Datasets:

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kejian

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SciRIFFEval-4096-0214-v2

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[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: Successful embryo implantation depends on a well-timed maternal-embryonic crosstalk. Human chorionic gonadotropin (hCG) secreted by the embryo is known to play a key role in this process and to trigger a complex signal transduction cascade allowing the apposition, attachment, and invasion of the embryo into the decidualized uterus. Production of hCG was reported to be dependent on blastocyst quality and several articles suggested that intrauterine hCG injection increases pregnancy and implantation rates in IVF patients. However, no study has as yet analysed birth rates as final outcome. Our objective was to determine whether clinical outcome after blastocyst transfer can be improved by intrauterine injection of hCG and whether this is dependent on blastocyst quality.\n\nABSTRACT.METHODS: A prospective randomised study was conducted in two settings. In cohort A, hCG application was performed two days before blastocyst transfer. In cohort B, the administration of hCG occurred just prior to embryo transfer on day 5. For both cohorts, patients were randomised to either intrauterine hCG application or to the control group that received culture medium. Clinical outcome was analysed according to blastocyst quality of transferred embryos.\n\nABSTRACT.RESULTS: The outcome of 182 IVF-cycles (cohort A) and 1004 IVF-cycles (cohort B) was analysed. All patients received a fresh autologous blastocyst transfer on day five. Primary outcomes were pregnancy rates (PR), clinical pregnancy rates (cPR), miscarriage rates (MR), and live birth rates (LBR). No improvement of clinical outcome after intrauterine hCG administration on day 3 (cohort A) or day 5 (cohort B) was found, independently of blastocyst quality transferred. The final outcome in cohort A: LBR after transfer of top blastocysts was 50.0 % with hCG and 53.3 % in the control group. With non-top blastocysts, LBR of 17.1 % (hCG) and 18.2 % (control) were observed (n.s.). In cohort B, LBR with top blastocysts was 53.3 % (hCG) and 48.4 % (control), with non-top blastocysts it came to 28.7 % (hCG) and 35.0 % (control). The differences between the groups were statistically not significant. Furthermore, we investigated a possible benefit of hCG administration in correlation with female age. In both age groups (<38 years and ≥ 38 years) we found similar LBR after treatment with hCG vs. medium. A LBR of 47.1 % vs. 48.7 % was obtained in the younger group and 26.6 % vs. 30.8 % in the older group.\n\nABSTRACT.CONCLUSIONS: In contrast to previous studies indicating a substantial benefit from intrauterine hCG application in cleavage stage embryo transfers, in our study we could not find any evidence for improvement of clinical outcome in blastocyst transfer cycles, neither with top nor with non-top quality morphology.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"cohort B, the administration of hCG occurred just prior to embryo transfer on day 5 with top blastocysts and with non-top blastocysts \", \"control\", \"live birth rates (LBR)\", \"no significant difference\", \"In cohort B, LBR with top blastocysts was 53.3 % (hCG) and 48.4 % (control), with non-top blastocysts it came to 28.7 % (hCG) and 35.0 % (control). The differences between the groups were statistically not significant.\"]]", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nMost termination events in Okazaki fragments are dictated by initiation patterns.\n\nResearch Abstract:\nMany fundamental aspects of DNA replication, such as the exact locations where DNA synthesis is initiated and terminated, how frequently origins are used, and how fork progression is influenced by transcription, are poorly understood.\nVia the deep sequencing of Okazaki fragments, we comprehensively document replication fork directionality throughout the S. cerevisiae genome, which permits the systematic analysis of initiation, origin efficiency, fork progression, and termination.\nWe show that leading-strand initiation preferentially occurs within a nucleosome-free region at replication origins.\nUsing a strain in which late origins can be induced to fire early, we show that replication termination is a largely passive phenomenon that does not rely on cis-acting sequences or replication fork pausing.\nThe replication profile is predominantly determined by the kinetics of origin firing, allowing us to reconstruct chromosome-wide timing profiles from an asynchronous culture.", "role": "user" }, { "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The replication profile is predominantly determined by the kinetics of origin firing, allowing us to reconstruct chromosome-wide timing profiles from an asynchronous culture.\"]}", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nAdversarial Reweighting for Partial Domain Adaptation\n\nMain Content:\n1 Introduction . Deep learning has achieved impressive success in image recognition [ 15 , 20 , 39 ] . However , deep learning models often rely on massive labeled training data , requiring an expensive and time-consuming labeling process . To alleviate the dependency of deep learning models on a large number of labeled data , domain adaptation ( DA ) [ 27 ] transfers the knowledge from a relevant source domain with rich labeled data to the target domain . The core of DA is to build a predictive model for the target domain using the training data of the source domain , and the model is expected to be robust to the distribution discrepancy ( a.k.a. , domain shift ) between source and target domains . Domain adaptation methods often train the robust model by aligning distributions of different domains by moment matching [ 17 , 19 , 24 , 28 , 40 , 48 ] or adversarial training [ 8 , 25 , 38 , 40 , 41 , 42 , 50 ] . The conventional closed-set DA methods generally assume that the source and target domains share the same label space . However , this assumption is often not realistic in practice . It is usually difficult to find a relevant source domain with identical label space as the target domain . This motivates the research on the learning problem of partial domain adaptation [ 3 , 49 ] . Partial domain adaptation ( PDA ) is an important subcategory of domain adaptation . PDA [ 3 , 4 , 49 ] tackles the scenario that the label space of the target domain is a subset of that of the source domain . Partial domain adaptation is more challenging than vanilla closed-set domain adaptation . Because , besides the challenge of the domain shift , the existence of source-only classes that do not exist in the target domain can cause class-wise feature mismatch when aligning distributions . This potentially leads to negative transfer [ 27 ] , i.e. , the DA approaches hurt the performance of learning in the target ∗The corresponding author . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . domain . To mitigate the negative transfer , current PDA methods [ 3 , 4 , 5 , 21 , 23 , 34 , 46 , 49 ] commonly reweight the source domain data to decrease the importance of data belonging to the source-only classes . The target and reweighted source domain data are used to train the feature extractor by adversarial training [ 3 , 4 , 5 , 23 , 46 , 49 ] or kernel mean matching [ 21 , 34 ] to align distributions . In this paper , we first propose to measure the hardness of a dataset for PDA using the probability of target domain data being misclassified into source-only classes . We then observe that some of the state-of-the-art reweighted distribution alignment losses , e.g. , PADA [ 4 ] , BAA [ 23 ] , and reweighted MMD [ 34 ] , cause negative transfer on VisDA-2017 [ 32 ] and DomainNet [ 31 ] datasets that are more challenging than Office-31 [ 36 ] , Office-Home [ 43 ] and ImageNet-Caltech [ 12 , 35 ] , according to our hardness measure . Specifically , learning/adapting the feature extractor by aligning the feature distributions of the reweighted source and target domain data can even worsen the performance of the baseline model without feature distribution alignment . We find that this negative domain transfer effect is mainly because that these reweighted feature distribution alignment methods are not robust to the “ noises ” of source data weights , i.e. , some source-only-class data are mistakenly assigned with non-zero weights in the alignment losses . To tackle the negative domain transfer in PDA , we propose a novel adversarial reweighting ( AR ) approach , which adversarially learns to reweight the source domain data for aligning the distributions of the source and target domains . Specifically , our approach relies on adversarial training to learn the weights of source domain data to minimize the Wasserstein distance between the reweighted source domain and target domain distributions . The weight learning process is conducted in an adversarial reweighting model , using the dual form of the Wasserstein distance . We then define a reweighted cross-entropy loss on the reweighted source domain data and use the conditional entropy loss on the target data to train the transferable recognition network for the target domain . The network training and weight learning are performed alternately in an iterative training algorithm . The current PDA methods [ 3 , 4 , 5 , 21 , 23 , 34 , 46 , 49 ] design/learn source data weights based on the classifier [ 3 , 4 , 21 , 23 , 34 , 46 ] or discriminator [ 5 , 49 ] . They then train the feature extractor using a reweighted distribution alignment loss defined on the target and reweighted source data . Different from them , firstly , we learn the weights of source data in our proposed adversarial reweighting model to decrease the weight of source-only-class data . Secondly , we reduce the domain gap by reweighting the source domain data , instead of directly optimizing the feature extractor to match feature distributions This strategy may mitigate the negative domain transfer when the source domain data contain “ noisy ” weights . Note that Balaji et al . [ 2 ] and Huang et al . [ 16 ] also align data distributions by data reweighing . Huang et al . [ 16 ] proposes a shallow method that reweights the source data using the density ratio estimated by kernel mean matching for closed-set DA . Differently , we reweight the source data using an adversarial reweighting model for PDA in feature space in deep learning framework . Balaji et al . [ 2 ] proposes a robust optimal transport model to decrease the importance of the outliers of target data in distribution alignment for closed-set DA . Different from Balaji et al . [ 2 ] , we reweight the source domain data to decrease the importance of source-only-class data in the source classification loss to mitigate the negative transfer for PDA . We conduct extensive experiments on five benchmark datasets i.e. , Office-31 , ImageNet-Caltech , Office-Home , VisDA-2017 , and DomainNet . The results show that our method achieves state-of-theart results for PDA on ImageNet-Caltech , Office-Home , VisDA-2017 , and DomainNet datasets , and achieves competitive results on Office-31 . Ablation studies indicate that our method significantly outperforms its baseline on all datasets . Our code is available at https : //github.com/XJTU-XGU/ Adversarial-Reweighting-for-Partial-Domain-Adaptation . In the following sections , we discuss the limitations of feature adaptation by domain distribution alignment in Sect . 2 . Section 3 presents the details of our approach . Section 4 reports the experimental results and Sect . 5 concludes this work . 2 Limitations of Feature Adaptation by Domain Distribution Alignment . In this section , we first introduce the setting of PDA and summarize the popular reweighted distribution alignment methods for PDA [ 3 , 4 , 5 , 21 , 23 , 34 , 49 ] . We then propose a hardness measure for PDA benchmarks and show the limitations of the reweighted distribution alignment . Problem setting . In PDA , we are given a labeled dataset S = { xsi , ysi } ns i=1 from source domain and an unlabeled dataset T = { xtj } nt j=1 from target domain , where x s and xt ∈ Rd respectively denote the source and target data , ysi ∈ Ys is the label of xsi , and Ys = { 0 , 1 , · · · , K } is the source label space . The goal is to train a recognition network to predict the label of xtj . Notably , in PDA , the target label space Yt is a subset of Ys , i.e. , Yt ⊂ Ys , which is different from the setting ( Yt = Ys ) in the closed-set DA . The PDA methods often use a feature extractor F to extract the features and a classifier C to predict labels , and optionally use a discriminator D to discriminate domains . Summary of reweighted distribution alignment methods in PDA . The challenges of PDA come from two aspects : the domain shift between source and target domains , and the negative effect of source-only-class data in adaptation . To tackle these challenges , most popular PDA methods ( e.g. , SAN [ 3 ] , PADA [ 4 ] , ETN [ 5 ] , DRCN [ 21 ] , BA3US [ 23 ] , TSCDA [ 34 ] , and IWAN [ 49 ] ) commonly adapt the feature extractor by minimizing the feature distribution distance between the reweighted source domain and the target domain , measured by the reweighted distribution alignment losses . The weights in the reweighted distribution alignment losses are usually designed based on the output of the classifier or the discriminator . The widely adopted distribution distance metrics include the Maximization Mean Discrepancy ( MMD ) and the Jensen–Shannon ( JS ) divergence . Minimizing the MMD matches the kernel mean embedding of distributions in the Reproducing Kernel Hilbert Space . Minimizing the JS divergence is equivalent to the adversarial training as in the Generative Adversarial Network [ 10 ] . More comparisons of the PDA methods are given in Supp . A . Measuring the hardness of a dataset for PDA . We propose to measure the hardness of a dataset for PDA using the probability of the target domain data being misclassified into the source-only classes . Specifically , we first train a model by minimizing the source classification loss and the entropy loss on target domain , which is the baseline for PDA . Then the hardness of a PDA task is defined as the average predicted probability of the target domain samples being misclassified into the source-only classes , using the trained model . The average hardness of all tasks in the dataset is taken as the hardness of the dataset . We report the hardness of five PDA benchmark datasets in Fig . 1 ( a ) . Figure 1 ( a ) indicates that VisDA-2017 and DomainNet are more challenging than the other datasets . Adapting feature extractor by reweighted distribution alignment may lead to negative domain transfer . We show the limitations of the reweighted distribution alignment in this paragraph . We report the average classification accuracies for different reweighted distribution alignment losses ( including the reweighted adversarial training losses of PADA [ 4 ] and BAA [ 23 ] , and the reweighted MMD loss in [ 34 ] ) and the baseline ( w/o Align ) on five benchmark datasets in Fig . 1 ( b ) . We also give the detailed results in Supp . B . We can observe in Fig . 1 ( b ) that additionally minimizing these reweighted distribution alignment losses by the feature extractor worsens the performance of the baseline on some challenging datasets , e.g. , VisDA-2017 and DomainNet . This indicates that adapting the feature extractor by the reweighted distribution alignment losses leads to negative transfer on these datasets . We next investigate the reasons for this finding . Adapting feature extractor by reweighted distribution alignment is not robust to noisy weights . In the methods of [ 3 , 4 , 21 , 23 , 34 ] , the weight of source domain data ( xsi , y s i ) is defined as the average predicted probability for category of ysi on the target domain data , i.e. , the y s i -th element of 1nt ∑nt j=1 C ( F ( x t j ) ) . When the domain gap between source and target domains is large , the prediction by the source classifier on the target domain may be uncertain . Then , the predicted probability of classifying the target data as source-only classes may be non-zero or even possibly significantly larger than zero . Hence , reweighting source classes based on the outputs of the classifier may assign non-zero weights to the source-only classes , i.e. , the designed weights may contain “ noises ” . We analyze that the ineffectiveness of adapting the feature extractor by the reweighted distribution alignment could be because it is not robust to the “ noisy ” weights . To illustrate this , we conduct experiments with simulating the source domain data weights with different noise levels . We assign the noise to each source-only class in proportion to its weight predicted by the classifier aforementioned . If the weight predicted by the classifier is ci , i ∈ Ys , for the noise level p ∈ [ 0 , 1 ] , the simulated weight for each source-only class is pci/ ∑ k∈Ys\\Yt c k , for i ∈ Ys\\Yt . Similarly , the simulated weight for each source-shared class ( the class exists in both source and target label spaces ) is ( 1 − p ) ci/ ∑ k∈Yt c k , for i ∈ Yt . If p = 0 , the data of source-only classes are ideally assigned with weights of 0 , and as the p increases , these data are assigned with larger weights , which are taken as the “ noisy ” weights . We also use the simulated weights to reweight the importance of source data in the classification loss , denoted as “ RC ” . Figures 1 ( c ) and 1 ( d ) show the results for different reweighted distribution alignment losses with the simulated weights under varying noise levels . We can see that when the noise level is near zero , all the alignment losses outperform “ w/o Align ” , indicating that the alignment losses lead to positive domain transfer in this case . However , as the noise level increases , the performance of the alignment losses decreases rapidly and even becomes significantly worse than that of “ w/o Align ” . Specifically , in task S→R ( Fig . 1 ( c ) ) , when the noise level is 0.1 , the performance of the alignment losses begin to be inferior to that of “ w/o Align ” . Similarly , in task C→P ( Fig . 1 ( d ) ) , the performance of the alignment losses begin to be inferior to that of “ w/o Align ” at noise level 0.3 . While the “ real ” noise level ( the sum of weights based on the classifier over source-only classes ) is larger than 0.3 ( resp . 0.4 ) in the two tasks . Therefore , the negative domain transfer of the reweighted distribution alignment losses may be because of the “ noise ” in weights . It is also seen that when the noise level ranges from 0 to 0.4 , the approach “ w/o Align & w/ RC ” that reweights data importance in source classification loss consistently outperforms the baseline “ w/o Align ” in both two tasks . This indicates that reweighting data importance in the source classification loss is more robust to weight noise , compared with the reweighted distribution alignment losses , in the two tasks . The above observations indicate that adapting the feature extractor by the reweighted distribution alignment is not robust to the noise in source data weights , and can cause negative domain transfer on some challenging datasets . Surprisingly , reweighting data importance in the source classification loss may be more robust to weight noise than the reweighted distribution alignment losses .", "role": "user" }, { "content": "The paper introduces the adversarial reweighting (AR) technique for estimating weights in the source domain. Compared to the previous methods on partial domain adaptation, the proposed method seems robust on the effect of negative domain transfer during the adaptation. Authors demonstrate the effectiveness of AR in several partial domain adaptation benchmarks.", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: Mannoheptulose (MH), a sugar found in avocados that inhibits glycolysis in vitro, has been preliminarily investigated as a novel food ingredient for dogs. This study aimed to determine the effects of dietary MH, delivered as an extract of un-ripened avocado, on energy expenditure (EE) in healthy adult Labrador Retriever dogs (total of 12 dogs, 26.99 ± 0.634 kg, 4.9 ± 0.2 y). The study was a double-blind, cross-over with each dog receiving both dietary treatments, control (CON) and MH (400 mg/kg of diet; 6 mg/kg BW), in random order. Resting and post-prandial (10 h) EE and respiratory quotient (RQ) were determined by indirect calorimetry (d 42). The following day, body composition was assessed using dual X-ray absorptiometry. Continuous activity monitoring was conducted using an Atical® accelerometer (d 43–47). A vastus lateralis muscle biopsy was obtained prior to the morning meal (d 49) and 4 h after consumption of their meal (d 56) to determine the protein content and phosphorylation of 5' adenosine monophosphate-activated protein kinase (AMPK). Diet did not affect body weight, resting EE or skeletal muscle AMPK phosphorylation. Dogs fed MH had significantly lower post-prandial RQ (p = 0.02) and ratio of fat to lean body mass (p = 0.02). Physical activity during light time periods (but not dark) was lower in dogs fed MH (p < 0.05) during weekends, but not on weekdays. These results suggest that MH affects energy balance of adult dogs, but that these effects are not dose dependent and not due to physical activity.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Mannoheptulose\", \"Control\", \"Body weight\", \"no significant difference\", \"Diet did not affect body weight, resting EE or skeletal muscle AMPK phosphorylation.\"], [\"Mannoheptulose\", \"Control\", \"Resting energy expenditure\", \"no significant difference\", \"Diet did not affect body weight, resting EE or skeletal muscle AMPK phosphorylation.\"], [\"Mannoheptulose\", \"Control\", \"Adenosine monophosphate-activated protein kinase phosphorylation\", \"no significant difference\", \"Diet did not affect body weight, resting EE or skeletal muscle AMPK phosphorylation.\"], [\"Mannoheptulose\", \"Control\", \"Ratio of fat to lean body mass\", \"significantly decreased\", \"Dogs fed MH had significantly lower post-prandial RQ (p = 0.02) and ratio of fat to lean body mass (p = 0.02).\"], [\"Mannoheptulose\", \"Control\", \"Post-prandial respiratory quotient\", \"significantly decreased\", \"Dogs fed MH had significantly lower post-prandial RQ (p = 0.02) and ratio of fat to lean body mass (p = 0.02).\"], [\"Mannoheptulose\", \"Control\", \"Physical activity during light time periods\", \"significantly decreased\", \"Physical activity during light time periods (but not dark) was lower in dogs fed MH (p \\u003c 0.05) during weekends, but not on weekdays.\"]]", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nDive Deeper Into Integral Pose Regression\n\nMain Content:\n1 INTRODUCTION . 2D human pose estimation aims to detect the image coordinates of the body and or the hand . In the last few years , detection-based methods ( Newell et al. , 2016 ; Xiao et al. , 2018 ; Sun et al. , 2019 ; Li et al. , 2019 ) and integral pose regression ( IPR ) methods ( Sun et al. , 2018 ; Iqbal et al. , 2018 ) have emerged as two common paradigms for performing human pose estimation . Both detection-based and IPR methods learn a heatmap representing pixel-wise likelihoods of the joint positions ; the heatmap is learned explicitly for detection-based methods , but remains implicit or “ latent ” for IPR . To decode the heatmaps to joint coordinates , detection methods use an argmax , while IPR methods take the expected value . As the expectation operation is differentiable , IPR has the benefit of being end-to-end learnable , even though detection methods seem more competitive accuracy-wise ( COC , 2020 ) . Gu et al . ( 2021 ) ’ s recent work showed a curious performance difference between IPR and detection methods . With a nuanced split over the evaluation set , IPR outperforms detection when test samples are “ harder ” , i.e . , with fewer keypoints present in the scene , under higher occlusion , and lower bounding box resolutions . Given that both detection and IPR methods work with fully convolutional feed-forward architectures , this naturally begs the question - what are the reasons behind this performance difference ? Why is it that IPR , which otherwise seems to lag behind detection methods in performance ( COC , 2020 ) , can excel on these hard cases ? This serves as the motivation for our closer study and analysis of integral pose regression . Detection and IPR methods differ in two aspects ( see Fig . 1 for an overview ) in the forwards and backwards pass . In the forwards pass , the heatmap is decoded with an argmax for detection versus a softmax normalization and expectation for IPR . In the backwards pass , detection methods are supervised with an explicitly defined Gaussian heatmap centered on the ground truth joint , while IPR is supervised by the joint coordinates directly . Through detailed theoretical analysis and experimentation on the decoding and back-propagation process , we make following findings and contributions : 1 . We propose a unified model of the heatmap to interpret and compare detection and integral regression methods . We verify the model experimentally and show that as samples progress from hard to easy , the region of activation on the heatmap decreases for both detection and integral regression methods . 2 . We demonstrate experimentally that degenerately small regions of activation degrades the accuracy of both detection and integral regression methods . 3 . Integral regression methods , by decoding the heatmap with an expectation operation , should result in a lower expected EPE than detection methods that decode with an argmax operation . In practice , this can be observed only for hard samples due to the shrinkage of the active region on the heatmap . 4 . Direct supervision with the joint coordinates in IPR , although end-to-end , suffers from gradient vanishing and provides less spatial cues for learning the heatmap than the explicit heatmap supervision of detection methods . As a result , the training of IPR is more inefficient and slower to converge than detection methods . In conclusion , our findings serve as insight to understand IPR which has a better decoding method theoretically and shows the potential to surpass the performance of detection by our experiments . 2 RELATED WORK . Since the concept of “ heatmap ” is proposed in ( Tompson et al. , 2014 ) , detection-based methods have been top performers for human pose . Existing detection-based works emphasize to extract high-quality multi-resolution features . Xiao et al . ( 2018 ) proposed to adapt ResNet with deconvolution layers while Hourglass ( Newell et al. , 2016 ) and Cascaded Pyramid Network ( CPN ) ( Li et al. , 2019 ) introduced cascaded network architectures with coarse-to-fine design paradigm . HighResolution Network ( HRNet ) ( Sun et al. , 2019 ) follows the coarse-to-fine paradigm and further improves the performance by adapting more dense connection among different resolution representations . IPR ( Sun et al. , 2018 ) introduced a competitive regression-based framework , but exceeded by more advanced detection-based works ( Xiao et al. , 2018 ; Sun et al. , 2019 ) . Numerical regression-based methods , which directly regress the joint coordinates , are commonly used in facial land-mark detection ( Feng et al. , 2018 ; Zhu et al. , 2020 ) . These methods however are not accurate for human pose because they fail to capture spatial knowledge . As a follow-up , IPR tries to merge the spatial knowledge from the heatmap implicitly , hence the “ latent ” heatmap . IPR methods are especially preferred in hand pose estimation ( Spurr et al. , 2020 ; Yang et al. , 2021 ) but are still less common for the human pose estimation ( Sun et al. , 2018 ; Nibali et al. , 2018 ) . Recently , two parallel lines of work have developed to further understand the behaviour of detection and IPR methods respectively . For detection-based methods , Huang et al . ( 2020 ) revealed a heatmap bias caused by the inconsistency of coordinate system transformations and solved it by redesigning the transformations during data processing . Moreover , as the predicted heatmaps during inference may violate the Gaussian assumption and worsen the performance , Huang et al . ( 2020 ) proposed to find the optimal Gaussian location based on the prediction and Zhang et al . ( 2020 ) directly modulated the predicted Gaussian distribution for decoding . For IPR methods , Nibali et al . ( 2018 ) experimentally compared different heatmap regularizers , heatmap normalization schemes and loss functions . They find using Jensen-Shannon regularization on the heatmap with a softmax normalization and L1 loss achieves the best performance for IPR . More recently , Gu et al . ( 2021 ) performed a systematic comparison of detection vs. IPR methods with a common backbone and discovered the performance advantage of IPR on the “ hard ” samples . Furthermore , Gu et al . ( 2021 ) revealed a bias that arises from taking the expectation after the softmax normalization ; they propose a compensation scheme to mitigate the bias which in turn improves the overall performance of IPR , making them competitive with detection methods . The work ( Zhang et al. , 2020 ; Huang et al. , 2020 ) focus on the explicit heatmaps of detection-based methods and investigate the bias under the Gaussian assumption . However , this is not applicable for IPR methods without the Gaussian assumption . Like ( Nibali et al. , 2018 ; Gu et al. , 2021 ) , our work also investigates the implicit heatmaps of IPR ; the previous works focus on experimental exploration , while we place more emphasis on theoretical analysis to pinpoint underlying causes . 3 PRELIMINARIES ON HUMAN POSE ESTIMATION . In this work , we target the more commonly used ‘ top-down ’ form of pose estimation in which a person detector already provides a cropped image I of the person . For simplicity , we focus our discussion on one given joint of the K total joints in the body . The pose estimation model outputs a heatmap Ĥ ∈ RM×N where M and N are the dimensions of the spatial heatmap . Typically , M and N are scaled down by a factor of 4 from the original input dimensions of I ( Xiao et al. , 2018 ; Sun et al. , 2019 ) . The heatmap Ĥ represents a ( discrete ) spatial likelihood P ( J|I ) , where J ∈ R1×2 is the 2D coordinates of that joint . In practice , all K heatmaps are predicted simultaneously by the same network , where each joint is one channel . In both detection and IPR methods , the coordinates Ĵ are decoded from Ĥ ; where the two methods differ is the manner of decoding ( see Sec . 3.1 ) and the form of supervision applied ( see Sec . 3.2 ) . 3.1 HEATMAP DECODING : MAX VERSUS EXPECTED VALUE . Detection Methods apply an argmax on Ĥ indexed by p to estimate the joint coordinates Ĵde : Ĵde = arg max p Ĥ ( p ) , ( 1 ) Taking an argmax can be interpreted as taking a maximum likelihood on the heatmap Ĥ , assuming that Ĥ is proportional to the likelihood . In practice , the final Ĵde value is determined as a linear combination of the highest and second-highest response on Ĥ as a way to account for quantization effects in the discrete heatmap ( Newell et al. , 2016 ) . A more recent work , DARK ( Zhang et al. , 2020 ) approximates the true prediction by a Taylor series evaluated at the maximum activation of the heatmap and shows this to be more accurate . Integral Pose Regression applies an expectation operation on Ĥ to give a mean estimate of the joint coordinates . To do so , the heatmap must first be normalized to sum up to 1 ; the most common and effective ( Nibali et al. , 2018 ) approach is to apply a softmax normalization . Afterwards , the predicted joint Ĵre with x and y components Ĵx and Ĵy1 is determined by taking the expectation on the normalized heatmap H̃ with elements h̃p at location p : Ĵre = [ Ĵx Ĵy ] = ∑ p∈Ω p · h̃p where h̃p = eβĥp∑ ( p′ ) ∈Ω eβĥp′ . ( 2 ) Here , Ω is the domain of the heatmap and β is a scaling parameter used in the softmax normalization . Note that softmax normalization assigns a non-zero value to all pixels in H̃ , even if it was originally zero in Ĥ . These values also contribute to the expected value , resulting in a center-biased estimated joint coordinate Ĵre ( Gu et al. , 2021 ) . The smaller the β is , the stronger the bias is . Although Gu 1For clarity , we drop the subscript ‘ re ’ , as we refer to the individual components for IPR only . et al . ( 2021 ) propose a compensation scheme , for the purposes of our analysis we will assume that β is sufficiently large that the impact of the bias is negligible . 3.2 SUPERVISION : EXPLICIT HEATMAP VS . GROUND TRUTH COORDINATES . Detection Methods are learned by providing supervision on the heatmap . The ground truth H is given as a circular Gaussian , with a mean centered at the ground truth joint coordinate ( see Fig . 1 ) . The loss applied is a pixel-wise MSE between the ground truth hp and the predicted ĥp : Lde = ||H− Ĥ||22 = ∑ p∈Ω ( hp − ĥp ) 2 , ( 3 ) where Ω is the domain of the heatmap . As the loss is defined in terms of the heatmap and not the actual variable of interest , i.e . the predicted joints , detection based methods are not end-to-end in their learning and this is often cited as a drawback ( Sun et al. , 2018 ; Zhang et al. , 2020 ) . Integral Pose Regression defines a loss based on the difference between the prediction Ĵre and ground truth joint location J . The L1 loss empirically performs better than the L2 ( Sun et al. , 2018 ) : Lre = ‖Jgt − Ĵre‖1 = ( |Ĵx − Jx|+ |Ĵy − Jy| ) . ( 4 ) IPR are end-to-end methods because they provide supervision at the joint level . As the estimated H is learned only implicitly , some works refer to the heatmap as “ latent ” ( Iqbal et al. , 2018 ) .", "role": "user" }, { "content": "This paper investigates the performance inconsistency of integral pose regression (IPR) methods on 'easy' pose samples and 'hard' pose samples (v.s. argmax methods). The authors find that the heatmap shrinkage caused by the IPR methods leads to the lower accuracy on 'easy' samples than argmax based methods. They give some toy examples and intuitively demonstrate how the shrinkage of heatmaps is caused by the gradient of the IPR method. They further propose a heatmap distribution prior loss to mitigate the shrinkage.", "role": "assistant" } ]

[ { "content": "Analyze the given abstract from a computer science paper. Identify and list entities in a structured JSON format. Focus on capturing distinct elements relevant to the research context.\n\nInput Abstract:\n\nWe present the first application of the head-driven statistical parsing model of Collins (1999) as a simultaneous language model and parser for large-vocabulary speech recognition. The model is adapted to an online left to right chart-parser for word lattices, integrating acoustic, n-gram, and parser probabilities. The parser uses structural and lexical dependencies not considered by n-gram models, conditioning recognition on more linguistically-grounded relationships. Experiments on the Wall Street Journal treebank and lattice corpora show word error rates competitive with the standard n-gram language model while extracting additional structural information useful for speech understanding.", "role": "user" }, { "content": "Output Entities:\n\n{\"Generic\": [], \"Material\": [\"lattice corpora\", \"Wall Street Journal treebank\"], \"Method\": [\"online left to right chart-parser\", \"n-gram models\", \"n-gram language model\", \"head-driven statistical parsing model\", \"simultaneous language model\"], \"Metric\": [\"word error rates\"], \"OtherScientificTerm\": [\"word lattices\", \"acoustic , n-gram , and parser probabilities\", \"structural information\", \"structural and lexical dependencies\"], \"Task\": [\"large-vocabulary speech recognition\", \"speech understanding\"]}", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nWord embedding models -such as that used in the approach to predicting compositionality of <cite>Salehi et al. (2015)</cite> -typically do not learn representations for low frequency items.", "role": "user" }, { "content": "[\"Background\"]", "role": "assistant" } ]

[ { "content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nGrammatical Error Correction and Style Transfer via Zero-shot Monolingual Translation\n\nBoth grammatical error correction and text style transfer can be viewed as monolingual sequence-to-sequence transformation tasks, but the scarcity of directly annotated data for either task makes them unfeasible for most languages. We present an approach that does both tasks within the same trained model, and only uses regular language parallel data, without requiring error-corrected or style-adapted texts. We apply our model to three languages and present a thorough evaluation on both tasks, showing that the model is reliable for a number of error types and style transfer aspects.\n\nMethod\nAs mentioned in the introduction, our approach is based on the idea of zero-shot MT BIBREF11 . There the authors show that after training a single model to translate from Portuguese to English as well as from English to Spanish, it can also translate Portuguese into Spanish, without seeing any translation examples for this language pair. We use the zero-shot effect to achieve monolingual translation by training the model on bilingual examples in both directions, and then doing translation into the same language as the input: illustrated on Figure FIGREF1 .\nWith regular sentences monolingual translation does not seem useful, as its behaviour mainly consists of copying. However, when the input sentence has characteristics unseen or rarely seen by the model at training time (like grammatical errors or different stylistic choices) – the decoder still generates the more regular version of the sentence (thus fixing the errors or adapting the style). Furthermore, in case of multilingual multi-domain NMT BIBREF12 , it is possible to switch between different domains or styles at runtime, thus performing “monolingual domain adaptation” or style transfer.\nTo create a multilingual multi-domain NMT system we use the self-attention architecture BIBREF13 . Instead of specifying the output language with a token inside the input sequence, as BIBREF11 did, we follow BIBREF12 and use word features (or factors). On one hand, this provides a stronger signal for the model, and on the other – allows for additional parametrization, which in our case is the text domain/style of the corpus.\nAs a result, a pre-processed English-Latvian training set sentence pair “Hello!”–“Sveiki!” looks like:\nHere 2lv and 2os specify Latvian and OpenSubtitles as the output language and domain; the output text has no factors to predict. At application time we simply use the same input and output languages, for example the grammatically incorrect input “we is” looks like the following, after pre-processing:\nThe intuition behind our approach is that a multilingual shared encoder produces semantically rich latent sentence representations BIBREF14 , which provide a solid ground for the effective style transfer on top.\nNext we present the technical details, the experiment setup and the data we used for training the model used in the experiments.\n\nLanguages and Data\nWe use three languages in our experiments: English, Estonian and Latvian. All three have different characteristics, for example Latvian and (especially) Estonian are morphologically complex and have loose word order, while English has a strict word order and the morphology is much simpler. Most importantly, all three languages have error-corrected corpora for testing purposes, though work on their automatic grammatical error correction is extremely limited (see Section SECREF3 ).\nThe corpora we use for training the model are OpenSubtitles2018 BIBREF15 , Europarl BIBREF16 , JRC-Acquis and EMEA BIBREF17 . We assume that there should be sufficient stylistic difference between these corpora, especially between the more informal OpenSubtitles2018 (comprised of movie and TV subtitles) on one hand and Europarl and JRC-Acquis (proceedings and documents of the European Parliament) on the other.\n\nEvaluation\nFirst, we evaluate our model in the context of MT, as the translation quality can be expected to have influence on the other tasks that the model performs. We use public benchmarks for Estonian-English and Latvian-English translations from the news translation shared tasks of WMT 2017 and 2018 BIBREF18 , BIBREF19 . The BLEU scores for each translation direction and all included styles/domains are shown in Table TABREF6 .\nSome surface notes on these results: the BLEU scores for translation from and into Latvian are below English-Estonian scores, which is likely explained by smaller datasets that include Latvian. Also, translation into English has higher scores than into Estonian/Latvian, which is also expected.\nAn interesting side-effect we have observed is the model's resilience to code-switching in the input text. The reason is that the model is trained with only the target language (and domain), and not the source language, as a result of which it learns language normalization of sorts. For example, the sentence “Ma tahan two saldējumus.” (“Ma tahan” / “I want” in Estonian, “two” and “saldējumus” / “ice-creams” in genitive, plural in Latvian) is correctly translated into English as “I want two ice creams.”. See more examples in Appendix SECREF8 .\n\nTest Data and Metrics\nWe use the following error-corrected corpora both for scoring and as basis for manual analysis:\nfor English: CoNLL-2014 BIBREF5 and JFLEG BIBREF20 corpora\nfor Estonian: the Learner Language Corpus BIBREF21 \nfor Latvian: the Error-annotated Corpus of Latvian BIBREF22 \nAll of these are based on language learner (L2) essays and their manual corrections.\nTo evaluate the model quantitatively we used two metrics: the Max-Match (M INLINEFORM0 ) metric from the CoNLL-2014 shared task scorer, and the GLEU score BIBREF23 for the other corpora. The main difference is that M INLINEFORM1 is based on the annotation of error categories, while the GLEU score compares the automatic correction to a reference without any error categorization.\n\nResults\nThe M INLINEFORM0 scores are computed based on error-annotated corpora. Since error annotations were only available for English, we calculated the scores on English CoNLL corpus, see Table TABREF12 ).\nOur model gets the M INLINEFORM0 score of 32.1. While it does not reach the score of the best CoNLL model BIBREF24 or the state-of-the-art BIBREF25 , these use annotated corpora to train. Our results count as restricted in CoNLL definitions and are more directly comparable to the classifier-based approach trained on unannotated corpora by BIBREF26 , while requiring even less effort.\nThe GLEU scores can be seen in Table TABREF13 . We calculated GLEU for both formal and informal style models for all three languages. For English our model's best score was 45.9 and for Estonian it was 38.1. Latvian corrected output in fact get worse scores than the original uncorrected corpus, which can be explained by smaller training corpora and worse MT quality for Latvian (see Table TABREF6 ).\n\nRelated Work\nGrammatical error correction: there have been four shared tasks for GEC with prepared error-tagged datasets for L2 learners of English in the last decade: HOO BIBREF27 , BIBREF28 and CoNLL BIBREF29 , BIBREF5 . This has given an opportunity to train new models on the shared datasets and get an objective comparison of results. The general approach for grammatical error correction has been to use either rule-based approach, machine learning on error-tagged corpora, MT models on parallel data of erroneous and corrected sentences, or a combination of these BIBREF5 . The top model of the CONLL shared task in 2014 used a combined model of rule-based approach and MT BIBREF24 . All of these require annotated data or considerable effort to create, whereas our model is much more resource-independent. Another focus of the newer research is on creating GEC models without human-annotated resources. For example BIBREF26 combine statistical MT with unsupervised classification using unannotated parallel data for MT and unannotated native data for the classification model. In this case parallel data of erroneous and corrected sentences is still necessary for MT; the classifier uses native data, but still needs definitions of possible error types to classify – this work needs to be done by a human and is difficult for some less clear error types. In our approach there is no need for parallel data nor to specify error types, only for native data.\nThere has been little work on Estonian and Latvian GEC, all limited with rule-based approaches BIBREF30 , BIBREF31 . For both languages, as well as any low-resourced languages, our approach gives a feasible way to do grammatical error correction without needing neither parallel nor error tagged corpora. Style transfer: Several approaches use directly annotated data: for example, BIBREF7 and BIBREF8 train MT systems on the corpus of modern English Shakespeare to original Shakespeare. BIBREF32 collect a dataset of 110K informal/formal sentence pairs and train rule-based, phrase-based, and neural MT systems using this data.\nOne line of work aims at learning a style-independent latent representation of content while building decoders that can generate sentences in the style of choice BIBREF9 , BIBREF33 , BIBREF34 , BIBREF35 , BIBREF36 , BIBREF37 , BIBREF38 , BIBREF39 . Unsupervised MT has also been adapted for the task BIBREF10 , BIBREF40 . Our system also does not require parallel data between styles, but leverages the stability of the off-the-shelf supervised NMT to avoid the hassle of training unsupervised NMT systems and making GANs converge. Another problem with many current (both supervised and unsupervised) style transfer methods is that they are bounded to solve a binary task, where only two styles are included (whether because of data or restrictions of the approach). Our method, on the other hand, can be extended to as many styles as needed as long as there are parallel MT corpora in these styles available.\nNotably, BIBREF41 use side constrains in order to translate in polite/impolite German, while we rely on multilingual encoder representations and use the system monolingually at inference time.\nFinally, the most similar to our work conceptually is the approach of BIBREF42 , where they translate a sentence into another language, hoping that it will lose some style indicators, and then translate it back into the original language with a desired style tag attached to the encoder latent space. We also use the MT encoder to obtain rich sentence representations, but learn them directly as a part of a single multilingual translation system.\n\nTable 1: BLEU scores of the multilingual MT model on WMT’17 (Latvian↔English) and WMT’18 (Estonian↔English) test sets\n\nTable 3: GLEU scores for all three languages. No scores have been previously reported elsewhere for Estonian and Latvian.\n\nQuery: Do they introduce errors in the data or does the data already contain them?\n\nJSON Response:", "role": "user" }, { "content": "{\"answer\": \"Data already contain errors\", \"evidence\": [\"Test Data and Metrics\", \"We use the following error-corrected corpora both for scoring and as basis for manual analysis:\", \"for English: CoNLL-2014 BIBREF5 and JFLEG BIBREF20 corpora\", \"for Estonian: the Learner Language Corpus BIBREF21\", \"for Latvian: the Error-annotated Corpus of Latvian BIBREF22\", \"All of these are based on language learner (L2) essays and their manual corrections.\"]}", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nConnecting Sphere Manifolds Hierarchically for Regularization\n\nMain Content:\n1 INTRODUCTION . Applying inductive biases or prior knowledge to inference models is a popular strategy to improve their generalization performance ( Battaglia et al. , 2018 ) . For example , a hierarchical structure is found based on the similarity or shared characteristics between samples and thus becomes a basic criterion to categorize particular objects . The known hierarchical structures provided by the datasets ( e.g. , ImageNet ( Deng et al. , 2009 ) classified based on the WordNet graph ; CIFAR100 ( Krizhevsky , 2009 ) in ten different groups ) can help the network identify the similarity between the given samples . In classification tasks , the final layer of neural networks maps embedding vectors to a discrete target space . However , there is no mechanism forcing similar categories to be distributed close to each other in the embedding . Instead , we may observe classes to be uniformly distributed after training , as this simplifies the separation by the last fully-connected layer . This behavior is a consequence of seeing the label structure as ‘ flat , ’ i.e. , when we omit to consider the hierarchical relationships between classes ( Bilal et al. , 2017 ) . To alleviate this problem , in this study , we force similar classes to be closer in the embedding by forcing their hyperplanes to follow a given hierarchy . One way to realize that is by making children nodes dependent on parent nodes and constraining their distance through a regularization term . However , the norm itself does not give a relevant information on the closeness between classifiers . Indeed , two classifiers are close if they classify two similar points in the same class . This means similar classifiers have to indicate a similar direction . Therefore , we have to focus on the angle between classifiers , which can be achieved through spherical constraints . Contributions . In this paper , we propose a simple strategy to incorporate hierarchical information in deep neural network architectures with minimal changes to the training procedure , by modifying only the last layer . Given a hierarchical structure in the labels under the form of a tree , we explicitly force the classifiers of classes to belong to a sphere , whose center is the classifier of their super-class , recursively until we reach the root ( see Figure 2 ) . We introduce the spherical fully-connected layer and the hierarchically connected layer , whose combination implements our technique . Finally , we investigate the impact of Riemannian optimization instead of simple norm normalization . By its nature , the proposed technique is quite versatile because the modifications only affect the structure of last fully-connected layer of the neural network . Thus , it can be combined with many other strategies ( like spherical CNN from Xie et al . ( 2017 ) , or other deep neural network architectures ) . Related works . Hierarchical structures are well-studied , and their properties can be effectively learned using manifold embedding . The design of the optimal embedding to learn the latent hierarchy is a complex task , and was extensively studied in the past decade . For example , Word2Vec ( Mikolov et al. , 2013b ; a ) and Poincaré embedding ( Nickel & Kiela , 2017 ) showed a remarkable performance in hierarchical representation learning . ( Du et al. , 2018 ) forced the representation of sub-classes to “ orbit ” around the representation of their super-class to find similarity based embedding . Recently , using elliptical manifold embedding ( Batmanghelich et al. , 2016 ) , hyperbolic manifolds ( Nickel & Kiela , 2017 ; De Sa et al. , 2018 ; Tifrea et al. , 2018 ) , and a combination of the two ( Gu et al. , 2019 ; Bachmann et al. , 2019 ) , shown that the latent structure of many data was non-Euclidean ( Zhu et al. , 2016 ; Bronstein et al. , 2017 ; Skopek et al. , 2019 ) . ( Xie et al. , 2017 ) showed that spheres ( with angular constraints ) in the hidden layers also induce diversity , thus reducing over-fitting in latent space models . Mixing hierarchical information and structured prediction is not new , especially in text analysis ( Koller & Sahami , 1997 ; McCallum et al. , 1998 ; Weigend et al. , 1999 ; Wang et al. , 1999 ; Dumais & Chen , 2000 ) . Partial order structure of the visual-semantic hierarchy is exploited using a simple order pair with max-margin loss function in ( Vendrov et al. , 2016 ) . The results of previous studies indicate that exploiting hierarchical information during training gives better and more resilient classifiers , in particular when the number of classes is large ( Cai & Hofmann , 2004 ) . For a given hierarchy , it is possible to design structured models incorporating this information to improve the efficiency of the classifier . For instance , for support vector machines ( SVMs ) , the techniques reported in ( Cai & Hofmann , 2004 ; 2007 ; Gopal et al. , 2012 ; Sela et al. , 2011 ) use hierarchical regularization , forcing the classifier of a super-class to be close to the classifiers of its sub-classes . However , the intuition is very different in this case , because SVMs do not learn the embedding . In this study , we consider that the hierarchy of the class labels is known . Moreover , we do not change prior layers of the deep neural network , and only work on the last layer that directly contributed to build hyperplanes for a classification purpose . Our work is thus orthogonal to those works on embedding learning , but not incompatible . Comparison with hyperbolic/Poincaré/graph networks . Hyperbolic network is a recent technique that shows impressive results for hierarchical representation learning . Poincaré networks ( Nickel & Kiela , 2017 ) were originally designed to learn the latent hierarchy of data using low-dimension embedding . To alleviate their drawbacks due to a transductive property which can not be used for unseen graph inference , hyperbolic neural networks equipped set aggregation operations have been proposed ( Chami et al. , 2019 ; Liu et al. , 2019 ) . These methods have been mostly focused on learning embedding using a hyperbolic activation function for hierarchical representation . Our technique is orthogonal to these works : First , we assume that the hierarchical structure is not learnt but already known . Second , our model focuses on generating individual hyperplanes of embedding vectors given by the network architecture . While spherical geometry has a positive curvature , moreover , that of hyperbolic space has a constant negative curvature . However , our technique and hyperbolic networks are not mutually exclusive . Meanwhile focusing on spheres embedded in Rd in this study , it is straightforward to consider spheres embedded in hyperbolic spaces . 2 HIERARCHICAL REGULARIZATION . 2.1 DEFINITION AND NOTATIONS We assume we have samples with hierarchically ordered classes . For instance , apple , banana , and orange are classes that may belong to the super-class “ fruits. ” This represents hierarchical relationships with trees , as depicted in Figure 1 . We identify nodes in the graph through the path taken in the tree . To represent the leaf ( highlighted in blue in Figure 1 ) , we use the notation n { 1,3,2 } . This means it is the second child of the super-class n { 1,3 } , and recursively , until we reach the root . More formally , we identify nodes as np , where p is the path to the node . A path uniquely defines a node where only one possible path exists . Using the concatenation , between the path p and its child i , a new path p̃ can be defined as follows , p̃ = 〈p , i〉 ( 1 ) We denote P the set of all paths in the tree starting from the root , with cardinality |P| . Notice that |P| is also the number of nodes in the tree ( i.e. , number of classes and super-classes ) . We distinguish the set P from the set L , the set of paths associated to nodes whose label appears in the dataset . Although L may equal to P , this is not the case in our experiments . We show an example in Appendix A . 2.2 SIMILARITY BETWEEN OBJECTS AND THEIR REPRESENTATION . Let X be the network input ( e.g . an image ) , and φθ ( X ) be its representation , i.e. , the features of X extracted by a deep neural network parameterized by θ . We start with the following observation : Given a representation , super-class separators should be similar to separators for their sub-classes . This assumption implies the following direct consequence . All objects whose labels belong to the same super-class have a similar representation . That is a natural property that we may expect from a good representation . For instance , two dogs from different breeds should share more common features than that of a dog shares with an apple . Therefore , the parameter of the classifiers that identify dog ’ s breed should also be similar . Their difference lies in the parameters associated to some specific features that differentiate breeds of dogs . Although this is not necessarily satisfied with arbitrary hierarchical classification , we observe this in many existing datasets . For instance , Caltech-UCSD Birds 200 and Stanford dogs are datasets that classify , respectively , birds and dogs in term of their breeds . A possible example where this assumption may not be satisfied is a dataset whose super-classes are “ labels whose first letter is « · » . ” 2.3 HIERARCHICAL REGULARIZATION . Starting from a simple observation in the previous section , we propose a regularization technique that forces the network to have similar representation for classes along a path p , which implies having similar representation between similar objects . More formally , if we have an optimal classifier wp for the super-class p and a classifier w〈p , i〉 for the class 〈p , i〉 , we expect that ‖wp − w〈p , i〉‖ is small . ( 2 ) If this is satisfied , separators for objects in the same super-class are also similar because ‖w〈p , i〉 − w〈p , j〉‖ = ‖ ( w〈p , i〉 − wp ) − ( w〈p , j〉 − wp ) ‖ ≤ ‖wp − w〈p , i〉‖︸ ︷︷ ︸ small + ‖wp − w〈p , j〉‖︸ ︷︷ ︸ small . ( 3 ) However , the optimal classifier for an arbitrary representation φθ ( X ) may not satisfy equation 2 . The naive and direct way to ensure equation 2 is through hierarchical regularization , which forces classifiers in the same path to be close to each other . 2.4 HIERARCHICAL LAYER AND HIERARCHICALLY CONNECTED LAYER . In the previous section , we described the hierarchical regularization technique given a hierarchical structure in the classes . In this section , we show how to conveniently parametrize equation 2 . We first express the classifier as a sum of vectors δ defined recursively as follows : w〈p , i〉 = wp + δ〈p , i〉 , δ { } = 0 , ( 4 ) where { } is the root . It is possible to consider δ { } 6= 0 , which shifts separating hyper-planes . We do not consider this case in this paper . Given equation 4 , we have that ‖δ〈p , i〉‖ is small in equation 2 . Finally , it suffices to penalize the norm of δ〈p , i〉 during the optimization . Notice that , by construction , the number of δ ’ s is equal to the number of nodes in the hierarchical tree . Next , consider the output of CNNs for classification , φθ ( · ) TW , ( 5 ) where θ denotes the parameters of the hidden layers , W = [ w1 , . . . , w|L| ] denotes the last fullyconnected layer , and wi denotes the separator for the class i . For simplicity , we omit potential additional nonlinear functions , such as a softmax , on top of the prediction . We have parametrized wi following the recursive formula in equation 4 . To define the matrix formulation of equation 4 , we first introduce the Hierarchical layer H which plays an important role . This hierarchical layer can be identified to the adjacency matrix of the hierarchical graph . Definition 1 . ( Hierarchical layer ) . Consider ordering over the sets P and L , i.e. , for i = 1 , . . . , |P| and j = 1 , . . . , |L| , P = { p1 , . . . , pi , . . . , p|P| } and L = { p1 , . . . , pj , . . . , p|L| } . In other words , we associate to all nodes an index . Then , the hierarchical layer H is defined as H ∈ B|P|×|L| , Hi , j = 1 if npi npj , 0 otherwise . ( 6 ) where npi npj means npj is a parent of npi . We illustrate an example of H in Appendix A . The next proposition shows that equation 5 can be written using a simple matrix-matrix multiplication , involving the hierarchical layer . Proposition 1 . Consider a representation φθ ( · ) , where φθ ( · ) ∈ Rd . LetW be the matrix of separators W = [ wp1 , . . . , wp|L| ] , pi ∈ L , ( 7 ) where the separators are parametrized as equation 4 . Let ∆ be defined as ∆ ∈ Rd×|P| , ∆ = [ δp1 , . . . , δp|P| ] , ( 8 ) where P and L are defined in Section 2.1 . Consider the hierarchical layer defined in Definition 1 . Then , the matrix of separators W can be expressed as W = ∆H . ( 9 ) We can see W = ∆H as a combination of an augmented fully-connected layer , combined with the hierarchical layer that selects the right columns of ∆ , hence the term hierarchically connected layer . The ` 2 regularization of the δ can be conducted by the parameter weight decay , which is widely used in training of neural networks . The hierarchical layer H is fixed , while ∆ is learnable . This does not affect the complexity of the back-propagation significantly , as ∆H is a simple linear form . The size of the last layer slightly increases , from |L| × d to |P| × d , where d is the dimension of the representation φθ ( · ) . For instance , in the case of Tiny-ImageNet , the number of parameters of the last layer only increases by roughly 36 % ; nevertheless , the increased number of parameters of the last layer is still usually negligible in comparison with the total number of parameters for classical network architectures .", "role": "user" }, { "content": "In this paper, the authors proposed a novel reparameterization framework of the last network layer that takes semantic hierarchy into account. Specifically, the authors assume a predefined hierarchy graph, and model the classifier of child classes as a parent classifier plus offsets $\\delta$ recursively. The authors show that such hierarchy can be parameterized a matrix multiplication $\\Delta \\mathbf{H}$ where $\\mathbf{H}$ is predefined by the graph. In addition, the authors further propose to fix the norm of $\\delta$ in a decaying manner with respect to path length. The resulting spherical objective is optimized via Riemannian gradient descent.", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nCharcoal is an effective treatment for acute paraquat poisoning.\n\nResearch Abstract:\nThe present study retrospectively analyzed 19 patients diagnosed with paraquat (PQ) poisoning with the aim to investigate the effect of activated charcoal hemoperfusion on renal function and PQ elimination.\nThe results indicated that 7 patients died and 12 survived.\nNon-oliguric renal failure occurred in all of the 7 patients who died.\nAmong the 12 surviving patients, 10 had normal renal function and 2 developed non-oliguric renal failure.\nThere was a linear correlation between plasma and urine paraquat concentration prior to and during activated charcoal hemoperfusion.\nThe equation parameters together with the correlation coefficient on admission were as follows: Y=0.5820+1.7348X (R2=0.678; F=35.768; P<0.0001).\nThe equation parameters together with the correlation coefficient were as follows during activated charcoal hemoperfusion: Y=0.6827+1.2649X (R2=0.626; F=50.308; P<0.0001).\nTherefore, it was concluded that in patients with normal renal function, the elimination kinetics of PQ by the kidneys were only associated with the plasma PQ concentration.\nActivated charcoal hemoperfusion had little effect on avoiding acute kidney injury in patients with severe PQ poisoning.", "role": "user" }, { "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Activated charcoal hemoperfusion had little effect on avoiding acute kidney injury in patients with severe PQ poisoning.\"]}", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nFACT and other histone chaperone(s) compensate for Histone 2A (H2A)-histone 2B (H2B) dimer eviction during the histone exchange process.\n\nResearch Abstract:\nFACT, a heterodimer of Spt16 and Pob3, is an essential histone chaperone.\nWe show that the H2A-H2B binding activity that is central to FACT function resides in short acidic regions near the C termini of each subunit.\nMutations throughout these regions affect binding and cause correlated phenotypes that range from mild to lethal, with the largest individual contributions unexpectedly coming from an aromatic residue and a nearby carboxylate residue within each domain.\nSpt16 and Pob3 bind overlapping sites on H2A-H2B, and Spt16-Pob3 heterodimers simultaneously bind two H2A-H2B dimers, the same stoichiometry as the components of a nucleosome.\nAn Spt16:H2A-H2B crystal structure explains the biochemical and genetic data, provides a model for Pob3 binding, and implies a mechanism for FACT reorganization that we confirm biochemically.\nMoreover, unexpected similarity to binding of ANP32E and Swr1 with H2A.Z-H2B reveals that diverse H2A-H2B chaperones use common mechanisms of histone binding and regulating nucleosome functions.", "role": "user" }, { "content": "{\"verdict\": \"NEI\", \"evidence\": []}", "role": "assistant" } ]

[ { "content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nUG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish\n\nThe present study describes our submission to SemEval 2018 Task 1: Affect in Tweets. Our Spanish-only approach aimed to demonstrate that it is beneficial to automatically generate additional training data by (i) translating training data from other languages and (ii) applying a semi-supervised learning method. We find strong support for both approaches, with those models outperforming our regular models in all subtasks. However, creating a stepwise ensemble of different models as opposed to simply averaging did not result in an increase in performance. We placed second (EI-Reg), second (EI-Oc), fourth (V-Reg) and fifth (V-Oc) in the four Spanish subtasks we participated in.\n\nIntroduction\nUnderstanding the emotions expressed in a text or message is of high relevance nowadays. Companies are interested in this to get an understanding of the sentiment of their current customers regarding their products and the sentiment of their potential customers to attract new ones. Moreover, changes in a product or a company may also affect the sentiment of a customer. However, the intensity of an emotion is crucial in determining the urgency and importance of that sentiment. If someone is only slightly happy about a product, is a customer willing to buy it again? Conversely, if someone is very angry about customer service, his or her complaint might be given priority over somewhat milder complaints.\n BIBREF0 present four tasks in which systems have to automatically determine the intensity of emotions (EI) or the intensity of the sentiment (Valence) of tweets in the languages English, Arabic, and Spanish. The goal is to either predict a continuous regression (reg) value or to do ordinal classification (oc) based on a number of predefined categories. The EI tasks have separate training sets for four different emotions: anger, fear, joy and sadness. Due to the large number of subtasks and the fact that this language does not have many resources readily available, we only focus on the Spanish subtasks. Our work makes the following contributions:\nOur submissions ranked second (EI-Reg), second (EI-Oc), fourth (V-Reg) and fifth (V-Oc), demonstrating that the proposed method is accurate in automatically determining the intensity of emotions and sentiment of Spanish tweets. This paper will first focus on the datasets, the data generation procedure, and the techniques and tools used. Then we present the results in detail, after which we perform a small error analysis on the largest mistakes our model made. We conclude with some possible ideas for future work.\n\nData\nFor each task, the training data that was made available by the organizers is used, which is a selection of tweets with for each tweet a label describing the intensity of the emotion or sentiment BIBREF1 . Links and usernames were replaced by the general tokens URL and @username, after which the tweets were tokenized by using TweetTokenizer. All text was lowercased. In a post-processing step, it was ensured that each emoji is tokenized as a single token.\n\nTranslating Lexicons\nMost lexical resources for sentiment analysis are in English. To still be able to benefit from these sources, the lexicons in the AffectiveTweets package were translated to Spanish, using the machine translation platform Apertium BIBREF5 .\nAll lexicons from the AffectiveTweets package were translated, except for SentiStrength. Instead of translating this lexicon, the English version was replaced by the Spanish variant made available by BIBREF6 .\nFor each subtask, the optimal combination of lexicons was determined. This was done by first calculating the benefits of adding each lexicon individually, after which only beneficial lexicons were added until the score did not increase anymore (e.g. after adding the best four lexicons the fifth one did not help anymore, so only four were added). The tests were performed using a default SVM model, with the set of word embeddings described in the previous section. Each subtask thus uses a different set of lexicons (see Table TABREF1 for an overview of the lexicons used in our final ensemble). For each subtask, this resulted in a (modest) increase on the development set, between 0.01 and 0.05.\n\nTranslating Data\nThe training set provided by BIBREF0 is not very large, so it was interesting to find a way to augment the training set. A possible method is to simply translate the datasets into other languages, leaving the labels intact. Since the present study focuses on Spanish tweets, all tweets from the English datasets were translated into Spanish. This new set of “Spanish” data was then added to our original training set. Again, the machine translation platform Apertium BIBREF5 was used for the translation of the datasets.\n\nAlgorithms Used\nThree types of models were used in our system, a feed-forward neural network, an LSTM network and an SVM regressor. The neural nets were inspired by the work of Prayas BIBREF7 in the previous shared task. Different regression algorithms (e.g. AdaBoost, XGBoost) were also tried due to the success of SeerNet BIBREF8 , but our study was not able to reproduce their results for Spanish.\nFor both the LSTM network and the feed-forward network, a parameter search was done for the number of layers, the number of nodes and dropout used. This was done for each subtask, i.e. different tasks can have a different number of layers. All models were implemented using Keras BIBREF9 . After the best parameter settings were found, the results of 10 system runs to produce our predictions were averaged (note that this is different from averaging our different type of models in Section SECREF16 ). For the SVM (implemented in scikit-learn BIBREF10 ), the RBF kernel was used and a parameter search was conducted for epsilon. Detailed parameter settings for each subtask are shown in Table TABREF12 . Each parameter search was performed using 10-fold cross validation, as to not overfit on the development set.\n\nSemi-supervised Learning\nOne of the aims of this study was to see if using semi-supervised learning is beneficial for emotion intensity tasks. For this purpose, the DISC BIBREF0 corpus was used. This corpus was created by querying certain emotion-related words, which makes it very suitable as a semi-supervised corpus. However, the specific emotion the tweet belonged to was not made public. Therefore, a method was applied to automatically assign the tweets to an emotion by comparing our scraped tweets to this new data set.\nFirst, in an attempt to obtain the query-terms, we selected the 100 words which occurred most frequently in the DISC corpus, in comparison with their frequencies in our own scraped tweets corpus. Words that were clearly not indicators of emotion were removed. The rest was annotated per emotion or removed if it was unclear to which emotion the word belonged. This allowed us to create silver datasets per emotion, assigning tweets to an emotion if an annotated emotion-word occurred in the tweet.\nOur semi-supervised approach is quite straightforward: first a model is trained on the training set and then this model is used to predict the labels of the silver data. This silver data is then simply added to our training set, after which the model is retrained. However, an extra step is applied to ensure that the silver data is of reasonable quality. Instead of training a single model initially, ten different models were trained which predict the labels of the silver instances. If the highest and lowest prediction do not differ more than a certain threshold the silver instance is maintained, otherwise it is discarded.\nThis results in two parameters that could be optimized: the threshold and the number of silver instances that would be added. This method can be applied to both the LSTM and feed-forward networks that were used. An overview of the characteristics of our data set with the final parameter settings is shown in Table TABREF14 . Usually, only a small subset of data was added to our training set, meaning that most of the silver data is not used in the experiments. Note that since only the emotions were annotated, this method is only applicable to the EI tasks.\n\nEnsembling\nTo boost performance, the SVM, LSTM, and feed-forward models were combined into an ensemble. For both the LSTM and feed-forward approach, three different models were trained. The first model was trained on the training data (regular), the second model was trained on both the training and translated training data (translated) and the third one was trained on both the training data and the semi-supervised data (silver). Due to the nature of the SVM algorithm, semi-supervised learning does not help, so only the regular and translated model were trained in this case. This results in 8 different models per subtask. Note that for the valence tasks no silver training data was obtained, meaning that for those tasks the semi-supervised models could not be used.\nPer task, the LSTM and feed-forward model's predictions were averaged over 10 prediction runs. Subsequently, the predictions of all individual models were combined into an average. Finally, models were removed from the ensemble in a stepwise manner if the removal increased the average score. This was done based on their original scores, i.e. starting out by trying to remove the worst individual model and working our way up to the best model. We only consider it an increase in score if the difference is larger than 0.002 (i.e. the difference between 0.716 and 0.718). If at some point the score does not increase and we are therefore unable to remove a model, the process is stopped and our best ensemble of models has been found. This process uses the scores on the development set of different combinations of models. Note that this means that the ensembles for different subtasks can contain different sets of models. The final model selections can be found in Table TABREF17 .\n\nResults and Discussion\nTable TABREF18 shows the results on the development set of all individuals models, distinguishing the three types of training: regular (r), translated (t) and semi-supervised (s). In Tables TABREF17 and TABREF18 , the letter behind each model (e.g. SVM-r, LSTM-r) corresponds to the type of training used. Comparing the regular and translated columns for the three algorithms, it shows that in 22 out of 30 cases, using translated instances as extra training data resulted in an improvement. For the semi-supervised learning approach, an improvement is found in 15 out of 16 cases. Moreover, our best individual model for each subtask (bolded scores in Table TABREF18 ) is always either a translated or semi-supervised model. Table TABREF18 also shows that, in general, our feed-forward network obtained the best results, having the highest F-score for 8 out of 10 subtasks.\nHowever, Table TABREF19 shows that these scores can still be improved by averaging or ensembling the individual models. On the dev set, averaging our 8 individual models results in a better score for 8 out of 10 subtasks, while creating an ensemble beats all of the individual models as well as the average for each subtask. On the test set, however, only a small increase in score (if any) is found for stepwise ensembling, compared to averaging. Even though the results do not get worse, we cannot conclude that stepwise ensembling is a better method than simply averaging.\nOur official scores (column Ens Test in Table TABREF19 ) have placed us second (EI-Reg, EI-Oc), fourth (V-Reg) and fifth (V-Oc) on the SemEval AIT-2018 leaderboard. However, it is evident that the results obtained on the test set are not always in line with those achieved on the development set. Especially on the anger subtask for both EI-Reg and EI-Oc, the scores are considerably lower on the test set in comparison with the results on the development set. Therefore, a small error analysis was performed on the instances where our final model made the largest errors.\n\nError Analysis\nDue to some large differences between our results on the dev and test set of this task, we performed a small error analysis in order to see what caused these differences. For EI-Reg-anger, the gold labels were compared to our own predictions, and we manually checked 50 instances for which our system made the largest errors.\nSome examples that were indicative of the shortcomings of our system are shown in Table TABREF20 . First of all, our system did not take into account capitalization. The implications of this are shown in the first sentence, where capitalization intensifies the emotion used in the sentence. In the second sentence, the name Imperator Furiosa is not understood. Since our texts were lowercased, our system was unable to capture the named entity and thought the sentence was about an angry emperor instead. In the third sentence, our system fails to capture that when you are so angry that it makes you laugh, it results in a reduced intensity of the angriness. Finally, in the fourth sentence, it is the figurative language me infla la vena (it inflates my vein) that the system is not able to understand.\nThe first two error-categories might be solved by including smart features regarding capitalization and named entity recognition. However, the last two categories are problems of natural language understanding and will be very difficult to fix.\n\nConclusion\nTo conclude, the present study described our submission for the Semeval 2018 Shared Task on Affect in Tweets. We participated in four Spanish subtasks and our submissions ranked second, second, fourth and fifth place. Our study aimed to investigate whether the automatic generation of additional training data through translation and semi-supervised learning, as well as the creation of stepwise ensembles, increase the performance of our Spanish-language models. Strong support was found for the translation and semi-supervised learning approaches; our best models for all subtasks use either one of these approaches. These results suggest that both of these additional data resources are beneficial when determining emotion intensity (for Spanish). However, the creation of a stepwise ensemble from the best models did not result in better performance compared to simply averaging the models. In addition, some signs of overfitting on the dev set were found. In future work, we would like to apply the methods (translation and semi-supervised learning) used on Spanish on other low-resource languages and potentially also on other tasks.\n\nTable 1: Lexicons included in our final ensemble. NRC-10 and SentiWordNet are left out of the table because they never improved the score for a task.\n\nTable 2: Parameter settings for the algorithms used. For feed-forward, we show the number of nodes per layer. The Dense column for LSTM shows whether a dense layer was added after the LSTM layers (with half the number of nodes as is shown in the Nodes column). The feed-forward networks always use a dropout of 0.001 after the first layer.\n\nTable 3: Statistics and parameter settings of the semi-supervised learning experiments.\n\nTable 4: Models included in our final ensemble.\n\nTable 5: Scores for each individual model per subtask. Best individual score per subtask is bolded.\n\nTable 6: Results on the dev and test set for averaging and stepwise ensembling the individual models. The last column shows our official results.\n\nTable 7: Error analysis for the EI-Reg-anger subtask, with English translations.\n\nQuery: What dataset did they use?\n\nJSON Response:", "role": "user" }, { "content": "{\"answer\": \" Selection of tweets with for each tweet a label describing the intensity of the emotion or sentiment provided by organizers and tweets translated form English to Spanish.\", \"evidence\": [\"For each task, the training data that was made available by the organizers is used, which is a selection of tweets with for each tweet a label describing the intensity of the emotion or sentiment BIBREF1 . Links and usernames were replaced by the general tokens URL and @username, after which the tweets were tokenized by using TweetTokenizer. All text was lowercased. In a post-processing step, it was ensured that each emoji is tokenized as a single token.\", \"The training set provided by BIBREF0 is not very large, so it was interesting to find a way to augment the training set. A possible method is to simply translate the datasets into other languages, leaving the labels intact. Since the present study focuses on Spanish tweets, all tweets from the English datasets were translated into Spanish. This new set of \\u201cSpanish\\u201d data was then added to our original training set. Again, the machine translation platform Apertium BIBREF5 was used for the translation of the datasets.\"]}", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nEnriching Knowledge Bases with Interesting Negative Statements\n\nMain Content:\nIn this paper , we make the case for explicitly stating interesting statements which are not true . Negative statements would be important to overcome current limitations of question answering , yet due to their potential abundance , any effort towards compiling them needs a tight coupling with ranking . We introduce two approaches towards automatically compiling negative statements . ( i ) In peer-based statistical inferences , we compare entities with highly related entities in order to derive potential negative statements , which we then rank using supervised and unsupervised features . ( ii ) In pattern-based query log extraction , we use a pattern-based approach for harvesting search engine query logs . Experimental results show that both approaches hold promising and complementary potential . Along with this paper , we publish the first datasets on interesting negative information , containing over 1.4M statements for 130K popular Wikidata entities . 1 . Introduction . Motivation and problem Structured knowledge is crucial in a range of applications like question answering , dialogue agents , and recommendation systems . The required knowledge is usually stored in KBs , and recent years have seen a rise of interest in KB construction , querying and maintenance , with notable projects being Wikidata [ Vrandečić and Krötzsch , 2014 ] , DBpedia [ Auer et al. , 2007 ] , Yago [ Suchanek et al. , 2007 ] , or the Google Knowledge Graph [ Singhal , 2012 ] . These KBs store positive statements such as “ Canberra is the capital of Australia ” , and are a key asset for many knowledge-intensive AI applications . A major limitation of most of these KBs is their inability to deal with negative information . At present , the major KBs contain virtually only positive statements , whereas statements such as that “ Tom Cruise did not win an Oscar ” could only be inferred with the major assumption that the KB is complete - the so-called closed-world assumption ( CWA ) . Yet as KBs are only pragmatic collections of positive statements , the CWA is not realistic to assume , and there remains uncertainty whether statements not contained in a KBs are false , or truth is merely unknown to the KB . Not being able to formally distinguish whether a statement is false or unknown poses challenges in a variety of applications . In medicine , for instance , it is important to distinguish between knowing about the absence of a biochemical reaction between substances , and not knowing about its existence at all . In corporate integrity , it is important to know whether a person was never employed by a certain competitor , while in anti-corruption investigations , absence of family relations needs to be ascertained . In data science and machine learning , on-the-spot counterexamples are important to ensure the correctness of learned extraction patterns and associations . State of the art and its limitations Current web-scale KBs contain almost only positive statements , and this is engraved in the open-world assumption ( OWA ) employed on the semantic web , which states that asserted statements are true , while the remainder is unknown ( a notable exception being Wikidata , which we discuss in details in Appendix E ) . Some formal entailment regimes like OWL [ McGuinness et al. , 2004 ] go beyond the OWA assumption , and allow to infer negation , yet are intended for use at query time , not for static materialization , and also lack ranking facilities . Similarly , data constraints [ Marx and Krötzsch , 2017 ] and association rules [ Ortona et al. , 2018 ] can in principle yield negative statements , but face the same challenges . This has consequences for usage of KBs : for instance , today ’ s question answering ( QA ) systems are well geared for positive questions , and questions where exactly one answer should be returned ( e.g. , quiz questions or reading comprehension tasks ) [ Fader et al. , 2014 , Yang et al. , 2015 ] . In contrast , for answering negative questions like “ Actors without Oscars ” , QA systems lack a data basis . Similarly , they struggle with positive questions that have no answer , like “ Children of Angela Merkel ” , too often still returning a besteffort answer even if it is incorrect . Materialized negative information would allow a better treatment of both cases . Approach and contribution In this paper , we make the case that important negative knowledge should be explicitly materialized . We motivate this selective materialization with the challenge of overseeing a near-infinite space of possibly true statements that are not asserted in KBs , and with the importance of explicit negation in search and question answering . We then develop two complementary approaches towards generating negative statements : statistical ranking methods for statements derived based on related entities , and pattern-based text extraction , applied to high-quality search engine query logs . We also present the first datasets on interesting negative information , and highlight the usefulness of negative knowledge in extrinsic use cases . Our salient contributions are : 1 . We make the first comprehensive case for materializing interesting negative statements in KBs ; 2 . We present two judiciously designed methods for collecting negative statements : peerbased statistical inference and pattern-based text extraction ; 3 . We produce two datasets containing over 1.4M interesting negative statements for 130K popular Wikidata subjects . 4 . We show the usefulness of negative knowledge in a QA use case . 2 . Problem and Design Space . Formalization For the remainder we assume that a KB is a set of statements , each being a triple ( s ; p ; o ) of subject s , property p and object o . Let Ki be an ( imaginary ) ideal KB that perfectly represents reality , i.e. , contains exactly those statements that hold in reality . Under the OWA , ( practically ) available KBs Ka contain correct statements , but may be incomplete , so the condition Ka ⊆ Ki holds , but not the converse [ Razniewski and Nutt , 2011 ] . We distinguish two forms of negative statements : Definition 1 ( Negative statements ) 1 . A grounded negative statement ¬ ( s ; p ; o ) is satisfied if ( s ; p ; o ) is not in Ki . 2 . A universally negative statement ¬∃ ( s ; p ; ) is satisfied if there exists no o such that ( s ; p ; o ) ∈ Ki . Both statements represent standard logical constructs , and could also be expressed in the OWL ontology language . Grounded negative statements could be expressed via negative property statements ( e.g. , NegativeObjectPropertyStatement ( : hasWife : Bill : Mary ) ) , while universally negative statements could be expressed via owl : complementOf and ObjectSomeValuesFrom [ Erxleben et al. , 2014 ] . For these classes of negative statements , checking that there is no conflict with a positive statement is trivial . Yet compiling negative statements faces two other challenges . First , being not in conflict with positive statements is a necessary but not a sufficient condition for correctness of negation , due to the OWA . In particular , Ki is only a virtual construct , so methods to derive correct negative statements have to rely on the limited positive information contained in Ka , or utilize external evidence , e.g. , from text . Second , the set of correct negative statements is near-infinite , especially for grounded negative statements . Thus , unlike for positive statements , negative statement construction/extraction needs a tight coupling with ranking methods . Problem 1 Given an entity e in a KB , compile a ranked list of interesting grounded negative and universally negative statements . Design space A first thought is that deletions from time-variant KBs are a natural source . For instance , in Wikidata , for subjects of type person within the last year , more than 500K triples have been deleted . Yet on careful inspection we found that most of these concern ontology restructuring , granularity refinements , or blatant typos , thus do not give rise to interesting negation . A second conceivable approach is to leverage the CWA , or its relaxed variant PCA ( Partial Completeness Assumption , aka . LCWA for Local CWA ) [ Galárraga et al. , 2015 ] , to generate negative statements . Using just the active domain of Wikidata for grounding , the CWA would give rise to about 6.4×1018 negative statements1 . Assuming that Wikidata covers 10 % of all true statements per entity , more than 99.999 % of the negative statements would be correct , but hardly interesting . For the PCA , the total would be about 3.2× 1016 negative statements ( assuming an average of 5 populated properties per entity ) , and almost all of these would be correct . But these approaches would miss the true issue : merely enumerating huge sets of negative statements is not insightful even with ( trivially ) high precision . The key challenge rather is to identify interesting statements that users find noteworthy . Instead , we propose methods that follow two major paradigms for KB construction and completion : Statistical inference and text extraction . 1 . 80 Million subjects times 1000 properties times 80 Million objects . Statistical inference methods , ranging from association rule mining such as AMIE and RuDiK [ Galárraga et al. , 2013 , Ortona et al. , 2018 ] to embedding models such as TransE and HolE [ Bordes et al. , 2013 , Nickel et al. , 2016 ] can predict positive statements and provide ranked lists of role fillers for KB relations . In Section 4 , we develop a statistical inference method for negative statements , which generates candidate sets from related entities , and uses a set of popularity and probability heuristics in order to rank these statements . Textual information extraction ( IE ) is a standard paradigm for KB construction . Common challenges in textual IE comprise noise and sparsity in observations , and canonicalization of entities and predicates . Our goal is to achieve maximal flexibility w.r.t . open predicates , and to overcome sparsity in negative statements in texts . Section 5 presents a method that combines pattern-based and open information extraction , and applies it to a particularly rich data source , search engine query logs . 3 . Related Work . The problem of compiling informative negative statements about entities is new , so there are no directly comparable methods . Nevertheless , there is prior work on rule mining over KBs [ Galárraga et al. , 2015 ] that is conceivably useful in our context . Most notably , [ Galárraga et al. , 2017 ] employed rule mining to predict the completeness of properties for given entities . This corresponds to learning whether the PCA holds in a local part of the KB , inferring that all absent values for a subject-predicate pair are false . For our task , this could be a building block , but it does not address the inference of interesting negative statements . [ Ortona et al. , 2018 ] devised a rule mining system that can learn rules with negative atoms in rule heads ( e.g. , people born in Germany can not be US president ) . This could be utilized towards predicting negative statements . Unfortunately , the mining also discovers many convoluted and exotic rules ( e.g. , people whose body weight is less than their birth year can not win the Nobel prize ) , often with a large number of atoms in the rule body , and such rules are among the top-ranked ones . Even good rules , such as “ people with birth year after 2000 do not win the Nobel prize ” , are not that useful for our task . Such rules predict way too many – correct , but uninformative – negative statements , essentially enumerating a huge set of people who are not Nobel laureates . [ Ortona et al. , 2018 ] also proposed a precision-oriented variant of PCA that assumes negation only if subject and object are connected by at least one other relation . Unfortunately , this condition is rarely met in interesting cases . For instance , none of the negative statements in Table 2 have alternative connections between subject and object in Wikidata . Another related line of work is learning which attributes are mandatory in a KB , for only non-mandatory absent predicates are candidates for universal absence . [ Lajus and Suchanek , 2018 ] exploits density differences along type hierarchies to this end . This could be an initial filter towards discovering negative statements , but does not address our key problem of inferring when a missing statement is truly negative and interesting .", "role": "user" }, { "content": "The paper addresses the problem of negative statements in knowledgebase. They formalize the types of negative statements: (a) grounded statement: [s,p,o] does not exist in KB, (b) not exist [s, p, o] (there's no object that satisfy s,p]. To find negative statements, they proposes two methods (a) peer-based candidate retrieval (i.e., heuristic of finding relation that is frequently populated in nearby entities but missing in the target entity) and (b) Using search logs with meta patterns (i.e., search query logs for pattern such as \"Why XXX not\", and find retrieved queries such as \"Why XXX never won the Oscar).", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nScore-Based Generative Modeling with Critically-Damped Langevin Diffusion\n\nMain Content:\n1 INTRODUCTION . Score-based generative models ( SGMs ) and denoising diffusion probabilistic models have emerged as a promising class of generative models ( Sohl-Dickstein et al. , 2015 ; Song et al. , 2021c ; b ; Vahdat et al. , 2021 ; Kingma et al. , 2021 ) . SGMs offer high quality synthesis and sample diversity , do not require adversarial objectives , and have found applications in image ( Ho et al. , 2020 ; Nichol & Dhariwal , 2021 ; Dhariwal & Nichol , 2021 ; Ho et al. , 2021 ) , speech ( Chen et al. , 2021 ; Kong et al. , 2021 ; Jeong et al. , 2021 ) , and music synthesis ( Mittal et al. , 2021 ) , image editing ( Meng et al. , 2021 ; Sinha et al. , 2021 ; Furusawa et al. , 2021 ) , super-resolution ( Saharia et al. , 2021 ; Li et al. , 2021 ) , image-to-image translation ( Sasaki et al. , 2021 ) , and 3D shape generation ( Luo & Hu , 2021 ; Zhou et al. , 2021 ) . SGMs use a diffusion process to gradually add noise to the data , transforming a complex data distribution to an analytically tractable prior distribution . A neural network is then utilized to learn the score function—the gradient of the log probability density—of the perturbed data . The learnt scores can be used to solve a stochastic differential equation ( SDE ) to synthesize new samples . This corresponds to an iterative denoising process , inverting the forward diffusion . In the seminal work by Song et al . ( 2021c ) , it has been shown that the score function that needs to be learnt by the neural network is uniquely determined by the forward diffusion process . Consequently , the complexity of the learning problem depends , other than on the data itself , only on the diffusion . Hence , the diffusion process is the key component of SGMs that needs to be revisited to further improve SGMs , for example , in terms of synthesis quality or sampling speed . Inspired by statistical mechanics ( Tuckerman , 2010 ) , we propose a novel forward diffusion process , the critically-damped Langevin diffusion ( CLD ) . In CLD , the data variable , xt ( time t along the diffusion ) , is augmented with an additional “ velocity ” variable vt and a diffusion process is run in the joint data-velocity space . Data and velocity are coupled to each other as in Hamiltonian dynamics , and noise is injected only into the velocity variable . Similarly as in Hamiltonian Monte Carlo ( Neal , 2011 ) , the Hamiltonian component helps to efficiently traverse the joint data-velocity space and to transform the data distribution into the prior distribution more smoothly . We derive the corresponding score matching objective and show that for CLD the neural network is tasked with learning only the score of the conditional distribution of velocity given data∇vt log pt ( vt|xt ) , which is arguably easier than learning the score of diffused data distribution directly . Using techniques from molecular dynamics ( Bussi & Parrinello , 2007 ; Tuckerman , 2010 ; Leimkuhler & Matthews , 2013 ) , we also derive a novel SDE integrator tailored to CLD ’ s reverse-time synthesis SDE . We extensively validate CLD and the new SDE solver : ( i ) We show that the neural networks learnt in CLD-based SGMs are smoother than those of previous SGMs . ( ii ) On the CIFAR-10 image modeling benchmark , we demonstrate that CLD-based models outperform previous diffusion models in synthesis quality for similar network architectures and sampling compute budgets . We attribute these positive results to the Hamiltonian component in the diffusion and to CLD ’ s easier score function target , the velocity-data conditional distribution . ( iii ) We show that our novel sampling scheme for CLD significantly outperforms the popular Euler–Maruyama method . ( iv ) We perform ablations on various aspects of CLD and find that CLD does not have difficult-to-tune hyperparameters . In summary , we make the following technical contributions : ( i ) We propose CLD , a novel diffusion process for SGMs . ( ii ) We derive a score matching objective for CLD , which requires only the conditional distribution of velocity given data . ( iii ) We propose a new type of denoising score matching ideally suited for scalable training of CLD-based SGMs . ( iv ) We derive a tailored SDE integrator that enables efficient sampling from CLD-based models . ( v ) Overall , we provide novel insights into SGMs and point out important new connections to statistical mechanics . 2 BACKGROUND . Consider a diffusion process ut ∈ Rd defined by the Itô SDE dut = f ( ut , t ) dt+G ( ut , t ) dwt , t ∈ [ 0 , T ] , ( 1 ) with continuous time variable t ∈ [ 0 , T ] , standard Wiener process wt , drift coefficient f : Rd × [ 0 , T ] → Rd and diffusion coefficientG : Rd× [ 0 , T ] → Rd×d . Defining ūt : = uT−t , a corresponding reverse-time diffusion process that inverts the above forward diffusion can be derived ( Anderson , 1982 ; Haussmann & Pardoux , 1986 ; Song et al. , 2021c ) ( with positive dt and t ∈ [ 0 , T ] ) : dūt = [ −f ( ūt , T − t ) +G ( ūt , T − t ) G ( ūt , T − t ) > ∇ūt log pT−t ( ūt ) ] dt+G ( ūt , T − t ) dwt , ( 2 ) where∇ūt log pT−t ( ūt ) is the score function of the marginal distribution over ūt at time T − t. The reverse-time process can be used as a generative model . In particular , Song et al . ( 2021c ) model data x , setting p ( u0 ) =pdata ( x ) . Currently used SDEs ( Song et al. , 2021c ; Kim et al. , 2021 ) have drift and diffusion coefficients of the simple form f ( xt , t ) =f ( t ) xt andG ( xt , t ) =g ( t ) Id . Generally , f and G are chosen such that the SDE ’ s marginal , equilibrium density is approximately Normal at time T , i.e. , p ( uT ) ≈N ( 0 , Id ) . We can then initialize x0 based on a sample drawn from a complex data distribution , corresponding to a far-from-equilibrium state . While we let the state x0 relax towards equilibrium via the forward diffusion , we can learn a model sθ ( xt , t ) for the score ∇xt log pt ( xt ) , which can be used for synthesis via the reverse-time SDE in Eq . ( 2 ) . If f and G take the simple form from above , the denoising score matching ( Vincent , 2011 ) objective for this task is : min θ Et∼ [ 0 , T ] Ex0∼p ( x0 ) Ext∼pt ( xt|x0 ) [ λ ( t ) ‖sθ ( xt , t ) −∇xt log pt ( xt|x0 ) ‖22 ] ( 3 ) If f and G are affine , the conditional distribution pt ( xt|x0 ) is Normal and available analytically ( Särkkä & Solin , 2019 ) . Different λ ( t ) result in different trade-offs between synthesis quality and likelihood in the generative model defined by sθ ( xt , t ) ( Song et al. , 2021b ; Vahdat et al. , 2021 ) . 3 CRITICALLY-DAMPED LANGEVIN DIFFUSION . We propose to augment the data xt ∈ Rd with auxiliary velocity1 variables vt ∈ Rd and utilize a diffusion process that is run in the joint xt-vt-space . With ut = ( xt , vt ) > ∈ R2d , we set f ( ut , t ) : = ( 0 βM−1 −β −ΓβM−1 ) ⊗ Id ut , G ( ut , t ) : = ( 0 0 0 √ 2Γβ ) ⊗ Id , ( 4 ) where ⊗ denotes the Kronecker product . The coupled SDE that describes the diffusion process is ( dxt dvt ) = ( M−1vt −xt ) βdt︸ ︷︷ ︸ Hamiltonian component= : H + ( 0 −ΓM−1vt ) βdt+ ( 0√ 2Γβ ) dwt︸ ︷︷ ︸ Ornstein-Uhlenbeck process= : O , ( 5 ) which corresponds to Langevin dynamics in each dimension . That is , each xi is independently coupled to a velocity vi , which explains the blockwise structure of f and G. The mass M ∈ R+ is a hyperparameter that determines the coupling between the xt and vt variables ; β ∈ R+ is a constant time rescaling chosen such that the diffusion converges to its equilibrium distribution within t ∈ [ 0 , T ] ( in practice , we set T=1 ) when initialized from a data-defined non-equilibrium state and is analogous to β ( t ) in previous diffusions ( we could also use time-dependent β ( t ) , but found constant β ’ s to work well , and therefore opted for simplicity ) ; Γ ∈ R+ is a friction coefficient that determines the strength of the noise injection into the velocities . Notice that the SDE in Eq . ( 5 ) consists of two components . The H term represents a Hamiltonian component . Hamiltonian dynamics are frequently used in Markov chain Monte Carlo methods to accelerate sampling and efficiently explore complex probability distributions ( Neal , 2011 ) . The Hamiltonian component in our diffusion process plays a similar role and helps to quickly and smoothly converge the initial joint data-velocity distribution to the equilibrium , or prior ( see Fig . 1 ) . Furthermore , Hamiltonian dynamics on their own are trivially invertible ( Tuckerman , 2010 ) , which intuitively is also beneficial in our situation when using this diffusion for training SGMs . TheO term corresponds to an OrnsteinUhlenbeck process ( Särkkä & Solin , 2019 ) in the velocity component , which injects noise such that the diffusion dynamics properly converge to equilibrium for any Γ > 0 . It can be shown that the equilibrium distribution of this diffusion is pEQ ( u ) = N ( x ; 0d , Id ) N ( v ; 0d , MId ) ( see App . B.2 ) . There is a crucial balance between M and Γ ( McCall , 2010 ) : For Γ2 < 4M ( underdamped Langevin dynamics ) the Hamiltonian component dominates , which implies oscillatory dynamics of xt and vt that slow down convergence to equilibrium . For Γ2 > 4M ( overdamped Langevin dynamics ) the Oterm dominates which also slows down convergence , since the accelerating effect by the Hamiltonian component is suppressed due to the strong noise injection . For Γ2=4M ( critical damping ) , an ideal balance is achieved and convergence to pEQ ( u ) occurs as fast as possible in a smooth manner without oscillations ( also see discussion in App . A.1 ) ( McCall , 2010 ) . Hence , we propose to set Γ2=4M and call the resulting diffusion critically-damped Langevin diffusion ( CLD ) ( see Fig . 1 ) . Diffusions such as the VPSDE ( Song et al. , 2021c ) correspond to overdamped Langevin dynamics with high friction coefficients Γ ( see App . A.2 ) . Furthermore , in previous works noise is injected directly into the data variables ( pixels , for images ) . In CLD , only the velocity variables are subject to direct noise and the data is perturbed only indirectly due to the coupling between xt and vt .", "role": "user" }, { "content": "The authors propose critically-damped Langevin dynamics (CLD) for score-based generative modeling. This consists of a higher-order Langevin dynamics scheme with particle velocity and position coupled to each other, as in Hamiltonian dynamics. The Langevin dynamics is critical in the sense that it is neither over- nor under-damped. A corresponding score matching objective is derived as an objective, with proof given that it is simply necessary to approximate the score of the velocity given the position. Empirical evidence is provided that this score is easier to estimate on a synthetic example. As DSM is analytically intractable for the higher-order scheme, Hybrid Score Matching (HSM) is proposed and the integration integral to this objective is addressed with a new numerical integration scheme. This approximation scheme, called Symmetric Splitting CLD Sample (SSCS), decomposes the SDE to be integrated into a tractable expression and a (hopefully small) Euler-Maruyama integration for improved accuracy (although still first order) overall. Synthetic examples are used in both the main text and the supplementary material to motivate the theory. Benchmark image datasets exhibit exceptionally strong performance, with improved sample efficiency after training and robust hyperparameters.", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nAndrogenetic haploid mouse embryonic stem cells (ESCs) can be derived and genetically manipulated in vitro.\n\nResearch Abstract:\nHaploid cells are amenable for genetic analysis.\nRecent success in the derivation of mouse haploid embryonic stem cells (haESCs) via parthenogenesis has enabled genetic screening in mammalian cells.\nHowever, successful generation of live animals from these haESCs, which is needed to extend the genetic analysis to the organism level, has not been achieved.\nHere, we report the derivation of haESCs from androgenetic blastocysts.\nThese cells, designated as AG-haESCs, partially maintain paternal imprints, express classical ESC pluripotency markers, and contribute to various tissues, including the germline, upon injection into diploid blastocysts.\nStrikingly, live mice can be obtained upon injection of AG-haESCs into MII oocytes, and these mice bear haESC-carried genetic traits and develop into fertile adults.\nFurthermore, gene targeting via homologous recombination is feasible in the AG-haESCs.\nOur results demonstrate that AG-haESCs can be used as a genetically tractable fertilization agent for the production of live animals via injection into oocytes.", "role": "user" }, { "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Strikingly, live mice can be obtained upon injection of AG-haESCs into MII oocytes, and these mice bear haESC-carried genetic traits and develop into fertile adults.\", \"Our results demonstrate that AG-haESCs can be used as a genetically tractable fertilization agent for the production of live animals via injection into oocytes.\"]}", "role": "assistant" } ]

[ { "content": "| Compound | Glass sample | Glass sample | Glass sample | Glass sample | Glass sample | Glass sample |\n| Compound | SBNx | SBNx | SBNxCs | SBNxCs | SBNxVy | SBNxVy |\n| Compound | Nominal (wt%) | EDS (wt%) | Nominal (wt%) | EDS (wt%) | Nominal (wt%) | EDS (wt%) |\n| SiO2 | 42.3-58.3 | 41-59+-1 | 42.5-57.2 | 41.8-58+-1 | 35-56.2 | 34-56+-1 |\n| B2O3 | 23.1-31.9 | - | 22.9-31.7 | - | 17-30.8 | - |\n| Na2O | 7-34.6 | 6-36+-2 | 7-34.6 | 5.5-34+-2 | 7-34.6 | 6-34+-2 |\n| | | | | | | |\n| Added compounds | Added compounds | Added compounds | Added compounds | Added compounds | Added compounds | Added compounds |\n| Na2SO4 | 6.7-8.8 | 0-2.8+-0.2 | | | 5-8 | 0.3-2.9+-0.2 |\n| Cs2SO4 | | | 17-22 | 0-1.4+-0.2 | | |\n| V2O5 | | | | | 5-15 | 4.3-15.2+-0.4 |\n| | | | | | | |\n| Structural parameters | Structural parameters | Structural parameters | Structural parameters | Structural parameters | Structural parameters | Structural parameters |\n| R | 0.3-1.36+-0.05 | 0.3-1.36+-0.05 | 0.3-1.36+-0.05 | 0.3-1.36+-0.05 | 0.3-1.36+-0.05 | 0.3-1.36+-0.05 |\n| K | 1.58-2.12+-0.05 | 1.58-2.12+-0.05 | 1.58-2.12+-0.05 | 1.58-2.12+-0.05 | 1.58-2.12+-0.05 | 1.58-2.12+-0.05 |\n| R * =3K/16 | 0.79-0.89+-0.01 | 0.79-0.89+-0.01 | 0.79-0.89+-0.01 | 0.79-0.89+-0.01 | 0.79-0.89+-0.01 | 0.79-0.89+-0.01 |\n| r | 2.29-2.52+-0.001 | 2.29-2.52+-0.001 | 2.4-2.52+-0.001 | 2.4-2.52+-0.001 | 2.30-2.52+-0.001 | 2.30-2.52+-0.001 |\n\nCaption: Nominal and EDS - measured composition (in weight%) and density of the simplified three- and four-oxide glass samples produced and analysed in this work\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"42.3\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"wt\", \"material\": \"SBNx\"}\n{\"value\": \"58.3\", \"type\": \"Other\"}\n{\"value\": \"41\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"wt\", \"material\": \"SBNx\"}\n{\"value\": \"59\", \"type\": \"Other\"}\n{\"value\": \"42.5\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"wt\", \"material\": \"SBNxCs\"}\n{\"value\": \"57.2\", \"type\": \"Other\"}\n{\"value\": \"41.8\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"wt\", \"material\": \"SBNxCs\"}\n{\"value\": \"58\", \"type\": \"Other\"}\n{\"value\": \"35\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"wt\", \"material\": \"SBNxVy\"}\n{\"value\": \"56.2\", \"type\": \"Other\"}\n{\"value\": \"34\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"wt\", \"material\": \"SBNxVy\"}\n{\"value\": \"56\", \"type\": \"Other\"}\n{\"value\": \"23.1\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"B2O3\", \"unit\": \"wt\", \"material\": \"SBNx\"}\n{\"value\": \"31.9\", \"type\": \"Other\"}\n{\"value\": \"22.9\", \"type\": \"Other\"}\n{\"value\": \"31.7\", \"type\": \"Other\"}\n{\"value\": \"17\", \"type\": \"Other\"}\n{\"value\": \"30.8\", \"type\": \"Other\"}\n{\"value\": \"7\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"wt\", \"material\": \"SBNx\"}\n{\"value\": \"34.6\", \"type\": \"Other\"}\n{\"value\": \"6\", \"type\": \"Other\"}\n{\"value\": \"36\", \"type\": \"Other\"}\n{\"value\": \"7\", \"type\": \"Other\"}\n{\"value\": \"34.6\", \"type\": \"Other\"}\n{\"value\": \"5.5\", \"type\": \"Other\"}\n{\"value\": \"34\", \"type\": \"Other\"}\n{\"value\": \"7\", \"type\": \"Other\"}\n{\"value\": \"34.6\", \"type\": \"Other\"}\n{\"value\": \"6\", \"type\": \"Other\"}\n{\"value\": \"34\", \"type\": \"Other\"}\n{\"value\": \"6.7\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2SO4\", \"unit\": \"wt\", \"material\": \"SBNx\"}\n{\"value\": \"8.8\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"2.8\", \"type\": \"Other\"}\n{\"value\": \"5\", \"type\": \"Other\"}\n{\"value\": \"8\", \"type\": \"Other\"}\n{\"value\": \"0.3\", \"type\": \"Other\"}\n{\"value\": \"2.9\", \"type\": \"Other\"}\n{\"value\": \"17\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Cs2SO4\", \"unit\": \"wt\", \"material\": \"SBNxCs\"}\n{\"value\": \"22\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"1.4\", \"type\": \"Other\"}\n{\"value\": \"5\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2SO4\", \"unit\": \"wt\", \"material\": \"SBNxVy\"}\n{\"value\": \"15\", \"type\": \"Other\"}\n{\"value\": \"4.3\", \"type\": \"Other\"}\n{\"value\": \"15.2\", \"type\": \"Other\"}\n{\"value\": \"0.3\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2SO4\", \"unit\": \"wt\", \"material\": \"SBNxVy\"}\n{\"value\": \"1.36\", \"type\": \"Other\"}\n{\"value\": \"0.3\", \"type\": \"Other\"}\n{\"value\": \"1.36\", \"type\": \"Other\"}\n{\"value\": \"0.3\", \"type\": \"Other\"}\n{\"value\": \"1.36\", \"type\": \"Other\"}\n{\"value\": \"0.3\", \"type\": \"Other\"}\n{\"value\": \"1.36\", \"type\": \"Other\"}\n{\"value\": \"0.3\", \"type\": \"Other\"}\n{\"value\": \"1.36\", \"type\": \"Other\"}\n{\"value\": \"0.3\", \"type\": \"Other\"}\n{\"value\": \"1.36\", \"type\": \"Other\"}\n{\"value\": \"1.58\", \"type\": \"Other\"}\n{\"value\": \"2.12\", \"type\": \"Other\"}\n{\"value\": \"1.58\", \"type\": \"Other\"}\n{\"value\": \"2.12\", \"type\": \"Other\"}\n{\"value\": \"1.58\", \"type\": \"Other\"}\n{\"value\": \"2.12\", \"type\": \"Other\"}\n{\"value\": \"1.58\", \"type\": \"Other\"}\n{\"value\": \"2.12\", \"type\": \"Other\"}\n{\"value\": \"1.58\", \"type\": \"Other\"}\n{\"value\": \"2.12\", \"type\": \"Other\"}\n{\"value\": \"1.58\", \"type\": \"Other\"}\n{\"value\": \"2.12\", \"type\": \"Other\"}\n{\"value\": \"0.79\", \"type\": \"Other\"}\n{\"value\": \"0.89\", \"type\": \"Other\"}\n{\"value\": \"0.79\", \"type\": \"Other\"}\n{\"value\": \"0.89\", \"type\": \"Other\"}\n{\"value\": \"0.79\", \"type\": \"Other\"}\n{\"value\": \"0.89\", \"type\": \"Other\"}\n{\"value\": \"0.79\", \"type\": \"Other\"}\n{\"value\": \"0.89\", \"type\": \"Other\"}\n{\"value\": \"0.79\", \"type\": \"Other\"}\n{\"value\": \"0.89\", \"type\": \"Other\"}\n{\"value\": \"0.79\", \"type\": \"Other\"}\n{\"value\": \"0.89\", \"type\": \"Other\"}\n{\"value\": \"2.29\", \"type\": \"Other\"}\n{\"value\": \"2.52\", \"type\": \"Other\"}\n{\"value\": \"2.29\", \"type\": \"Other\"}\n{\"value\": \"2.52\", \"type\": \"Other\"}\n{\"value\": \"2.4\", \"type\": \"Other\"}\n{\"value\": \"2.52\", \"type\": \"Other\"}\n{\"value\": \"2.4\", \"type\": \"Other\"}\n{\"value\": \"2.52\", \"type\": \"Other\"}\n{\"value\": \"2.30\", \"type\": \"Other\"}\n{\"value\": \"2.52\", \"type\": \"Other\"}\n{\"value\": \"2.30\", \"type\": \"Other\"}\n{\"value\": \"2.52\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nEGR2 is important to myelination of the peripheral nervous system.\n\nResearch Abstract:\nLymphocytes provide optimal responses against pathogens with minimal inflammatory pathology.\nHowever, the intrinsic mechanisms regulating these responses are unknown.\nHere, we report that deletion of both transcription factors Egr2 and Egr3 in lymphocytes resulted in a lethal autoimmune syndrome with excessive serum proinflammatory cytokines but also impaired antigen receptor-induced proliferation of B and T cells.\nEgr2- and Egr3-defective B and T cells had hyperactive signal transducer and activator of transcription-1 (STAT1) and STAT3 while antigen receptor-induced activation of transcription factor AP-1 was severely impaired.\nWe discovered that Egr2 and/or Egr3 directly induced expression of suppressor of cytokine signaling-1 (SOCS1) and SOCS3, inhibitors of STAT1 and STAT3, and also blocked the function of Batf, an AP-1 inhibitor, in B and T cells.\nThus, Egr2 and Egr3 regulate B and T cell function in adaptive immune responses and homeostasis by promoting antigen receptor signaling and controlling inflammation.", "role": "user" }, { "content": "{\"verdict\": \"NEI\", \"evidence\": []}", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nTaxation of sugar-sweetened beverages reduced the incidence rate of type II diabetes in India.\n\nResearch Abstract:\nBACKGROUND Taxing sugar-sweetened beverages (SSBs) has been proposed in high-income countries to reduce obesity and type 2 diabetes.\nWe sought to estimate the potential health effects of such a fiscal strategy in the middle-income country of India, where there is heterogeneity in SSB consumption, patterns of substitution between SSBs and other beverages after tax increases, and vast differences in chronic disease risk within the population.\nMETHODS AND FINDINGS Using consumption and price variations data from a nationally representative survey of 100,855 Indian households, we first calculated how changes in SSB price alter per capita consumption of SSBs and substitution with other beverages.\nWe then incorporated SSB sales trends, body mass index (BMI), and diabetes incidence data stratified by age, sex, income, and urban/rural residence into a validated microsimulation of caloric consumption, glycemic load, overweight/obesity prevalence, and type 2 diabetes incidence among Indian subpopulations facing a 20% SSB excise tax.\nThe 20% SSB tax was anticipated to reduce overweight and obesity prevalence by 3.0% (95% CI 1.6%-5.9%) and type 2 diabetes incidence by 1.6% (95% CI 1.2%-1.9%) among various Indian subpopulations over the period 2014-2023, if SSB consumption continued to increase linearly in accordance with secular trends.\nHowever, acceleration in SSB consumption trends consistent with industry marketing models would be expected to increase the impact efficacy of taxation, averting 4.2% of prevalent overweight/obesity (95% CI 2.5-10.0%) and 2.5% (95% CI 1.0-2.8%) of incident type 2 diabetes from 2014-2023.\nGiven current consumption and BMI distributions, our results suggest the largest relative effect would be expected among young rural men, refuting our a priori hypothesis that urban populations would be isolated beneficiaries of SSB taxation.\nKey limitations of this estimation approach include the assumption that consumer expenditure behavior from prior years, captured in price elasticities, will reflect future behavior among consumers, and potential underreporting of consumption in dietary recall data used to inform our calculations.\nCONCLUSION Sustained SSB taxation at a high tax rate could mitigate rising obesity and type 2 diabetes in India among both urban and rural subpopulations.", "role": "user" }, { "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"The 20% SSB tax was anticipated to reduce overweight and obesity prevalence by 3.0% (95% CI 1.6%-5.9%) and type 2 diabetes incidence by 1.6% (95% CI 1.2%-1.9%) among various Indian subpopulations over the period 2014-2023, if SSB consumption continued to increase linearly in accordance with secular trends.\", \"However, acceleration in SSB consumption trends consistent with industry marketing models would be expected to increase the impact efficacy of taxation, averting 4.2% of prevalent overweight/obesity (95% CI 2.5-10.0%) and 2.5% (95% CI 1.0-2.8%) of incident type 2 diabetes from 2014-2023.\", \"CONCLUSION Sustained SSB taxation at a high tax rate could mitigate rising obesity and type 2 diabetes in India among both urban and rural subpopulations.\"]}", "role": "assistant" } ]

[ { "content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nIdentification of apolipoprotein E Guangzhou (arginine 150 proline), a new variant associated with lipoprotein glomerulopathy. BACKGROUND/AIMS: Lipoprotein glomerulopathy (LPG) is a rare disease characterized by thrombus-like substances in markedly dilated glomerular capillaries and elevated plasma levels of apolipoprotein E (apoE). Previous studies have shown that genetic disorders of apoE may contribute to the pathogenesis of LPG, but LPG may not be caused by apoE gene mutations in Chinese patients. This study investigated the association of a new variant of apoE with LPG in a Chinese family. METHODS: The apoE gene in a family with 4 LPG patients was sequenced. The polymerase chain reaction product of coding region of apoE exon 4 was cloned into pMD 18-T vector and then sequenced. RESULTS: A novel point mutation in exon 4 of the apoE gene was identified in all 4 LPG patients and 1 asymptomatic family member. Sequence analysis confirmed a nucleotide G to C point mutation in exon 4 (base 308) of the apoE gene in all patients and the asymptomatic family member. This missense mutation denotes amino acid substitution of the proline residue for arginine residue at position 150 of apoE. Those patients were all heterozygotes with apoE Guangzhou. One of 2 grandsons was a heterozygous carrier of apoE Guangzhou, although he did not have proteinuria. CONCLUSION: The results of this study suggest that apoE (arginine 150 proline) is a novel apoE variant that etiologically related to LPG. This variant (apoE Guangzhou) may cause a marked molecular conformational change of the apoE and thus impair its binding ability to lipids.", "role": "user" }, { "content": "{\"CellLine\": [], \"Chemical\": [\"lipids\"], \"Disease\": [\"LPG\", \"thrombus\", \"lipoprotein glomerulopathy\", \"Lipoprotein glomerulopathy\", \"dilated glomerular capillaries\", \"proteinuria\"], \"Gene\": [\"apolipoprotein E\", \"apoE\"], \"Species\": [\"patients\"], \"Variant\": [\"proline residue for arginine residue at position 150\", \"arginine 150 proline\", \"G to C point mutation in exon 4 (base 308)\"]}", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: Learned irrelevance (LIrr) refers to a reduction in associative learning after pre-exposure of the conditioned and unconditioned stimulus in a non-contingent fashion. This paradigm might serve as a translational model for (pre)attentive information processing deficits in schizophrenia. This is the first study to investigate the event-related potentials (ERPs) of a within-subject LIrr paradigm in humans. Furthermore, the effects of the muscarinic M1 antagonist biperiden on LIrr were assessed. As expected, LIrr was found to be intact in young healthy volunteers after placebo. Furthermore, in the placebo condition P3b latency was decreased for target stimuli, which were pre-cued. This suggests that the predictability of the occurrence of these stimuli is mainly reflected by this ERP component. Biperiden had no effect on the behavioural LIrr measures, although prolonged reaction times were evident. Biperiden increased the N1 amplitude of the pre-exposed predictor letters, suggesting an effect of this drug on early perceptual processing. In conclusion, the within-subject paradigm used in the current study in combination with electroencephalography can reveal brain mechanisms involved in LIrr. M1 antagonism did not affect LIrr performance but seemed to influence early information processing.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Biperiden\", \"Placebo\", \"Learned irrelevance index\", \"no significant difference\", \"Biperiden had no effect on the behavioural LIrr measures,\"]]", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nAngiotensin converting enzyme inhibitors are associated with decreased risk for functional renal insufficiency.\n\nResearch Abstract:\nAngiotensin converting enzyme (ACE) inhibitors are now one of the most frequently used classes of antihypertensive drugs.\nBeyond their utility in the management of hypertension, their use has been extended to the long-term management of patients with congestive heart failure (CHF), as well as diabetic and nondiabetic nephropathies.\nAlthough ACE inhibitor therapy usually improves renal blood flow (RBF) and sodium excretion rates in CHF and reduces the rate of progressive renal injury in chronic renal disease, its use can also be associated with a syndrome of “functional renal insufficiency” and/or hyperkalemia.\nThis form of acute renal failure (ARF) most commonly develops shortly after initiation of ACE inhibitor therapy but can be observed after months or years of therapy, even in the absence of prior ill effects.\nARF is most likely to occur when renal perfusion pressure cannot be sustained because of substantial decreases in mean arterial pressure (MAP) or when glomerular filtration rate (GFR) is highly angiotensin II (Ang II) dependent.\nConditions that predict an adverse hemodynamic effect of ACE inhibitors in patients with CHF are preexisting hypotension and low cardiac filling pressures.\nThe GFR is especially dependent on Ang II during extracellular fluid (ECF) volume depletion, high-grade bilateral renal artery stenosis, or stenosis of a dominant or single kidney, as in a renal transplant recipient.\nUnderstanding the pathophysiological mechanisms and the common risk factors for ACE inhibitor–induced functional ARF is critical, because preventive strategies for ARF exist, and if effectively used, they may permit use of these compounds in a less restricted fashion.\nUnder normal physiological conditions, renal autoregulation adjusts renal vascular resistance, so that RBF and GFR remain constant over a wide range of MAPs.1 The intrinsic renal autoregulation mechanism is adjusted by Ang II and the sympathetic nervous system.\nWhen renal perfusion pressure falls (as in …", "role": "user" }, { "content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Although ACE inhibitor therapy usually improves renal blood flow (RBF) and sodium excretion rates in CHF and reduces the rate of progressive renal injury in chronic renal disease, its use can also be associated with a syndrome of \\u201cfunctional renal insufficiency\\u201d and/or hyperkalemia.\", \"Understanding the pathophysiological mechanisms and the common risk factors for ACE inhibitor\\u2013induced functional ARF is critical, because preventive strategies for ARF exist, and if effectively used, they may permit use of these compounds in a less restricted fashion.\"]}", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nRUNX1 is downregulated or mutated in TLX1 T-ALL.\n\nResearch Abstract:\nThe TLX1 and TLX3 transcription factor oncogenes have a key role in the pathogenesis of T cell acute lymphoblastic leukemia (T-ALL).\nHere we used reverse engineering of global transcriptional networks to decipher the oncogenic regulatory circuit controlled by TLX1 and TLX3.\nThis systems biology analysis defined T cell leukemia homeobox 1 (TLX1) and TLX3 as master regulators of an oncogenic transcriptional circuit governing T-ALL.\nNotably, a network structure analysis of this hierarchical network identified RUNX1 as a key mediator of the T-ALL induced by TLX1 and TLX3 and predicted a tumor-suppressor role for RUNX1 in T cell transformation.\nConsistent with these results, we identified recurrent somatic loss-of-function mutations in RUNX1 in human T-ALL.\nOverall, these results place TLX1 and TLX3 at the top of an oncogenic transcriptional network controlling leukemia development, show the power of network analyses to identify key elements in the regulatory circuits governing human cancer and identify RUNX1 as a tumor-suppressor gene in T-ALL.", "role": "user" }, { "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"Notably, a network structure analysis of this hierarchical network identified RUNX1 as a key mediator of the T-ALL induced by TLX1 and TLX3 and predicted a tumor-suppressor role for RUNX1 in T cell transformation.\", \"Overall, these results place TLX1 and TLX3 at the top of an oncogenic transcriptional network controlling leukemia development, show the power of network analyses to identify key elements in the regulatory circuits governing human cancer and identify RUNX1 as a tumor-suppressor gene in T-ALL.\"]}", "role": "assistant" } ]

[ { "content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nSuccimer chelation improves learning, attention, and arousal regulation in lead-exposed rats but produces lasting cognitive impairment in the absence of lead exposure. BACKGROUND: There is growing pressure for clinicians to prescribe chelation therapy at only slightly elevated blood lead levels. However, very few studies have evaluated whether chelation improves cognitive outcomes in Pb-exposed children, or whether these agents have adverse effects that may affect brain development in the absence of Pb exposure. OBJECTIVES: The present study was designed to answer these questions, using a rodent model of early childhood Pb exposure and treatment with succimer, a widely used chelating agent for the treatment of Pb poisoning. RESULTS: Pb exposure produced lasting impairments in learning, attention, inhibitory control, and arousal regulation, paralleling the areas of dysfunction seen in Pb-exposed children. Succimer treatment of the Pb-exposed rats significantly improved learning, attention, and arousal regulation, although the efficacy of the treatment varied as a function of the Pb exposure level and the specific functional deficit. In contrast, succimer treatment of rats not previously exposed to Pb produced lasting and pervasive cognitive and affective dysfunction comparable in magnitude to that produced by the higher Pb exposure regimen. CONCLUSIONS: These are the first data, to our knowledge, to show that treatment with any chelating agent can alleviate cognitive deficits due to Pb exposure. These findings suggest that it may be possible to identify a succimer treatment protocol that improves cognitive outcomes in Pb-exposed children. However, they also suggest that succimer treatment should be strongly discouraged for children who do not have elevated tissue levels of Pb or other heavy metals.", "role": "user" }, { "content": "{\"CellLine\": [], \"Chemical\": [\"Succimer\", \"succimer\", \"Pb\", \"lead\"], \"Disease\": [\"impairments in learning, attention, inhibitory control, and arousal regulation\", \"Pb poisoning\", \"cognitive deficits\", \"cognitive impairment\", \"cognitive and affective dysfunction\"], \"Gene\": [], \"Species\": [\"rats\"], \"Variant\": []}", "role": "assistant" } ]

[ { "content": "| No. | (1) | (2) | (3) | (4) | (6) | (7) | (8) | (9) | (12) | (13) | (14) | (15) |\n| T0 (%) | 78.59 | 79.63 | 79.11 | 79.06 | 76.58 | 78.45 | 76.84 | 79.31 | 79.01 | 75.33 | 70.60 | 74.46 |\n\nCaption: The maximum transmission of glass samples.\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"1\", \"type\": \"Other\"}\n{\"value\": \"2\", \"type\": \"Other\"}\n{\"value\": \"3\", \"type\": \"Other\"}\n{\"value\": \"4\", \"type\": \"Other\"}\n{\"value\": \"6\", \"type\": \"Other\"}\n{\"value\": \"7\", \"type\": \"Other\"}\n{\"value\": \"8\", \"type\": \"Other\"}\n{\"value\": \"9\", \"type\": \"Other\"}\n{\"value\": \"12\", \"type\": \"Other\"}\n{\"value\": \"13\", \"type\": \"Other\"}\n{\"value\": \"14\", \"type\": \"Other\"}\n{\"value\": \"15\", \"type\": \"Other\"}\n{\"value\": \"78.59\", \"type\": \"Other\"}\n{\"value\": \"79.63\", \"type\": \"Other\"}\n{\"value\": \"79.11\", \"type\": \"Other\"}\n{\"value\": \"79.06\", \"type\": \"Other\"}\n{\"value\": \"76.58\", \"type\": \"Other\"}\n{\"value\": \"78.45\", \"type\": \"Other\"}\n{\"value\": \"76.84\", \"type\": \"Other\"}\n{\"value\": \"79.31\", \"type\": \"Other\"}\n{\"value\": \"79.01\", \"type\": \"Other\"}\n{\"value\": \"75.33\", \"type\": \"Other\"}\n{\"value\": \"70.60\", \"type\": \"Other\"}\n{\"value\": \"74.46\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nSLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression\n\nMain Content:\n1 Introduction . Logistic regression is a workhorse in statistics , machine learning , data science , and many applied fields . It is a generalized linear model that models a binary scalar outcome Y ∈ { 0 , 1 } conditional on observed features X ∈ Rd via E [ Y | X = x ] = g ( β > x ) , with g ( t ) : = 11+exp ( −t ) , ( 1.1 ) with the coefficients β fit using observed data . Logistic regression is popular as a scientific tool because the model is often accurate , and comes with well-established statistical inference procedures for quantifying uncertainty about the parameters β and predictions g ( β > x ) at test inputs x . For example , most statistical software packages not only produce predictions from the model , but also summaries such as confidence intervals ( CIs ) and p-values that enable practitioners to understand the strength of evidence for the prediction in a quantitative way . These widely adopted estimation and ∗Google Research , Brain Team †Google Health 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . statistical inference routines are based on approximations from large-sample asymptotic theory of the maximum likelihood estimator ( MLE ) β̂ . These approximations come from the limiting distribution of β̂ as the sample size n tends toward infinity , but the number of covariates d remains fixed . Unfortunately , these standard approximations perform poorly when the number of covariates d is of a similar order to the number of samples n , even when the sample size is large [ Fan et al. , 2019 ] . Recently , Sur and Candès [ 2019 ] showed that , in this setting , the behavior of β̂ in finite samples is better approximated by its limiting distribution in another regime , where the aspect ratio κ : = d/n > 0 is held fixed as both n and d grow to infinity . They show that the estimated coefficients β̂ ( and therefore , the predictions ) have systematically inflated magnitude and larger variance than the standard approximation predicts . The precise characterization of the limiting distribution of β̂ in Sur and Candès [ 2019 ] and Zhao et al . [ 2020 ] justify a new approximation , which , in principle , facilitates debiasing estimates and constructing CIs and p-values for parameters and test predictions , alike . The goal of this paper is to make the application of these groundbreaking results practical . A major barrier to adoption currently is that the calculation of these bias and variance corrections requires knowing the signal strength γ2 : = Var ( β > X ) , which is challenging to estimate because it is a function of the unknown parameter vector β. Sur and Candès [ 2019 ] proposed a heuristic called ProbeFrontier to estimate this quantity , but this approach is computationally expensive , conceptually complex , and hard to analyze statistically . We propose a simpler estimation procedure . Our approach reparameterizes the problem in terms of the corrupted signal strength parameter η2 : = limn→∞Var ( β̂ > X ) that includes the noise in the estimate β̂ . This is more straightforward ( though non-trivial ) to estimate . We propose the Signal Strength Leave-One-Out Estimator ( SLOE ) , which consistently estimates η2 , and show that using this for inference yields more accurate CIs in finite samples . Importantly , SLOE takes orders of magnitude less computation than ProbeFrontier , having similar runtime to the standard logistic regression fitting routine . 2 Preliminaries . In this section , we revisit some fundamentals of statistical inference with logistic regression , review recent advances in high dimensional settings by Sur and Candès [ 2019 ] and Zhao et al . [ 2020 ] , and discuss the implications of this paradigm shift in terms of better characterizing practice . 2.1 Logistic Regression and Statistical Inference . Estimates of β : = ( β1 , · · · , βd ) in the logistic regression model are usually obtained through maximum likelihood estimation , by maximizing the empirical log-likelihood β̂ : = argmax β∈Rd 1 n n∑ i=1 Yi log ( g ( β > Xi ) ) + ( 1− Yi ) log ( 1− g ( β > Xi ) ) . ( 2.1 ) The log likelihood is concave , and has a unique maximizer whenever the outcomes are not linearly separable in the covariates . We will use logistic regression synonymously with maximum likelihood estimation in the logistic regression model , and call β̂ the MLE . The large-sample asymptotic statistical theory [ Lehmann and Romano , 2005 ] taught in nearly every university Statistics program characterizes the behavior of the estimated coefficients and predictions in the limit as n → ∞ while holding the number of features d fixed . Under this theory , estimates converge to their true value , β̂ p→ β , and the estimation error β̂ − β and prediction error g ( β̂ > x ) − g ( β > x ) will be small to observe , unless amplified by a factor of √ n , in which case √ n ( β̂ − β ) d N ( 0 , I−1β ) , where Iβ : = E [ DβXX > ] is the Fisher information matrix , with Dβ : = g ( β > X ) ( 1− g ( β > X ) ) . Of course , when analyzing real data , data scientists only have access to a finite number of samples , and so this theory serves as an approximation characterization of the behavior expected in practice . If the approximation is good , one can make inferences about the underlying data generating distribution . For example , for δ ∈ ( 0 , 1 ) , we can construct confidence intervals ( CIs ) that contain the true parameters with probability 1−δ . Propagating the uncertainty to predictions gives CIs for the outcome probabilities that can help contextualize machine learning predictions for users such as scientists or clinicians [ Kompa et al. , 2021 ] . The large sample approximation is a good one when the number of predictors is small relative to the number of observations . However , in modern scientific studies and data-driven technology , while many samples may be collected , many features of each sample are also collected . In such settings , the number of features d may be significant compared to the sample size n ; then , the large sample characterization can be quite misleading . We can visualize the extent to which this breakdown in assumptions leads to bias . Figure 1 shows the estimated coefficients and calibration curve for a logistic regression model fit on simulated data with n = 4000 and p = 800 . Despite n being reasonably large , these illustrate significant departures from the behavior predicted by the large sample asymptotics . The magnitude of the estimated coefficients is systematically overestimated and they are noisy . Additionally , the model is overconfident in its predictions . Despite this , the classification accuracy of the resulting model on the in-distribution test data is 0.70 , which is close to the optimal accuracy of 0.77 . Recently , Sur and Candès [ 2019 ] , Mai et al . [ 2019 ] , and Zhao et al . [ 2020 ] showed that the behavior of the MLE in high-dimensional settings is better characterized by a different asymptotic approximation . In this approximation , the aspect ratio κ = d/n , instead of the number of predictors d , remains fixed as n grows to infinity . Formally , they study a sequence of problems in which n , d → ∞ ; d/n→ κ > 0 , γ2 = Var ( β > X ) is fixed as d grows , 3 and the MLE exists asymptotically for ( κ , γ ) . Sur and Candès [ 2019 ] , Zhao et al . [ 2020 ] , and our work assume Xi is marginally Gaussian , and are complementary to the mixture-of-Gaussians generative model studied by Mai et al . [ 2019 ] . The semantics of this asymptotic regime are a bit more complicated than the classical one ; we can no longer think of the argument in terms of a fixed problem with increasing data . However , The characterization from this asymptotic theory better reflects the finite-sample behavior of the MLE β̂ when the data has a moderate aspect ratio κ . For simplicity , we state the key result from this work in the case where the covariates are drawn from a spherical Gaussian , i.e. , X ∼ N ( 0 , Id ) , but note that these results are generalized in Zhao et al . [ 2020 ] to arbitrary covariance structures.4 So far , proof techniques only work for normally distributed features , however empirical results ( Fig . 4b and previous work ) the work for sub-Gaussian features . In this regime , the distribution of the MLE β̂ converges to a normal distribution asymptotically 3This prevents the expectations g ( β > X ) from converging to 0 or 1 as n grows . To satisfy this , the effect of each covariate shrinks as n grows . It is complementary to sparsity models , being more realistic for many problems where predictive performance comes from pooling many weak signals . 4Simulations suggest that our methods generalize to models including an intercept term when using the intercept correction conjectured in Zhao et al . [ 2020 ] . centered around the inflated parameter αβ , for some α > 1 . In particular , for any coefficient βj , √ n ( β̂j − αβj ) d N ( 0 , σ2 ? ) , ( 2.2 ) and for the predicted logit of an input5 x ∈ Rd with asymptotically finite norm and √ nx > β = O ( 1 ) , √ n‖x‖−12 ( β̂ > x− αβ > x ) d N ( 0 , σ2 ? ) , ( 2.3 ) for constants α and σ ? , to be discussed later , that are only functions of κ and γ . The general convergence result when X has an arbitrary covariance is stated in Theorem 3.1 of Zhao et al . [ 2020 ] . If α and σ ? were known , this theory provides corrected approximate CIs and p-values . For example , taking Φ−1 to be the quantile function of a standard normal distribution : ( 1− δ ) CI for βj : ( β̂j α ± Φ−1 ( 1− δ/2 ) σ ? αn1/2 ) ( 2.4 ) p-value under βj = 0 : p = Φ−1 ( Z ) where Z = β̂j/ σ ? αn1/2 . ( 2.5 ) Figure 2 shows how the p-values obtained based on these adjustments have the expected behavior ( i.e. , they are uniform when the null is true ) , whereas those from the standard approximation do not . 2.2 Calculating Dimensionality Corrections . Sur and Candès [ 2019 ] show that the constants α and σ ? are determined by the solution to a system of three non-linear equations , which depend implicitly on the aspect ratio κ and the signal strength γ2 : = Var ( β > X ) : κ2σ2 ? = E [ 2g ( Q1 ) ( λg ( proxλG ( Q2 ) ) ) 2 ] , 0 = E [ g ( Q1 ) Q1λg ( proxλG ( Q2 ) ) ] , 1− κ = E [ 2g ( Q1 ) 1+λg′ ( proxλG ( Q2 ) ) ] , ( 2.6 ) where proxλG ( s ) = argmint λG ( t ) + 1 2 ( s− t ) 2 , for G the anti-derivative of g , and ( Q1 Q2 ) ∼ N ( 0 , [ γ2 −αγ2 −αγ2 α2γ2 + κσ2 ? ] ) . ( 2.7 ) Here , Q1 is a random variable with the same distribution ( asymptotically ) as the population logits β > X , and Q2 is a random variable with the same distribution ( asymptotically ) as the logits with the biased MLE plugged in β̂ > X . The auxiliary parameter λ corresponds to the limiting average eigenvalue of the inverse Hessian at the MLE , ( 1/n ∑n i=1Xig ′ ( β̂ > Xi ) X > i ) −1 , which is useful for making comparisons to asymptotic approximations from standard theory . Sur and Candès [ 2019 ] note that this system has a unique solution so long as ( κ , γ ) take values at which the MLE exists asymptotically . Once set up , these equations are straightforward to solve with numerical methods . The key quantity needed to set up these equations is the signal strength parameter γ , which is not directly observed . Thus , to correct for high dimensions in practice requires that γ be estimated . This is difficult , as γ is itself a function of the unknown parameter β , and is the main focus of this paper . Sur and Candès [ 2019 ] suggest a heuristic method , called ProbeFrontier , for estimating γ in practice . The idea is to search for the ( sub ) sample size n′ < n such that the observations in the subsample are linearly separable . Sub-sampling the data changes the aspect ratio κ without changing the signal strength γ . For a fixed signal strength γ , there is a sharp aspect ratio cutoff κ ? ( γ ) ( the “ frontier ” ) above which the data is separable with high probability . Based on these ideas , ProbeFrontier then inverts κ ? ( γ ) to estimate γ from the empirical aspect ratio p/n′ . This requires subsampling the data repeatedly at various candidate aspect ratios , and for each subsample , checking whether the data are linearly separable using a linear program . Repeatedly checking the separability is computationally expensive , and the statistical behavior near the frontier makes analysis tricky . 5Note here that x is a sequence in n. Up to a 1/ √ d scale factor , these constraints are satisfied for a fixed sequences of examples drawn from an iid test dataset with high probability .", "role": "user" }, { "content": "The paper is based on a work by Sur and Candès (2019) who propose a way to correct the bias of the MLE estimator and correct confidence intervals and p-values for logistic regression. The results in Sur Candes are asymptotic results n \\to \\infty but assume a fixed aspect ration $p / n = \\kappa$ where p is the number of regression coefficients. The results have been refined in Zhao et al (2020),: in Zhao, the regressors are Gaussian but with an arbitrary covariance matrix whereas the regressors are spherical Gaussian in Sur Candes (the distribution of the regressor plays a key role to compute the correction factor). The original algorithm by Sur and Candes require to estimate the signal strength, defined as the $\\gamma^2 = Var(\\beta^T X)$. This is of course challenging because the parameter is unknown. The authors proposed to use the \"corrupted signal strength, defined as $\\Var(\\hat{\\beta}^T X)$ where $\\hat{\\beta}$ is the MLE (it is claimed that the corrupted signal strength has been introduced in Zhao (2020) but on the arxiv version of this paper I did not find such quantity). The correction factors can be computed by solving an equation to involving the corrupted signal strength. The purpose of this paper is to compute an estimator of the corrupted signal strength.", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nInterpretable agent communication from scratch (with a generic visual processor emerging on the side)\n\nMain Content:\n1 Introduction . As deep networks become more effective at solving specialized tasks , there has been interest in letting them develop a language-like communication protocol so that they can flexibly interact to address joint tasks [ 1 ] . One line of work within this tradition has focused on what is arguably the most basic function of language , namely to point out , or refer to , objects through discrete symbols . Such ability would for example allow deep-net-controlled agents , such as self-driving cars , to inform each other about the presence and nature of potentially dangerous objects , besides being a basic requirement to support more advanced capabilities ( e.g. , denoting relations between objects ) . While discreteness is not a necessary prerequisite for agent communication [ 2 , 3 ] , practical and ethical problems might arise if communication is incomprehensible to humans . A discrete code analogous to language is certainly easier to decode for us , helping us to understand the agents ’ decisions , and ultimately contributing to the larger goal of explainable AI [ 4 ] . In this paper , we study emergent discrete referential communication between two deep network agents that are trained from scratch on the task . We observe that the referential discrimination task played by the networks is closely related to pretext contrastive objectives used in self-supervised visual representation learning [ 5–8 ] . We exploit this insight to develop a robust end-to-end variant of a communication game . Our experiments confirm that , in our setup : i ) the nets develop a set of discrete symbols allowing them to successfully discriminate objects in natural images , including novel ones that were not shown during training ; ii ) these symbols denote partially interpretable categories , so that their emergence can be seen as a first step towards fully unsupervised image annotation ; iii ) the visual representations induced as a by-product can be used as high-quality general-purpose features , 1Code : https : //github.com/facebookresearch/EGG/tree/master/egg/zoo/emcom_as_ssl . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . whose performance in various object classification tasks is not lagging much behind that of features induced by a popular self-supervised representation method specifically designed for this task . 2 Background . Deep net emergent communication There has recently been interest in letting deep nets communicate through learned protocols . This line of work has addressed various challenges , such as communication in a dynamic environment or how to interface the emergent protocol with natural language ( see [ 1 ] for a survey ) . Probably the most widely studied aspect of emergent communication is the ability of deep net agents to use the protocol to refer to objects in their environment [ e.g. , 9–16 ] . The typical setup is that of a referential , or discriminative , communication game . In the simplest scenario , which we adopt here , an agent , the Sender , sees one input ( the target ) and it sends a discrete symbol to another agent , the Receiver , that sees an array of items including the target , and has to point to the latter for communication to be deemed successful . Importantly , task success is the only training objective ; the communication protocol emerges purely as a by-product of game-playing , without any direct supervision on the symbol-transmission channel . In one of the earliest papers in this line of research , Lazaridou et al . [ 9 ] used images from ImageNet [ 17 ] as input to the discrimination game ; Havrylov and Titov [ 11 ] used MSCOCO [ 18 ] ; and Evtimova et al . [ 12 ] used animal images from Flickr . While they relied on natural images , all these studies were limited to small sets of carefully selected object categories . Moreover , in all these works , the agents processed images with convolutional networks pretrained on supervised object recognition . While this sped up learning , it also meant that all the proposed systems de facto relied on the large amount of human annotated data used for object recognition training . Lazaridou et al . [ 13 ] and Choi et al . [ 14 ] dispensed with pre-trained CNNs , but they used synthetically generated geometric shapes as inputs . Results on the interpretability of symbols in games with realistic inputs have generally been mixed . Indeed , Bouchacourt and Baroni [ 19 ] showed that , after training Lazaridou et al . [ 9 ] ’ s networks on real pictures , the networks could use the learned protocol to successfully communicate about blobs of Gaussian noise , suggesting that their code ( also ) denoted low-level image features , differently from the general semantic categories that words in human language refer to . In part for this reason , recent work tends to focus on controlled symbolic inputs , where it is easier to detect degenerate communication strategies [ e.g. , 10 , 15 , 16 ] . Learning discrete latent representations with variational autoencoders Like emergent communication , variational autoencoders and related techniques have been used to induce discrete variables without direct supervision [ 20–22 ] . There are however important differences stemming from the fact that this research line is interested in inducing latent representations to be used in tasks such as image generation , whereas the goal of emergent communication is to induce discrete symbols for inter-agent coordination . As a result , for example , discrete representations derived with variational autoencoders are typically of much higher dimensionality , making a direct comparison in terms of interpretability difficult . Still , it is remarkable that the goal of learning discrete representations in an unsupervised way independently arose in different fields , and future work should explore connections between these ideas . Self-supervised representation learning Self-supervised learning of general-purpose visual features has received much attention in recent years . The main idea is to train a network on a pretext task that does not require manual annotation . After convergence , the net is used to extract high-quality features from images , to be applied in various “ downstream ” tasks of interest . This is often done by training a simple classifier on top of the frozen trained architecture [ 23–26 ] . Early models used image-patch prediction as the proxy task [ 8 , 27 ] . Recent work has instead focused on an instance-level contrastive discrimination objective [ 5–7 , 28 ] . Two symmetric networks encode different views of the same input images obtained through a stochastic data augmentation pipeline . Optimization is done with variants of the InfoNCE loss [ 27 , 29 ] , that maximizes similarity among representations of the same image while minimizing similarity of different ones . Interestingly , the contrastive pretext task is very close to the one of identifying a target image among distractors , as in the standard emergent communication referent discrimination game . The influential SimCLR model proposed by Chen et al . [ 5 ] is particularly similar to our setup . It uses two twin networks with a shared convolutional module optimizing the ( dis ) similarity of sets of target/distractor images . The main conceptual differences are that there is no discrete bottleneck imposed on “ communication ” between the networks , and there is no asymmetry , so that both networks act simultaneously as Sender and Receiver ( both networks produce a continuous “ message ” that must be as discriminative for the other network as possible ) . We rely here on the connection with self-supervised learning in two ways . First , we import the idea of data augmentation from this literature into the communication game , showing how it helps in evolving a more semantically interpretable protocol . Second , we evaluate the discrimination game as a self-supervised feature extraction method . We find that the visual features induced by the CNNs embedded in our agents are virtually as good as those induced by SimCLR , while the emergent protocol is better for communication than the one obtained by adapting SimCLR to the discrete communication setup . 3 Setup . 3.1 The discrimination game . A Sender network receives as input a target picture , and it produces as output one of |V | symbols . A Receiver network receives in input this symbol , as well as a list of n pictures , one of them ( randomly placed in the ith position of the list ) being the same target presented to the Sender . Receiver produces a probability distribution of cardinality n , interpreted as its guess over the position of the target . The guess is correct iff Receiver concentrates the largest probability mass on the ith position , corresponding to the target slot . Agent architecture Agent architecture and game flow are schematically shown in Fig . 1 . Sender reads the target image through a convolutional module , followed by a one-layer network mapping the output of the CNN onto |V | dimensions and applying batch normalization [ 30 ] , to obtain vector v. Following common practice when optimizing through discrete bottlenecks , we then compute the Gumbel-Softmax continuous relaxation [ 31 , 32 ] , which was shown to also be effective in the emergent communication setup [ 11 ] . At train time , Sender produces an approximation to a one-hot symbol vector with each component given by mi = exp [ ( si+vi ) /τ ] ∑ j exp [ ( sj+vj ) /τ ] , where si is a random sample from Gumbel ( 0,1 ) and vi a dimension of v. The approximation is controlled by temperature parameter τ : as τ approaches 0 , the approximation approaches a one-hot vector , and as τ approaches +∞ , the relaxation becomes closer to uniform . Importantly , at test time the Sender ’ s output is generated by directly argmax-ing v , so that it is a discrete one-hot vector indexing one of |V | possible symbols . Receiver passes each input image through its visual module ( a CNN architecture ) , followed by a two-layer MLP with batch normalization and ReLU after the first layer [ 33 ] . It then computes temperature-weighted cosine scores for the linearly embedded symbol compared to each image representation . The resulting vector of cross-modal ( symbol-image ) similarities is transformed into a probability distribution over which image is the likely target by applying the softmax operation . For both Sender and Receiver , we use ResNet-50 [ 34 ] as visual module . As they are different agents , that could ( in future experiments ) have very different architectures and interact with further agents , the most natural assumption is that each of them does visual processing with its own CNN ( no weight sharing ) . We consider however also a setup in which the CNN module is shared ( closer to earlier emergent-communication work , where the agents relied on the same pre-trained CNN ) . Optimization Optimization is performed end-to-end and the error signal , backpropagated through Receiver and Sender , is computed using the cross-entropy cost function by comparing the Receiver ’ s output with a one-hot vector representing the position of the target in the image list . SimCLR as a comparison model Given the similarity between the referential communication game and contrastive self-supervised learning in SimCLR [ 5 ] , we use the latter as a comparison point for our approach . Fig . 2 schematically shows the SimCLR architecture . The crucial differences between SimCLR and our communication game are the following : i ) In SimCLR , the agents are parameterized by the same network , that is , the visual encoder and transformation modules in the two branches of Fig . 2 are instances of the same net . ii ) The setup is fully symmetric . Like our Receiver , both agents get a set of images in input , and , like our Sender , both agents can be seen as producing “ messages ” representing each input image . iii ) Instead of ( a probability distribution over ) symbols , the exchanged information takes the form of continuous vectors ( s in the figure ) . iv ) The loss is based on directly comparing embeddings of these continuous vectors ( z in the figure ) , maximizing the similarity between pairs representing the same images ( positive examples in contrastive-loss terminology ) and minimizing that of pairs representing different images ( negative examples ) . This differs from our loss , that maximizes the similarity of the Receiver embedding of the Sender-produced discrete symbol with its own representation of the target image , while minimizing the similarity of the symbol embedding with its representation of the distractors . It is important to stress the different roles that SimCLR as a comparison model will play in the experiments below . When playing the communication game ( Section 4.1 ) , our discretized SimCLR method must be simply seen as an interesting baseline , as the system was not designed for discrete communication in the first place ( it is indeed interesting that it performs as well as it does ) . When performing protocol analysis ( Section 4.2 ) , the discrete clusters explicitly built on SimCLR representations act as a challenging comparison point for the categories implicitly induced by our system through game playing . Finally , when evaluating the visual features learnt by the two systems ( Section 4.3 ) , the roles are inverted , with SimCLR being a standard method developed for these purposes , whereas in our setup the emergence of good visual representations is a by-product of communication-based model training . Data augmentation In the original discrimination game proposed in [ 9 ] , the agents are shown exactly the same target image.2 In self-supervised learning , on the other hand , it is common practice to “ augment ” images in different ways , e.g. , by applying different croppings or color perturbations 2Lazaridou and colleagues [ 9 ] also considered a variant of the game in which the agents see different pictures of the same category ( e.g. , the shared target is dog , but the agents get different dog pictures ) . This version of the game is however severely limited by the requirement of manual category annotation . Lazaridou et al . [ 13 ] also provide different images to Sender and Receiver , by feeding them different viewpoints of the same synthetically generated objects : again , a strategy that will not scale up to natural images . [ 5 , 35 , 36 ] . In standard contrastive learning frameworks , where all the weights are shared and there is no communication bottleneck , it is necessary to create these different views , or else the system would trivially succeed at the pretext contrastive task without any actual feature learning . We conjecture that data augmentation , while not strictly needed , might also be beneficial in the communication game setup : presenting different views of the target to Sender and Receiver should make it harder for them to adopt degenerate strategies based on low-level image information [ 19 ] . We follow the same data augmentation pipeline as [ 5 ] , stochastically applying crop-and-resize , color perturbation , and random Gaussian blurring to every image . Note that , for the experiments reported in the main paper , we do not apply data augmentation at test time . Results with augmentation also applied at test time are reported in Appendix A.4 . Implementation details All hidden and output layers are set to dimensionality 2048.3 Note that this implies |V | = 2048 , more than double the categories in the dataset we use to train the model ( see Section 3.2 below ) , to avoid implicit supervision on optimal symbol count.4 We fix Gumbel-Softmax temperature at 5.0 , and Receiver cosine temperature at 0.1 . The latter value is also used for the equivalent τ parameter in the NTXent-loss of our SimCLR implementation . We train with mixed precision [ 37 ] for 100 epochs , with a batch of size 16× 128 = 2048 , divided across 16 GPUs . Rather than sampling distractors from the entire dataset , we take them from the current device ’ s batch , thus playing the communication game with 127 distractor images in all reported experiments.5 We do not share distractors ( negative samples ) across devices . As in SimCLR , we use the LARS optimizer [ 38 ] with linear scaling [ 39 ] , resulting in an initial learning rate of 2.4 . We apply a cosine decay schedule without warmup nor restart [ 40 ] . Compute requirements are reported in Appendix A.1 . All models are implemented with the EGG toolkit [ 41 ] .", "role": "user" }, { "content": "The model aims to train a pair of two ConvNets in the setup of the so-called discrimination game -- one ConvNet (sender) can pass discrete information to the other ConvNet (receiver) to identify the same image presented to the Sender among a set of images. Generating such discrete (bottlenecked) information is learned in an end-to-end manner, which is claimed to provide a human-interpretable inter-ConvNet communication. Compared to SimCLR (Chen et al., ICML 2020), the proposed method shows better or matched performance in terms of discrimination game-playing accuracy and downstream image classification task.", "role": "assistant" } ]

[ { "content": "Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.\n\nContext: Recently, Gordon syndrome has been associated to heterozygous mutations in the piezo-type mechanosensitive ion channel component 2 gene (PIEZO2). Different mutations of this gene also cause distal arthrogryposis type 5 and Marden-Walker syndrome.\nMutations in PIEZO2 cause Gordon syndrome, Marden-Walker syndrome, and distal arthrogryposis type 5.\nBoth types of changes in kinetics result in increased channel activity in response to a given mechanical stimulus, suggesting that Distal Arthrogryposis Type 5 can be caused by gain-of-function mutations in PIEZO2.\nHere, we describe two distinct PIEZO2 mutations in patients with a subtype of Distal Arthrogryposis Type 5 characterized by generalized autosomal dominant contractures with limited eye movements, restrictive lung disease, and variable absence of cruciate knee ligaments.\nRecently, Gordon syndrome has been associated to heterozygous mutations in the piezo-type mechanosensitive ion channel component 2 gene (PIEZO2).\nIn contrast, heterozygous gain-of-function missense mutations, mainly localized at the C terminus, cause dominant distal arthrogryposis 3 (DA3), distal arthrogryposis 5 (DA5), or Marden-Walker syndrome (MWKS), which encompass contractures of hands and feet, scoliosis, ophthalmoplegia, and ptosis.\nDominant mutations in PIEZO2, which codes for the principal mechanotransduction channel for proprioception and touch sensation, have been found to cause different forms of distal arthrogryposi\n\nQuestion: Please list 3 diseases associated with the PIEZO2 gene.", "role": "user" }, { "content": "[\"Gordon syndrome\", \"distal arthrogryposis type 5\", \"Marden-Walker syndrome\"]", "role": "assistant" } ]

[ { "content": "| Transition | Mean wavelength (nm) | A (s-1) | b (%) | t rad (ms) |\n| 3H4 - 3H5 | 2369.3 | 17.4 | 2.2 | 1.2 |\n| - 3F4 | 1459.5 | 62.7 | 8.0 | |\n| - 3H6 | 801.6 | 703.9 | 89.8 | |\n| | | | | |\n| 3H5 - 3F4 | 3800.5 | 4.4 | 2.3 | 5.1 |\n| - 3H6 | 1211.4 | 191.2 | 97.7 | |\n| | | | | |\n| 3F4 - 3H6 | 1778.2 | 141.0 | 100.0 | 7.1 |\n\nCaption: Radiative transition probabilities (A), branching ratio (b) and radiative lifetime (t rad) in 0.70(Ge0.25As0.10S0.65)-0.15GaS3/2-0.15CsBr glass doped with Tm3+\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"2369.3\", \"type\": \"Other\"}\n{\"value\": \"17.4\", \"type\": \"Other\"}\n{\"value\": \"2.2\", \"type\": \"Other\"}\n{\"value\": \"1.2\", \"type\": \"Other\"}\n{\"value\": \"1459.5\", \"type\": \"Other\"}\n{\"value\": \"62.7\", \"type\": \"Other\"}\n{\"value\": \"8.0\", \"type\": \"Other\"}\n{\"value\": \"801.6\", \"type\": \"Other\"}\n{\"value\": \"703.9\", \"type\": \"Other\"}\n{\"value\": \"89.8\", \"type\": \"Other\"}\n{\"value\": \"3800.5\", \"type\": \"Other\"}\n{\"value\": \"4.4\", \"type\": \"Other\"}\n{\"value\": \"2.3\", \"type\": \"Other\"}\n{\"value\": \"5.1\", \"type\": \"Other\"}\n{\"value\": \"1211.4\", \"type\": \"Other\"}\n{\"value\": \"191.2\", \"type\": \"Other\"}\n{\"value\": \"97.7\", \"type\": \"Other\"}\n{\"value\": \"1778.2\", \"type\": \"Other\"}\n{\"value\": \"141.0\", \"type\": \"Other\"}\n{\"value\": \"100.0\", \"type\": \"Other\"}\n{\"value\": \"7.1\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "| b (K/min) | First crystallization curve | First crystallization curve | First crystallization curve | | Second crystallization curve | Second crystallization curve | Second crystallization curve | Second crystallization curve |\n| b (K/min) | T p1 (K) | 103(dkh/dt)(s-1) | n 1 | <n 1> | T p2 (K) | 103(dkh/dt) (s-1) | n 2 | <n 2> |\n| 5 | 1061 | 0.0014 | 2.034 | 2.1257 | 1077 | 0.0019 | 3.149 | 3.125 |\n| 10 | 1086 | 0.0027 | 2.0548 | | 1112 | 0.0035 | 3.092 | |\n| 20 | 1120 | 0.0053 | 2.145 | | 1149 | 0.0067 | 3.159 | |\n| 30 | 1138 | 0.0080 | 2.2285 | | 1169 | 0.0094 | 3.059 | |\n| 40 | 1153 | 0.0101 | 2.1661 | | 1182 | 0.0127 | 3.169 | |\n\nCaption: Peak temperature of crystallization, maximum crystallization rate, corresponding maximum crystallization temperature, and kinetic exponent for the different heating rates\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"103\", \"type\": \"Other\"}\n{\"value\": \"103\", \"type\": \"Other\"}\n{\"value\": \"5\", \"type\": \"Other\"}\n{\"value\": \"1061\", \"type\": \"Other\"}\n{\"value\": \"0.0014\", \"type\": \"Other\"}\n{\"value\": \"2.034\", \"type\": \"Other\"}\n{\"value\": \"2.1257\", \"type\": \"Other\"}\n{\"value\": \"1077\", \"type\": \"Other\"}\n{\"value\": \"0.0019\", \"type\": \"Other\"}\n{\"value\": \"3.149\", \"type\": \"Other\"}\n{\"value\": \"3.125\", \"type\": \"Other\"}\n{\"value\": \"10\", \"type\": \"Other\"}\n{\"value\": \"1086\", \"type\": \"Other\"}\n{\"value\": \"0.0027\", \"type\": \"Other\"}\n{\"value\": \"2.0548\", \"type\": \"Other\"}\n{\"value\": \"1112\", \"type\": \"Other\"}\n{\"value\": \"0.0035\", \"type\": \"Other\"}\n{\"value\": \"3.092\", \"type\": \"Other\"}\n{\"value\": \"20\", \"type\": \"Other\"}\n{\"value\": \"1120\", \"type\": \"Other\"}\n{\"value\": \"0.0053\", \"type\": \"Other\"}\n{\"value\": \"2.145\", \"type\": \"Other\"}\n{\"value\": \"1149\", \"type\": \"Other\"}\n{\"value\": \"0.0067\", \"type\": \"Other\"}\n{\"value\": \"3.159\", \"type\": \"Other\"}\n{\"value\": \"30\", \"type\": \"Other\"}\n{\"value\": \"1138\", \"type\": \"Other\"}\n{\"value\": \"0.0080\", \"type\": \"Other\"}\n{\"value\": \"2.2285\", \"type\": \"Other\"}\n{\"value\": \"1169\", \"type\": \"Other\"}\n{\"value\": \"0.0094\", \"type\": \"Other\"}\n{\"value\": \"3.059\", \"type\": \"Other\"}\n{\"value\": \"40\", \"type\": \"Other\"}\n{\"value\": \"1153\", \"type\": \"Other\"}\n{\"value\": \"0.0101\", \"type\": \"Other\"}\n{\"value\": \"2.1661\", \"type\": \"Other\"}\n{\"value\": \"1182\", \"type\": \"Other\"}\n{\"value\": \"0.0127\", \"type\": \"Other\"}\n{\"value\": \"3.169\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nZero-Shot Coordination via Semantic Relationships Between Actions and Observations\n\nMain Content:\n1 INTRODUCTION . Successful collaboration between agents requires coordination ( Tomasello et al. , 2005 ; Misyak et al. , 2014 ; Kleiman-Weiner et al. , 2016 ) , which is challenging because coordinated strategies can be arbitrary ( Lewis , 1969 ; Young , 1993 ; Lerer & Peysakhovich , 2018 ) . A priori , one can neither deduce which side of the road to drive , nor which utterance to use to refer to ♥ ( Pal et al. , 2020 ) . In these cases coordination can arise from actors best responding to what others are already doing i.e. , following a convention . For example , Americans drive on the right side of the road and say “ heart ” to refer to ♥ while Japanese drive on the left and say “ shinzo ” . Yet in many situations prior conventions may not be available and agents may be faced with entirely novel situations or partners . In this work we study ways that agents may learn to leverage abstract relations between observations and actions to coordinate with agents they have had no experience interacting with before . To illustrate , consider the following situations where people can figure out how to coordinate without prior experienced or shared conventions . Imagine a store that sells strawberries and blueberries . You want to buy strawberries but you don ’ t share any common language with the clerk . You are however wearing a red hat and you wave the hat at the clerk to hint that the strawberries are what you want . The clerk has two baskets of strawberries remaining , and so you raise a single finger to indicate that you only want one of the baskets . The clerk produces a paper and plastic bag and you point to the paper bag to indicate that you want the paper one . These examples are so simple that they seem obvious : the red hat matches the colors of the strawberries , the number of fingers matches the number of baskets you want , and you extend a finger in the direction of the desired packaging ( Grice , 1975 ) . While obvious to people , who rely on a theory-of-mind in understanding others , we will show that these inferences remain a challenge for multi-agent reinforcement learning agents . Less obvious examples are common in the cognitive science literature . Consider the shapes in Fig . 1 . When asked to assign the names “ Boubo ” and “ Kiki ” to the two shapes people name the jagged object “ Kiki ” and the curvy object “ Boubo ” ( Köhler , 1929 ) . This finding is robust across different linguistic communities and cultures and is even found in young children ( Maurer et al. , 2006 ) . The causal explanation is that people match a “ jaggedness ” -feature and “ curvey ” -feature in both the visual and auditory data . Across the above these cases , there seem to be a generalized mechanism for mapping the features of the persons action with the features of the desired action . All are examples of where in the absence of norms or conventions , people minimize the distance between features when making a choice . This basic form of zero-shot coordination predates verbal behavior ( Tomasello et al. , 2007 ) and this capability has been hypothesized as a key predecessor to more sophisticated language development and acquisition ( Tomasello et al. , 2005 ) . Modeling these capacities is key for building machines that can robustly coordinate with other agents and with people ( Kleiman-Weiner et al. , 2016 ; Dafoe et al. , 2020 ) . However , as we will show , naively training reinforcement learning ( RL ) agents with self-play fails to learn to coordinate even in these obvious ways . Instead , they develop arbitrary private languages that are uninterpretable to both the same models trained with a different random seed as well as to human partners ( Hu et al. , 2020 ) . For instance in the examples above , they would be equally likely to wave a red-hat to hint they want strawberries as they would to indicate that they want blueberries . These problems also emerge at scale in the decentralized partially observable Markov decision process ( Dec-POMDP ) benchmark Hanabi ( Bard et al. , 2019 ) . When agents are trained with self-play using standard architectures they do not develop strategies that take into account the correspondence between the features of the actions ( colored and numbered cards ) and the observation of the game state ( other colored and numbered cards ) . Unfortunately , describing the kind of abstract knowledge that these agents lack in closed form is challenging . Rather than attempting to do so , we take a learning-based approach . Our aim is to build an agent with the capacity to develop these kinds of abstract correspondences during self-play such that they can robustly succeed during cross-play or during play with humans . Our contributions are as follows : ( 1 ) We extend Dec-POMDPs to allow actions and observations to be represented using shared features and develop a novel human-interpretable environment for studying coordination in this setting . ( 2 ) We evaluate the role of neural network ( NN ) architectures including feedforward , recurrent , and attention mechanisms on both cross-play generalization and ability to create human-interpretable conventions . ( 3 ) We show that an attention architecture which takes both the action and observations as input allows the agent to exploit the semantic relationships for coordination , resulting in strong cross-play and human compatible policies that outperform baseline ZSC methods . This model also demonstrates sophisticated coordination patterns that exploit mutual exclusivity and implicature , two well-known phenomena studied in cognitive science ( Markman & Wachtel , 1988 ; Grice , 1975 ) . 2 RELATED WORK . Cooperative MARL . The standard method for training multi-agent reinforcement learning ( MARL ) agents in fully cooperative , partially observable settings is self-play ( SP ) . However , the failure of SP policies in cross-play ( XP ) has been recently explored . Carroll et al . ( 2019 ) used grid-world MDPs to show that both SP and population-based training fail when paired with human collaborators . In Hanabi , agents trained via SP develop conventions that fail to generalize to independently trained agents from the same algorithm with different random seeds ( Bard et al. , 2019 ; Hu et al. , 2020 ) . Zero-Shot Coordination . To address this issue , Hu et al . ( 2020 ) formally introduced the zeroshot coordination ( ZSC ) framework , where the goal is to maximize the XP returns of independently trained agents , allowing them to coordinated at test time . Thus formulated , ZSC is an alternative to ad-hoc teamplay , a framework for measuring coordinated team success when faced with players with unknown behavior ( Stone et al. , 2010 ; Barrett et al. , 2011 ) , which can be formalized as playing a best response to a distribution of a-priori known agents . A few methods have attempted to address the ZSC framework . Other-Play ( OP ) ( Hu et al. , 2020 ) exploits the symmetries in a given Dec-POMDP to prevent agents from learning equivalent but mutually incompatible policies . OP prohibits arbitrary tie-breaking , thereby preventing equivalent conventions from forming . However , OP requires experimenter-coded symmetries , and discovering such symmetries is computationally challenging . In contrast , our learning based approach requires no experimenter-coding . Another recent method , Off-Belief Learning ( OBL ) ( Hu et al. , 2021 ) , regularizes agents ’ ability to make inferences based on the behavior of others . Compared to prior work on Hanabi where SP scores were high but XP scores were near chance , both of OP and OBL drastically improve XP scores and show promising preliminary results in play with people . However , neither of these algorithms can exploit the correspondences between features of an action and the observation of the state as we show in this work , unless this falls out of the environment dynamics . Attention for Modeling Input-Output Relationships . Attention ( Vaswani et al. , 2017 ; Bahdanau et al. , 2015 ; Xu et al. , 2016 ) is an important tool for large sequence models , and exploiting semantic relationships between inputs and outputs via an attention-based model has been studied in the deep learning literature . In natural language processing , such an idea is commonly applied to question answering models ( dos Santos et al. , 2016 ; Tan et al. , 2016 ; Yang et al. , 2016 ) . For instance , Yang et al . ( 2016 ) form a matrix that represents the semantic matching information of term pairs from a question and answer pair , and then use dot-product attention to model question term importance . For regression tasks , Kim et al . ( 2019 ) proposed Attentive Neural Processes ( ANP ) that use dot-product attention to allow each input location to attend to the relevant context points for the prediction , and applied ANP to vision problems . However , to our knowledge , we are the first to apply attention to exploit shared features of actions and observations in a Dec-POMDP setting for coordination . Human Coordination . Our work is also inspired by how humans coordinate in cooperative settings . Theory-of-mind , the mechanism people use to infer intentions from the actions of others , plays a key role in structuring coordination ( Wu et al. , 2021 ; Shum et al. , 2019 ) . In particular , Rational Speech Acts ( RSA ) is a influential model of pragmatic implicature ( Frank & Goodman , 2012 ; Goodman & Stuhlmüller , 2013 ) . At the heart of these approaches are probabilistic representations of belief which allow for the modeling of uncertainty and recursive reasoning about each others beliefs , enabling higher-order mental state inferences . This recursive reasoning step also underlies the cognitive hierarchy and level-K reasoning models , and is useful for explaining certain focal points ( Camerer , 2011 ; Stahl & Wilson , 1995 ; Camerer et al. , 2004 ) . However , constructing recursive models of players beliefs and behavior is computationally expensive as each agent must construct an exponentially growing number of models of each agent modeling each other agent . As a result , recursive models are often limited to one or two levels of recursion . Furthermore , none of these approaches can by itself take advantage of the shared features across actions and observations . 3 BACKGROUND . Dec-POMDPs . We use decentralized partially observable Markov decision processes ( DecPOMDPs ) to formalize our setting ( Nair et al. , 2003 ) . In a Dec-POMDP , each player i observes the underlying state s partially through an observation function Ωi ( s ) ∈ Oi , and takes action ai ∈ Ai . Players receive a common reward R ( s , a ) and the state follows the transition function T ( s , a ) . The historical trajectory is denoted as τ = ( s1 , a1 , . . . , at−1 , st ) . Player i ’ s action-observation history ( AOH ) is denoted as ( Ωi ( s1 ) , ai1 , . . . , a i t−1 , Ω i ( st ) ) . The policy for player i takes as input an AOH and outputs a distribution over actions , denoted by πi ( ai | τ it ) . The joint policy is denoted by π. Dot-Product Attention . Given a sequence of input vectors ( x1 , ... , xm ) , dot-product attention uses three weight matrices ( Q , K , V ) to obtain triples ( Qxi , Kxi , V xi ) for each i ∈ { 1 , . . . , m } , called query vectors , key vectors , and value vectors . We abbreviate these as ( qi , ki , vi ) . Next , for each i , j , dot-product attention computes logits using dot products qi · kj . These logits are in turn used to compute an output matrix [ softmax ( qi · k1/ √ m , . . . , qi · km/ √ m ) · vj ] i , j . We denote this output matrix as Attention ( x1 , . . . , xm ) .", "role": "user" }, { "content": "This paper explores several architectures for reinforcement-learning in the context of a newly proposed card game (Hinter-Guesser). A number of the architectures seem to have poor performance on the game. Moreover, whatever they learn is not compatible with what other learners learn, so that they cannot mutually understand Hints & Guesses. The stated aim is: \"to build an agent with the capacity to develop these kinds of abstract correspondences during self-play such that they can robustly succeed during cross-play or during play with people.\" The architectures generally succeed (70-90%) at playing against themselves (with the shared learned policy) but perform badly when playing with independently trained agents, except when trained with the desired action as part of the input (unclear how this can be tested on unknown cases when 'answer' is given as part of the inputs).", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nWe note that in<cite> Wang and Lan (2015)</cite> the numbers that correspond to the entire sentence baselines are not the same as those that we obtain, so we do not report a direct comparison with their models.", "role": "user" }, { "content": "[\"Differences\"]", "role": "assistant" } ]

[ { "content": "| Atomic pair | Distance (A) | Coordination number | FWHM (A) |\n| Si-O | 1.62 | 4.1+-0.1 | 0.060+-0.005 |\n| Ti-O(short) | 1.68 | 0.8+-0.1 | 0.060+-0.005 |\n| Ti-O(long) | 1.96 | 4.0+-0.1 | 0.08+-0.05 |\n| O-O | 2.66 | 5.0+-0.1 | 0.088+-0.02 |\n| K-O | 2.9 | 8.0+-0.2 | 0.10+-0.01 |\n\nCaption: Parameters for the Gaussian functions used to fit the experimental correlation function at 273 K to 3.2 A\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"1.62\", \"type\": \"Other\"}\n{\"value\": \"4.1\", \"type\": \"Other\"}\n{\"value\": \"0.060\", \"type\": \"Other\"}\n{\"value\": \"1.68\", \"type\": \"Other\"}\n{\"value\": \"0.8\", \"type\": \"Other\"}\n{\"value\": \"0.060\", \"type\": \"Other\"}\n{\"value\": \"1.96\", \"type\": \"Other\"}\n{\"value\": \"4.0\", \"type\": \"Other\"}\n{\"value\": \"0.08\", \"type\": \"Other\"}\n{\"value\": \"2.66\", \"type\": \"Other\"}\n{\"value\": \"5.0\", \"type\": \"Other\"}\n{\"value\": \"0.088\", \"type\": \"Other\"}\n{\"value\": \"2.9\", \"type\": \"Other\"}\n{\"value\": \"8.0\", \"type\": \"Other\"}\n{\"value\": \"0.10\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nDestination container port throughput(CPT) is positively related to dengue virus (DENV-1) diffusion in air traffic shipments.\n\nResearch Abstract:\nBACKGROUND Aedes aegypti, the major vector of dengue viruses, often breeds in water storage containers used by households without tap water supply, and occurs in high numbers even in dense urban areas.\nWe analysed the interaction between human population density and lack of tap water as a cause of dengue fever outbreaks with the aim of identifying geographic areas at highest risk.\nMETHODS AND FINDINGS We conducted an individual-level cohort study in a population of 75,000 geo-referenced households in Vietnam over the course of two epidemics, on the basis of dengue hospital admissions (n = 3,013).\nWe applied space-time scan statistics and mathematical models to confirm the findings.\nWe identified a surprisingly narrow range of critical human population densities between around 3,000 to 7,000 people/km² prone to dengue outbreaks.\nIn the study area, this population density was typical of villages and some peri-urban areas.\nScan statistics showed that areas with a high population density or adequate water supply did not experience severe outbreaks.\nThe risk of dengue was higher in rural than in urban areas, largely explained by lack of piped water supply, and in human population densities more often falling within the critical range.\nMathematical modeling suggests that simple assumptions regarding area-level vector/host ratios may explain the occurrence of outbreaks.\nCONCLUSIONS Rural areas may contribute at least as much to the dissemination of dengue fever as cities.\nImproving water supply and vector control in areas with a human population density critical for dengue transmission could increase the efficiency of control efforts.\nPlease see later in the article for the Editors' Summary.", "role": "user" }, { "content": "{\"verdict\": \"NEI\", \"evidence\": []}", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nEvaluated on the blind test set of WIKIHOP, our proposed end-to-end trained single neural model beats the current stateof-the-art results in<cite> (Zhong et al., 2019)</cite> 1 , without using pretrained contextual ELMo embedding (Peters et al., 2018) .", "role": "user" }, { "content": "[\"Differences\"]", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nIn addition, we wish to explore more fully our negative result with the reimplementation of the <cite>Collins et al. (2005)</cite> system, to investigate the effect of balancing features in the lattice, and to examine the variability of the BLEU scores for each system.", "role": "user" }, { "content": "[\"FutureWork\"]", "role": "assistant" } ]

[ { "content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nEmotion Detection in Text: Focusing on Latent Representation\n\nIn recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc. In this work, we argue that current methods which are based on conventional machine learning models cannot grasp the intricacy of emotional language by ignoring the sequential nature of the text, and the context. These methods, therefore, are not sufficient to create an applicable and generalizable emotion detection methodology. Understanding these limitations, we present a new network based on a bidirectional GRU model to show that capturing more meaningful information from text can significantly improve the performance of these models. The results show significant improvement with an average of 26.8 point increase in F-measure on our test data and 38.6 increase on the totally new dataset.\n\nIntroduction\nThere have been many advances in machine learning methods which help machines understand human behavior better than ever. One of the most important aspects of human behavior is emotion. If machines could detect human emotional expressions, it could be used to improve on verity of applications such as marketing BIBREF0 , human-computer interactions BIBREF1 , political science BIBREF2 etc.\nEmotion in humans is complex and hard to distinguish. There have been many emotional models in psychology which tried to classify and point out basic human emotions such as Ekman's 6 basic emotions BIBREF3 , Plutchik's wheel of emotions BIBREF4 , or Parrott's three-level categorization of emotions BIBREF5 . These varieties show that emotions are hard to define, distinguish, and categorize even for human experts.\nBy adding the complexity of language and the fact that emotion expressions are very complex and context dependant BIBREF6 , BIBREF7 , BIBREF8 , we can see why detecting emotions in textual data is a challenging task. This difficulty can be seen when human annotators try to assign emotional labels to the text, but using various techniques the annotation task can be accomplished with desirable agreement among the annotators BIBREF9 .\n\nRelated Work\nA lot of work has been done on detecting emotion in speech or visual data BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 . But detecting emotions in textual data is a relatively new area that demands more research. There have been many attempts to detect emotions in text using conventional machine learning techniques and handcrafted features in which given the dataset, the authors try to find the best feature set that represents the most and the best information about the text, then passing the converted text as feature vectors to the classifier for training BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 , BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 . During the process of creating the feature set, in these methods, some of the most important information in the text such as the sequential nature of the data, and the context will be lost.\nConsidering the complexity of the task, and the fact that these models lose a lot of information by using simpler models such as the bag of words model (BOW) or lexicon features, these attempts lead to methods which are not reusable and generalizable. Further improvement in classification algorithms, and trying out new paths is necessary in order to improve the performance of emotion detection methods. Some suggestions that were less present in the literature, are to develop methods that go above lexical representations and consider the flow of the language.\nDue to this sequential nature, recurrent and convolutional neural networks have been used in many NLP tasks and were able to improve the performance in a variety of classification tasks BIBREF27 , BIBREF28 , BIBREF29 , BIBREF30 . There have been very few works in using deep neural network for emotion detection in text BIBREF31 , BIBREF32 . These models can capture the complexity an context of the language better not only by keeping the sequential information but also by creating hidden representation for the text as a whole and by learning the important features without any additional (and often incomplete) human-designed features.\nIn this work, we argue that creating a model that can better capture the context and sequential nature of text , can significantly improve the performance in the hard task of emotion detection. We show this by using a recurrent neural network-based classifier that can learn to create a more informative latent representation of the target text as a whole, and we show that this can improve the final performance significantly. Based on that, we suggest focusing on methodologies that increase the quality of these latent representations both contextually and emotionally, can improve the performance of these models. Based on this assumption we propose a deep recurrent neural network architecture to detect discrete emotions in a tweet dataset. The code can be accessed at GitHub [https://github.com/armintabari/Emotion-Detection-RNN].\n\nBaseline Approaches\nWe compare our approach to two other, the first one uses almost the same tweet data as we use for training, and the second one is the CrowdFlower dataset annotated for emotions.\nIn the first one Wang et al. BIBREF21 downloaded over 5M tweets which included one of 131 emotional hashtags based on Parrott's three-level categorization of emotions in seven categories: joy, sadness, anger, love, fear, thankfulness, surprise. To assess the quality of using hashtags as labels, the sampled 400 tweets randomly and after comparing human annotations by hashtag labels they came up with simple heuristics to increase the quality of labeling by ignoring tweets with quotations and URLs and only keeping tweets with 5 terms or more that have the emotional hashtags at the end of the tweets. Using these rules they extracted around 2.5M tweets. After sampling another 400 random tweets and comparing it to human annotation the saw that hashtags can classify the tweets with 95% precision. They did some pre-processing by making all words lower-case, replaced user mentions with @user, replaced letters/punctuation that is repeated more than twice with the same two letters/punctuation (e.g., ooooh INLINEFORM0 ooh, !!!!! INLINEFORM1 !!); normalized some frequently used informal expressions (e.g., ll → will, dnt INLINEFORM2 do not); and stripped hash symbols. They used a sub-sample of their dataset to figure out the best approaches for classification, and after trying two different classifiers (multinomial Naive Bayes and LIBLINEAR) and 12 different feature sets, they got their best results using logistic regression branch for LIBLINEAR classifier and a feature set consist of n-gram(n=1,2), LIWC and MPQA lexicons, WordNet-Affect and POS tags.\nIn the second one, the reported results are from a paper by BIBREF33 in which they used maximum entropy classifier with bag of words model to classify various emotional datasets. Here we only report part of their result for CrowdFlower dataset that can be mapped to one of our seven labels.\n\nData and preparation\nThere are not many free datasets available for emotion classification. Most datasets are subject-specific (i.e. news headlines, fairy tails, etc.) and not big enough to train deep neural networks. Here we use the tweet dataset created by Wang et al. As mentioned in the previous section, they have collected over 2 million tweets by using hashtags for labeling their data. They created a list of words associated with 7 emotions (six emotions from BIBREF34 love, joy, surprise, anger, sadness fear plus thankfulness (See Table TABREF3 ), and used the list as their guide to label the sampled tweets with acceptable quality.\nAfter pre-processing, they have used 250k tweets as the test set, around 250k as development test and the rest of the data (around 2M) as training data. their best results using LIBLINEAR classifier and a feature set containing n-gram(n=1,2), LIWC and MPQA lexicons, WordNet-Affect and POS tags can be seen in Table TABREF4 . It can be seen that their best results were for high count emotions like joy and sadness as high as 72.1 in F-measure and worst result was for a low count emotion surprise with F-measure of 13.9.\nAs Twitter is against polishing this many tweets, Wang et al. provided the tweet ids along with their label. For our experiment, we retrieved the tweets in Wang et al.'s dataset by tweet IDs. As the dataset is from 7 years ago We could only download over 1.3 million tweets from around 2.5M tweet IDs in the dataset. The distribution of the data can be seen in Table TABREF5 .\nIn our experiment, we used simpler pre-processing steps which will be explained later on in the \"Experiment\" section.\n\nExperiment\nMinimal pre-processing was done by converting text to lower case, removing the hashtags at the end of tweets and separating each punctuation from the connected token (e.g., awesome!! INLINEFORM0 awesome !!) and replacing comma and new-line characters with white space. The text, then, was tokenized using TensorFlow-Keras tokenizer. Top N terms were selected and added to our dictionary where N=100k for higher count emotions joy, sadness, anger, love and N=50k for thankfulness and fear and N=25k for surprise. Seven binary classifiers were trained for the seven emotions with a batch size of 250 and for 20 epochs with binary cross-entropy as the objective function and Adam optimizer. The architecture of the model can be seen in Figure FIGREF6 . For training each classifier, a balanced dataset was created with selecting all tweets from the target set as class 1 and a random sample of the same size from other classes as class 0. For each classifier, 80% of the data was randomly selected as the training set, and 10% for the validation set, and 10% as the test set. As mentioned before we used the two embedding models, ConceptNet Numberbatch and fastText as the two more modern pre-trained word vector spaces to see how changing the embedding layer can affect the performance. The result of comparison among different embeddings can be seen in Table TABREF10 . It can be seen that the best performance was divided between the two embedding models with minor performance variations.\nThe comparison of our result with Wang et al. can be seen in Table TABREF9 . as shown, the results from our model shows significant improvement from 10% increase in F-measure for a high count emotion joy up to 61.7 point increase in F-measure for a low count emotion surprise. on average we showed 26.8 point increase in F-measure for all categories and more interestingly our result shows very little variance between different emotions compare to results reported by Wang et al.\n\nModel Performances on New Dataset\nTo asses the performance of these models on a totally unseen data, we tried to classify the CrowdFlower emotional tweets dataset. The CrowdFlower dataset consists of 40k tweets annotated via crowd-sourcing each with a single emotional label. This dataset is considered a hard dataset to classify with a lot of noise. The distribution of the dataset can be seen in Table TABREF18 . The labeling on this dataset is non-standard, so we used the following mapping for labels:\nsadness INLINEFORM0 sadness\nworry INLINEFORM0 fear\nhappiness INLINEFORM0 joy\nlove INLINEFORM0 love\nsurprise INLINEFORM0 surprise\nanger INLINEFORM0 anger\nWe then classified emotions using the pre-trained models and emotionally fitted fastText embedding. The result can be seen in Table TABREF19 . The baseline results are from BIBREF33 done using BOW model and maximum entropy classifier. We saw a huge improvement from 26 point increase in F-measure for the emotion joy (happiness) up to 57 point increase for surprise with total average increase of 38.6 points. Bostan and Klinger did not report classification results for the emotion love so we did not include it in the average. These results show that our trained models perform exceptionally on a totally new dataset with a different method of annotation.\n\nConclusion and Future Work\nIn this paper, we have shown that using the designed RNN based network we could increase the performance of classification dramatically. We showed that keeping the sequential nature of the data can be hugely beneficial when working with textual data especially faced with the hard task of detecting more complex phenomena like emotions. We accomplished that by using a recurrent network in the process of generating our hidden representation. We have also used a max-pooling layer to capture the most relevant features and an average pooling layer to capture the text as a whole proving that we can achieve better performance by focusing on creating a more informative hidden representation. In future we can focus on improving these representations for example by using attention networks BIBREF37 , BIBREF38 to capture a more contextual representation or using language model based methods like BERT BIBREF39 that has been shown very successful in various NLP tasks.\n\nTable 1: Statistics in the original dataset from Wang et al.\n\nTable 2: Results of final classification in Wang et al.\n\nFigure 1: Bidirectional GRU architecture used in our experiment.\n\nTable 5: Results of classification using two embedding models and bidirectional GRU. No meaningful differences was seen between the two models. Reported numbers are F1measures.\n\nTable 4: Results of classification using bidirectional GRU. Reported numbers are F1-measures.\n\nTable 7: Results from classifying CrowdFlower data using pre-trained model. Reported numbers are F1-measure.\n\nQuery: What are the hyperparameters of the bi-GRU?\n\nJSON Response:", "role": "user" }, { "content": "{\"answer\": \"They use the embedding layer with a size 35 and embedding dimension of 300. They use a dense layer with 70 units and a dropout layer with a rate of 50%.\", \"evidence\": [\"Minimal pre-processing was done by converting text to lower case, removing the hashtags at the end of tweets and separating each punctuation from the connected token (e.g., awesome!! INLINEFORM0 awesome !!) and replacing comma and new-line characters with white space. The text, then, was tokenized using TensorFlow-Keras tokenizer. Top N terms were selected and added to our dictionary where N=100k for higher count emotions joy, sadness, anger, love and N=50k for thankfulness and fear and N=25k for surprise. Seven binary classifiers were trained for the seven emotions with a batch size of 250 and for 20 epochs with binary cross-entropy as the objective function and Adam optimizer. The architecture of the model can be seen in Figure FIGREF6 . For training each classifier, a balanced dataset was created with selecting all tweets from the target set as class 1 and a random sample of the same size from other classes as class 0. For each classifier, 80% of the data was randomly selected as the training set, and 10% for the validation set, and 10% as the test set. As mentioned before we used the two embedding models, ConceptNet Numberbatch and fastText as the two more modern pre-trained word vector spaces to see how changing the embedding layer can affect the performance. The result of comparison among different embeddings can be seen in Table TABREF10 . It can be seen that the best performance was divided between the two embedding models with minor performance variations.\"]}", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND:: Nowadays, there has been a growing trend of caesarean sections in Iran. One reason would be the mother's desire. Increased maternal self-efficacy can be an important step to reduce percentage of cesarean. This study aimed to determine the effectiveness of training-based strategies to increase the self-efficacy for choosing delivery method among pregnant women in Shahrekord city.\n\nABSTRACT.METHODS:: This quasi-experimental study was conducted on 130 pregnant women who attended urban health centers in Shahrekord city in 2013. Intervention was applied in the experimental group in three sessions in about 60-90 min while control group did not receive any intervention. Fear of childbirth and self-efficacy questionnaire was completed before and after training.\n\nABSTRACT.RESULTS:: While mean scores of the fear of childbirth, expectations and childbirth self-efficacy before the intervention between the two groups were not significantly different (P > 0.05), mean scores of childbirth in intervention group was reduced and expectation and childbirth self-efficacy had a significant increase after intervention (P < 0.05). In this study, 71.4% of mothers in intervention group and 53.8% of control mothers naturally delivered their children. Most of intervention group mothers desired to deliver through cesarean and had more fear (P < 0.001) but lower childbirth expectation (P > 0.05) and self-efficacy (P < 0.001) than those who chose normal method.\n\nABSTRACT.CONCLUSIONS:: Our findings showed that training-based self-efficacy procedure has been effective in encouraging mothers to choose natural childbirth. Therefore, the design and implementation of curriculum-based strategies for increasing self-efficacy is suggested for pregnant women.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Training-based strategies (experimental group)\", \"No intervention (control group)\", \"Means scores of expectations and childbirth self-efficacy\", \"significantly increased\", \"mean scores of childbirth in intervention group was reduced and expectation and childbirth self-efficacy had a significant increase after intervention (P \\u003c 0.05).\"]]", "role": "assistant" } ]

[ { "content": "| Barium glasses | T p 1 | T p 1 | T p 1 | T p 2 | T p 2 | T p 2 | T p 3 | T p 3 | T p 3 |\n| S value | 0.00 | 0.50 | 1.00 | 0.00 | 0.50 | 1.00 | 0.00 | 0.50 | 1.00 |\n| E a Marotta (kJ/mol) | 307 | 221 | 266 | 156 | 245 | 306 | None | None | 209 |\n| Linear fit (R 2) | 0.98 | 0.96 | 0.98 | 0.99 | 0.98 | 0.95 | None | None | 0.94 |\n| E a Matusita (kJ/mol) | 301 | 215 | 261 | 149 | 239 | 300 | None | None | 203 |\n| Linear fit (R 2) | 0.98 | 0.96 | 0.98 | 0.98 | 0.98 | 0.95 | None | None | 0.94 |\n| | | | | | | | | | |\n| Potassia glasses | Tpnuc2 | Tpnuc2 | Tpnuc2 | T p 1 | T p 1 | T p 1 | T p 2 | T p 2 | T p 2 |\n| S value | 0.00 | 0.50 | 1.00 | 0.00 | 0.50 | 1.00 | 0.00 | 0.50 | 1.00 |\n| E a Marotta (kJ/mol) | None | 309 | 245 | 254 | 317 | 331 | 249 | None | 387 |\n| Linear fit (R 2) | None | 0.93 | 0.99 | 0.99 | 0.99 | 0.98 | 0.99 | None | 0.97 |\n| E a Matusita (kJ/mol) | None | 304 | 240 | 248 | 311 | 325 | 243 | None | 381 |\n| Linear fit (R 2) | None | 0.93 | 0.99 | 0.99 | 0.99 | 0.98 | 0.99 | None | 0.97 |\n\nCaption: Activation energy and respective linear fit values (R 2) for barium-containing a potassium containing (8SiO23Al2O3(S)Li2O(3-S)MgO3MgF2BaO/K2O) compositions calculated by E a Marotta and E a Matusita methods\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"0.00\", \"type\": \"Other\"}\n{\"value\": \"0.50\", \"type\": \"Other\"}\n{\"value\": \"1.00\", \"type\": \"Other\"}\n{\"value\": \"0.00\", \"type\": \"Other\"}\n{\"value\": \"0.50\", \"type\": \"Other\"}\n{\"value\": \"1.00\", \"type\": \"Other\"}\n{\"value\": \"0.00\", \"type\": \"Other\"}\n{\"value\": \"0.50\", \"type\": \"Other\"}\n{\"value\": \"1.00\", \"type\": \"Other\"}\n{\"value\": \"307\", \"type\": \"Other\"}\n{\"value\": \"221\", \"type\": \"Other\"}\n{\"value\": \"266\", \"type\": \"Other\"}\n{\"value\": \"156\", \"type\": \"Other\"}\n{\"value\": \"245\", \"type\": \"Other\"}\n{\"value\": \"306\", \"type\": \"Other\"}\n{\"value\": \"209\", \"type\": \"Other\"}\n{\"value\": \"0.98\", \"type\": \"Other\"}\n{\"value\": \"0.96\", \"type\": \"Other\"}\n{\"value\": \"0.98\", \"type\": \"Other\"}\n{\"value\": \"0.99\", \"type\": \"Other\"}\n{\"value\": \"0.98\", \"type\": \"Other\"}\n{\"value\": \"0.95\", \"type\": \"Other\"}\n{\"value\": \"0.94\", \"type\": \"Other\"}\n{\"value\": \"301\", \"type\": \"Other\"}\n{\"value\": \"215\", \"type\": \"Other\"}\n{\"value\": \"261\", \"type\": \"Other\"}\n{\"value\": \"149\", \"type\": \"Other\"}\n{\"value\": \"239\", \"type\": \"Other\"}\n{\"value\": \"300\", \"type\": \"Other\"}\n{\"value\": \"203\", \"type\": \"Other\"}\n{\"value\": \"0.98\", \"type\": \"Other\"}\n{\"value\": \"0.96\", \"type\": \"Other\"}\n{\"value\": \"0.98\", \"type\": \"Other\"}\n{\"value\": \"0.98\", \"type\": \"Other\"}\n{\"value\": \"0.98\", \"type\": \"Other\"}\n{\"value\": \"0.95\", \"type\": \"Other\"}\n{\"value\": \"0.94\", \"type\": \"Other\"}\n{\"value\": \"0.00\", \"type\": \"Other\"}\n{\"value\": \"0.50\", \"type\": \"Other\"}\n{\"value\": \"1.00\", \"type\": \"Other\"}\n{\"value\": \"0.00\", \"type\": \"Other\"}\n{\"value\": \"0.50\", \"type\": \"Other\"}\n{\"value\": \"1.00\", \"type\": \"Other\"}\n{\"value\": \"0.00\", \"type\": \"Other\"}\n{\"value\": \"0.50\", \"type\": \"Other\"}\n{\"value\": \"1.00\", \"type\": \"Other\"}\n{\"value\": \"309\", \"type\": \"Other\"}\n{\"value\": \"245\", \"type\": \"Other\"}\n{\"value\": \"254\", \"type\": \"Other\"}\n{\"value\": \"317\", \"type\": \"Other\"}\n{\"value\": \"331\", \"type\": \"Other\"}\n{\"value\": \"249\", \"type\": \"Other\"}\n{\"value\": \"387\", \"type\": \"Other\"}\n{\"value\": \"0.93\", \"type\": \"Other\"}\n{\"value\": \"0.99\", \"type\": \"Other\"}\n{\"value\": \"0.99\", \"type\": \"Other\"}\n{\"value\": \"0.99\", \"type\": \"Other\"}\n{\"value\": \"0.98\", \"type\": \"Other\"}\n{\"value\": \"0.99\", \"type\": \"Other\"}\n{\"value\": \"0.97\", \"type\": \"Other\"}\n{\"value\": \"304\", \"type\": \"Other\"}\n{\"value\": \"240\", \"type\": \"Other\"}\n{\"value\": \"248\", \"type\": \"Other\"}\n{\"value\": \"311\", \"type\": \"Other\"}\n{\"value\": \"325\", \"type\": \"Other\"}\n{\"value\": \"243\", \"type\": \"Other\"}\n{\"value\": \"381\", \"type\": \"Other\"}\n{\"value\": \"0.93\", \"type\": \"Other\"}\n{\"value\": \"0.99\", \"type\": \"Other\"}\n{\"value\": \"0.99\", \"type\": \"Other\"}\n{\"value\": \"0.99\", \"type\": \"Other\"}\n{\"value\": \"0.98\", \"type\": \"Other\"}\n{\"value\": \"0.99\", \"type\": \"Other\"}\n{\"value\": \"0.97\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nStochastic bandits with groups of similar arms.\n\nMain Content:\n1 Introduction . The finite stochastic multi-armed bandit problem is a popular framework for studying sequential decision making problems in which a learner sequentially samples from a finite set of distributions called arms . It was first introduced in the context of medical trials [ Thompson , 1933b , 1935 ] and later formalized under this name by Robbins in Robbins [ 1952 ] . We refer the interested reader to Lattimore and Szepesvári [ 2020 ] for a recent survey . This is one of the simplest theoretical framework in which one can study the notion of exploration-exploitation tradeoff . This tension between exploration and exploitation arises from the sequential optimization problem a learner is trying to perform while being uncertain about the very problem it is optimizing . Formally , a multi-armed bandit configuration is specified by a set of unknown real-valued probability distributions ν= ( νa ) a∈A with means ( µa ) a∈A , indexed by a set of arms A . We hereafter consider a finite A , and that all νa , a∈A belong to the same family of distributions F ( e.g . Bernoulli , Gaussian , etc . ) , that is ν ∈ FA . The bandit game proceeds as follows . At each time t∈N , the learner chooses an arm at ∈A based on the past observations and decisions , then receives and observes a sample Xt ( called the reward ) , conditionally independent , sampled from νat . Her goal is to maximize the cumulative reward received over time . The mean of each arm is unknown , which makes the problem non-trivial , hence the learner should adjust her sampling strategy based on past information obtained from drawing different arms in order to maximize the expected sum of rewards . The maximal expected value of a finite bandit configuration is denoted by µ ? , defined as µ ? = max a∈A µa . The performance of the strategy used by the agent is measured by the ( pseudo ) regret , that compares the 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . expected sum of rewards obtained by an oracle that would constantly pull an optimal arm and the ones obtained by the learner , up to some time horizon T ( that we assume is unknown to the learner ) . Definition 1 ( Regret ) . The regret incurred by a sampling strategy after T time steps on a bandit configuration ν is given by : R ( ν , T ) = Eν ( T∑ t=1 ( µ∗ − µat ) ) = ∑ a∈A ( µ∗ − µa ) Eν ( Na ( T ) ) , where Na ( T ) = T∑ t=1 I { at = a } denotes the number of selection of arm a after T time steps . Group of similar arms Motivated by various practical reasons , one may want to restrict to a subset B ⊂ FA of allowed bandit configurations instead of the full set FA . In this paper , we study a variant of the multi-armed bandit problem in which the reward function , µ : a ∈ A → µa , is assumed to satisfy a cluster-like structural property . A bandit configuration ν is said to satisfy the q-equivalence property if for every arm a ∈ A , there are at least q−1 distinct arms having the same expected value : ∀a ∈ A , | { a′ ∈ A|µa′ = µa } | > q . Assuming the set of arms A and base distributions D are known to the learner , we denote by Bq the set of bandit configurations having the q-equivalence property . We also denote by Bq ( µ ) the set of all expected values in Bq . Formally , Bq ( µ ) is the image of Bq under the µ mapping . Definition 2 ( Arm equivalence and equivalence class ) . Given a bandit configuration ν , two arms a , a′ ∈ A are said to be equivalent if their associated distributions have the same expected values : a ∼ a′ ⇔ µa = µa′ . An equivalence class c in ν is a maximal subset of arms in A having the same mean , i.e. , for all arms a , a′ in c , µa = µa′ and for all arm a ∈ c and a′ ∈ A \\ c , µa 6= µa′ . This situation typically appears in practical situations when each arm can be described with a list of categorical attributes , and the ( unknown ) mean reward function only depends on a subset of them , the others being redundant . In this case , q is naturally linked to the number of attributes considered redundant ( or useless descriptors ) , and the number of categories of each attribute . Precisely , q = ∏ i∈R ci whereR is the set of redundant attributes and ci the number of categories for attribute i . The learner may know that there exists such a structure while not knowing a closed form formula mapping the list of categorical attributes to the significant subset . In this case , q might be a lower bound on the sizes of the class since the setR might not be the largest possible one or because the number of redundant attributes depends on the number of relevant attributes . In all cases , the smallest possible number of redundant attributes can be naturally linked to q . We hereafter consider the learner only knows q but would like to exploit the prior knowledge of this structure in a bandit problem . Regret lower bounds overview In order to assess the performance of a bandit algorithm on a set of configurations B , one naturally studies the best guarantee achievable by a uniformly efficient algorithm on B , i.e with sub-linear regret on any instance ν ∈ B of the bandit problem . When B = FA , such a guarantee was first provided by Lai and Robbins [ 1985 ] for parametric families F , and then extended by Burnetas and Katehakis [ 1996 ] for more general families . It states that any algorithm that is uniformly efficient1 on a family of distributions F must satisfy lim inf T→∞ R ( ν , T ) log ( T ) > ∑ a : µ ? > µa0 µ ? − µa KF ( νa‖µ ? ) , KF ( νa‖µ ? ) = inf G∈F { KL ( νa‖G ) : EG ( X ) > µ ? } . ( 1 ) This popular result entails that any strategy having the desirable property to have sub-linear regret on any instance in F must incur a non-trivial minimal regret . When B is a strict subset of FA , the bandit problem is called structured , as in this case pulling an arm may reveal information that makes it possible to refine estimation of other arms ( e.g . think of the set of bandit configurations having Lipschitz mean function with respect to A⊂Rd ) . The presence of structure may considerably modify the achievable lowest regret , as shown in Burnetas and Katehakis [ 1996 ] and Graves and Lai 1Formally , for each bandit on F , for each arm k with ∆k > 0 , then E [ Nk ( T ) ] =o ( Tα ) for all α∈ ( 0 , 1 ] . [ 1997 ] , who extended the ( unstructured ) lower bounds to arbitrarily structured bandit problems ( and beyond ) . These lower bound take the generic form lim infT→∞ R ( ν , T ) log ( T ) > CB ( ν ) , where CB ( ν ) is a constant term solution of a constrained linear-optimization problem . A bandit algorithm is then called asymptotically optimal for a set B when its regret asymptotically matches this lower bound . Existing strategies In order to minimize the regret , a learner faces the classical exploration/exploitation dilemma : it needs to balance exploration , that is gaining information about the expected values of the arms by sampling them , and exploitation , that is playing the most promising arm sufficiently often . Many algorithms have been proposed to solve the multi-armed bandits problem ( see Lattimore and Szepesvári [ 2020 ] for a survey ) . The study of the lower bounds had a crucial impact on the development of provably asymptotically optimal strategies . In the case of unstructured bandit B = FA , this includes strategies that build on the concept of Optimism in Face of Uncertainty ( the most celebrated of which being the Upper Confidence Bound ( UCB ) algorithms Agrawal [ 1995 ] , Auer et al . [ 2002 ] ) , such as KLUCB [ Lai , 1987 , Cappé et al. , 2013 , Maillard , 2018 ] , DMED and IMED Honda and Takemura [ 2011 , 2015 ] , that are proven asymptotically optimal for various families F ( e.g . one-dimensional exponential families ) , and directly exploit the lower bound in their structure . Alternative asymptotically optimal strategies include the Thompson Sampling ( TS ) Thompson [ 1933a ] , Agrawal and Goyal [ 2012 ] , which uses a Bayesian posterior distribution given a specific prior , whose optimality was shown in Korda et al . [ 2013 ] . See also Kveton et al . [ 2019 ] for other randomized algorithms and Kveton et al . [ 2020 ] , Chan [ 2020 ] , Baudry et al . [ 2020 ] for recent non-parametric extensions using re-sampling methods . Further , some authors also allow many optimal arms , see de Heide et al . [ 2021 ] , or even countably many arms , see Kalvit and Zeevi [ 2020 ] . However , these works do not consider nor exploit a constraint on the level-sets of the mean function and follow an optimistic paradigm while we follow an information minimization targeting optimality . On the other hand , several instances of structured bandits received considerable attention in the last few years . This is the case for instance of linear bandits , see [ Abbasi-Yadkori et al. , 2011 , Srinivas et al. , 2010 , Durand et al. , 2017 , Kveton et al. , 2020 ] and Lattimore and Szepesvari [ 2017 ] , Lipschitz bandits Magureanu et al . [ 2014 ] , Wang et al . [ 2020 ] , Lu et al . [ 2019 ] , unimodal bandits Yu and Mannor [ 2011 ] , Combes and Proutiere [ 2014 ] , Saber et al . [ 2020 ] , or combinatorial bandits Kveton et al . [ 2015 ] , Magureanu [ 2018 ] , and more recently Cuvelier et al . [ 2021b ] . A generic asymptotically optimal algorithm , called OSSB ( Optimal Structured Stochastic Bandit ) , has been introduced in the work of Combes et al . [ 2017 ] , and proven to be asymptotically optimal for all structures satisfying some weak properties that include all the aforementioned structures . Although being asymptotically optimal this algorithm often suffers from a long burn-in phase that may hinder its finite practical performance . It further comes with high computational price as it requires to solve an empirical version of the optimization problem CB ( ν ) at each step . This motivates the quest for alternative strategies , perhaps less generic but better suited to a specific structure . Inspired by combinatorial structures for which computing CD ( ν ) is simply not feasible , a relaxation of the generic constrained optimization problem was recently proposed in Cuvelier et al . [ 2021a ] . The authors show that this comes at the price of trading-off regret optimality for computational efficiency . Indeed in some structure , combinatorial properties are at stake and asymptotically optimal algorithms may require solving combinatorial optimization problems ( see Cuvelier et al . [ 2021a ] ) related to CB ( ν ) . In order to exploit the combinatorial structures in a numerically efficient way , research has been made in how to relax these combinatorial optimization problems while preserving theoretical properties on the regret of the relaxed algorithms ( see Cuvelier et al . [ 2021b , a ] ) . Our work consider similar computational issues , with a different perspective . Goal For the structure Bq , as we show in Theorem 1 below , the term CBq ( ν ) unfortunately makes appear in general a combinatorial optimization problem . This makes resorting to OSSB or any strategy targeting exact asymptotic optimality a daunting task for the practitioner . In this paper , our goal is to provide a computationally efficient strategy adapted to the structure Bq , that is able to reach optimality up to controlled error term . Outline and contributions The rest of this paper is organized as follows . In section 2 , we derive a lower bound on the regret for the structured set of bandit configurations Bq . This bound makes appear two components , one that we call non-combinatorial as optimizing it can be done efficiently , and a second term that we term combinatorial as it involves solving a combinatorial problem . Interestingly , using in Lemma 1 and Theorem 3 that the contribution of the combinatorial part of the lower bound can be controlled . Owing to this key insight , we introduce in section 3 , IMED-EC , an adaptation of the IMED strategy from Honda and Takemura [ 2015 ] to the structured set Bq . One advantage of IMED over a KL-UCB alternative is its reduced complexity , which translates to the equivalence class setup . At each time step , the complexity of computing the next arm to be pulled by IMED-EC is no more than the one of sorting a list of |A| elements once the IMED indexes have been computed , which is only log |A| times larger than looking for the minimal IMED index . In Section 4 , we prove that IMED-EC achieves a controlled asymptotic regret that matches the non-combinatorial part of the lower bound and is at most ( less than ) a factor of 2 times the optimal regret bound . Last , we illustrate the benefit of the IMED-EC over its unstructured version in section 5 , where it shows a substantial improvement . Our experiments also highlights the robustness of the algorithm to a misspecified parameter q , which is a desirable feature for the practitioner .", "role": "user" }, { "content": "This paper considers a new variant of the classic stochastic bandit problem, analysing the complexity of the problem and proposing an effective algorithm. The new problem matches the classic problem definition of the K-armed bandit, except all arms are grouped into classes with identical distributions. Each class contains at least q arms, and the decision-maker is assumed to know q a priori but not the classes. A motivating scenario where this may arise is one where arms have multiple categorical features but certain features are uniformly redundant in the sense that the reward distributions do not vary with respect to these features. The decision-maker would in such a setting know (a lower bound upon) the number of redundant features but not which features are redundant (and as such which arms are equivalent). The authors derive an asymptotic lower bound on the number of plays of suboptimal arms, and then propose an algorithm based on the IMED indices of Honda and Takemura (2015). The regret of this algorithm is shown to coincide with the lower bound in certain settings - where classes are equally sized (roughly speaking) – and differ from the lower bound by no more than a constant factor in other settings. The algorithm is shown to outperform non-bespoke approaches which treat are unaware of the arm class structure, and have some robustness to misspecification of q.", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.PURPOSE: The evaluation of syncope is often disorganized and ineffective. The objective of this study was to examine whether implementation of a standardized emergency department (ED) protocol improves the quality of syncope evaluation.\n\nABSTRACT.MATERIALS AND METHODS: This study was a prospective, non-randomized study conducted at a 1900-bed, tertiary teaching hospital in South Korea. We compared two specific periods, including a 12-month observation period (control group, January-December 2009) and a 10-month intervention period after the implementation of standardized approaches, comprising risk stratification, hospital order sets and establishment of a syncope observational unit (intervention group, March-December 2010). Primary end points were hospital admission rates and medical costs related to syncope evaluation.\n\nABSTRACT.RESULTS: A total of 244 patients were enrolled in this study (116 patients in the control group and 128 patients in the intervention group). The admission rate decreased by 8.3% in the intervention group (adjusted odds ratio 0.31, 95% confidence interval 0.13-0.70, p=0.005). There was a cost reduction of about 30% during the intervention period [369000 Korean won (KRW), interquartile range (IQR) 240000-602000 KRW], compared with the control period (542000 KRW, IQR 316000-1185000 KRW). The length of stay in the ED was also reduced in the intervention group (median: 4.6 hours vs. 3.4 hours).\n\nABSTRACT.CONCLUSION: Standardized approaches to syncope evaluation reduced hospital admissions, medical costs and length of stay in the overcrowded emergency department of a tertiary teaching hospital in South Korea.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Educational program and ED protocol for syncope evaluation\", \"No standardized guidelines or educational programs\", \"Admission rate\", \"significantly decreased\", \"The admission rate decreased by 8.3% in the intervention group (adjusted odds ratio 0.31, 95% confidence interval 0.13-0.70, p=0.005).\"]]", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nMoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees\n\nMain Content:\n1 INTRODUCTION . Deep Reinforcement Learning ( DRL ) has achieved many recent breakthroughs in challenging domains such as Go ( Silver et al. , 2016 ) . While using neural networks for encoding state representations allow DRL agents to learn policies for tasks with large state spaces , the learned policies are not interpretable , which hinders their use in safety-critical applications . Some recent works leverage programs and decision trees as representations for interpreting the learned agent policies . PIRL ( Verma et al. , 2018 ) uses program synthesis to generate a program in a Domain-Specific Language ( DSL ) that is close to the DRL agent policy . The design of the DSL with desired operators is a tedious manual effort and the enumerative search for synthesis is difficult to scale for larger programs . In contrast , Viper ( Bastani et al. , 2018 ) learns a Decision Tree ( DT ) policy by mimicking the DRL agent , which not only allows for a general representation for different policies , but also allows for verification of these policies using integer linear programming solvers . Viper uses the DAGGER ( Ross et al. , 2011 ) imitation learning approach to collect state action pairs for training the student DT policy given the teacher DRL policy . It modifies the DAGGER algorithm to use the Q-function of teacher policy to prioritize states of critical importance during learning . However , learning a single DT for the complete policy leads to some key shortcomings such as i ) less faithful representation of original agent policy measured by the number of mispredictions , ii ) lower overall performance ( reward ) , and iii ) larger DT sizes that make them harder to interpret . In this paper , we present MOËT ( Mixture of Expert Trees ) , a technique based on Mixture of Experts ( MOE ) ( Jacobs et al. , 1991 ; Jordan and Xu , 1995 ; Yuksel et al. , 2012 ) , and reformulate its learning procedure to support DT experts . MOE models can typically use any expert as long as it is a differentiable function of model parameters , which unfortunately does not hold for DTs . Similar to MOE training with Expectation-Maximization ( EM ) algorithm , we first observe that MOËT can be trained by interchangeably optimizing the weighted log likelihood for experts ( independently from one another ) and optimizing the gating function with respect to the obtained experts . Then , we propose a procedure for DT learning in the specific context of MOE . To the best of our knowledge we are first to combine standard non-differentiable DT experts , which are interpretable , with MOE model . Existing combinations which rely on differentiable tree or treelike models , such as soft decision trees ( Irsoy et al. , 2012 ) and hierarchical mixture of experts ( Zhao et al. , 2019 ) are not interpretable . We adapt the imitation learning technique of Viper to use MOËT policies instead of DTs . MOËT creates multiple local DTs that specialize on different regions of the input space , allowing for simpler ( shallower ) DTs that more accurately mimic the DRL agent policy within their regions , and combines the local trees into a global policy using a gating function . We use a simple and interpretable linear model with softmax function as the gating function , which returns a distribution over DT experts for each point in the input space . While standard MOE uses this distribution to average predictions of DTs , we also consider selecting just one most likely expert tree to improve interpretability . While decision boundaries of Viper DT policies must be axis-perpendicular , the softmax gating function supports boundaries with hyperplanes of arbitrary orientations , allowing MOËT to more faithfully represent the original policy . We evaluate our technique on four different environments : CartPole , Pong , Acrobot , and Mountaincar . We show that MOËT achieves significantly better rewards and lower misprediction rates with shallower trees . We also visualize the Viper and MOËT policies for Mountaincar , demonstrating the differences in their learning capabilities . Finally , we demonstrate how a MOËT policy can be translated into an SMT formula for verifying properties for CartPole game using the Z3 theorem prover ( De Moura and Bjørner , 2008 ) under similar assumptions made in Viper . In summary , this paper makes the following key contributions : 1 ) We propose MOËT , a technique based on MOE to learn mixture of expert decision trees and present a learning algorithm to train MOËT models . 2 ) We use MOËT models with a softmax gating function for interpreting DRL policies and adapt the imitation learning approach used in Viper to learn MOËT models . 3 ) We evaluate MOËT on different environments and show that it leads to smaller , more faithful , and performant representations of DRL agent policies compared to Viper while preserving verifiability . 2 RELATED WORK . Interpretable Machine Learning : In numerous contexts , it is important to understand and interpret the decision making process of a machine learning model . However , interpretability does not have a unique definition that is widely accepted . Accoding to Lipton ( Lipton , 2016 ) , there are several properties which might be meant by this word and we adopt the one which Lipton names transparency which is further decomposed to simulability , decomposability , and algorithmic transparency . A model is simulable if a person can in reasonable time compute the outputs from given inputs and in that way simulate the model ’ s inner workings . That holds for small linear models and small decision trees ( Lipton , 2016 ) . A model is decomposable if each part of a models admits an intuitive explanation , which is again the case for simple linear models and decision trees ( Lipton , 2016 ) . Algorithmic transparency is related to our understanding of the workings of the training algorithm . For instance , in case of linear models the shape of the error surface and properties of its unique minimum towards which the algorithm converges are well understood ( Lipton , 2016 ) . MOËT models focus on transparency ( as we discuss at the end of Section 5 ) . Explainable Machine Learning : There has been a lot of recent interest in explaining decisions of black-box models ( Guidotti et al. , 2018a ; Doshi-Velez and Kim , 2017 ) . For image classification , activation maximization techniques can be used to sample representative input patterns ( Erhan et al. , 2009 ; Olah et al. , 2017 ) . TCAV ( Kim et al. , 2017 ) uses human-friendly high-level concepts to associate their importance to the decision . Some recent works also generate contrastive robust explanations to help users understand a classifier decision based on a family of neighboring inputs ( Zhang et al. , 2018 ; Dhurandhar et al. , 2018 ) . LORE ( Guidotti et al. , 2018b ) explains behavior of a blackbox model around an input of interest by sampling the black-box model around the neighborhood of the input , and training a local DT over the sampled points . Our model presents an approach that combines local trees into a global policy . Tree-Structured Models : Irsoy et al . ( Irsoy et al. , 2012 ) propose a a novel decision tree architecture with soft decisions at the internal nodes where both children are chosen with probabilities given by a sigmoid gating function . Similarly , binary tree-structured hierarchical routing mixture of experts ( HRME ) model , which has classifiers as non-leaf node experts and simple regression models as leaf node experts , were proposed in ( Zhao et al. , 2019 ) . Both models are unfortunately not interpretable . Knowledge Distillation and Model Compression : We rely on ideas already explored in fields of model compression ( Bucilu et al. , 2006 ) and knowledge distillation ( Hinton et al. , 2015 ) . The idea is to use a complex well performing model to facilitate training of a simpler model which might have some other desirable properties ( e.g. , interpretability ) . Such practices have been applied to approximate decision tree ensemble by a single tree ( Breiman and Shang , 1996 ) , but this is different from our case , since we approximate a neural network . In a similar fashion a neural network can be used to train another neural network ( Furlanello et al. , 2018 ) , but neural networks are hard to interpret and even harder to formally verify , so this is also different from our case . Such practices have also been applied in the field of reinforcement learning in knowledge and policy distillation ( Rusu et al. , 2016 ; Koul et al. , 2019 ; Zhang et al. , 2019 ) , which are similar in spirit to our work , and imitation learning ( Bastani et al. , 2018 ; Ross et al. , 2011 ; Abbeel and Ng , 2004 ; Schaal , 1999 ) , which provide a foundation for our work . 3 MOTIVATING EXAMPLE : GRIDWORLD . We now present a simple motivating example to showcase some of the key differences between Viper and MOËT approaches . Consider the N ×N Gridworld problem shown in Figure 1a ( for N = 5 ) . The agent is placed at a random position in a grid ( except the walls denoted by filled rectangles ) and should find its way out . To move through the grid the agent can choose to go up , left , right or down at each time step . If it hits the wall it stays in the same position ( state ) . State is represented using two integer values ( x , y coordinates ) which range from ( 0 , 0 ) —bottom left to ( N − 1 , N − 1 ) —top right . The grid can be escaped through either left doors ( left of the first column ) , or right doors ( right of the last column ) . A negative reward of −0.1 is received for each agent action ( negative reward encourages the agent to find the exit as fast as possible ) . An episode finishes as soon as an exit is reached or if 100 steps are made whichever comes first . The optimal policy ( π∗ ) for this problem consists of taking the left ( right resp . ) action for each state below ( above resp . ) the diagonal . We used π∗ as a teacher and used imitation learning approach of Viper to train an interpretable DT policy that mimics π∗ . The resulting DT policy is shown in Figure 1b . The DT partitions the state space ( grid ) using lines perpendicular to x and y axes , until it separates all states above diagonal from those below . This results in a DT of depth 3 with 9 nodes . On the other hand , the policy learned by MOËT is shown in Figure 1c . The MOËT model with 2 experts learns to partition the space using the line defined by a linear function 1.06x + 1.11y = 4 ( roughly the diagonal of the grid ) . Points on the different sides of the line correspond to two different experts which are themselves DTs of depth 0 always choosing to go left ( below ) or right ( above ) . We notice that DT policy needs much larger depth to represent π∗ while MOËT can represent it as only one decision step . Furthermore , with increasing N ( size of the grid ) , complexity of DT will grow , while MOËT complexity stays the same ; we empirically confirm this for N = [ 5 , 10 ] . For N = 5 , 6 , 7 , 8 , 9 , 10 DT depths are 3 , 4 , 4 , 4 , 4 , 5 and number of nodes are 9 , 11 , 13 , 15 , 17 , 21 respectively . In contrast , MOËT models of same complexity and structure as the one shown in Figure 1c are learned for all values of N ( models differ in the learned partitioning linear function ) .", "role": "user" }, { "content": "The paper proposes an extension to the Viper[1] method for interpreting and verifying deep RL policies by learning a mixture of decision trees to mimic the originally learned policy. The proposed approach can imitate the deep policy better compared with Viper while preserving verifiability. Empirically the proposed method demonstrates improvement in terms of cumulative reward and misprediction rate over Viper in four benchmark tasks.", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.OBJECTIVE: Statins treatment may modify the levels of zinc and selenium, minerals that can improve vascular function and reduce oxidative damage and inflammation in atherosclerotic patients. This study aimed to evaluate the effects of rosuvastatin, alone or associated with zinc and selenium supplementation, on lipid profile, antioxidant enzymes and mineral status in coronary artery disease patients.\n\nABSTRACT.MATERIAL AND METHODS: A double-blind randomized clinical trial was performed in which patients (n = 76) were treated with 10 mg rosuvastatin over 4 months associated or not with zinc (30 mg/d) and selenium (150 μg/d) supplementation. The following parameters were analyzed before and after the intervention: anthropometric measurements, lipid profile, high sensitivity C-reactive protein (hs-CRP), electronegative low density lipoprotein (LDL(-)) concentrations, activities of glutathione peroxidase (GPx), superoxide dismutase (SOD), zinc and selenium concentrations in blood plasma and erythocytes. Significance was determined using an α of 5% (two-tailed).\n\nABSTRACT.RESULTS: We found that rosuvastatin therapy was efficient in reducing total cholesterol, LDL-cholesterol, non-HDL cholesterol, triglycerides, and hs-CRP independently of mineral supplementation. Neither treatment was associated with significant changes in LDL(-). Similarly, the antioxidant enzymes GPx and SOD activity were unchanged by treatments. Neither treatment was associated with significant differences in concentrations of zinc or selenium in blood plasma and erythocytes of studied groups.\n\nABSTRACT.CONCLUSION: Rosuvastatin treatment did not affect zinc and selenium levels in coronary artery disease patients. The zinc and selenium supplementation at doses used in this study did not change lipid profile or SOD and GPx activity in patients receiving rosuvastatin. Further studies should be focused on testing alternative doses and supplements in different populations to contribute for a consensus on the ideal choice of antioxidants to be used as possible complementary therapies in atherosclerotic patients.\n\nABSTRACT.TRIAL REGISTRATION: ClinicalTrials.gov NCT01547377\n\n\nExtracted Data:", "role": "user" }, { "content": "[[\"10 mg rosuvastatin over 4 months with zinc (30 mg/d) and selenium (150 I\\u03021\\u20444g/d) supplementation.\", \"10 mg rosuvastatin over 4 months\", \"glutathione peroxidase (GPx) and superoxide dismutase (SOD) levels\", \"no significant difference\", \"Similarly, the activities of the antioxidant enzymes GPx and SOD were unchanged by treatments (P \\u003e 0.42) and were similar in both groups (P \\u003e 0.33).\"], [\"10 mg rosuvastatin over 4 months with zinc (30 mg/d) and selenium (150 I\\u03021\\u20444g/d) supplementation.\", \"10 mg rosuvastatin over 4 months\", \"glutathione peroxidase (GPx) and superoxide dismutase (SOD) levels\", \"no significant difference\", \"Similarly, the antioxidant enzymes GPx and SOD activity were unchanged by treatments.\"], [\"10 mg rosuvastatin over 4 months with zinc (30 mg/d) and selenium (150 I\\u03021\\u20444g/d) supplementation.\", \"10 mg rosuvastatin over 4 months\", \"total cholesterol, LDL-c, and non-HDL-c levels\", \"no significant difference\", \"Significant decreases in total cholesterol, LDL-c, and non-HDL-c levels were observed for all patients, regardless of group; however, there were no differences between the rosuvastatin + mineral-supplemented and rosuvastatin + placebo groups (P \\u003e 0.21).\"], [\"10 mg rosuvastatin over 4 months with zinc (30 mg/d) and selenium (150 I\\u03021\\u20444g/d) supplementation.\", \"10 mg rosuvastatin over 4 months\", \"Triglycerides concentrations\", \"no significant difference\", \"Triglycerides concentrations dropped noticeably in both groups, though this pattern was significant only for the placebo group (P = 0.034); despite this, differences between the two groups were not significant (P = 0.76).\"], [\"10 mg rosuvastatin over 4 months with zinc (30 mg/d) supplementation.\", \"10 mg rosuvastatin over 4 months with selenium (150 I\\u03021\\u20444g/d) supplementation.\", \"concentrations of zinc and selenium of studied groups.\", \"no significant difference\", \"Neither treatment was associated with significant differences in concentrations of zinc (plasma: P \\u003e 0.14; erythrocyte: P \\u003e 0.14) or selenium (plasma: P \\u003e 0.63; erythrocyte: P \\u003e 0.07) of studied groups.\"], [\"10 mg rosuvastatin over 4 months with zinc (30 mg/d) supplementation.\", \"10 mg rosuvastatin over 4 months with selenium (150 I\\u03021\\u20444g/d) supplementation.\", \"concentrations of zinc and selenium of studied groups.\", \"no significant difference\", \"Neither treatment was associated with significant differences in concentrations of zinc or selenium in blood plasma and erythocytes of studied groups.\"]]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.PURPOSE: The purpose of this multi-institute, single-group clinical trial was to evaluate the effectiveness and safety of toric orthokeratology lenses for the treatment of patients with combined myopia and astigmatism.\n\nABSTRACT.METHODS: A total of 44 patients were included in this clinical trial. The patients ranged in age from 7 to 49 years, with myopia of -0.75 to -6.0 diopters (D) and astigmatism of 1.25 to 4.0 D. After excluding 21 subjects, 23 subjects (39 eyes) were analyzed after toric orthokeratology lens use. The subjects underwent ophthalmologic examination after 1 day and 1, 2, 3, and 4 weeks of wearing overnight toric orthokeratology lenses.\n\nABSTRACT.RESULTS: A total of 19 subjects (31 eyes) completed the trial after five subjects (eight eyes) dropped out. In the patients who completed the study by wearing lenses for 4 weeks, the myopic refractive error decreased significantly by 2.60 ± 2.21 D (p < 0.001), from -3.65 ± 1.62 to -1.05 ± 1.64 D. The astigmatic refractive error were also significantly decreased by 0.63 ± 0.98 D (p = 0.001), from 2.07 ± 0.83 to 1.44 ± 0.99 D. The mean uncorrected and corrected visual acuities before wearing the lenses were 2.14 ± 0.80 logarithm of the logMAR (logMAR) and 0.05 ± 0.13 logMAR, respectively, which changed to 0.12 ± 0.30 logarithm of the logMAR (p < 0.001) and 0.01 ± 0.04 logMAR (p = 0.156) after 4 weeks. No serious adverse reactions were reported during the clinical trial.\n\nABSTRACT.CONCLUSIONS: Our results suggest that toric orthokeratology is an effective and safe treatment for correcting visual acuity in patients with combined myopia and astigmatism.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"astigmatism of 1.25 to 4.0 D\", \"baseline\", \"The mean astigmatic refractive error\", \"significantly decreased\", \"A total of 19 subjects (31 eyes) completed the trial after five subjects (eight eyes) dropped out. In the patients who completed the study by wearing lenses for 4 weeks, the myopic refractive error decreased significantly by 2.60 \\u00b1 2.21 D (p \\u003c 0.001), from -3.65 \\u00b1 1.62 to -1.05 \\u00b1 1.64 D. The astigmatic refractive error were also significantly decreased by 0.63 \\u00b1 0.98 D (p = 0.001), from 2.07 \\u00b1 0.83 to 1.44 \\u00b1 0.99 D.\"]]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: Amylin is co‐secreted with insulin and is therefore lacking in patients with type 1 diabetes. Replacement with fixed ratio co‐administration of insulin and the amylin analogue pramlintide may be superior to separate dosing. This concept was evaluated in a ratio‐finding study. Patients with type 1 diabetes were enrolled in a randomized, single‐masked, standard breakfast crossover study using regular human insulin injected simultaneously with pramlintide 6, 9 or 12 mcg/unit insulin or placebo. Insulin dosage was reduced by 30% from patients' usual estimates. Plasma glucose, glucagon and pramlintide and adverse events were assessed. All ratios reduced 0–3‐h glucose and glucagon increments by >50%. No hypoglycaemia occurred. Adverse events were infrequent and generally mild. All pramlintide/insulin ratios markedly and safely reduced glycaemic excursions and suppressed glucagon secretion in the immediate postprandial state. Further study using one of these ratios to explore the efficacy and safety of longer‐term meal‐time and basal hormone replacement is warranted.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Insulin + Pramlintide (6 mcg/U)\", \"Insulin + Pramlintide (12 mcg/U)\", \"Elevation of glucose and glucagon after 1 hour\", \"no significant difference\", \"All ratios reduced 0\\u20133\\u2010h glucose and glucagon increments by \\u003e50%.\"], [\"Insulin + Pramlintide (all doses)\", \"Insulin alone\", \"Hypoglycemia after 24 hours\", \"no significant difference\", \"No hypoglycaemia occurred.\"]]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: We investigated the effects of glucose and diverse breakfasts on glucose increment and ghrelin suppression and cognitive processing of sensory information assessed by frontal P300 evoked potentials. In a randomized crossover design, 12 healthy individuals (6M/6F; BMI 22.2 ± 0.4 kg/m2; 27 ± 1.3 years, mean ± SEM) underwent 50 g OGTT (A) and 3 breakfasts (B1: milk and cereals; B2: milk, apple, and chocolate cream-filled sponge cake; B3: milk, apple, bread, and hazelnut chocolate cream) to assess plasma glucose-, insulin-, and ghrelin excursions. An electroencephalography was performed before and 100 min after consumption of each load to measure the latency of frontal P300 evoked potentials as index of cognitive performance. Breakfasts B1 and B2 exhibited significantly lower glycemic and insulinemic responses as compared to A. Breakfast B3 exhibited significantly lower glycemic, but not insulinemic response, as compared to A. Final plasma ghrelin inhibition was more pronounced, albeit not significantly, in all breakfasts with respect to A. P300 latency tended to decrease following each of the three breakfasts, but B3 was the only breakfast capable to elicit a statistically significant reduction in P300 latency with respect to A (p < 0.01), suggesting ameliorated cognitive performance. Such amelioration was correlated with the 2-hour final inhibition of plasma ghrelin concentration (r = 0.61, p = 0.01).\n\nExtracted Data:", "role": "user" }, { "content": "[[\"3 breakfasts (B1: milk and cereals; B2: milk, apple, and chocolate cream-filled sponge cake; B3: milk, apple, bread, and hazelnut chocolate cream)\", \"50?g OGTT (A)\", \"glucose incremental peaks\", \"significantly decreased\", \"the repeated measures ANOVA with Greenhouse-Geisser correction resulted significant (p \\u003c 0.001) and the Tukey-Kramer post hoc test revealed that B1 (36.2 \\u00b1 3.8 mg/dL), B2 (19.6 \\u00b1 2.9 mg/dL), and B3 (27.3 \\u00b1 4.0 mg/dL) had significantly smaller glucose incremental peaks than A (55.9 \\u00b1 6.1 mg/dL) with p values associated to such post hoc comparisons that were p = 0.04, p \\u003c 0.001, and p = 0.002, respectively. \"], [\"3 breakfasts (B1: milk and cereals; B2: milk, apple, and chocolate cream-filled sponge cake; B3: milk, apple, bread, and hazelnut chocolate cream)\", \"50?g OGTT (A)\", \"insulin excursions \", \"significantly decreased\", \"All three commercial breakfasts showed somewhat smaller insulin excursions with respect to the one elicited by glucose administration (Figure 2(a), 2(b), and 2(c)). As for the comparison among the insulinemic AUC values (Figure 2(d)), the repeated measures ANOVA was significant (p \\u003c 0.001) (without requiring any correction for the sphericity condition) and the Tukey-Kramer post hoc test showed that both B1 and B2 had smaller insulin AUCs than A (p = 0.002 and p = 0.016, resp.).\"], [\"3 breakfasts (B1: milk and cereals; B2: milk, apple, and chocolate cream-filled sponge cake; B3: milk, apple, bread, and hazelnut chocolate cream)\", \"50?g OGTT (A)\", \"the 2-hour plasma ghrelin\", \"no significant difference\", \"After glucose/breakfast ingestion, the 2-hour plasma ghrelin attained the lowest level in B3 (332.1 \\u00b1 30.2 pg/mL), but the decrement with respect to A (420.6 \\u00b1 44.2 pg/mL) did not achieve the statistical significance (Figure 3(c)).\"], [\"B3: milk, apple, bread, and hazelnut chocolate cream\", \"50?g OGTT (A)\", \"P300 latency incremental values\", \"significantly decreased\", \"The repeated measures ANOVA with Greenhouse-Geisser correction was significant (p \\u003c 0.01) and the Tukey-Kramer post hoc test showed that the only significant difference among means was the one between breakfast B3 and glucose (\\u221233.6 \\u00b1 5.9 ms versus 9.7 \\u00b1 6.6 ms, p \\u003c 0.01).\"], [\"B3: milk, apple, bread, and hazelnut chocolate cream\", \"50?g OGTT (A)\", \"P300 latency incremental values\", \"significantly decreased\", \"P300 latency tended to decrease following each of the three breakfasts, but B3 was the only breakfast capable to elicit a statistically significant reduction in P300 latency with respect to A (p \\u003c 0.01),\"]]", "role": "assistant" } ]

[ { "content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nTwo distinct mutations at a single BamHI site in phenylketonuria. Classical phenylketonuria is an autosomal recessive disease caused by a deficiency of hepatic phenylalanine hydroxylase (PAH). The abolition of an invariant BamHI site located in the coding sequence of the PAH gene (exon 7) led to the recognition of two new point mutations at codon 272 and 273 (272gly----stop and 273ser----phe, respectively). Both mutations were detected in north eastern France or Belgium and occurred on the background of RFLP haplotype 7 alleles. The present study supports the view that the clinical heterogeneity in PKU is accounted for by the large variety of mutant genotypes associated with PAH deficiencies..", "role": "user" }, { "content": "{\"CellLine\": [], \"Chemical\": [], \"Disease\": [\"phenylketonuria\", \"deficiency of hepatic phenylalanine hydroxylase\", \"PKU\", \"PAH deficiencies\", \"autosomal recessive disease\", \"Classical phenylketonuria\"], \"Gene\": [\"PAH\"], \"Species\": [], \"Variant\": [\"273ser----phe\", \"272gly----stop\"]}", "role": "assistant" } ]

[ { "content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nIdentification of three F5 gene mutations associated with inherited coagulation factor V deficiency in two Chinese pedigrees. To investigate the molecular defects in two Chinese pedigrees with inherited factor V (FV) deficiency. A 37-year-old male (proband 1) and an 18-month-old boy (proband 2) were diagnosed as inherited coagulation FV deficiency by severely reduced plasma levels of FV activity and antigen. All 25 exons and their flanking sequence of F5 gene were amplified by polymerase chain reaction (PCR) for both probands and the PCR products were directly sequenced. Total RNA was extracted from the peripheral lymphocytes of proband 1 for detecting the changes at mRNA level. The homozygous deletion IVS8 -2A>G was identified in the F5 gene of proband 1 and complementary DNA (cDNA) analysis revealed the abolishment of the canonical splicing site by the mutation and the activation of the cryptic acceptor site 24 bp upstream instead. The insertion introduced eight additional amino acids (AA) into the FV protein. Two heterozygous mutations of F5 gene were discovered in proband 2. The 2238-9del AG in exon 13 introduced a premature termination code at 689 AA and the substitution of G6410 by T in exon 23 lead to the missense mutation Gly2079Val. Three F5 gene mutations, IVS8 -2A>G, 2238-9del AG and G6410T, have been identified in two Chinese pedigree with congenital FV deficiency, respectively.", "role": "user" }, { "content": "{\"CellLine\": [], \"Chemical\": [], \"Disease\": [\"inherited coagulation FV deficiency\", \"inherited factor V (FV) deficiency\", \"inherited coagulation factor V deficiency\", \"congenital FV deficiency\"], \"Gene\": [\"F5\", \"FV\"], \"Species\": [], \"Variant\": [\"G6410 by T\", \"Gly2079Val\", \"IVS8 -2A\\u003eG\", \"insertion introduced eight additional amino acids\", \"2238-9del AG\", \"G6410T\", \"689 AA\"]}", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nThe supervised neural model on a single dataset was introduced by Zeng et al. (2014) and followed by many others (Nguyen and Grishman, 2015; Zhou et al., 2016; Miwa and Bansal, 2016; Nguyen and Grishman, 2016;<cite> Fu et al., 2017)</cite> .", "role": "user" }, { "content": "[\"Background\"]", "role": "assistant" } ]

[ { "content": "Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.\n\nContext: The prevention of paediatric bacterial meningitis and septicaemia has recently entered a new era with the availability of two vaccines against capsular group B meningococcus\nMeningitis and sepsis caused by serogroup B meningococcus are two severe diseases that still cause significant mortality.\nSepsis and meningitis caused by serogroup B meningococcus are devastating diseases of infants and young adults , which cannot yet be prevented by vaccination .\nThe prevention of paediatric bacterial meningitis and septicaemia has recently entered a new era with the availability of two vaccines against capsular group B meningococcus ( MenB) .\nMeningitis and sepsis caused by serogroup B meningococcus are two severe diseases that still cause significant mortality.\n\nQuestion: List diseases that are caused by the Meningococcus B?", "role": "user" }, { "content": "[\"meningitis\", \"septicemia\"]", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nTwo methods have been proposed on how to jointly provide highlights along with classification. (1) an extraction-based method (Lei et al., 2016) , which first extracts evidences from the original text and then makes a prediction solely based on the extracted evidences; (2) an attentionbased method <cite>(Lin et al., 2017</cite>; Mullenbach et al., 2018) , which leverages the self-attention mechaMedical Report: The patient was admitted to the Neurological Intensive Care Unit for close observation.", "role": "user" }, { "content": "[\"Background\"]", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nIn order to compare the performance with the state-of-the-art, we have also evaluated ROOT9 in subsets of the<cite> Weeds et al. (2014)</cite> datasets, proving that it is in fact competitive.", "role": "user" }, { "content": "[\"Uses\"]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.OBJECTIVES:: The purpose of this study was to evaluate the effect of salivary pH on the shear bond strength (SBS) of orthodontic brackets to tooth surface.\n\nABSTRACT.MATERIALS AND METHODS:: Eighty intact premolars were randomly divided into four groups of 20. After bonding a bracket on each tooth, the groups one to four were stored in artificial saliva at a pH of 3.8, 4.8, 5.8, and 6.8, respectively for two months. The artificial saliva solutions were refreshed weekly. Each tooth was then embedded in an acrylic block so that the crown was exposed and its buccal surface was parallel to the direction of the force during SBS testing. All brackets were debonded using Dartec universal testing machine, and the mean values of SBS in different groups were compared using one-way analysis of variance (ANOVA).\n\nABSTRACT.RESULTS:: The mean SBS value in group one (pH 3.8) was significantly lower than that in other groups (P<0.05). The differences between other groups were not significant (P>0.05).\n\nABSTRACT.CONCLUSION:: Decreased salivary pH due to poor oral hygiene and/or frequent consumption of acidic beverages may be responsible for orthodontic bracket bond failure.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"pH 3.8 saliva \", \"pH 6.8 saliva\", \"Shear bond strength\", \"significantly decreased\", \"The mean SBS value in group one (pH 3.8) was significantly lower than that in other groups (P\\u003c0.05). The differences between other groups were not significant (P\\u003e0.05).\"], [\"pH 6.8 saliva\", \"pH 5.8 saliva\", \"Shear bond strength\", \"no significant difference\", \"The mean SBS value in group one (pH 3.8) was significantly lower than that in other groups (P\\u003c0.05). The differences between other groups were not significant (P\\u003e0.05).\"], [\"pH 3.8 saliva \", \"pH 5.8 saliva\", \"Shear bond strength\", \"significantly decreased\", \"The mean SBS value in group one (pH 3.8) was significantly lower than that in other groups (P\\u003c0.05).\"], [\"pH 6.8 saliva\", \"pH 4.8 saliva\", \"Shear bond strength\", \"no significant difference\", \"The mean SBS value in group one (pH 3.8) was significantly lower than that in other groups (P\\u003c0.05). The differences between other groups were not significant (P\\u003e0.05).\"], [\"pH 3.8 saliva \", \"pH 4.8 saliva\", \"Shear bond strength\", \"significantly decreased\", \"The mean SBS value in group one (pH 3.8) was significantly lower than that in other groups (P\\u003c0.05).\"], [\"pH 5.8 saliva\", \"pH 4.8 saliva\", \"Shear bond strength\", \"no significant difference\", \"The mean SBS value in group one (pH 3.8) was significantly lower than that in other groups (P\\u003c0.05). The differences between other groups were not significant (P\\u003e0.05).\"]]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: The efficacy of an enhanced version of the Aussie Optimism Program (AOP) was investigated in a cluster randomized controlled trial. Grade 6 students aged 10–11 years of age (N = 2288) from 63 government primary schools in Perth, Western Australia, participated in the pre, post, and follow-up study. Schools were randomly assigned to one of three conditions: Aussie Optimism with teacher training, Aussie Optimism with teacher training plus coaching, or a usual care condition that received the regular Western Australian Health Education Curriculum. Students in the Aussie Optimism conditions received 20, 1-h lessons relating to social and interpersonal skills and optimistic thinking skills over the last 2 years of primary school. Parents in the active conditions received a parent information booklet each year, plus a self-directed program in Grade 7. Students and parents completed the Extended Strengths and Difficulties Questionnaire. Students who scored in the clinical range on the Emotional Symptoms Scale were given The Diagnostic Interview for Children and Adolescents IV, to assess suicidal ideation and behavior, and depressive and anxiety disorders. Results indicated that Aussie Optimism with teacher training plus coaching was associated with the best outcomes: a significant increase in student-reported pro-social behavior from pre-test to post-test 1 (maintained at post-test 2) and significantly lower incidence rates from suicidal ideation at post-test 2 and follow-up. No significant intervention effects on anxiety and depressive disorders, and total difficulties were reported. These findings suggest that the AOP with teacher training along with coaching may have the potential to positively impact on suicidality and pro-social behavior in the pre-adolescent years.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Training + coaching \", \"Control\", \"Suicidal ideas\", \"significantly decreased\", \"teacher training plus coaching was associated with the best outcomes: a significant increase in student-reported pro-social behavior from pre-test to post-test 1 (maintained at post-test 2) and significantly lower incidence rates from suicidal ideation at post-test 2 and follow-up.\"]]", "role": "assistant" } ]

[ { "content": "| Host materials | FOM for gain (t x s em) (10-20 mscm2) | FOM for bandwidth (Dl x s em) (10-20 nmcm2) | Reference |\n| GC containing BaF2 | 5.7 | 16.7 | Present work |\n| GC containing CaF2 | 4.4 | 40.6 | [16] |\n| GC containing PbF2 | 2.1 | 32.3 | [17,33] |\n| Al silica fiber | 5.7 | 13.4 | [26] |\n\nCaption: Comparison of FOM for gain and for bandwidth of Er3+ in different oxyfluoride glass ceramics (GC) and the commercial Al silica fiber used for device fabrication\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"5.7\", \"type\": \"Other\"}\n{\"value\": \"16.7\", \"type\": \"Other\"}\n{\"value\": \"4.4\", \"type\": \"Other\"}\n{\"value\": \"40.6\", \"type\": \"Other\"}\n{\"value\": \"2.1\", \"type\": \"Other\"}\n{\"value\": \"32.3\", \"type\": \"Other\"}\n{\"value\": \"5.7\", \"type\": \"Other\"}\n{\"value\": \"13.4\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.SUMMARY: Total knee arthroplasty (TKA) is generally carried out using a tourniquet and blood loss occurring mainly post operatively is collected in drains. Tranexamic acid is an antifibrinolytic agent which decreases the total blood loss. Patients had unilateral / bilateral cemented TKA using combined spinal and epidural anaesthesia. In a double-blind fashion, they received either placebo (n=25) or tranexamic acid (n=25)10 mg.kg−1 i.v., just before tourniquet inflation, followed by 1 mg kg−1 h-1 i.v. till closure of the wound. The postoperative blood loss, transfusion requirement, cost effectiveness and complications were noted. The groups had similar characteristics. The mean volume of drainage fluid was 270 ml and 620 ml for unilateral(U/L) and bilateral(B/L) TKR patients in placebo group. Whereas it was 160ml and 286 ml respectively in unilateral(U/L) and bilateral(B/L) TKR patients who received tranexamic acid. This was considered statistically significant. Control group patients received 26 units of PRBC as compared to 4 units in tranexamic acid groups (p<0.001). This was again statistically significant. None of the patients in any of the groups developed deep vein thrombosis. Tranexamic acid decreased total blood loss by nearly 54% in B/L TKR and 40% in U/L TKR and drastically reduced (> 80%) blood transfusion.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Tranexamic acid\", \"Placebo\", \"Deep vein thrombosis\", \"no significant difference\", \"None of the patients in any of the groups developed deep vein thrombosis.\"], [\"Tranexamic acid\", \"Placebo\", \"Units of PRBC needed\", \"significantly decreased\", \"Control group patients received 26 units of PRBC as compared to 4 units in tranexamic acid groups (p\\u003c0.001).\"], [\"Tranexamic acid\", \"Placebo\", \"Mean volume of drainage fluid\", \"significantly decreased\", \"The mean volume of drainage fluid was 270 ml and 620 ml for unilateral(U/L) and bilateral(B/L) TKR patients in placebo group. Whereas it was 160ml and 286 ml respectively in unilateral(U/L) and bilateral(B/L) TKR patients who received tranexamic acid. This was considered statistically significant.\"]]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: Disturbance in sleep quality is a symptom of Major Depressive Disorder (MDD) and Bipolar Disorder (BD) and thus improving quality of sleep is an important aspect of successful treatment. Here, a prospective, double-blind, randomized, placebo-controlled study examined the effect of olanzapine (an atypical antipsychotic) augmentation therapy on sleep architecture, specifically slow wave sleep (SWS), in the treatment of depression. The effect of olanzapine augmentation therapy on other features of sleep (e.g., sleep continuity) and depression (e.g., illness severity and cognitive function) were also determined.\n\nABSTRACT.METHODS: Patients currently experiencing a major depressive episode and who were on a stable medication were included. Sleep architecture was measured by overnight ambulatory polysomnography. Illness severity was determined using the Montgomery-Asberg Depression Rating Scale (MADRS). Cognitive function was examined using Cambridge Neuropsychological Test Automated Battery (CANTAB): Spatial Working Memory (SWM), Spatial Span (SSP), and Reaction Time (RTI) tasks. Polysomnographs, clinical measures and cognitive tests were administered at baseline, after 2–4 days of treatment and after 28–31 days of treatment. Twenty-five patients participated in the study (N = 10, N = 15 for placebo and olanzapine treated groups respectively).\n\nABSTRACT.RESULTS: The primary objective of the study was to assess the objective (polysomnographic) changes in sleep quality, defined as changes in SWS, following olanzapine treatment for depression. Latency to but not duration of SWS was found to significantly differ between olanzapine- and placebo-treated participants (Hedge's g: 0.97, 0.13 respectively). A significant improvement in olanzapine-treated participants over placebo-treated participants was observed in secondary outcome measures, including sleep efficiency, total sleep time, and sleep latency. Secondary objectives assessed the subjective changes in sleep quality parameters and correlated them with measures of illness severity and changes in cognition. MADRS scores were significantly improved in olanzapine-treated participants over time but not more than placebo treatment. There was no significant difference between olanzapine- and placebo-treated participants in SWM, SSP or RTI tasks.\n\nABSTRACT.CONCLUSIONS: Olanzapine augmentation treatment generally did not improve SWS but did improve sleep continuity and depression. Olanzapine may be one of few medications that improve sleep continuity, thus directly targeting symptoms of depression.\n\nABSTRACT.TRIAL REGISTRATION: ClinicalTrials.gov,\nNCT00520507.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"olanzapine\", \"placebo\", \"sleep efficiency \", \"significantly increased\", \"A significant improvement in olanzapine-treated participants over placebo-treated participants was observed in secondary outcome measures, including sleep efficiency, total sleep time, and sleep latency.\"], [\"olanzapine\", \"placebo\", \"Spatial Working Memory (SWM), Spatial Span (SSP), and Reaction Time (RTI) tasks\", \"no significant difference\", \"There was no significant difference between olanzapine- and placebo-treated participants in SWM, SSP or RTI tasks.\"]]", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nUnlike <cite>Kottur et al. (2017)</cite> , we use a slightly harder version of their dataset which aligns better with the goal of compositional language.", "role": "user" }, { "content": "[\"Differences\"]", "role": "assistant" } ]

[ { "content": "| Powder | Mass% | Mass% | Mass% | Mass% | Mass% | Mass% | Mass% | Mass% | Mass% | CaO/SiO2 | NBO/T |\n| Powder | CaO | SiO2 | Na2O | Al2O3 | CaF2 | MnO | MgO | B2O3 | Li2O | CaO/SiO2 | NBO/T |\n| CAS-1 | 32.6 | 23.4 | 10.0 | 10.0 | 16.4 | 1.0 | 3.0 | 2.0 | 1.6 | 1.4 | 3.1 |\n| CAS-2 | 29.2 | 20.8 | 10.0 | 16.0 | 16.4 | 1.0 | 3.0 | 2.0 | 1.6 | 1.4 | 2.4 |\n| CAS-3 | 25.7 | 18.3 | 10.0 | 22.0 | 16.4 | 1.0 | 3.0 | 2.0 | 1.6 | 1.4 | 1.9 |\n| CAS-4 | 22.2 | 15.8 | 10.0 | 28.0 | 16.4 | 1.0 | 3.0 | 2.0 | 1.6 | 1.4 | 1.5 |\n| CAS-5 | 18.7 | 13.3 | 10.0 | 34.0 | 16.4 | 1.0 | 3.0 | 2.0 | 1.6 | 1.4 | 1.1 |\n\nCaption: Designed composition of high Al2O3 content mold fluxes.\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"32.6\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"wt\", \"material\": \"CAS-1\"}\n{\"value\": \"23.4\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"wt\", \"material\": \"CAS-1\"}\n{\"value\": \"10.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"wt\", \"material\": \"CAS-1\"}\n{\"value\": \"10.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Al2O3\", \"unit\": \"wt\", \"material\": \"CAS-1\"}\n{\"value\": \"16.4\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaF2\", \"unit\": \"wt\", \"material\": \"CAS-1\"}\n{\"value\": \"1.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"MnO\", \"unit\": \"wt\", \"material\": \"CAS-1\"}\n{\"value\": \"3.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"MgO\", \"unit\": \"wt\", \"material\": \"CAS-1\"}\n{\"value\": \"2.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"B2O3\", \"unit\": \"wt\", \"material\": \"CAS-1\"}\n{\"value\": \"1.6\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Li2O\", \"unit\": \"wt\", \"material\": \"CAS-1\"}\n{\"value\": \"1.4\", \"type\": \"Other\"}\n{\"value\": \"3.1\", \"type\": \"Other\"}\n{\"value\": \"29.2\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"wt\", \"material\": \"CAS-2\"}\n{\"value\": \"20.8\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"wt\", \"material\": \"CAS-2\"}\n{\"value\": \"10.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"wt\", \"material\": \"CAS-2\"}\n{\"value\": \"16.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Al2O3\", \"unit\": \"wt\", \"material\": \"CAS-2\"}\n{\"value\": \"16.4\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaF2\", \"unit\": \"wt\", \"material\": \"CAS-2\"}\n{\"value\": \"1.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"MnO\", \"unit\": \"wt\", \"material\": \"CAS-2\"}\n{\"value\": \"3.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"MgO\", \"unit\": \"wt\", \"material\": \"CAS-2\"}\n{\"value\": \"2.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"B2O3\", \"unit\": \"wt\", \"material\": \"CAS-2\"}\n{\"value\": \"1.6\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Li2O\", \"unit\": \"wt\", \"material\": \"CAS-2\"}\n{\"value\": \"1.4\", \"type\": \"Other\"}\n{\"value\": \"2.4\", \"type\": \"Other\"}\n{\"value\": \"25.7\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"wt\", \"material\": \"CAS-3\"}\n{\"value\": \"18.3\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"wt\", \"material\": \"CAS-3\"}\n{\"value\": \"10.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"wt\", \"material\": \"CAS-3\"}\n{\"value\": \"22.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Al2O3\", \"unit\": \"wt\", \"material\": \"CAS-3\"}\n{\"value\": \"16.4\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaF2\", \"unit\": \"wt\", \"material\": \"CAS-3\"}\n{\"value\": \"1.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"MnO\", \"unit\": \"wt\", \"material\": \"CAS-3\"}\n{\"value\": \"3.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"MgO\", \"unit\": \"wt\", \"material\": \"CAS-3\"}\n{\"value\": \"2.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"B2O3\", \"unit\": \"wt\", \"material\": \"CAS-3\"}\n{\"value\": \"1.6\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Li2O\", \"unit\": \"wt\", \"material\": \"CAS-3\"}\n{\"value\": \"1.4\", \"type\": \"Other\"}\n{\"value\": \"1.9\", \"type\": \"Other\"}\n{\"value\": \"22.2\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"wt\", \"material\": \"CAS-4\"}\n{\"value\": \"15.8\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"wt\", \"material\": \"CAS-4\"}\n{\"value\": \"10.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"wt\", \"material\": \"CAS-4\"}\n{\"value\": \"28.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Al2O3\", \"unit\": \"wt\", \"material\": \"CAS-4\"}\n{\"value\": \"16.4\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaF2\", \"unit\": \"wt\", \"material\": \"CAS-4\"}\n{\"value\": \"1.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"MnO\", \"unit\": \"wt\", \"material\": \"CAS-4\"}\n{\"value\": \"3.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"MgO\", \"unit\": \"wt\", \"material\": \"CAS-4\"}\n{\"value\": \"2.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"B2O3\", \"unit\": \"wt\", \"material\": \"CAS-4\"}\n{\"value\": \"1.6\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Li2O\", \"unit\": \"wt\", \"material\": \"CAS-4\"}\n{\"value\": \"1.4\", \"type\": \"Other\"}\n{\"value\": \"1.5\", \"type\": \"Other\"}\n{\"value\": \"18.7\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"wt\", \"material\": \"CAS-5\"}\n{\"value\": \"13.3\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"wt\", \"material\": \"CAS-5\"}\n{\"value\": \"10.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"wt\", \"material\": \"CAS-5\"}\n{\"value\": \"34.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Al2O3\", \"unit\": \"wt\", \"material\": \"CAS-5\"}\n{\"value\": \"16.4\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaF2\", \"unit\": \"wt\", \"material\": \"CAS-5\"}\n{\"value\": \"1.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"MnO\", \"unit\": \"wt\", \"material\": \"CAS-5\"}\n{\"value\": \"3.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"MgO\", \"unit\": \"wt\", \"material\": \"CAS-5\"}\n{\"value\": \"2.0\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"B2O3\", \"unit\": \"wt\", \"material\": \"CAS-5\"}\n{\"value\": \"1.6\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Li2O\", \"unit\": \"wt\", \"material\": \"CAS-5\"}\n{\"value\": \"1.4\", \"type\": \"Other\"}\n{\"value\": \"1.1\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.OBJECTIVE: To evaluate the effects of canagliflozin, a sodium glucose co-transporter 2 inhibitor, on body weight in overweight and obese subjects (body mass index [BMI] ≥27 and <50 kg/m2).\n\nABSTRACT.METHODS: This 12-week, Phase 2b, randomized, double-blind study enrolled 376 subjects without diabetes mellitus who received canagliflozin 50, 100, or 300 mg or placebo once daily. The primary endpoint was the percent change in body weight from baseline through Week 12.\n\nABSTRACT.RESULTS: Canagliflozin increased urinary glucose excretion in a dose-dependent manner and produced statistically significant reductions in body weight compared with placebo (least squares mean percent changes from baseline of −2.2%, −2.9%, −2.7%, and −1.3% with canagliflozin 50, 100, and 300 mg and placebo; P < 0.05 for all comparisons). Overall adverse event (AE) rates were similar across groups. Canagliflozin was associated with higher rates of genital mycotic infections in women, which were generally mild and led to few study discontinuations. Osmotic diuresis-related AE rates were low and similar across groups.\n\nABSTRACT.CONCLUSIONS: In overweight and obese subjects without diabetes mellitus, canagliflozin significantly reduced body weight compared with placebo and was generally well tolerated.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Canagliflozin\", \"Placebo\", \"Glycosuria\", \"significantly increased\", \"Canagliflozin increased urinary glucose excretion in a dose-dependent manner and produced statistically significant reductions in body weight compared with placebo (least squares mean percent changes from baseline of \\u22122.2%, \\u22122.9%, \\u22122.7%, and \\u22121.3% with canagliflozin 50, 100, and 300 mg and placebo; P \\u003c 0.05 for all comparisons).\"], [\"Canagliflozin\", \"Placebo\", \"Adverse events\", \"no significant difference\", \"Overall adverse event (AE) rates were similar across groups.\"], [\"Canagliflozin\", \"Placebo\", \"Body weight reduction\", \"significantly increased\", \"Canagliflozin increased urinary glucose excretion in a dose-dependent manner and produced statistically significant reductions in body weight compared with placebo (least squares mean percent changes from baseline of \\u22122.2%, \\u22122.9%, \\u22122.7%, and \\u22121.3% with canagliflozin 50, 100, and 300 mg and placebo; P \\u003c 0.05 for all comparisons).\"]]", "role": "assistant" } ]

[ { "content": "| | Ge [1,22] | Se [1,22] | Sb [1] | Ag [15,22,25] |\n| Density (g/cm3) | 5.32 | 4.80 | 6.68 | 10.50 |\n| Coordination number | 4 | 2 | 3 | 2 |\n| Hs (kcal/mol) | 90.0 | 49.4 | 62.0 | 68.0 |\n| Bond energy (kcal/mol) | 37.78 | 44.04 | 30.22 | 39.75 |\n| Electronegativity | 2.01 | 2.55 | 2.05 | 1.93 |\n\nCaption: Values of the density, the coordination number, the heat of atomization, the bond energy and the electronegativity of Ge, Se, Sb and Ag, used for the calculations.\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"5.32\", \"type\": \"Other\"}\n{\"value\": \"4.80\", \"type\": \"Other\"}\n{\"value\": \"6.68\", \"type\": \"Other\"}\n{\"value\": \"10.50\", \"type\": \"Other\"}\n{\"value\": \"4\", \"type\": \"Other\"}\n{\"value\": \"2\", \"type\": \"Other\"}\n{\"value\": \"3\", \"type\": \"Other\"}\n{\"value\": \"2\", \"type\": \"Other\"}\n{\"value\": \"90.0\", \"type\": \"Other\"}\n{\"value\": \"49.4\", \"type\": \"Other\"}\n{\"value\": \"62.0\", \"type\": \"Other\"}\n{\"value\": \"68.0\", \"type\": \"Other\"}\n{\"value\": \"37.78\", \"type\": \"Other\"}\n{\"value\": \"44.04\", \"type\": \"Other\"}\n{\"value\": \"30.22\", \"type\": \"Other\"}\n{\"value\": \"39.75\", \"type\": \"Other\"}\n{\"value\": \"2.01\", \"type\": \"Other\"}\n{\"value\": \"2.55\", \"type\": \"Other\"}\n{\"value\": \"2.05\", \"type\": \"Other\"}\n{\"value\": \"1.93\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.\n\nContext: The combination of neurological symptoms, thrombocytopenia, fever, renal failure and hemolytic anemia in a patient taking ticlopidine points to a diagnosis of TTP.\nIn addition to the typical manifestations of thrombotic-thrombocytopenic purpura like thrombocytopenia, haemolysis, fever, coma and renal failure, signs of a beginning DIC could be seen in a patient after abdominal surgery.\nMoschcowitz syndrome or thrombotic thrombocytopenic purpura is a rare disorder with a poor prognosis. This syndrome is characterized by a microangiopathic hemolytic anemia with thrombocytopenia, neurologic symptoms and renal disease.\nMoschcowitz's syndrome is a rare condition with poor prognosis. It is characterized by a microangiopathic haemolytic anaemia associated with thrombocytopenia, neurological symptoms and renal involvement.\nThrombotic thrombocytopenic purpura (TTP), in 1924 first described by Moschcowitz, is a clinically heterogeneous syndrome associated with thrombocytopenia, Coombs-negative hemolytic anemia, neurologic changes, renal impairment, and fever.\nThrombotic thrombocytopenic purpura (TTP), in 1924 first described by Moschcowitz, is a clinically heterogeneous syndrome associated with thrombocytopenia, Coombs-negative hemolytic anemia, neurologic changes, renal impairment, and fever\nThrombotic thrombocytopenic purpura (TTP, Moschcowitz disease) is characterized by thrombotic microangiopathy leading to microvascular occlusion and ischemic dysfunction of various organs including the brain\nSevere deficiency of von Willebrand factor-cleaving protease (ADAMTS-13) activity (<5% of normal) is specific for classical thrombotic thrombocytopenic purpura (TTP), a disorder presenting with thrombocytopenia, microangiopathic haemolytic anaemia and often with organ dysfunction such as neurological symptoms, renal failure, and fever.\nIn a 57-year-old female patient, who was admitted with fluctuating central neurological abnormalities and generalized purpura, was made the diagnosis of a thrombotic thrombocytopenic purpura (TTP, Moschcowitz' syndrome).\nThrombotic thrombocytopenic purpura (TTP) is a syndrome that occurs mainly in adults with multiorgan microvascular thrombosis consisting of thrombocytopenia, microangiopathic hemolytic anemia, neurologic symptoms, renal involvement, and fever.\n\nQuestion: List the classical symptoms of the Moschcowitz syndrome (Thrombotic thrombocytopenic purpura).", "role": "user" }, { "content": "[\"thrombocytopenia\", \"haemolysis\", \"fever\", \"coma\", \"renal failure\"]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: Hyaluronic acid has been extensively used for treatment of knee osteoarthritis due to its anti-inflammatory properties and its ability to act as a synovial lubricant. Furthermore, it has found application in combination with other drugs in the dermatological field and in pre-clinical studies in animal models of osteoarthritis. Experimental evidence suggests that a combination of this macromolecule with other drugs may act as a slow-release depot. However, to date, to the best of our knowledge, no one has tested local intra-articular delivery of highly cross-linked hyaluronic acid combined with bisphosphonate or nonsteroidal anti-inflammatory drugs for management of knee osteoarthritis pain in the clinical setting. The aim of the present randomized double-blind study was to investigate, for the first time, the effect of a highly cross-linked hyaluronic acid, Variofill®, alone or in combination with diclofenac sodium or sodium clodronate, for management of bilateral knee osteoarthritis-related pain.\n\nABSTRACT.METHODS: Sixty-two patients with symptomatic bilateral medial tibiofemoral knee osteoarthritis (Kellgren–Lawrence grade II and III) and pain in both knees corresponding to a daily visual analog scale (VAS) score ≥ 30 in the month before the beginning of the study were included in this investigation. Patients were divided into three groups: group 1, treated with an injection of hyaluronic acid alone (66 mg) into each knee; group 2, treated with an injection of hyaluronic acid (49.5 mg) plus diclofenac sodium (5 mg) into each knee; group 3, treated with an injection of hyaluronic acid (49.5 mg) plus sodium clodronate (5 mg) into each knee. Patients also underwent blood tests for measurement of erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) immediately before and at 6-month follow-up.\n\nABSTRACT.RESULTS: Hyaluronic acid alone and in combination with sodium clodronate or diclofenac sodium produced a significant improvement in mean VAS pain score at 3 and 6-month follow-up. At 6-month follow-up, therapy with hyaluronic acid plus sodium clodronate was the most beneficial in terms of percentage improvement in VAS pain score. A significant improvement in ESR and CRP was observed at 6-month follow-up in each treatment group. No significant difference was observed when the percentage change from baseline related to these parameters was compared among the groups. No dropout was observed in any group. No serious adverse events were observed.\n\nABSTRACT.CONCLUSION: Further studies are necessary to determine the effect of a therapy based on hyaluronic acid combined with diclofenac sodium or sodium clodronate in larger cohorts of patients affected by knee osteoarthritis and in longer-term follow-up.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"group 1, treated with an injection of hyaluronic acid alone (66 mg) into each knee; group 2, treated with an injection of hyaluronic acid (49.5 mg) plus diclofenac sodium (5 mg) into each knee;\", \"group 3, treated with an injection of hyaluronic acid (49.5 mg) plus sodium clodronate (5 mg) into each knee\", \"erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP)\", \"no significant difference\", \"A significant improvement in ESR and CRP was observed at 6-month follow-up in each treatment group. No significant difference was observed when the percentage change from baseline related to these parameters was compared among the groups\"]]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: Macrophage activation determined by levels of soluble sCD163 is associated with obesity, insulin resistance, diabetes mellitus type 2 (DM2) and non-alcoholic fatty liver disease (NAFLD). This suggests that macrophage activation is involved in the pathogenesis of conditions is characterised by adaptions in the lipid metabolism. Since sCD163 is shed to serum by inflammatory signals including lipopolysaccharides (LPS, endotoxin), we investigated sCD163 and correlations with lipid metabolism following LPS exposure.\n\nABSTRACT.METHODS: Eight healthy male subjects were investigated on two separate occasions: (i) following an LPS exposure and (ii) following saline exposure. Each study day consisted of a four-hour non-insulin-stimulated period followed by a two-hour hyperinsulinemic euglycemic clamp period. A 3H-palmitate tracer was used to calculate the rate of appearance (Rapalmitate). Blood samples were consecutively obtained throughout each study day. Abdominal subcutaneous adipose tissue was obtained for western blotting.\n\nABSTRACT.RESULTS: We observed a significant two-fold increase in plasma sCD163 levels following LPS exposure (P < 0.001), and sCD163 concentrations correlated positively with the plasma concentration of free fatty acids, Rapalmitate, lipid oxidation rates and phosphorylation of the hormone-sensitive lipase at serine 660 in adipose tissue (P < 0.05, all). Furthermore, sCD163 concentrations correlated positively with plasma concentrations of cortisol, glucagon, tumour necrosis factor (TNF)-α, interleukin (IL)-6 and IL-10 (P < 0.05, all).\n\nABSTRACT.CONCLUSION: We observed a strong correlation between sCD163 and stimulation of lipolysis and fat oxidation following LPS exposure. These findings support preexisting theory that inflammation and macrophage activation play a significant role in lipid metabolic adaptions under conditions such as obesity, DM2 and NAFLD.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"LPS exposure\", \"Saline axposure\", \"macrophage activation\", \"significantly increased\", \"We observed a significant two-fold increase in plasma sCD163 levels following LPS exposure (P \\u003c 0.001), and sCD163 concentrations correlated positively with the plasma concentration of free fatty acids, Rapalmitate, lipid oxidation rates and phosphorylation of the hormone-sensitive lipase at serine 660 in adipose tissue (P \\u003c 0.05, all). Furthermore, sCD163 concentrations correlated positively with plasma concentrations of cortisol, glucagon, tumour necrosis factor (TNF)-\\u03b1, interleukin (IL)-6 and IL-10 (P \\u003c 0.05, all).\"], [\"LPS exposure\", \"placebo exposure\", \"concentration of sCD163\", \"significantly increased\", \"We observed a significant two-fold increase in plasma sCD163 levels following LPS exposure (P \\u003c 0.001), and sCD163 concentrations correlated positively with the plasma concentration of free fatty acids, Rapalmitate, lipid oxidation rates and phosphorylation of the hormone-sensitive lipase at serine 660 in adipose tissue (P \\u003c 0.05, all).\"]]", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\n3 The verbal models have the best performance here, which is consistent with previous sentiment analysis studies on multiple databases (e.g., <cite>Zadeh et al. (2018a)</cite> ). This suggests that lexical information remains the most effective for sentiment analysis.", "role": "user" }, { "content": "[\"Similarities\"]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: The Internet is a promising medium in the field of health promotion for offering tailored and targeted lifestyle interventions applying computer-tailored (CT) techniques to the general public. Actual exposure to CT interventions is not living up to its high expectations, as only a (limited) proportion of the target group is actually using these programs.\n\nABSTRACT.OBJECTIVE: To investigate exposure to an Internet-delivered, CT lifestyle intervention, targeting physical activity, fruit and vegetable intake, smoking behavior, and alcohol intake, we focused on three processes: first use, prolonged use, and sustained use. The first objectives were to identify user characteristics that predict initiation of an online CT lifestyle program (first use) and completion of this program (prolonged use). Furthermore, we studied the effect of using a proactive strategy, consisting of periodic email prompts, on program revisits (sustained use).\n\nABSTRACT.METHODS: The research population for this study consisted of Dutch adults participating in the Adult Health Monitor, offered by the regional public health services. We used a randomized controlled trial design to assess predictors of first use, prolonged use, and sustained use. Demographics and behavioral characteristics, as well as the strategy used for revisiting, were included as predictors in the model.\n\nABSTRACT.RESULTS: A total of 9169 participants indicated their interest in the new program and 5168 actually logged in to the program. Participants significantly more likely to initiate one of the CT modules were male, older, and employed, and had a lower income, higher body mass index, and relatively unhealthy lifestyle. Participants significantly more likely to complete one of the CT modules were older and had a higher income and a relatively healthier lifestyle. Finally, using a proactive strategy influenced sustained use, with people from the prompting condition being more likely to revisit the program (odds ratio 28.92, 95% confidence interval 10.65–78.52; P < .001).\n\nABSTRACT.CONCLUSIONS: Older, male, and employed participants, and those with a lower income, higher body mass index, and a relatively unhealthy lifestyle were more likely to initiate a CT module. Module completers predominantly had a higher income and age. The current program therefore succeeded in reaching those people who benefit most from online lifestyle interventions. However, these people tended to disengage from the program. This underlines the importance of additional research into program adjustments and strategies that can be used to stimulate prolonged program use. Furthermore, sending periodic email prompts significantly increased revisits to the program. Though promising, this effect was modest and needs to be further examined, in order to maximize the potential of periodic email prompting.\n\nABSTRACT.TRIAL REGISTRATION: Nederlands Trial Register (NTR: 1786) and Medical Ethics Committee of Maastricht University and the University Hospital Maastricht (NL2723506809/MEC0903016); http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=1786 (Archived by WebCite at http://www.webcitation.org/65hBXA6V7)\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Periodic Email Prompt\", \"Control\", \"Revisiting the program\", \"significantly increased\", \"Finally, using a proactive strategy influenced sustained use, with people from the prompting condition being more likely to revisit the program (odds ratio 28.92, 95% confidence interval 10.65\\u201378.52; P \\u003c .001).\"]]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: The objective of the study was to examine the feasibility and benefits of a group resistance training exercise programme for improving muscle function in institutionalised older adults. A feasibility and acceptability study was designed for a residential aged care (RAC) facility, based on the Gold Coast, Australia. Thirty-seven adults, mean age 86.8 ± 6.1 years (30 females) living in a RAC facility. Participants were allocated into an exercise (n = 20) or control (n = 17) group. The exercise group, the Group Aged Care Exercise (GrACE) programme, performed 12 weeks of twice weekly resistance exercises. Feasibility was measured via recruitment rate, measurement (physiological and surveys) completion rate, loss-to-follow-up, exercise session adherence, adverse events, and ratings of burden and acceptability. Muscle function was assessed using gait speed, sit-to-stand and handgrip strength assessments. All intervention participants completed pre- and post-assessments, and the exercise intervention, with 85% (n = 17) of the group attending ≥ 18 of the 24 sessions and 15% (n = 3) attending all sessions. Acceptability was 100% with exercise participants, and staff who had been involved with the programme strongly agreed that the participants \"Benefited from the programme.\" There were no adverse events reported by any participants during the exercise sessions. When compared to the control group, the exercise group experienced significant improvements in gait speed (F(4.078) = 8.265, p = 0.007), sit to stand performance (F(3.24) = 11.033, p = 0.002) and handgrip strength (F(3.697) = 26.359, p < 0.001). Resistance training via the GrACE programme is feasible, safe and significantly improves gait speed, sit-to-stand performance and handgrip strength in RAC adults.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Exercise program\", \"No exercise\", \"Gait speed\", \"significantly increased\", \"When compared to the control group, the exercise group experienced significant improvements in gait speed (F(4.078) = 8.265, p = 0.007), sit to stand performance (F(3.24) = 11.033, p = 0.002) and handgrip strength (F(3.697) = 26.359, p \\u003c 0.001).\"], [\"Exercise program\", \"No exercise\", \"Handgrip strength\", \"significantly increased\", \"When compared to the control group, the exercise group experienced significant improvements in gait speed (F(4.078) = 8.265, p = 0.007), sit to stand performance (F(3.24) = 11.033, p = 0.002) and handgrip strength (F(3.697) = 26.359, p \\u003c 0.001).\"], [\"Exercise program\", \"No exercise\", \"Sit to stand performance\", \"significantly increased\", \"When compared to the control group, the exercise group experienced significant improvements in gait speed (F(4.078) = 8.265, p = 0.007), sit to stand performance (F(3.24) = 11.033, p = 0.002) and handgrip strength (F(3.697) = 26.359, p \\u003c 0.001).\"]]", "role": "assistant" } ]

[ { "content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nFast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring\n\nData for human-human spoken dialogues for research and development are currently very limited in quantity, variety, and sources; such data are even scarcer in healthcare. In this work, we investigate fast prototyping of a dialogue comprehension system by leveraging on minimal nurse-to-patient conversations. We propose a framework inspired by nurse-initiated clinical symptom monitoring conversations to construct a simulated human-human dialogue dataset, embodying linguistic characteristics of spoken interactions like thinking aloud, self-contradiction, and topic drift. We then adopt an established bidirectional attention pointer network on this simulated dataset, achieving more than 80% F1 score on a held-out test set from real-world nurse-to-patient conversations. The ability to automatically comprehend conversations in the healthcare domain by exploiting only limited data has implications for improving clinical workflows through red flag symptom detection and triaging capabilities. We demonstrate the feasibility for efficient and effective extraction, retrieval and comprehension of symptom checking information discussed in multi-turn human-human spoken conversations.\n\nHuman-human Spoken Conversations\nHuman-human spoken conversations are a dynamic and interactive flow of information exchange. While developing technology to comprehend such spoken conversations presents similar technical challenges as machine comprehension of written passages BIBREF6 , the challenges are further complicated by the interactive nature of human-human spoken conversations:\n(1) Zero anaphora is more common: Co-reference resolution of spoken utterances from multiple speakers is needed. For example, in Figure FIGREF5 (a) headaches, the pain, it, head bulging all refer to the patient's headache symptom, but they were uttered by different speakers and across multiple utterances and turns. In addition, anaphors are more likely to be omitted (see Figure FIGREF5 (a) A4) as this does not affect the human listener’s understanding, but it might be challenging for computational models.\n(2) Thinking aloud more commonly occurs: Since it is more effortless to speak than to type, one is more likely to reveal her running thoughts when talking. In addition, one cannot retract what has been uttered, while in text communications, one is more likely to confirm the accuracy of the information in a written response and revise if necessary before sending it out. Thinking aloud can lead to self-contradiction, requiring more context to fully understand the dialogue; e.g., in A6 in Figure FIGREF5 (a), the patient at first says he has none of the symptoms asked, but later revises his response saying that he does get dizzy after running.\n(3) Topic drift is more common and harder to detect in spoken conversations: An example is shown in Figure FIGREF5 (a) in A3, where No is actually referring to cough in the previous question, and then the topic is shifted to headache. In spoken conversations, utterances are often incomplete sentences so traditional linguistic features used in written passages such as punctuation marks indicating syntactic boundaries or conjunction words suggesting discourse relations might no longer exist.\n\nNLP for Healthcare\nThere is emerging interest in research and development activities at the intersection of machine learning and healthcare , of which much of the NLP related work are centered around social media or online forums (e.g., BIBREF19 , BIBREF20 ), partially due to the world wide web as a readily available source of information. Other work in this area uses public data sources such as MIMIC in electronic health records: text classification approaches have been applied to analyze unstructured clinical notes for ICD code assignment BIBREF21 and automatic intensive emergency prediction BIBREF22 . Sequence-to-sequence textual generation has been used for readable notes based on medical and demographic recordings BIBREF23 . For mental health, there has been more focus on analyzing dialogues. For example, sequential modeling of audio and text have helped detect depression from human-machine interviews BIBREF24 . However, few studies have examined human-human spoken conversations in healthcare settings.\n\nData Preparation\nWe used recordings of nurse-initiated telephone conversations for congestive heart failure patients undergoing telemonitoring, post-discharge from the hospital. The clinical data was acquired by the Health Management Unit at Changi General Hospital. This research study was approved by the SingHealth Centralised Institutional Review Board (Protocol 1556561515). The patients were recruited during 2014-2016 as part of their routine care delivery, and enrolled into the telemonitoring health management program with consent for use of anonymized versions of their data for research.\nThe dataset comprises a total of 353 conversations from 40 speakers (11 nurses, 16 patients, and 13 caregivers) with consent to the use of anonymized data for research. The speakers are 38 to 88 years old, equally distributed across gender, and comprise a range of ethnic groups (55% Chinese, 17% Malay, 14% Indian, 3% Eurasian, and 11% unspecified). The conversations cover 11 topics (e.g., medication compliance, symptom checking, education, greeting) and 9 symptoms (e.g., chest pain, cough) and amount to 41 hours.\nData preprocessing and anonymization were performed by a data preparation team, separate from the data analysis team to maintain data confidentiality. The data preparation team followed standard speech recognition transcription guidelines, where words are transcribed verbatim to include false starts, disfluencies, mispronunciations, and private self-talk. Confidential information were marked and clipped off from the audio and transcribed with predefined tags in the annotation. Conversation topics and clinical symptoms were also annotated and clinically validated by certified telehealth nurses.\n\nLinguistic Characterization on Seed Data\nTo analyze the linguistic structure of the inquiry-response pairs in the entire 41-hour dataset, we randomly sampled a seed dataset consisting of 1,200 turns and manually categorized them to different types, which are summarized in Table TABREF14 along with the corresponding occurrence frequency statistics. Note that each given utterance could be categorized to more than one type. We elaborate on each utterance type below.\nOpen-ended Inquiry: Inquiries about general well-being or a particular symptom; e.g., “How are you feeling?” and “Do you cough?”\nDetailed Inquiry: Inquiries with specific details that prompt yes/no answers or clarifications; e.g., “Do you cough at night?”\nMulti-Intent Inquiry: Inquiring more than one symptom in a question; e.g., “Any cough, chest pain, or headache?”\nReconfirmation Inquiry: The nurse reconfirms particular details; e.g., “Really? At night?” and “Serious or mild?”. This case is usually related to explicit or implicit coreferencing.\nInquiry with Transitional Clauses: During spoken conversations, one might repeat what the other party said, but it is unrelated to the main clause of the question. This is usually due to private self-talk while thinking aloud, and such utterances form a transitional clause before the speaker starts a new topic; e.g., “Chest pain... no chest pain, I see... any cough?”.\nYes/No Response: Yes/No responses seem straightforward, but sometimes lead to misunderstanding if one does not interpret the context appropriately. One case is tag questions: A:“You don't cough at night, do you?” B:`Yes, yes” A:“cough at night?” B:“No, no cough”. Usually when the answer is unclear, clarifying inquiries will be asked for reconfirmation purposes.\nDetailed Response: Responses that contain specific information of one symptom, like “I felt tightness in my chest”.\nResponse with Revision: Revision is infrequent but can affect comprehension significantly. One cause is thinking aloud so a later response overrules the previous one; e.g., “No dizziness, oh wait... last week I felt a bit dizzy when biking”.\nResponse with Topic Drift: When a symptom/topic like headache is inquired, the response might be: “Only some chest pain at night”, not referring to the original symptom (headache) at all.\nResponse with Transitional Clauses: Repeating some of the previous content, but often unrelated to critical clinical information and usually followed by topic drift. For example, “Swelling... swelling... I don't cough at night”.\n\nTemplate Construction\nEach utterance in the seed data is categorized according to Table TABREF14 and then abstracted into templates by replacing entity phrases like cough and often with respective placeholders “#symptom#” and “#frequency#”. The templates are refined through verifying logical correctness and injecting expression diversity by linguistically trained researchers. As these replacements do not alter the syntactic structure, we interchange such placeholders with various verbal expressions to enlarge the simulated training set in Section SECREF20 . Clinical validation was also conducted by certified telehealth nurses.\nFor the 9 symptoms (e.g. chest pain, cough) and 5 attributes (e.g., extent, frequency), we collect various expressions from the seed data, and expand them through synonym replacement. Some attributes are unique to a particular symptom; e.g., “left leg” in #location# is only suitable to describe the symptom swelling, but not the symptom headache. Therefore, we only reuse general expressions like “slight” in #extent# across different symptoms to diversify linguistic expressions.\nTwo linguistically trained researchers constructed expression pools for each symptom and each attribute to account for different types of paraphrasing and descriptions. These expression pools are used in Section SECREF20 (c).\n\nSimulated Data Generation Framework\nFigure FIGREF15 shows the five steps we use to generate multi-turn symptom monitoring dialogue samples.\n(a) Topic Selection: While nurses might prefer to inquire the symptoms in different orders depending on the patient's history, our preliminary analysis shows that modeling results do not differ noticeably if topics are of equal prior probabilities. Thus we adopt this assumption for simplicity.\n(b) Template Selection: For each selected topic, one inquiry template and one response template are randomly chosen to compose a turn. To minimize adverse effects of underfitting, we redistributed the frequency distribution in Table TABREF14 : For utterance types that are below 15%, we boosted them to 15%, and the overall relative distribution ranking is balanced and consistent with Table TABREF14 .\n(c) Enriching Linguistic Expressions: The placeholders in the selected templates are substituted with diverse expressions from the expression pools in Section UID19 to characterize the symptoms and their corresponding attributes.\n(d) Multi-Turn Dialogue State Tracking: A greedy algorithm is applied to complete conversations. A “completed symptoms” list and a “to-do symptoms” list are used for symptom topic tracking. We also track the “completed attributes\" and “to-do attributes\". For each symptom, all related attributes are iterated. A dialogue ends only when all possible entities are exhausted, generating a multi-turn dialogue sample, which encourages the model to learn from the entire discussion flow rather than a single turn to comprehend contextual dependency. The average length of a simulated dialogue is 184 words, which happens to be twice as long as an average dialogue from the real-world evaluation set. Moreover, to model the roles of the respondents, we set the ratio between patients and caregivers to 2:1; this statistic is inspired by the real scenarios in the seed dataset. For both the caregivers and patients, we assume equal probability of both genders. The corresponding pronouns in the conversations are thus determined by the role and gender of these settings.\n(e) Multi-Turn Sample Annotation: For each multi-turn dialogue, a query is specified by a symptom and an attribute. The groundtruth output of the QA system is automatically labeled based on the template generation rules, but also manually verified to ensure annotation quality. Moreover, we adopt the unanswerable design in BIBREF6 : when the patient does not mention a particular symptom, the answer is defined as “No Answer”. This process is repeated until all logical permutations of symptoms and attributes are exhausted.\n\nModel Design\n We implemented an established model in reading comprehension, a bi-directional attention pointer network BIBREF1 , and equipped it with an answerable classifier, as depicted in Figure FIGREF21 . First, tokens in the given dialogue INLINEFORM0 and query INLINEFORM1 are converted into embedding vectors. Then the dialogue embeddings are fed to a bi-directional LSTM encoding layer, generating a sequence of contextual hidden states. Next, the hidden states and query embeddings are processed by a bi-directional attention layer, fusing attention information in both context-to-query and query-to-context directions. The following two bi-directional LSTM modeling layers read the contextual sequence with attention. Finally, two respective linear layers with softmax functions are used to estimate token INLINEFORM2 's INLINEFORM3 and INLINEFORM4 probability of the answer span INLINEFORM5 .\nIn addition, we add a special tag “[SEQ]” at the head of INLINEFORM0 to account for the case of “No answer” BIBREF4 and adopt an answerable classifier as in BIBREF25 . More specifically, when the queried symptom or attribute is not mentioned in the dialogue, the answer span should point to the tag “[SEQ]” and answerable probability should be predicted as 0.\n\nImplementation Details\nThe model was trained via gradient backpropagation with the cross-entropy loss function of answer span prediction and answerable classification, optimized by Adam algorithm BIBREF26 with initial learning rate of INLINEFORM0 . Pre-trained GloVe BIBREF3 embeddings (size INLINEFORM1 ) were used. We re-shuffled training samples at each epoch (batch size INLINEFORM2 ). Out-of-vocabulary words ( INLINEFORM3 ) were replaced with a fixed random vector. L2 regularization and dropout (rate INLINEFORM4 ) were used to alleviate overfitting BIBREF27 .\n\nFigure 1: Dialogue comprehension of symptom checking conversations.\n\nFigure 2: Simulated data generation framework.\n\nTable 2: QA model evaluation results. Each sample is a simulated multi-turn conversation.\n\nTable 3: Ablation experiments on 100K training size.\n\nQuery: Which data do they use as a starting point for the dialogue dataset?\n\nJSON Response:", "role": "user" }, { "content": "{\"answer\": \"A sample from nurse-initiated telephone conversations for congestive heart failure patients undergoing telepmonitoring, post-discharge from the Health Management Unit at Changi General Hospital\", \"evidence\": [\"We used recordings of nurse-initiated telephone conversations for congestive heart failure patients undergoing telemonitoring, post-discharge from the hospital. The clinical data was acquired by the Health Management Unit at Changi General Hospital. This research study was approved by the SingHealth Centralised Institutional Review Board (Protocol 1556561515). The patients were recruited during 2014-2016 as part of their routine care delivery, and enrolled into the telemonitoring health management program with consent for use of anonymized versions of their data for research.\", \"To analyze the linguistic structure of the inquiry-response pairs in the entire 41-hour dataset, we randomly sampled a seed dataset consisting of 1,200 turns and manually categorized them to different types, which are summarized in Table TABREF14 along with the corresponding occurrence frequency statistics. Note that each given utterance could be categorized to more than one type. We elaborate on each utterance type below.\"]}", "role": "assistant" } ]

[ { "content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nTNFR-associated factor 2 deficiency in B lymphocytes predisposes to chronic lymphocytic leukemia/small lymphocytic lymphoma in mice. We have previously shown that transgenic (tg) mice expressing in B lymphocytes both BCL-2 and a TNFR-associated factor 2 (TRAF2) mutant lacking the really interesting new gene and zinc finger domains (TRAF2DN) develop small lymphocytic lymphoma and chronic lymphocytic leukemia with high incidence (Zapata et al. 2004. Proc. Nat. Acad. Sci. USA 101: 16600-16605). Further analysis of the expression of TRAF2 and TRAF2DN in purified B cells demonstrated that expression of both endogenous TRAF2 and tg TRAF2DN was negligible in Traf2DN-tg B cells compared with wild-type mice. This was the result of proteasome-dependent degradation, and rendered TRAF2DN B cells as bona fide TRAF2-deficient B cells. Similar to B cells with targeted Traf2 deletion, Traf2DN-tg mice show expanded marginal zone B cell population and have constitutive p100 NF-kappaB2 processing. Also, TRAF3, X-linked inhibitor of apoptosis, and Bcl-X(L) expression levels were increased, whereas cellular inhibitors of apoptosis 1 and 2 levels were drastically reduced compared with those found in wild-type B cells. Moreover, consistent with previous results, we also show that TRAF2 was required for efficient JNK and ERK activation in response to CD40 engagement. However, TRAF2 was deleterious for BCR-mediated activation of these kinases. In contrast, TRAF2 deficiency had no effect on CD40-mediated p38 MAPK activation but significantly reduced BCR-mediated p38 activation. Finally, we further confirm that TRAF2 was required for CD40-mediated proliferation, but its absence relieved B cells of the need for B cell activating factor for survival. Altogether, our results suggest that TRAF2 deficiency cooperates with BCL-2 in promoting chronic lymphocytic leukemia/small lymphocytic lymphoma in mice, possibly by specifically enforcing marginal zone B cell accumulation, increasing X-linked inhibitor of apoptosis expression, and rendering B cells independent of B cell activating factor for survival.", "role": "user" }, { "content": "{\"CellLine\": [\"Traf2DN-tg\", \"Traf2DN-tg B\"], \"Chemical\": [], \"Disease\": [\"small lymphocytic lymphoma\", \"chronic lymphocytic leukemia\"], \"Gene\": [\"X-linked inhibitor of apoptosis\", \"p38\", \"TRAF3\", \"TNFR-associated factor 2\", \"cellular inhibitors of apoptosis 1 and 2\", \"CD40\", \"TRAF2\", \"proteasome\", \"Bcl-X(L)\", \"p38 MAPK\", \"B cell activating factor\", \"BCL-2\", \"p100 NF-kappaB2\", \"JNK\", \"Traf2\", \"BCR\", \"ERK\"], \"Species\": [\"mice\"], \"Variant\": []}", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: Functional constipation is a common clinical complaint of patients with unsatisfactory treatment outcome. We designed this study to evaluate the efficiency of a traditional herbal preparation (Lax-Asab) in treating chronic constipation. In this double-blind, randomized, placebo-controlled clinical trial, participants with chronic constipation (n = 48) were randomly selected to receive either the Lax-Asab powder (n = 24) or placebo (n = 24) on alternative days for 4 weeks. The Lax-Asab powder contains equal amounts of Cassia angustifolia Vahl. (狹葉番瀉葉 xiá yè fān xiè yè), Mentha piperita L. (胡椒薄荷 hú jiāo bò hé), Zingiber officinale Rosc. (生薑 shēng jiāng), Glycyrrhiza glabra L. (甘草 gān cǎo). A total of 40 patients completed the study. We determined the severity of constipation based on defecation frequency (per week) and defecation difficulties. Of the total of 48 patients who participated, 40 completed the trial [24 men (60%), mean age, 21.0 ± 4.2 years; 16 women (40%), mean age, 20.1 ± 4.3 years]. The mean of weekly defecation frequency increased in both groups; from 1.8 ± 0.41 to 4.8 ± 1.12 times in patients who received Lax-Asab and from 1.7 ± 0.44 to 2.2 ± 0.61 times in patients who received placebo. A time–treatment interaction showed that this increase was significantly higher in the intervention group. Defecation difficulties improved significantly more in patients who received Lax-Asab than patients who received placebo. There was no statistically significant difference between the two groups with regard to the side effects observed. This study confirms the efficacy and tolerability of an Iranian herbal preparation, Lax-Asab, in treating patients with chronic functional constipation.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Lax-Asab powder\", \"placebo\", \"efficacy of treating patients with chronic functional constipation\", \"significantly increased\", \"This study confirms the efficacy and tolerability of an Iranian herbal preparation, Lax-Asab, in treating patients with chronic functional constipation.\"], [\"Lax-Asab powder\", \"placebo\", \"mean of weekly defecation frequency increase\", \"significantly increased\", \"The mean of weekly defecation frequency increased in both groups; from 1.8 \\u00b1 0.41 to 4.8 \\u00b1 1.12 times in patients who received Lax-Asab and from 1.7 \\u00b1 0.44 to 2.2 \\u00b1 0.61 times in patients who received placebo. A time\\u2013treatment interaction showed that this increase was significantly higher in the intervention group.\"], [\"Lax-Asab powder\", \"placebo\", \"side effects observed\", \"no significant difference\", \"There was no statistically significant difference between the two groups with regard to the side effects observed.\"]]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND:: The purpose of this randomised phase III trial was to evaluate whether the addition of simvastatin, a synthetic 3-hydroxy-3methyglutaryl coenzyme A reductase inhibitor, to XELIRI/FOLFIRI chemotherapy regimens confers a clinical benefit to patients with previously treated metastatic colorectal cancer.\n\nABSTRACT.METHODS:: We undertook a double-blind, placebo-controlled phase III trial of 269 patients previously treated for metastatic colorectal cancer and enrolled in 5 centres in South Korea. Patients were randomly assigned (1 : 1) to one of the following groups: FOLFIRI/XELIRI plus simvastatin (40 mg) or FOLFIRI/XELIRI plus placebo. The FOLFIRI regimen consisted of irinotecan at 180 mg m−2 as a 90-min infusion, leucovorin at 200 mg m−2 as a 2-h infusion, and a bolus injection of 5-FU 400 mg m−2 followed by a 46-h continuous infusion of 5-FU at 2400 mg m−2. The XELIRI regimen consisted of irinotecan at 250 mg m−2 as a 90-min infusion with capecitabine 1000 mg m−2 twice daily for 14 days. The primary end point was progression-free survival (PFS). Secondary end points included response rate, duration of response, overall survival (OS), time to progression, and toxicity.\n\nABSTRACT.RESULTS:: Between April 2010 and July 2013, 269 patients were enrolled and assigned to treatment groups (134 simvastatin, 135 placebo). The median PFS was 5.9 months (95% CI, 4.5–7.3) in the XELIRI/FOLFIRI plus simvastatin group and 7.0 months (95% CI, 5.4–8.6) in the XELIRI/FOLFIRI plus placebo group (P=0.937). No significant difference was observed between the two groups with respect to OS (median, 15.9 months (simvastatin) vs 19.9 months (placebo), P=0.826). Grade ⩾3 nausea and anorexia were noted slightly more often in patients in the simvastatin arm compared with with the placebo arm (4.5% vs 0.7%, 3.0% vs 0%, respectively).\n\nABSTRACT.CONCLUSIONS:: The addition of 40 mg simvastatin to the XELIRI/FOLFIRI regimens did not improve PFS in patients with previously treated metastatic colorectal cancer nor did it increase toxicity.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Simvastatine\", \"Placebo\", \"Overall survival\", \"no significant difference\", \"No significant difference was observed between the two groups with respect to OS (median, 15.9 months (simvastatin) vs 19.9 months (placebo), P=0.826).\"], [\"Simvastatine\", \"Placebo\", \"Toxicity \", \"no significant difference\", \"The addition of 40 mg simvastatin to the XELIRI/FOLFIRI regimens did not improve PFS in patients with previously treated metastatic colorectal cancer nor did it increase toxicity.\"], [\"Simvastatine\", \"Placebo\", \"Progression-free survival\", \"no significant difference\", \"The median PFS was 5.9 months (95% CI, 4.5\\u20137.3) in the XELIRI/FOLFIRI plus simvastatin group and 7.0 months (95% CI, 5.4\\u20138.6) in the XELIRI/FOLFIRI plus placebo group (P=0.937).\"]]", "role": "assistant" } ]

[ { "content": "| Sample | O 2 | O 4 | O 6 | Rms (%) | O 4/O 6 |\n| Glass | 4.88 | 0.95 | 0.37 | 1.6% | 2.57 |\n| Glass ceramic | 3.81 | 0.57 | 0.18 | 2.9% | 3.17 |\n\nCaption: Judd-Ofelt parameters, O t (in unit of 10-20 cm2), of Er3+ in the precursor glass and glass ceramic (rms: the root-mean-square errors)\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"4.88\", \"type\": \"Other\"}\n{\"value\": \"0.95\", \"type\": \"Other\"}\n{\"value\": \"0.37\", \"type\": \"Other\"}\n{\"value\": \"2.57\", \"type\": \"Other\"}\n{\"value\": \"3.81\", \"type\": \"Other\"}\n{\"value\": \"0.57\", \"type\": \"Other\"}\n{\"value\": \"0.18\", \"type\": \"Other\"}\n{\"value\": \"3.17\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nRAR: Region-Aware Point Cloud Registration\n\nMain Content:\nPoint set registration is a challenging but meaningful task , which has wide application in many fields Bai et al . ( 2007 ) ; Bai & Latecki ( 2008 ) ; Myronenko & Song ( 2009 ) ; Ma et al . ( 2016 ) ; Wu et al . ( 2012 ) ; Klaus et al . ( 2006 ) ; Maintz & Viergever ( 1998 ) ; Besl & McKay ( 1992 ) ; Raguram et al . ( 2008 ) ; Yuille & Grzywacz ( 1988 ) ; Sonka et al . ( 2014 ) . Most existing non-learning methods solve the registration problem through an iterative optimization process to search the optimal geometric transformation to minimize a pre-defined alignment loss between transformed source point set and target point set Myronenko et al . ( 2007 ) ; Ma et al . ( 2013 ; 2014 ) ; Ling & Jacobs ( 2005 ) . The geometric transformation can be modeled by a specific type of parametric transformation ( e.g . rotation , translation , thin-plate spline , and so on ) Besl & McKay ( 1992 ) . For example , one of the most commonly applied methods , iterative closest point ( ICP ) Besl & McKay ( 1992 ) , estimates the rigid transformation based on a set of corresponding points . The ICP model , however , strongly depends on the initialization and has limited performance in choosing corresponding points . Moreover , iterative methods usually treat registration as an independent optimization process for each given pair of source and target point sets , which can not transfer knowledge from registering one pair to another . In recent years , deep-learning-based algorithms have been implemented in various industries and achieved great success , researchers are increasingly interested in bringing deep-learning-based solutions to the field of point set registration . Instead of directly optimizing the transformation matrix towards minimization of alignment loss in non-learning-based methods , learning-based methods usually leverage modern feature extraction technologies for feature learning and then regress the transformation matrix based on the mutual information and correlation defined on the extracted features of source and target shapes . The most recent model , deep closest point ( DCP ) Wang & Solomon ( 2019 ) , leverages DGCNN Wang et al . ( 2019 ) for feature learning and a pointer network to perform soft matching . To refine the soft matching results to predict the final rigid transformation , the DCP model further proposes a singular value decomposition layer for fine-tuning . However , it is still challenging to design an explicit module for learning both the features from unstructured point clouds and their “ geometric relationship ” Wang et al . ( 2018 ) . Existing works developed various models to compute the spatial correlation feature . For example , FlowNet3D Liu et al . ( 2019 ) tied to concatenate two global descriptors of source and target point sets ; Balakrishnan et al . ( 2018 ) used a U-Net-based structure to mix the source and target volumetric shapes ; Rocco et al . ( 2017 ) proposed a correlation tensor calculated from source and target feature map and so on . The learning of robust point cloud registration models with deep neural networks has emerged as a powerful paradigm , offering promising performance in predicting the global geometric transformation for a pair of point sets . Those methods share a similar pipeline by firstly leveraging an encoder to regress a latent shape embedding , which is then decoded into a shape-conditioned transformation via concatenation-based conditioning . In this paper , we observe that different regions of a 3D shape vary in their geometric structures which makes it more sense that we have a region-conditioned ( in contrast to shape-conditioned ) transformation decoder via concatenation-based conditioning . As shown in Figure 1 , the shapeconditioned transformation predicts one global transformation for point sets alignment whereas the region-conditioned transformation predicts a set of transformations for different implicit regions , which are then weighted fused to form a global transformation . With this observation , as illustrated in Figure 2 , we present a region-aware point cloud registration , denoted as RAR , to predict transformation for pairwise point sets in a self-supervised learning fashion . Our proposed RAR framework contains three main components . The first component is a region-aware decoder ( RAD ) module that is formed with an implicit neural region representation parameterized by neural networks conditioned on a shape embedding . The implicit neural region representation is learned with a self-supervised 3D shape reconstruction loss without the need for region labels . The second component is a region-aware transformation ( RAT ) module which decodes shape embedding features to regress a set of region-specific transformations . The third component is the region-aware weight ( RAW ) module which generates the weights for different regions of the 3D shape to be aligned . The global geometric transformation from source point set to target one is then formed by weighted fusion of region-aware transforms . Our contribution is as follows : • We introduce a new concept of region-conditioned transformation that contributes to a novel region-aware point cloud registration ( RAR ) as the learning approach for robust point set alignment . Our RAR models are realized with the development of three new modules : region-aware decoder ( RAD ) module , region-aware transformation ( RAT ) module , and region-aware weight ( RAW ) module . • Our RAR is a novel unsupervised learning model for point cloud registration without the need of training on labeled datasets . • Experimental results demonstrate the effectiveness of the proposed method for point set registration , our RAR achieved superior performance compared to unsupervised and supervised state-of-the-art approaches even without labeled data for training . 1 RELATED WORKS . 1.1 ITERATIVE REGISTRATION METHODS . The development of optimization algorithms to estimate rigid and non-rigid geometric transformations in an iterative routine has attracted extensive research attention in past decades . Assuming that a pair of point sets are related by a rigid transformation , the standard approach is to estimate the best translation and rotation parameters in the iterative search routine , therein aiming to minimize a distance metric between two sets of points . The iterative closest point ( ICP ) algorithm Besl & McKay ( 1992 ) is one successful solution for rigid registration . It initializes an estimation of a rigid function and then iteratively chooses corresponding points to refine the transformation . However , the ICP algorithm is reported to be vulnerable to the selection of corresponding points for initial transformation estimation . Go-ICP Yang et al . ( 2015 ) was further proposed by Yang et al . to leverage the BnB scheme for searching the entire 3D motion space to solve the local initialization problem brought by ICP . Zhou et al . proposed fast global registration Zhou et al . ( 2016 ) for the registration of partially overlapping 3D surfaces . The TPS-RSM algorithm was proposed by Chui and Rangarajan Chui & Rangarajan ( 2000 ) to estimate parameters of non-rigid transformations with a penalty on secondorder derivatives . Existing classical algorithms have achieved great success on the registration task . Although the independent iterative optimization process limits the efficiency of registering a large number of pairs , inspiring us to design a learning-based system for this task . 1.2 LEARNING-BASED REGISTRATION METHODS . In recent years , learning-based methods have achieved great success in many fields of computer vision Su et al . ( 2015 ) ; Sharma et al . ( 2016 ) ; Maturana & Scherer ( 2015 ) ; Bai et al . ( 2016 ) ; Qi et al . ( 2017 ) ; Verma et al . ( 2018 ) ; Masci et al . ( 2015 ) ; Zeng et al . ( 2017 ) . In particular , recent works have started a trend of directly learning geometric features from cloud points ( especially 3D points ) , which motivates us to approach the point set registration problem using deep neural networks Rocco et al . ( 2017 ) ; Balakrishnan et al . ( 2018 ) ; Zeng et al . ( 2017 ) ; Qi et al . ( 2017 ) ; Verma et al . ( 2018 ) ; Masci et al . ( 2015 ) . PointNetLK Aoki et al . ( 2019 ) was proposed by Aoki et al . to leverage the newly proposed PointNet algorithm for directly extracting features from the point cloud with the classical Lucas & Kanade algorithm for the rigid registration of 3D point sets . Liu et al . proposed FlowNet3D Liu et al . ( 2019 ) to treat 3D point cloud registration as a motion process between points . Wang et al . proposed a deep closest point Wang & Solomon ( 2019 ) model , which first leverages the DGCNN structure to exact the features from point sets and then regress the desired transformation based on it . Balakrishnan et al . Balakrishnan et al . ( 2018 ) proposed a voxelMorph CNN architecture to learn the registration field to align two volumetric medical images . For the learning-based registration solutions listed above , the main challenge concerns how to effectively model the “ geometric relationship ” between source and target objects in a learning-based approach . For example , Rocco et al . ( 2017 ) proposed a correlation tensor between the feature maps of source and target images . Balakrishnan et al . ( 2018 ) leveraged a U-Net-based structure to concatenate features of source and target voxels . Liu et al . ( 2019 ) ; Aoki et al . ( 2019 ) used a PointNet-based structure , and Wang & Solomon ( 2019 ) used a DGCNN structure to learn the features from a point set for further registration decoding . In contrast , we introduce a region-aware point cloud registration , denoted as RAR , to predict transformation for pairwise point sets in the self-supervised learning fashion . 2 METHODS . We introduce our approach in the following sections . The problem statement of our method is introduced in section 2.1 . We explain the learning shape descriptor in section 2.2 . Section 2.3 illustrates the network structure of our region-aware decoder module . The region-aware weight module is defined in section 2.4 . In section 2.5 , we describe the region-aware transformation module . The loss function is also discussed in section 2.6 . 2.1 PROBLEM STATEMENT . We define the optimization task of the deep learning-based methods which directly use unordered point clouds as input at first . Giving a training dataset D = { ( Si , Gi ) } , where Si , Gi ⊂ R3 . Si denotes the input source point clouds and Gi denotes the input target point clouds . We aim to obtain a parametric function gθ ( Si , Gi ) using a neural network structure that can predict the rotation matrix R ∈ SO ( 3 ) and a translation vector t ∈ R3 that can deform the source point cloud towards the target point cloud . A pre-defined alignment metric between the transformed source point cloud and the target point cloud can be defined as the objective loss function to update the parameters θ . For a given dataset D , a stochastic gradient-descent based algorithm can usually be utilized to optimize the parameters θ by minimizing the pre-defined loss function : θ∗ = argmin θ [ E ( Si , Gi ) ∼D [ L ( Si , Gi , gθ ( Si , Gi ) ) ] ( 1 ) where L represents the pre-defined loss function . 2.2 LEARNING SHAPE EMBEDDING . For the input point clouds , the learning shape embedding is a non-linear multi-layer perceptron ( MLP ) -based function neural network that can extract shape features and capture the geometric information . Formally , let Pi denotes the input point clouds and fx ⊂ Rm denotes the feature of x , ∀x ∈ Pi , where m is the dimension of output layer . Our Learning Shape Descriptor includes two key components : encoding network and feature information . We define the encoding network g1 : R3 → Rm which uses multi-layer perceptrons ( MLP ) with ReLu activation function for feature extraction : fx = g1 ( x ) x∈Pi ( 2 ) The feature information is combined by extracted feature and point coordinates . Specifically , ∀x ∈ Pi , we concatenate the learned feature fx with the coordinates x as the combined feature [ fx , x ] ∈ R ( m+3 ) . Thus , the shape descriptor of input point cloud Pi is : { [ fx , x ] } x∈Pi .", "role": "user" }, { "content": "This paper aims to solve the rigid registration of 3D point clouds using a deep neural network. The key difference from previous methods is that this paper proposes a region-conditioned transformation. Specifically, this method first estimates k transformation matrices and then adopts a region segmentation module to divide the shape, which is further utilized to estimate the region-aware weights to combine the k transformations", "role": "assistant" } ]

[ { "content": "| Li/V | 0 | 0.5 | 1 | 1.5 | 2 |\n| E a (eV) | 0.44 | 0.57 | 0.95 | 0.84 | 0.77 |\n| %V4+ (+-0.01) | 9.20 | 5.92 | 3.75 | 2.78 | 3.11 |\n\nCaption: Determined activation energy and V4+ amount of Li2CO3 based glasses\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"0.5\", \"type\": \"Other\"}\n{\"value\": \"1\", \"type\": \"Other\"}\n{\"value\": \"1.5\", \"type\": \"Other\"}\n{\"value\": \"2\", \"type\": \"Other\"}\n{\"value\": \"0.44\", \"type\": \"Other\"}\n{\"value\": \"0.57\", \"type\": \"Other\"}\n{\"value\": \"0.95\", \"type\": \"Other\"}\n{\"value\": \"0.84\", \"type\": \"Other\"}\n{\"value\": \"0.77\", \"type\": \"Other\"}\n{\"value\": \"9.20\", \"type\": \"Other\"}\n{\"value\": \"5.92\", \"type\": \"Other\"}\n{\"value\": \"3.75\", \"type\": \"Other\"}\n{\"value\": \"2.78\", \"type\": \"Other\"}\n{\"value\": \"3.11\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nImitation with Neural Density Models\n\nMain Content:\n1 Introduction . Imitation Learning ( IL ) algorithms aim to learn optimal behavior by mimicking expert demonstrations . Perhaps the simplest IL method is Behavioral Cloning ( BC ) ( Pomerleau , 1991 ) which ignores the dynamics of the underlying Markov Decision Process ( MDP ) that generated the demonstrations , and treats IL as a supervised learning problem of predicting optimal actions given states . Prior work showed that if the learned policy incurs a small BC loss , the worst case performance gap between the expert and imitator grows quadratically with the number of decision steps ( Ross & Bagnell , 2010 ; Ross et al. , 2011a ) . The crux of their argument is that policies that are `` close '' as measured by BC loss can induce disastrously different distributions over states when deployed in the environment . One family of solutions to mitigating such compounding errors is Interactive IL ( Guo et al. , 2014 ; Ross et al. , 2011b , 2013 ) , which involves running the imitator ’ s policy and collecting corrective actions from an interactive expert . However , interactive expert queries are expensive and seldom available . Another family of approaches ( Fu et al. , 2017 ; Ho & Ermon , 2016 ; Ke et al. , 2020 ; Kim & Park , 2018 ; Kostrikov et al. , 2020 ; Wang et al. , 2017 ) that have gained much traction is to directly minimize a statistical distance between state-action distributions induced by policies of the expert and imitator , i.e the occupancy measures ⇢⇡E and ⇢⇡✓ . As ⇢⇡✓ is an implicit distribution induced by the policy and environment1 , distribution matching with ⇢⇡✓ typically requires likelihood-free methods involving sampling . Sampling from ⇢⇡✓ entails running the imitator policy in the environment , which was not required by BC . While distribution matching IL requires additional access to an environment simulator , it has been shown to drastically improve demonstration efficiency , i.e the number of demonstrations needed to succeed at IL ( Ho & Ermon , 2016 ) . A wide suite of distribution matching IL algorithms use adversarial methods to match ⇢⇡✓ and ⇢⇡E , which requires alternating between reward ( discriminator ) and policy ( generator ) updates ( Fu et al. , 2017 ; Ho & Ermon , 2016 ; Ke et al. , 2020 ; Kim et al. , 2019 ; Kostrikov et al. , 2020 ) . A key drawback to such Adversarial Imitation Learning ( AIL ) methods is that they inherit the instability of alternating min-max optimization ( Miyato et al. , 2018 ; Salimans et al. , 2016 ) which is generally not guaranteed to converge ( Jin et al. , 2019 ) . Furthermore , this instability is exacerbated in the IL setting where generator updates involve high-variance policy optimization and leads to sub-optimal demonstration efficiency . To alleviate this instability , ( Brantley et al. , 2020 ; Reddy et al. , 2017 ; Wang et al. , 2019 ) have proposed to do RL with fixed heuristic rewards . Wang et al . ( 2019 ) , for example , uses a heuristic reward that estimates the 1we assume only samples can be taken from the environment dynamics and its density is unknown 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . support of ⇢⇡E which discourages the imitator from visiting out-of-support states . While having the merit of simplicity , these approaches have no guarantee of recovering the true expert policy . In this work , we propose a new framework for IL via obtaining a density estimate q of the expert ’ s occupancy measure ⇢⇡E followed by Maximum Occupancy Entropy Reinforcement Learning ( MaxOccEntRL ) ( Islam et al. , 2019 ; Lee et al. , 2019 ) . In the MaxOccEntRL step , the density estimate q is used as a fixed reward for RL and the occupancy entropy H ( ⇢⇡✓ ) is simultaneously maximized , leading to the objective max✓ E⇢⇡✓ [ log q ( s , a ) ] +H ( ⇢⇡✓ ) . Intuitively , our approach encourages the imitator to visit high density state-action pairs under ⇢⇡E while maximally exploring the state-action space . There are two main challenges to this approach . First , we require accurate density estimation of ⇢⇡E , which is particularly challenging when the state-action space is high dimensional and the number of expert demonstrations are limited . Second , in contrast to Maximum Entropy RL ( MaxEntRL ) , MaxOccEntRL requires maximizing the entropy of an implicit density ⇢⇡✓ . We address the former challenge leveraging advances in density estimation ( Du & Mordatch , 2018 ; Germain et al. , 2015 ; Song et al. , 2019 ) . For the latter challenge , we derive a non-adversarial model-free RL objective that provably maximizes a lower bound to occupancy entropy . As a byproduct , we also obtain a model-free RL objective that lower bounds reverse Kullback-Lieber ( KL ) divergence between ⇢⇡✓ and ⇢⇡E . The contribution of our work is introducing a novel family of distribution matching IL algorithms , named Neural Density Imitation ( NDI ) , that ( 1 ) optimizes a principled lower bound to the additive inverse of reverse KL , thereby avoiding adversarial optimization and ( 2 ) . advances state-of-the-art demonstration efficiency in IL . 2 Imitation Learning via density estimation . We model an agent ’ s decision making process as a discounted infinite-horizon Markov Decision Process ( MDP ) M = ( S , A , P , P0 , r , ) . Here S , A are state-action spaces , P : S ⇥ A ! ⌦ ( S ) is a transition dynamics where ⌦ ( S ) is the set of probability measures on S , P0 : S ! R is an initial state distribution , r : S ⇥ A ! R is a reward function , and 2 [ 0 , 1 ) is a discount factor . A parameterized policy ⇡✓ : S ! ⌦ ( A ) distills the agent ’ s decision making rule and { st , at } 1t=0 is the stochastic process realized by sampling an initial state from s0 ⇠ P0 ( s ) then running ⇡✓ in the environment , i.e at ⇠ ⇡✓ ( ·|st ) , st+1 ⇠ P ( ·|st , at ) . We denote by p✓ , t : t+k the joint distribution of states { st , st+1 , ... , st+k } , where setting p✓ , t recovers the marginal of st . The ( unnormalized ) occupancy measure of ⇡✓ is defined as ⇢⇡✓ ( s , a ) = P1 t=0 t p✓ , t ( s ) ⇡✓ ( a|s ) . Intuitively , ⇢⇡✓ ( s , a ) quantifies the frequency of visiting the state-action pair ( s , a ) when running ⇡✓ for a long time , with more emphasis on earlier states . We denote policy performance as J ( ⇡✓ , r̄ ) = E⇡✓ [ P1 t=0 t r̄ ( st , at ) ] = E ( s , a ) ⇠⇢⇡✓ [ r̄ ( s , a ) ] where r̄ is a ( potentially ) augmented reward function and E denotes the generalized expectation operator extended to non-normalized densities p̂ : X ! R+ and functions f : X ! Y so that Ep̂ [ f ( x ) ] = P x p̂ ( x ) f ( x ) . The choice of r̄ depends on the RL framework . In standard RL , we simply have r̄ = r , while in Maximum Entropy RL ( MaxEntRL ) ( Haarnoja et al. , 2017 ) , we have r̄ ( s , a ) = r ( s , a ) log ⇡✓ ( a|s ) . We denote the entropy of ⇢⇡✓ ( s , a ) as H ( ⇢⇡✓ ) = E⇢⇡✓ [ log ⇢⇡✓ ( s , a ) ] and overload notation to denote the -discounted causal entropy of policy ⇡✓ as H ( ⇡✓ ) = E⇡✓ [ P1 t=0 t log ⇡✓ ( at|st ) ] = E⇢⇡✓ [ log ⇡✓ ( a|s ) ] . Note that we use a generalized notion of entropy where the domain is extended to non-normalized densities . We can then define the Maximum Occupancy Entropy RL ( MaxOccEntRL ) ( Islam et al. , 2019 ; Lee et al. , 2019 ) objective as J ( ⇡✓ , r̄ = r ) +H ( ⇢⇡✓ ) . Note the key difference between MaxOccEntRL and MaxEntRL : entropy regularization is on the occupancy measure instead of the policy , i.e seeks state diversity instead of action diversity . We will later show in section 2.2 , that a lower bound on this objective reduces to a complete model-free RL objective with an augmented reward r̄ . Let ⇡E , ⇡✓ denote an expert and imitator policy , respectively . Given only demonstrations D = { ( s , a ) i } ki=1 ⇠ ⇡E of state-action pairs sampled from the expert , Imitation Learning ( IL ) aims to learn a policy ⇡✓ which matches the expert , i.e ⇡✓ = ⇡E . Formally , IL can be recast as a distribution matching problem ( Ho & Ermon , 2016 ; Ke et al. , 2020 ) between occupancy measures ⇢⇡✓ and ⇢⇡E : maximize✓ d ( ⇢⇡✓ , ⇢⇡E ) ( 1 ) where d ( p̂ , q̂ ) is a generalized statistical distance defined on the extended domain of ( potentially ) non-normalized probability densities p̂ ( x ) , q̂ ( x ) with the same normalization factor Z > 0 , i.eR x p̂ ( x ) /Z = R x q̂ ( x ) /Z = 1 . For ⇢⇡ and ⇢⇡E , we have Z = 1 1 . As we are only able to take samples from the transition kernel and its density is unknown , ⇢⇡✓ is an implicit distribution2 . Thus , optimizing Eq . 1 typically requires likelihood-free approaches leveraging samples from ⇢⇡✓ , i.e running ⇡✓ in the environment . Current state-of-the-art IL approaches use likelihood-free adversarial methods to approximately optimize Eq . 1 for various choices of d such as reverse Kullback-Liebler ( KL ) divergence ( Fu et al. , 2017 ; Kostrikov et al. , 2020 ) and Jensen-Shannon ( JS ) divergence ( Ho & Ermon , 2016 ) . However , adversarial methods are known to suffer from optimization instability which is exacerbated in the IL setting where one step in the alternating optimization involves RL . We instead derive a non-adversarial objective for IL . In this work , we choose d to be ( generalized ) reverse-KL divergence and leave derivations for alternate choices of d to future work . DKL ( ⇢⇡✓ ||⇢⇡E ) = E⇢⇡✓ [ log ⇢⇡E ( s , a ) log ⇢⇡✓ ( s , a ) ] = J ( ⇡✓ , r̄ = log ⇢⇡E ) +H ( ⇢⇡✓ ) ( 2 ) We see that maximizing negative reverse-KL with respect to ⇡✓ is equivalent to Maximum Occupancy Entropy RL ( MaxOccEntRL ) with log ⇢⇡E as the fixed reward . Intuitively , this objective drives ⇡✓ to visit states that are most likely under ⇢⇡E while maximally spreading out probability mass so that if two state-action pairs are equally likely , the policy visits both . There are two main challenges associated with this approach which we address in the following sections . 1. log ⇢⇡E is unknown and must be estimated from the demonstrations D. Density estimation remains a challenging problem , especially when there are a limited number of samples and the data is high dimensional ( Liu et al. , 2007 ) . Note that simply extracting the conditional ⇡ ( a|s ) from an estimate of the joint ⇢⇡E ( s , a ) is an alternate way to do BC and does not resolve the compounding error problem ( Ross et al. , 2011a ) . 2 . H ( ⇢⇡✓ ) is hard to maximize as ⇢⇡✓ is an implicit density . This challenge is similar to the difficulty of entropy regularizing generators ( Belghazi et al. , 2018 ; Dieng et al. , 2019 ; Mohamed & Lakshminarayanan , 2016 ) for Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) , and most existing approaches ( Dieng et al. , 2019 ; Lee et al. , 2019 ) use adversarial optimization .", "role": "user" }, { "content": "This paper presents an imitation learning approach based on density estimation of the state-action pairs. The underlying idea is to take expert demonstrations and find a distribution over the inherent state occupancy and then follow-up with a reinforcement learning step. As a result, the learner visits the high density state action pairs while also exploring new states during training. The paper makes a number of interesting contributions, among a new cost function for distribution matching IL and a proof showing that it is equivalent to maximizing a non-adversarial model-free RL objective. In contrast to other methods, like AIL, no adversarial optimization is needed; which typically would have resulted in instabilities. Instead the new objective pushes the lower bound in a single direction. The paper also features a list of tradeoffs between different distribution matching IL methods. Experiments on Mujoco tasks show state of the art performance.", "role": "assistant" } ]

[ { "content": "| SUMo2-12c | SUMo2-12c |\n| SiO2 | 35.99 |\n| Na2O | 8.79 |\n| B2O3 | 12.96 |\n| Al2O3 | 6.18 |\n| P2O5 | 3.69 |\n| MoO3 | 12.00 |\n| ZnO | 5.62 |\n| ZrO2 | 7.14 |\n| CaO | 5.67 |\n| Others | 1.96 |\n\nCaption: Glass composition (wt%)\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"35.99\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"wt\", \"material\": \"SUMo2-12c\"}\n{\"value\": \"8.79\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"wt\", \"material\": \"SUMo2-12c\"}\n{\"value\": \"12.96\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"B2O3\", \"unit\": \"wt\", \"material\": \"SUMo2-12c\"}\n{\"value\": \"6.18\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Al2O3\", \"unit\": \"wt\", \"material\": \"SUMo2-12c\"}\n{\"value\": \"3.69\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"P2O5\", \"unit\": \"wt\", \"material\": \"SUMo2-12c\"}\n{\"value\": \"12.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"MoO3\", \"unit\": \"wt\", \"material\": \"SUMo2-12c\"}\n{\"value\": \"5.62\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"ZnO\", \"unit\": \"wt\", \"material\": \"SUMo2-12c\"}\n{\"value\": \"7.14\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"ZrO2\", \"unit\": \"wt\", \"material\": \"SUMo2-12c\"}\n{\"value\": \"5.67\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"wt\", \"material\": \"SUMo2-12c\"}\n{\"value\": \"1.96\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Others\", \"unit\": \"wt\", \"material\": \"SUMo2-12c\"}", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nContrastive Learning for Neural Topic Model\n\nMain Content:\n1 Introduction . Topic models have been successfully applied in Natural Language Processing with various applications such as information extraction , text clustering , summarization , and sentiment analysis [ 1–6 ] . The most popular conventional topic model , Latent Dirichlet Allocation [ 7 ] , learns document-topic and topic-word distribution via Gibbs sampling and mean field approximation . To apply deep neural network for topic model , Miao et al . [ 8 ] proposed to use neural variational inference as the training method while Srivastava and Sutton [ 9 ] employed the logistic normal prior distribution . However , recent studies [ 10 , 11 ] showed that both Gaussian and logistic normal prior fail to capture multimodality aspects and semantic patterns of a document , which are crucial to maintain the quality of a topic model . To cope with this issue , Adversarial Topic Model ( ATM ) [ 10–13 ] was proposed with adversarial mechanisms using a combination of generator and discriminator . By seeking the equilibrium between the generator and discriminator , the generator is capable of learning meaningful semantic patterns of the document . Nonetheless , this framework has two main limitations . First , ATM relies on the key ingredient : leveraging the discrimination of the real distribution from the fake ( negative ) distribution to guide the training . Since the sampling of the fake distribution is not conditioned on the real distribution , it barely generates positive samples which largely preserves the semantic content of the real sample . This limits the behavior concerning the mutual information in the positive sample and the real one , which has been demonstrated as key driver to learn useful representations ∗Corresponding author 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . in unsupervised learning [ 14–18 ] . Second , ATM takes random samples from a prior distribution to feed to the generator . Previous work [ 19 ] has shown that incorporating additional variables , such as metadata or the sentiment , to estimate the topic distribution aids the learning of coherent topics . Relying on a pre-defined prior distribution , ATM hinders the integration of those variables . To address the above drawbacks , in this paper we propose a novel method to model the relations among samples without relying on the generative-discriminative architecture . In particular , we formulate the objective as an optimization problem that aims to move the representation of the input ( or prototype ) closer to the one that shares the semantic content , i.e. , positive sample . We also take into account the relation of the prototype and the negative sample by forming an auxiliary constraint to enforce the model to push the representation of the negative farther apart from the prototype . Our mathematical framework ends with a contrastive objective , which will be jointly optimized with the evidence lower bound of neural topic model . Nonetheless , another challenge arises : how to effectively generate positive and negative samples under neural topic model setting ? Recent efforts have addressed positive sampling strategies and methods to generate hard negative samples for images [ 20–23 ] . However , relevant research to adapt the techniques to neural topic model setting has been neglected in the literature . In this work , we introduce a novel sampling method that mimics the way human being seizes the similarity of a pair of documents , which is based on the following hypothesis : Hypothesis 1 . The common theme of the prototype and the positive sample can be realized due to their relative frequency of salient words . We use the example in Fig . 1 to explain the idea of our method . Humans are able to tell the similarity of the input with positive sample due to the reason that the frequency of salient words such as “ league ” and “ teams '' is proportional to their counterpart in the positive sample . On the other hand , the separation between the input and the negative sample can be induced since those words in the input do not occur in negative sample , though they both contain words “ billions '' and “ dollars '' , which are not salient in the context of the input . Based on this intuition , our method generates the positive and negative samples for topic model by maintaining the weights of salient entries and altering those of unimportant ones in the prototype to construct the positive samples while performing the opposite procedure for the negative ones . Inherently , since our method is not depended on a fixed prior distribution to draw our samples , we are not restrained in incorporating external variables to provide additional knowledge for better learning topics . In a nutshell , the contributions of our paper are as follows : • We target the problem of capturing meaningful representations through modeling the relations among samples from a new mathematical perspective and propose a novel contrastive objective which is jointly optimized with evidence lower bound of neural topic model . We find that capturing the mutual information between the prototype and its positive samples provides a strong foundation for constructing coherent topics , while differentiating the prototype from the negative samples plays a less important role . • We propose a novel sampling strategy that is motivated by human behavior when comparing different documents . By relying on the reconstructed output , we adapt the sampling to the learning process of the model , and produce the most informative samples compared with other sampling strategies . • We conduct extensive experiments in three common topic modeling datasets and demonstrate the effectiveness of our approach by outperforming other state-of-the-art approaches in terms of topic coherence , on both global and topic-by-topic basis . 2 Related Work . Neural Topic Model ( NTM ) has been studied to encode a large set of documents using latent vectors . Inspired by Variational Autoencoder , NTM inherit most techniques from VAE-specific early works , such as reparameterization trick [ 24 ] and neural variational inference [ 25 ] . Subsequent works attempting to apply for topic model [ 9 , 26 , 8 ] focus on studying various prior distributions , e.g . Gaussian or logistic normal . Recently , researches directly target to improve topic coherence through formulating it as an optimizing objective [ 27 ] , incorporating contextual language knowledge [ 28 ] , or passing external information , e.g . sentiment , group of documents , as input [ 19 ] . Generating topics that are human-interpretable has become the goal of a wide variety of latest efforts . Adversarial Topic Model [ 4 ] is a topic modeling approach that models the topics with GAN-based architecture . The key components in that architecture consist of a generator projecting randomly sampled document-topic distribution to gain the most realistic document-word distribution as possible and a discriminator trying to distinguish between the generated and the true sample [ 10 , 11 ] . To better learn informative representations of a document , Hu et al . [ 12 ] proposed adding two cycle-consistent constraints to encourage the coordination between the encoder and generator . Contrastive Framework and Sampling Techniques There are various efforts studying contrastive method to learn meaningful representations . For visual information , contrastive framework is applied for tasks such as image classification [ 29 , 30 ] , object detection [ 31–33 ] , image segmentaion [ 34–36 ] , etc . Other applications different from image include adversarial training [ 37–39 ] , graph [ 40–43 ] , and sequence modeling [ 44–46 ] . Specific positive sampling strategies have been proposed to improve the performance of contrastive learning , e.g . applying view-based transformations that preserve semantic content in the image [ 22 , 17 , 18 ] . On the other hand , there is a recent surge of interest in studying negative sampling methods . Chuang et al . [ 20 ] propose a debiasing method which is to correct the fact in false negative samples . For object detection , Jin et al . [ 47 ] employ temporal structure of video to generate negative examples . Although widely studied , little effort has been made to adapt contrastive techniques to neural topic model . In this paper , we re-formulate our goal of learning document representations in neural topic model as a contrastive objective . The form of our objective is mostly related to Robinson et al . [ 21 ] . However , there are two key differences : ( 1 ) As they use the weighting factor associated with the impact of negative sample as a tool to search for the distribution of hard negative samples , we consider it as an adaptive parameter to control the impact of the positive and negative sample on the learning . ( 2 ) We regard the effect of positive sample as the main driver to achieve meaningful representations , while they exploit the impact of negative ones . Our approach is more applicable to topic modeling , as proven in the investigation into human behavior of distinguishing among documents . 3 Methodology . 3.1 Notations and Problem Setting . In this paper , we focus on improving the performance of neural topic model ( NTM ) , measured via topic coherence . NTM inherits the architecture of Variational Autoencoder , where the latent vector is taken as topic distribution . Suppose the vocabulary has V unique words , each document is represented as a word count vector x ∈ RV and a latent distribution over T topics : z ∈ RT . NTM assumes that z is generated from a prior distribution p ( z ) and x is generated from the conditional distribution over the topic pφ ( x|z ) by a decoder φ . The aim of model is to infer the document-topic distribution given the word count . In other words , it must estimate the posterior distribution p ( z|x ) , which is approximated by the variational distribution qθ ( z|x ) modelled by an encoder θ. NTM is trained by minimizing the following objective LVAE ( x ) = −Eqθ ( z|x ) [ log pφ ( x|z ) ] +KL [ qθ ( z|x ) ||p ( z ) ] ( 1 ) Algorithm 1 Approximate β Input : Dataset D = { xi } Ni=1 , model parameter θ , model f , total training steps T 1 : Randomly pick a batch of L samples from the training set 2 : for each sample xl in the chosen batch do 3 : Draw the negative sample x−l and a positive sample x + l 4 : Obtain the latent distribution associated with the drawn samples : z−l = f ( x − l ) , f ( x + l ) = z + l 5 : Obtain the candidate β value with γl = ( z · z+ ) / ( z · z− ) . 6 : end for 7 : Initialize β as the mean of the candidate list β0 = 1L · ∑L l=1 γl 8 : for t = 1 to T do 9 : Train the model with βt = 12 − 1 T ∣∣T 2 − t ∣∣+ β0 10 : end for 3.2 Contrastive objective derivation . Let X = { x } denote the set of document bag-of-words . Each vector x is associated with a negative sample x− and a positive sample x+ . We assume a discrete set of latent classes C , so that ( x , x+ ) have the same latent class while ( x , x− ) does not . In this work , we choose to use the semantic dot product to measure the similarity between prototype x and the drawn samples . Our goal is to learn a mapping function fθ : RV → RT of the encoder θ which transforms x to the latent distribution z ( x− and x+ are transformed to z− and z+ , respectively ) . A reasonable mapping function must fulfill two qualities : ( 1 ) x and x+ are mapped onto nearby positions ; ( 2 ) x and x− are projected distantly . Regarding goal ( 1 ) as the main objective and goal ( 2 ) as the constraint enforcing the model to learn the relations among dissimilar samples , we specify the constrained optimization problem , in which denotes the strength of the constraint max θ Ex∼X ( z · z+ ) subject to Ex∼X ( z · z− ) < ( 2 ) Rewriting Eq . 2 as a Lagragian under KKT conditions [ 48 , 49 ] , we attain : F ( θ , x , x+ , x− ) = Ex∼X ( z · z+ ) − α · [ Ex∼X ( z · z− ) − ] ( 3 ) where the positive KKT multiplier α is the regularisation coefficient that controls the effect of the negative sample on training . Eq . 3 can be derived to arrive at the weighted-contrastive loss . F ( θ , x , x+ , x− ) ≥ Lcont ( θ , x , x+ , x− ) = Ex∼X [ log exp ( z · z+ ) exp ( z · z+ ) + β · exp ( z · z− ) ] ( 4 ) where α = exp ( β ) . The full proof of ( 4 ) can be found in the Appendix . Previous works [ 39 , 35 , 40 , 29 , 20 , 50 ] consider the positive and negative sample equally likely as setting β = 1 . In this paper , we leverage different values of β to guide the model concentration on the sample which is distinct from the input . In consequence , a reasonable value of β will provide a clear separation among topics in the dataset . We demonstrate our procedure to estimate β in the following section .", "role": "user" }, { "content": "This paper proposed a Neural Topic Model based on contrastive learning, which considers both effects of positive and negative pairs. They proposed a sampling strategy that takes inspiration from human behavior in differentiating documents. Experimental results show that it outperforms other state-of-the-art neural topic models in terms of topic coherence.", "role": "assistant" } ]

[ { "content": "| | T m [K] | T d [K] | e(T d ) | T g [K] | e(T g ) | log e T g e T d |\n| PS [11] | 1037 | 775 | 2.97106 | 683 | 2.71011 | 4.959 |\n| L2B [22] | 1190 | 795 | 2.20108 | 768 | 2.091011 | 2.979 |\n| L3S [11] | 1306 | 953 | 5.36106 | 734 | 1.451012 | 5.433 |\n| L2S [13] | 1307 | 809 | 1.33108 | 727 | 7.331011 | 3.742 |\n| Li-7.5 [19] | 1435 | 788 | 1.28109 | 748 | 5.691011 | 2.648 |\n| N1C3S [32] | 1469 | 830 | 4.71106 | 743 | 8.461011 | 5.255 |\n| Li-0 [19] | 1564 | 907 | 9.40108 | 838 | 2.491012 | 3.422 |\n| Cordierite [11] | 1643 | 1192 | 2.47108 | 1083 | 3.91012 | 4.198 |\n| Diopside [11] | 1664 | 1072 | 1.17108 | 995 | 9.21011 | 3.894 |\n| B2S [33] | 1693 | 1016 | 3.20109 | 961 | 1.681012 | 2.721 |\n| Fresnoite [35] | 1714 | 1009 | 1.101010 | 983 | 8.71011 | 1.896 |\n\nCaption: Melting (T m ), decoupling (T d ), and glass transition (T g ) temperatures, viscosities at T d and T g , and ratios of the viscosities at these temperatures for a set of liquids. The glass transition temperature refers to a good approximation (cf. Fig. 7) to a viscosity e(T g )1012Pas, i.e., the glass transition temperature refers widely to the value one obtains according to Tammann's definition of T g . The following systems are analyzed: PbO*SiO2 (PS), Li2O*2B2O3 (L2B), Li2O*3SiO2 (L3S), Li2O*2SiO2 (L2S), Na2O*2CaO*3SiO2-Li2O (Li-7.5), 2Na2O*1Cao*3SiO2 (2N1C3S), Na2O*2CaO*3SiO2 (Li-0), cordierite, CaO*MgO*2SiO2 (diopside), BaO*2SiO2 (B2S), and Ba2Ti(Si2O7)O (fresnoite).\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"1037\", \"type\": \"Other\"}\n{\"value\": \"775\", \"type\": \"Other\"}\n{\"value\": \"2.97106\", \"type\": \"Other\"}\n{\"value\": \"683\", \"type\": \"Other\"}\n{\"value\": \"2.71011\", \"type\": \"Other\"}\n{\"value\": \"4.959\", \"type\": \"Other\"}\n{\"value\": \"1190\", \"type\": \"Other\"}\n{\"value\": \"795\", \"type\": \"Other\"}\n{\"value\": \"2.20108\", \"type\": \"Other\"}\n{\"value\": \"768\", \"type\": \"Other\"}\n{\"value\": \"2.091011\", \"type\": \"Other\"}\n{\"value\": \"2.979\", \"type\": \"Other\"}\n{\"value\": \"1306\", \"type\": \"Other\"}\n{\"value\": \"953\", \"type\": \"Other\"}\n{\"value\": \"5.36106\", \"type\": \"Other\"}\n{\"value\": \"734\", \"type\": \"Other\"}\n{\"value\": \"1.451012\", \"type\": \"Other\"}\n{\"value\": \"5.433\", \"type\": \"Other\"}\n{\"value\": \"1307\", \"type\": \"Other\"}\n{\"value\": \"809\", \"type\": \"Other\"}\n{\"value\": \"1.33108\", \"type\": \"Other\"}\n{\"value\": \"727\", \"type\": \"Other\"}\n{\"value\": \"7.331011\", \"type\": \"Other\"}\n{\"value\": \"3.742\", \"type\": \"Other\"}\n{\"value\": \"1435\", \"type\": \"Other\"}\n{\"value\": \"788\", \"type\": \"Other\"}\n{\"value\": \"1.28109\", \"type\": \"Other\"}\n{\"value\": \"748\", \"type\": \"Other\"}\n{\"value\": \"5.691011\", \"type\": \"Other\"}\n{\"value\": \"2.648\", \"type\": \"Other\"}\n{\"value\": \"1469\", \"type\": \"Other\"}\n{\"value\": \"830\", \"type\": \"Other\"}\n{\"value\": \"4.71106\", \"type\": \"Other\"}\n{\"value\": \"743\", \"type\": \"Other\"}\n{\"value\": \"8.461011\", \"type\": \"Other\"}\n{\"value\": \"5.255\", \"type\": \"Other\"}\n{\"value\": \"1564\", \"type\": \"Other\"}\n{\"value\": \"907\", \"type\": \"Other\"}\n{\"value\": \"9.40108\", \"type\": \"Other\"}\n{\"value\": \"838\", \"type\": \"Other\"}\n{\"value\": \"2.491012\", \"type\": \"Other\"}\n{\"value\": \"3.422\", \"type\": \"Other\"}\n{\"value\": \"1643\", \"type\": \"Other\"}\n{\"value\": \"1192\", \"type\": \"Other\"}\n{\"value\": \"2.47108\", \"type\": \"Other\"}\n{\"value\": \"1083\", \"type\": \"Other\"}\n{\"value\": \"3.91012\", \"type\": \"Other\"}\n{\"value\": \"4.198\", \"type\": \"Other\"}\n{\"value\": \"1664\", \"type\": \"Other\"}\n{\"value\": \"1072\", \"type\": \"Other\"}\n{\"value\": \"1.17108\", \"type\": \"Other\"}\n{\"value\": \"995\", \"type\": \"Other\"}\n{\"value\": \"9.21011\", \"type\": \"Other\"}\n{\"value\": \"3.894\", \"type\": \"Other\"}\n{\"value\": \"1693\", \"type\": \"Other\"}\n{\"value\": \"1016\", \"type\": \"Other\"}\n{\"value\": \"3.20109\", \"type\": \"Other\"}\n{\"value\": \"961\", \"type\": \"Other\"}\n{\"value\": \"1.681012\", \"type\": \"Other\"}\n{\"value\": \"2.721\", \"type\": \"Other\"}\n{\"value\": \"1714\", \"type\": \"Other\"}\n{\"value\": \"1009\", \"type\": \"Other\"}\n{\"value\": \"1.101010\", \"type\": \"Other\"}\n{\"value\": \"983\", \"type\": \"Other\"}\n{\"value\": \"8.71011\", \"type\": \"Other\"}\n{\"value\": \"1.896\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: Different amounts of ingested alcohol can have distinct effects on the human body. However, there is limited research on chronic alcohol consumption with Helicobacter pylori infection. We sought to investigate the relationship between the cytokine profile, oxidative balance and H. pylori infection in subjects with chronic alcohol consumption. A total of 142 subjects were divided into three groups: 59 subjects with chronic alcohol ingestion and H. pylori infection (group A); 53 subjects with chronic alcohol ingestion without H. pylori infection (group B); and 30 control subjects (group C). The serum levels of CagA, interleukin (IL)-10, E-selectin, TNF-α, malondialdehyde (MDA) and superoxide dismutase (SOD) activity were measured by enzyme-linked immunosorbent assay (ELISA). We found that the ages and serum H. pylori CagA levels among the three groups, as well as both the mean drinking age and the mean daily alcohol consumption between groups A and B, were matched and comparable. Comparing the BMIs among the three groups, the BMI differences were found to be statistically significant (F=3.921, P<0.05). Compared with group C, the BMIs in groups A and B were significantly higher (P<0.001 and P<0.01, respectively); however, the BMI differences between group A and group B were not statistically significant (P>0.05). Additionally, no differences in the serum CagA levels were found in comparisons among the groups (all P>0.05). The serum IL-10 and E-selectin levels in group A were significantly lower than those in group B (serum IL-10: P<0.05; E-selectin: P<0.05). The serum IL-10 in group A was significantly higher than that in group C (P<0.01); the serum E-selectin levels in group A did not significantly differ compared with those in group C (P>0.05). Furthermore, the serum IL-10 and E-selectin levels in group B were significantly higher than those in group C (serum IL-10: P<0.001; E-selectin: P<0.05); however, the serum TNF-α levels did not differ among groups (all P>0.05). Although the serum levels of MDA and SOD in groups A and B were slightly lower than those in group C, there were no significant differences among groups (all P>0.05). In conclusion, we believe that H. pylori infection might cause a significant inhibition of certain cytokine profiles in subjects with chronic alcohol ingestion. Moreover, chronically ingested alcohol may exert an adjusted inflammatory effect, but there was no association between H. pylori infection, chronic alcohol consumption and oxidative balance.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Alcohol consumption \", \"Control\", \"TNF-\\u03b1 \", \"no significant difference\", \"the serum TNF-\\u03b1 levels did not differ among groups (all P\\u003e0.05).\"], [\"Alcohol consumption + H. pylori \", \"Alcohol consumption\", \"E-selectin\", \"significantly decreased\", \"The serum IL-10 and E-selectin levels in group A were significantly lower than those in group B (serum IL-10: P\\u003c0.05; E-selectin: P\\u003c0.05).\"], [\"Alcohol consumption + H. pylori \", \"Control\", \"E-selectin\", \"no significant difference\", \"The serum IL-10 in group A was significantly higher than that in group C (P\\u003c0.01); the serum E-selectin levels in group A did not significantly differ compared with those in group C (P\\u003e0.05).\"], [\"Alcohol consumption \", \"Control\", \"E-selectin\", \"significantly increased\", \"Furthermore, the serum IL-10 and E-selectin levels in group B were significantly higher than those in group C (serum IL-10: P\\u003c0.001; E-selectin: P\\u003c0.05);\"], [\"Alcohol consumption + H. pylori \", \"Alcohol consumption\", \"IL-10\", \"significantly decreased\", \"The serum IL-10 and E-selectin levels in group A were significantly lower than those in group B (serum IL-10: P\\u003c0.05; E-selectin: P\\u003c0.05).\"], [\"Alcohol consumption + H. pylori \", \"Control\", \"IL-10\", \"significantly increased\", \"The serum IL-10 in group A was significantly higher than that in group C (P\\u003c0.01);\"]]", "role": "assistant" } ]

[ { "content": "| Glass | SiO2 | CaO | Na2O |\n| G1 | 51.50 | 13.50 | 35.00 |\n| G2 | 55.00 | 13.50 | 31.50 |\n| G3 | 55.00 | 35.00 | 10.00 |\n| G4 | 42.00 | 23.00 | 35.00 |\n| G5 | 42.00 | 48.00 | 10.00 |\n| G6 | 42.00 | 35.50 | 22.50 |\n| G7 | 55.00 | 24.25 | 20.75 |\n| G8 | 53.25 | 13.50 | 33.25 |\n| G9 | 48.50 | 41.50 | 10.00 |\n| G10 | 46.75 | 18.25 | 35.00 |\n| G11 | 49.10 | 26.60 | 24.30 |\n\nCaption: Molar composition of the 11 interesting points determined from JMP 5.1 software.\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"51.50\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"mol\", \"material\": \"G1\"}\n{\"value\": \"13.50\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"mol\", \"material\": \"G1\"}\n{\"value\": \"35.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"mol\", \"material\": \"G1\"}\n{\"value\": \"55.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"mol\", \"material\": \"G2\"}\n{\"value\": \"13.50\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"mol\", \"material\": \"G2\"}\n{\"value\": \"31.50\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"mol\", \"material\": \"G2\"}\n{\"value\": \"55.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"mol\", \"material\": \"G3\"}\n{\"value\": \"35.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"mol\", \"material\": \"G3\"}\n{\"value\": \"10.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"mol\", \"material\": \"G3\"}\n{\"value\": \"42.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"mol\", \"material\": \"G4\"}\n{\"value\": \"23.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"mol\", \"material\": \"G4\"}\n{\"value\": \"35.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"mol\", \"material\": \"G4\"}\n{\"value\": \"42.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"mol\", \"material\": \"G5\"}\n{\"value\": \"48.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"mol\", \"material\": \"G5\"}\n{\"value\": \"10.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"mol\", \"material\": \"G5\"}\n{\"value\": \"42.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"mol\", \"material\": \"G6\"}\n{\"value\": \"35.50\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"mol\", \"material\": \"G6\"}\n{\"value\": \"22.50\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"mol\", \"material\": \"G6\"}\n{\"value\": \"55.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"mol\", \"material\": \"G7\"}\n{\"value\": \"24.25\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"mol\", \"material\": \"G7\"}\n{\"value\": \"20.75\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"mol\", \"material\": \"G7\"}\n{\"value\": \"53.25\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"mol\", \"material\": \"G8\"}\n{\"value\": \"13.50\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"mol\", \"material\": \"G8\"}\n{\"value\": \"33.25\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"mol\", \"material\": \"G8\"}\n{\"value\": \"48.50\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"mol\", \"material\": \"G9\"}\n{\"value\": \"41.50\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"mol\", \"material\": \"G9\"}\n{\"value\": \"10.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"mol\", \"material\": \"G9\"}\n{\"value\": \"46.75\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"mol\", \"material\": \"G10\"}\n{\"value\": \"18.25\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"mol\", \"material\": \"G10\"}\n{\"value\": \"35.00\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"mol\", \"material\": \"G10\"}\n{\"value\": \"49.10\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"SiO2\", \"unit\": \"mol\", \"material\": \"G11\"}\n{\"value\": \"26.60\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"CaO\", \"unit\": \"mol\", \"material\": \"G11\"}\n{\"value\": \"24.30\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Na2O\", \"unit\": \"mol\", \"material\": \"G11\"}", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nEliminating the last sporadic cases of malaria requires considerable funding.\n\nResearch Abstract:\nThe marginal costs and benefits of converting malaria programmes from a control to an elimination goal are central to strategic decisions, but empirical evidence is scarce.\nWe present a conceptual framework to assess the economics of elimination and analyse a central component of that framework-potential short-term to medium-term financial savings.\nAfter a review that showed a dearth of existing evidence, the net present value of elimination in five sites was calculated and compared with effective control.\nThe probability that elimination would be cost-saving over 50 years ranged from 0% to 42%, with only one site achieving cost-savings in the base case.\nThese findings show that financial savings should not be a primary rationale for elimination, but that elimination might still be a worthy investment if total benefits are sufficient to outweigh marginal costs.\nRobust research into these elimination benefits is urgently needed.", "role": "user" }, { "content": "{\"verdict\": \"NEI\", \"evidence\": []}", "role": "assistant" } ]

[ { "content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nDistant supervision for emotion detection using Facebook reactions\n\nWe exploit the Facebook reaction feature in a distant supervised fashion to train a support vector machine classifier for emotion detection, using several feature combinations and combining different Facebook pages. We test our models on existing benchmarks for emotion detection and show that employing only information that is derived completely automatically, thus without relying on any handcrafted lexicon as it's usually done, we can achieve competitive results. The results also show that there is large room for improvement, especially by gearing the collection of Facebook pages, with a view to the target domain.\n\nIntroduction\nThis work is licenced under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/\nIn the spirit of the brevity of social media's messages and reactions, people have got used to express feelings minimally and symbolically, as with hashtags on Twitter and Instagram. On Facebook, people tend to be more wordy, but posts normally receive more simple “likes” than longer comments. Since February 2016, Facebook users can express specific emotions in response to a post thanks to the newly introduced reaction feature (see Section SECREF2 ), so that now a post can be wordlessly marked with an expression of say “joy\" or “surprise\" rather than a generic “like”.\nIt has been observed that this new feature helps Facebook to know much more about their users and exploit this information for targeted advertising BIBREF0 , but interest in people's opinions and how they feel isn't limited to commercial reasons, as it invests social monitoring, too, including health care and education BIBREF1 . However, emotions and opinions are not always expressed this explicitly, so that there is high interest in developing systems towards their automatic detection. Creating manually annotated datasets large enough to train supervised models is not only costly, but also—especially in the case of opinions and emotions—difficult, due to the intrinsic subjectivity of the task BIBREF2 , BIBREF3 . Therefore, research has focused on unsupervised methods enriched with information derived from lexica, which are manually created BIBREF3 , BIBREF4 . Since go2009twitter have shown that happy and sad emoticons can be successfully used as signals for sentiment labels, distant supervision, i.e. using some reasonably safe signals as proxies for automatically labelling training data BIBREF5 , has been used also for emotion recognition, for example exploiting both emoticons and Twitter hashtags BIBREF6 , but mainly towards creating emotion lexica. mohammad2015using use hashtags, experimenting also with highly fine-grained emotion sets (up to almost 600 emotion labels), to create the large Hashtag Emotion Lexicon. Emoticons are used as proxies also by hallsmarmulti, who use distributed vector representations to find which words are interchangeable with emoticons but also which emoticons are used in a similar context.\nWe take advantage of distant supervision by using Facebook reactions as proxies for emotion labels, which to the best of our knowledge hasn't been done yet, and we train a set of Support Vector Machine models for emotion recognition. Our models, differently from existing ones, exploit information which is acquired entirely automatically, and achieve competitive or even state-of-the-art results for some of the emotion labels on existing, standard evaluation datasets. For explanatory purposes, related work is discussed further and more in detail when we describe the benchmarks for evaluation (Section SECREF3 ) and when we compare our models to existing ones (Section SECREF5 ). We also explore and discuss how choosing different sets of Facebook pages as training data provides an intrinsic domain-adaptation method.\n\nAffective Text dataset\nTask 14 at SemEval 2007 BIBREF7 was concerned with the classification of emotions and valence in news headlines. The headlines where collected from several news websites including Google news, The New York Times, BBC News and CNN. The used emotion labels were Anger, Disgust, Fear, Joy, Sadness, Surprise, in line with the six basic emotions of Ekman's standard model BIBREF8 . Valence was to be determined as positive or negative. Classification of emotion and valence were treated as separate tasks. Emotion labels were not considered as mututally exclusive, and each emotion was assigned a score from 0 to 100. Training/developing data amounted to 250 annotated headlines (Affective development), while systems were evaluated on another 1000 (Affective test). Evaluation was done using two different methods: a fine-grained evaluation using Pearson's r to measure the correlation between the system scores and the gold standard; and a coarse-grained method where each emotion score was converted to a binary label, and precision, recall, and f-score were computed to assess performance. As it is done in most works that use this dataset BIBREF3 , BIBREF4 , BIBREF9 , we also treat this as a classification problem (coarse-grained). This dataset has been extensively used for the evaluation of various unsupervised methods BIBREF2 , but also for testing different supervised learning techniques and feature portability BIBREF10 .\n\nFairy Tales dataset\nThis is a dataset collected by alm2008affect, where about 1,000 sentences from fairy tales (by B. Potter, H.C. Andersen and Grimm) were annotated with the same six emotions of the Affective Text dataset, though with different names: Angry, Disgusted, Fearful, Happy, Sad, and Surprised. In most works that use this dataset BIBREF3 , BIBREF4 , BIBREF9 , only sentences where all annotators agreed are used, and the labels angry and disgusted are merged. We adopt the same choices.\n\nISEAR\nThe ISEAR (International Survey on Emotion Antecedents and Reactions BIBREF11 , BIBREF12 ) is a dataset created in the context of a psychology project of the 1990s, by collecting questionnaires answered by people with different cultural backgrounds. The main aim of this project was to gather insights in cross-cultural aspects of emotional reactions. Student respondents, both psychologists and non-psychologists, were asked to report situations in which they had experienced all of seven major emotions (joy, fear, anger, sadness, disgust, shame and guilt). In each case, the questions covered the way they had appraised a given situation and how they reacted. The final dataset contains reports by approximately 3000 respondents from all over the world, for a total of 7665 sentences labelled with an emotion, making this the largest dataset out of the three we use.\n\nModel\nThere are two main decisions to be taken in developing our model: (i) which Facebook pages to select as training data, and (ii) which features to use to train the model, which we discuss below. Specifically, we first set on a subset of pages and then experiment with features. Further exploration of the interaction between choice of pages and choice of features is left to future work, and partly discussed in Section SECREF6 . For development, we use a small portion of the Affective data set described in Section SECREF4 , that is the portion that had been released as development set for SemEval's 2007 Task 14 BIBREF7 , which contains 250 annotated sentences (Affective development, Section SECREF4 ). All results reported in this section are on this dataset. The test set of Task 14 as well as the other two datasets described in Section SECREF3 will be used to evaluate the final models (Section SECREF4 ).\n\nFeatures\nIn selecting appropriate features, we mainly relied on previous work and intuition. We experimented with different combinations, and all tests were still done on Affective development, using the pages for the best model (B-M) described above as training data. Results are in Table TABREF20 . Future work will further explore the simultaneous selection of features and page combinations.\nWe use a set of basic text-based features to capture the emotion class. These include a tf-idf bag-of-words feature, word (2-3) and character (2-5) ngrams, and features related to the presence of negation words, and to the usage of punctuation.\nThis feature is used in all unsupervised models as a source of information, and we mainly include it to assess its contribution, but eventually do not use it in our final model.\nWe used the NRC10 Lexicon because it performed best in the experiments by BIBREF10 , which is built around the emotions anger, anticipation, disgust, fear, joy, sadness, and surprise, and the valence values positive and negative. For each word in the lexicon, a boolean value indicating presence or absence is associated to each emotion. For a whole sentence, a global score per emotion can be obtained by summing the vectors for all content words of that sentence included in the lexicon, and used as feature.\nAs additional feature, we also included Word Embeddings, namely distributed representations of words in a vector space, which have been exceptionally successful in boosting performance in a plethora of NLP tasks. We use three different embeddings:\nGoogle embeddings: pre-trained embeddings trained on Google News and obtained with the skip-gram architecture described in BIBREF14 . This model contains 300-dimensional vectors for 3 million words and phrases.\nFacebook embeddings: embeddings that we trained on our scraped Facebook pages for a total of 20,000 sentences. Using the gensim library BIBREF15 , we trained the embeddings with the following parameters: window size of 5, learning rate of 0.01 and dimensionality of 100. We filtered out words with frequency lower than 2 occurrences.\nRetrofitted embeddings: Retrofitting BIBREF16 has been shown as a simple but efficient way of informing trained embeddings with additional information derived from some lexical resource, rather than including it directly at the training stage, as it's done for example to create sense-aware BIBREF17 or sentiment-aware BIBREF18 embeddings. In this work, we retrofit general embeddings to include information about emotions, so that emotion-similar words can get closer in space. Both the Google as well as our Facebook embeddings were retrofitted with lexical information obtained from the NRC10 Lexicon mentioned above, which provides emotion-similarity for each token. Note that differently from the previous two types of embeddings, the retrofitted ones do rely on handcrafted information in the form of a lexical resource.\n\nResults\nIn Table TABREF26 we report the results of our model on the three datasets standardly used for the evaluation of emotion classification, which we have described in Section SECREF3 .\nOur B-M model relies on subsets of Facebook pages for training, which were chosen according to their performance on the development set as well as on the observation of emotions distribution on different pages and in the different datasets, as described in Section SECREF4 . The feature set we use is our best on the development set, namely all the features plus Google-based embeddings, but excluding the lexicon. This makes our approach completely independent of any manual annotation or handcrafted resource. Our model's performance is compared to the following systems, for which results are reported in the referred literature. Please note that no other existing model was re-implemented, and results are those reported in the respective papers.\n\nDiscussion, conclusions and future work\nWe have explored the potential of using Facebook reactions in a distant supervised setting to perform emotion classification. The evaluation on standard benchmarks shows that models trained as such, especially when enhanced with continuous vector representations, can achieve competitive results without relying on any handcrafted resource. An interesting aspect of our approach is the view to domain adaptation via the selection of Facebook pages to be used as training data.\nWe believe that this approach has a lot of potential, and we see the following directions for improvement. Feature-wise, we want to train emotion-aware embeddings, in the vein of work by tang:14, and iacobacci2015sensembed. Retrofitting FB-embeddings trained on a larger corpus might also be successful, but would rely on an external lexicon.\nThe largest room for yielding not only better results but also interesting insights on extensions of this approach lies in the choice of training instances, both in terms of Facebook pages to get posts from, as well as in which posts to select from the given pages. For the latter, one could for example only select posts that have a certain length, ignore posts that are only quotes or captions to images, or expand posts by including content from linked html pages, which might provide larger and better contexts BIBREF23 . Additionally, and most importantly, one could use an entropy-based measure to select only posts that have a strong emotion rather than just considering the majority emotion as training label. For the former, namely the choice of Facebook pages, which we believe deserves the most investigation, one could explore several avenues, especially in relation to stance-based issues BIBREF24 . In our dataset, for example, a post about Chile beating Colombia in a football match during the Copa America had very contradictory reactions, depending on which side readers would cheer for. Similarly, the very same political event, for example, would get very different reactions from readers if it was posted on Fox News or The Late Night Show, as the target audience is likely to feel very differently about the same issue. This also brings up theoretical issues related more generally to the definition of the emotion detection task, as it's strongly dependent on personal traits of the audience. Also, in this work, pages initially selected on availability and intuition were further grouped into sets to make training data according to performance on development data, and label distribution. Another criterion to be exploited would be vocabulary overlap between the pages and the datasets.\nLastly, we could develop single models for each emotion, treating the problem as a multi-label task. This would even better reflect the ambiguity and subjectivity intrinsic to assigning emotions to text, where content could be at same time joyful or sad, depending on the reader.\n\nAcknowledgements\nIn addition to the anonymous reviewers, we want to thank Lucia Passaro and Barbara Plank for insightful discussions, and for providing comments on draft versions of this paper.\n\nTable 1: Emotion labels in existing datasets, Facebook, and resulting mapping for the experiments in this work. The last row indicates which role each dataset has in our experiments.\n\nQuery: What was their performance on emotion detection?\n\nJSON Response:", "role": "user" }, { "content": "{\"answer\": \"Answer with content missing: (Table 3) Best author\\u0027s model B-M average micro f-score is 0.409, 0.459, 0.411 on Affective, Fairy Tales and ISEAR datasets respectively. \", \"evidence\": [\"In Table TABREF26 we report the results of our model on the three datasets standardly used for the evaluation of emotion classification, which we have described in Section SECREF3 .\"]}", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nConfidence-Aware Imitation Learning from Demonstrations with Varying Optimality\n\nMain Content:\n1 Introduction . We consider an imitation learning setting that learns a well-performing policy from a mixture of demonstrations with varying optimality ranging from random trajectories to optimal demonstrations . As opposed to standard imitation learning , where the demonstrations come from experts and thus are optimal , this benefits from a larger and more diverse source of data . Note that different from setting that the demonstrations are optimal but lack some causal factors [ 31 ] , in our setting , the demonstrations can be suboptimal . However , this introduces a new set of challenges . First , one needs to select useful demonstrations beyond the optimal ones . We are interested in settings where we do not have sufficient expert demonstrations in the mixture so we have to rely on learning from sub-optimal demonstrations that can still be successful at parts of the task . Second , we need to be able to filter the negative effects of useless or even malicious demonstrations , e.g. , demonstrations that implicitly fail the tasks . To address the above challenges , we propose to use a measure of confidence to indicate the likelihood that a demonstration is optimal . A confidence score can provide a fine-grained characterization of each demonstration ’ s optimality . For example , it can differentiate between near-optimal demonstrations or adversarial ones . By reweighting demonstrations with a confidence score , we can simultaneously learn from useful but sub-optimal demonstrations while avoiding the negative effects of malicious ones . So ∗Equal contribution . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . our problem reduces to learning an accurate confidence measure for demonstrations . Previous work learns the confidence from manually annotated demonstrations [ 30 ] , which are difficult to obtain and might contain bias—For example , a conservative and careful demonstrator may assign lower confidence compared to an optimistic demonstrator to the same demonstration . In this paper , we remove restrictive assumptions on the confidence , and propose an approach that automatically learns the confidence score for each demonstration based on evaluation of the outcome of imitation learning . This evaluation often requires access to limited evaluation data . We propose a new algorithm , Confidence-Aware Imitation Learning ( CAIL ) , to jointly learn a wellperforming policy and the confidence for every state-action pair in the demonstrations . Specifically , our method adopts a standard imitation learning algorithm and evaluates its performance to update the confidence scores with an evaluation loss , which we refer to as the outer loss . In our implementation , we use a limited amount of ranked demonstrations as our evaluation data for the outer loss . We then update the policy parameters using the loss of the imitation learning algorithm over the demonstrations reweighted by the confidence , which we refer to as the inner loss . Our framework can accommodate any imitation learning algorithm accompanied with an evaluation loss to assess the learned policy . We optimize for the inner and outer loss using a bi-level optimization [ 5 ] , and prove that our algorithm converges to the optimal confidence assignments under mild assumptions . We further implement the framework using Adversarial Inverse Reinforcement Learning ( AIRL ) [ 14 ] as the underlying imitation learning algorithm along with its corresponding learning loss as our inner loss . We design a ranking loss as the outer loss , which is compatible with the AIRL model and only requires easy-to-access ranking annotations rather than the exact confidence values . The main contributions of the paper can be summarized as : • We propose a novel framework , Confidence-Aware Imitation Learning ( CAIL ) , that jointly learns confidence scores and a well-performing policy from demonstrations with varying optimality . • We formulate our problem as a modified bi-level optimization with a pseudo-update step and prove that the confidence learned by CAIL converges to the optimal confidence in O ( 1/ √ T ) ( T is the number of steps ) under some mild assumptions . • We conduct experiments on several simulation and robot environments . Our results suggest that the learned confidence can accurately characterize the optimality of demonstrations , and that the learned policy achieves higher expected return compared to other imitation learning approaches . 2 Related Work . Imitation Learning . The most common approaches for imitation learning are Behavioral Cloning ( BC ) [ 20 , 4 , 23 , 22 , 3 ] , which treats the problem as a supervised learning problem , and Inverse Reinforcement Learning ( IRL ) , which recovers the reward function from expert demonstrations and finds the optimal policy through reinforcement learning over the learned reward [ 1 , 21 , 32 ] . More recently , Generative Adversarial Imitation Learning ( GAIL ) [ 18 ] learns the policy by matching the occupancy measure between demonstrations and the policy in an adversarial manner [ 15 ] . Adversarial Inverse Reinforcement Learning ( AIRL ) [ 14 ] and some other approaches [ 13 , 17 ] improve upon GAIL by simultaneously learning the reward function , and the optimal policy . However , these approaches assume that all the demonstrations are expert demonstrations , and can not learn a well-performing policy when learning from demonstrations with varying optimality . Learning from Demonstrations with Varying Optimality : Ranking-based . Ranking-based methods learn a policy from a sequence of demonstrations annotated with rankings [ 2 , 25 , 29 , 10 ] . T-REX learns a reward from the ranking of the demonstrations and learns a policy using reinforcement learning [ 8 ] . In our work , we assume access to rankings of a small subset of the demonstrations . The reward function learned from such a small number of rankings by T-REX may have low generalization ability to out of distribution states . D-REX improves T-REX by automatically generating the rankings of demonstrations [ 9 ] , and SSRR further finds the structure of the reward function [ 12 ] . These techniques automatically generate rankings under the assumption that a perturbed demonstration will have a lower reward than the original demonstration , which is not necessarily true for random or malicious demonstrations that can be present in our mixture . DPS utilizes partial orders and pairwise comparisons over trajectories to learn and generate new policies [ 19 ] . However , it requires interactively collecting feedback , which is not feasible in our offline learning setting . Learning from Demonstrations with Varying Optimality : Confidence-based . Confidence-based methods assume each demonstration or demonstrator holds a confidence value indicating their optimality and then reweight the demonstrations based on this value for imitation learning . To learn the confidence , 2IWIL requires access to ground-truth confidence values for the demonstrations to accurately learn a confidence predictor [ 30 ] . Tangkaratt et al . require that all the actions for a demonstration are drawn from the same noisy distribution with sufficiently small variance [ 27 ] . ICGAIL implicitly learns the confidence score by aligning the occupancy measure of the learned policy with the expert policy , but requires a set of ground-truth labels to estimate the average confidence [ 30 ] . Following works relax the assumption of access to the ground-truth confidence , but still require more optimal demonstrations than non-optimal ones in the dataset [ 26 ] . Other works require access to the reward of each demonstration [ 11 ] . All of these methods either rely on a specific imitation learning algorithm or require strong assumptions on the confidence . To move forward , we propose a general framework to jointly learn the confidence and the policy . Our framework is flexible as it can use any imitation learning algorithm as long as there exists a compatible outer loss , i.e. , the outer loss can evaluate the quality of the imitation learning model . 3 Problem Setting . We formulate the problem of learning from demonstrations with varying optimality as a Markov decision process ( MDP ) : M = 〈S , A , T , R , ρ0 , γ〉 , where S is the state space , A is the action space , T : S × A × S → [ 0 , 1 ] is the transition probability , ρ0 is the distribution of initial states , R : S × A → R is the reward function , and γ is the discount factor . A policy π : S × A → [ 0 , 1 ] defines a probability distribution over the action space in a given state . The expected return , which evaluates the quality of a policy , can be defined as ηπ = Es0∼ρ0 , π [ ∑∞ t=0 γ tR ( st , at ) ] , where t indicates the time step . We aim to learn a policy that imitates the behavior of a demonstrator d following policy πd who provides a set of demonstrations Ξ = { ξ1 , . . . , ξD } and ξi ∼ πd . Each trajectory is a sequence of stateaction pairs ξ = { s0 , a0 , . . . , sN } , and the expected return of a trajectory is ηξ = ∑N−1 t=0 γ tR ( st , at ) . A common assumption in classical imitation learning work is that the demonstrations are drawn from the expert policy πd = π∗ , i.e. , , the policy that optimizes the expected return of the MDPM [ 18 , 14 ] . Here , we relax this assumption so that the demonstrations may contain non-expert demonstrations or even failures—drawn from policies other than π∗ . Given the demonstration set D , we need to assess our confidence in each demonstration . To achieve learning confidence over this mixture of demonstrations , we rely on the ability to evaluate the performance of imitation learning . This can be achieved by using an evaluation loss trained on evaluation data , DE ( as shown in Fig . 1 ) . In our implementation , we rely on a small amount of rankings between trajectories as our evaluation data : DE = ηξ1 ≥ · · · ≥ ηξm . To summarize , our framework takes a set of demonstrations with varying optimality D as well as a limited amount of evaluation data DE along with an evaluation loss to find a well-performing policy . Note that unlike prior work [ 30 ] , we do not assume that optimal demonstrations always exist in the demonstration set , and CAIL can still extract useful information from D while avoiding negative effects of non-optimal demonstrations . 4 Confidence-Aware Imitation Learning . In our framework , we adopt an imitation learning algorithm with a model Fθ parameterized by θ and a corresponding imitation learning loss Lin , which we refer to as inner loss ( as shown in Figure 1 ) . We assign each state-action pair a confidence value indicating the likelihood of the state-action pair appearing in the well-performing policy . The confidence can be defined as a function mapping from a state-action pair to a scalar value β : S ×A → R. We aim to find the optimal confidence assignments β∗ to reweight state-action pairs within the demonstrations . We then conduct imitation learning from the reweighted demonstrations using the inner imitation loss Lin to learn a well-performing policy . Here , we first define the optimal confidence β∗ and describe how to learn it automatically . Defining the Optimal Confidence . We define the distribution of state-action pairs visited by a policy π based on the occupancy measure ρπ : S×A → R : ρπ ( s , a ) = π ( a|s ) ∑∞ t=0 γ tP ( st = s|π ) , which can be explained as the un-normalized distribution of state transitions that an agent encounters when navigating the environment with the policy π . We can normalize the occupancy measure to form the state-action distribution : pπ ( s , a ) = ρπ ( s , a ) ∑ s , a ρπ ( s , a ) . Recall that πd is the policy that the demonstrations are derived from , which can potentially be a mixture of different expert , suboptimal , or even malicious policies . We reweight the state-action distribution of the demonstrations to derive a new state-action distribution , which corresponds to another policy πnew : pπnew ( s , a ) = β ( s , a ) pπd ( s , a ) . Our goal is to find the optimal confidence β∗ that ensures the derived policy πnew maximizes the expected return : β∗ ( s , a ) = arg max β ηπnew . ( 1 ) With such β∗ ( s , a ) , we can conduct imitation learning from the reweighted demonstrations to maximize the expected return with the provided demonstrations . Learning the Confidence . We will learn an estimate of the confidence score β without access to any annotations of the ground-truth values based on optimizing two loss functions : The inner loss and the outer loss . The inner loss Lin is accompanied with the imitation learning algorithm encouraging imitation , while the outer loss Lout captures the quality of imitation learning , and thus optimizing it finds the confidence value that maximizes the performance of the imitation learning algorithm . Specifically , we first learn the imitation learning model parameters θ∗ that minimize the inner loss : θ∗ ( β ) = arg min θ E ( s , a ) ∼β ( s , a ) p πd ( s , a ) Lin ( s , a ; θ , β ) ( 2 ) We note that the inner loss Lin ( s , a ; θ , β ) refers to settings where ( s , a ) is sampled from the distribution β ( s , a ) pπd ( s , a ) , and hence implicitly depends on β . Thus we need to find the optimal β∗ , which can be estimated by minimizing an outer loss Lout : β∗out = arg min β Lout ( θ∗ ( β ) ) . ( 3 ) This evaluates the performance of the underlying imitation learning algorithm with respect to the reward with limited evaluation dataDE ( e.g . limited rankings if we select a ranking loss as our choice of Lout ; which we will discuss in detail in Sec . 4.3 ) .", "role": "user" }, { "content": "The paper proposes Confidence-Aware Imitation Learning (CAIL) that learns from sub-optimal demonstrations collected by different policies. The main idea is to train an imitating policy from demonstrations weighted by an estimated confidence scores, while the confidence scores are simultaneously estimated by using the imitating policy and ranked demonstrations. The paper also presents the convergence proofs of the algorithm. Experiments on simulated and real-world environments indicate that CAIL is more effective at learning from sub-optimal demonstrations when compared to existing algorithms.", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nUsing less restricted forms of cross-lingual supervision, we achieve a much higher coverage than <cite>Ziering and Van der Plas (2014)</cite> .", "role": "user" }, { "content": "[\"Differences\"]", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nLearning with Feature-Dependent Label Noise: A Progressive Approach\n\nMain Content:\n1 INTRODUCTION . Addressing noise in training set labels is an important problem in supervised learning . Incorrect annotation of data is inevitable in large-scale data collection , due to intrinsic ambiguity of data/class and mistakes of human/automatic annotators ( Yan et al. , 2014 ; Andreas et al. , 2017 ) . Developing methods that are resilient to label noise is therefore crucial in real-life applications . Classical approaches take a rather simplistic i.i.d . assumption on the label noise , i.e. , the label corruption is independent and identically distributed and thus is feature-independent . Methods based on this assumption either explicitly estimate the noise pattern ( Reed et al. , 2014 ; Patrini et al. , 2017 ; Dan et al. , 2019 ; Xu et al. , 2019 ) or introduce extra regularizer/loss terms ( Natarajan et al. , 2013 ; Van Rooyen et al. , 2015 ; Xiao et al. , 2015 ; Zhang & Sabuncu , 2018 ; Ma et al. , 2018 ; Arazo et al. , 2019 ; Shen & Sanghavi , 2019 ) . Some results prove that the commonly used losses are naturally robust against such i.i.d . label noise ( Manwani & Sastry , 2013 ; Ghosh et al. , 2015 ; Gao et al. , 2016 ; Ghosh et al. , 2017 ; Charoenphakdee et al. , 2019 ; Hu et al. , 2020 ) . Although these methods come with theoretical guarantees , they usually do not perform as well as expected in practice due to the unrealistic i.i.d . assumption on noise . This is likely because label noise is heterogeneous and feature-dependent . A cat with an intrinsically ambiguous appearance is more likely to be mislabeled as a dog . An image with poor lighting or severe occlusion can be mislabeled , as important visual clues are imperceptible . Methods that can combat label noise of a much more general form are very much needed to address real-world challenges . To adapt to the heterogeneous label noise , state-of-the-arts ( SOTAs ) often resort to a data-recalibrating strategy . They progressively identify trustworthy data or correct data labels , and then train using these data ( Tanaka et al. , 2018 ; Wang et al. , 2018 ; Lu et al. , 2018 ; Li et al. , 2019 ) . The models gradually improve as more clean data are collected or more labels are corrected , eventually converging to models of high accuracy . These data-recalibrating methods best leverage the learning power of deep neural nets and achieve superior performance in practice . However , their underlying mechanism remains a mystery . No methods in this category can provide theoretical insights as to why the model ∗Equal contributions . can converge to an ideal one . Thus , these methods require careful hyperparameter tuning and are hard to generalize . In this paper , we propose a novel and principled method that specifically targets the heterogeneous , feature-dependent label noise . Unlike previous methods , we target a much more general family of noise , called Polynomial Margin Diminishing ( PMD ) label noise . In this noise family , we allow arbitrary noise level except for data far away from the true decision boundary . This is consistent with the real-world scenario ; data near the decision boundary are harder to distinguish and more likely to be mislabeled . Meanwhile , a datum far away from the decision boundary is a typical example of its true class and should have a reasonably bounded noise level . Assuming this new PMD noise family , we propose a theoretically-guaranteed data-recalibrating algorithm that gradually corrects labels based on the noisy classifier ’ s confidence . We start from data points with high confidence , and correct the labels of these data using the predictions of the noisy classifier . Next , the model is improved using cleaned labels . We continue alternating the label correction and model improvement until it converges . See Figure 1 for an illustration . Our main theorem shows that with a theory-informed criterion for label correction at each iteration , the improvement of the label purity is guaranteed . Thus the model is guaranteed to improve with sufficient rate through iterations and eventually becomes consistent with the Bayes optimal classifier . Beside the theoretical strength , we also demonstrate the power of our method in practice . Our method outperforms others on CIFAR-10/100 with various synthetic noise patterns . We also evaluate our method against SOTAs on three real-world datasets with unknown noise patterns . To the best of our knowledge , our method is the first data-recalibrating method that is theoretically guaranteed to converge to an ideal model . The PMD noise family encompasses a broad spectrum of heterogeneous and feature-dependent noise , and better approximates the real-world scenario . It also provides a novel theoretical setting for the study of label noise . Related works . We review works that do not assume an i.i.d . label noise . Menon et al . ( 2018 ) generalized the work of ( Ghosh et al. , 2015 ) and provided an elegant theoretical framework , showing that loss functions fulfilling certain conditions naturally resist instance-dependent noise . The method can achieve even better theoretical properties ( i.e. , Bayes-consistency ) with stronger assumption on the clean posterior probability η . In practice , this method has not been extended to deep neural networks . Cheng et al . ( 2020 ) proposed an active learning method for instance-dependent label noise . The algorithm iteratively queries clean labels from an oracle on carefully selected data . However , this approach is not applicable to settings where kosher annotations are unavailable . Another contemporary work ( Chen et al. , 2021 ) showed that the noise in real-world dataset is unlikely to be i.i.d. , and proposed to fix the noisy labels by averaging the network predictions on each instance over the whole training process . While being effective , their method lacks theoretical guarantees . Chen et al . ( 2019 ) showed by regulating the topology of a classifier ’ s decision boundary , one can improve the model ’ s robustness against label noise . Data-recalibrating methods use noisy networks ’ predictions to iteratively select/correct data and improve the models . Tanaka et al . ( 2018 ) introduced a joint training framework which simultaneously enforces the network to be consistent with its own predictions and corrects the noisy labels during training . Wang et al . ( 2018 ) identified noisy labels as outliers based on their label consistencies with surrounding data . Lu et al . ( 2018 ) used a curriculum learning strategy where the teacher net is trained on a small kosher dataset to determine if a datum is clean ; then the learnt curriculum that gives the weight to each datum is fed into the student net for the training and inference . ( Yu et al. , 2019 ; Bo et al. , 2018 ) trained two synchronized networks ; the confidence and consistency of the two networks are utilized to identify clean data . Wu et al . ( 2020 ) selected the clean data by investigating the topological structures of the training data in the learned feature space . For completeness , we also refer to other methods of similar design ( Li et al. , 2017 ; Vahdat , 2017 ; Andreas et al. , 2017 ; Ma et al. , 2018 ; Thulasidasan et al. , 2019 ; Arazo et al. , 2019 ; Shu et al. , 2019 ; Yi & Wu , 2019 ) . As for theoretical guarantees , Ren et al . ( 2018 ) proposed an algorithm that iteratively re-weights each data point by solving an optimization problem . They proved the convergence of the training , but provided no guarantees that the model converges to an ideal one . Amid et al . ( 2019b ) generalized the work of ( Amid et al. , 2019a ) and proposed a tempered matching loss . They showed that when the final softmax layer is replaced by the bi-tempered loss , the resulting classifier will be Bayes consistent . Zheng et al . ( 2020 ) proved a one-shot guarantee for their data-recalibrating method ; but the convergence of the model is not guaranteed . Our method is the first data-recalibrating method which is guaranteed to converge to a well-behaved classifier . 2 METHOD . We start by introducing the family of Poly-Margin Diminishing ( PMD ) label noise . In Section 2.2 , we present our main algorithm . Finally , we prove the correctness of our algorithm in Section 3 . Notations and preliminaries . Although the noise setting and algorithm naturally generalize to multiclass , for simplicity we focus on binary classification . Let the feature space be X . We assume the data ( x , y ) is sampled from an underlying distribution D on X × { 0 , 1 } . Define the posterior probability η ( x ) = P [ y = 1 | x ] . Let τ0,1 ( x ) = P [ ỹ = 1 | y = 0 , x ] and τ1,0 ( x ) = P [ ỹ = 0 | y = 1 , x ] be the noise functions , where ỹ denotes the corrupted label . For example , if a datum x has true label y = 0 , it has τ0,1 ( x ) chance to be corrupted to 1 . Similarly , it has τ1,0 ( x ) chance to be corrupted from 1 to 0 . Let η̃ ( x ) = P [ ỹ = 1 | x ] be the noisy posterior probability of ỹ = 1 given feature x . Let η∗ ( x ) = I { η ( x ) ≥ 12 } be the ( clean ) Bayes optimal classifier , where IA equals 1 if A is true , and 0 otherwise . Finally , let f ( x ) : X → [ 0 , 1 ] be the classifier scoring function ( the softmax output of a neural network in this paper ) . 2.1 POLY-MARGIN DIMINISHING NOISE . We first introduce the family of noise functions τ this paper will address . We introduce the concept of polynomial margin diminishing noise ( PMD noise ) , which only upper bounds the noise τ in a certain level set of η ( x ) , thus allowing τ to be arbitrarily high outside the restricted domain . This formulation not only covers the feature-independent scenario but also generalizes scenarios proposed by ( Du & Cai , 2015 ; Menon et al. , 2018 ; Cheng et al. , 2020 ) . Definition 1 ( PMD noise ) . A pair of noise functions τ0,1 ( x ) and τ1,0 ( x ) are polynomial-margin diminishing ( PMD ) , if there exist constants t0 ∈ ( 0 , 12 ) , and c1 , c2 > 0 such that : τ1,0 ( x ) ≤ c1 [ 1− η ( x ) ] 1+c2 ; ∀η ( x ) ≥ 1 2 + t0 , and τ0,1 ( x ) ≤ c1η ( x ) 1+c2 ; ∀η ( x ) ≤ 1 2 − t0 . ( 1 ) We abuse notation by referring to t0 as the “ margin ” of τ . Note that the PMD condition only requires the upper bound on τ to be polynomial and monotonically decreasing in the region where the Bayes classifier is fairly confident . For the region { x : |η ( x ) − 12 | < t0 } , we allow both τ0,1 ( x ) and τ1,0 ( x ) to be arbitrary . Figure 2 ( d ) illustrates the upper bound ( orange curve ) and a sample noise function ( blue curve ) . We also show the corrupted data according to this noise function ( black points are the clean data whereas red points are the data with corrupted labels ) . The PMD noise family is much more general than existing noise assumptions . For example , the boundary consistent noise ( BCN ) ( Du & Cai , 2015 ; Menon et al. , 2018 ) assumes a noise function that monotonically decreases as the data are moving away from the decision boundary . See Figure 2 ( c ) for an illustration . This noise is much more restrictive compared to our PMD noise which ( 1 ) only requires a monotonic upper bound , and ( 2 ) allows arbitrary noise strength in a wide buffer near the decision boundary . Figure 2 ( b ) shows a traditional feature-independent noise pattern ( Reed et al. , 2014 ; Patrini et al. , 2017 ) , which assumes τ0,1 ( x ) ( resp . τ1,0 ( x ) ) to be a constant independent of x .", "role": "user" }, { "content": "Label noise is very frequently in many real world applications. However, the noise can be with different distributions. If we build the learning model under a certain distribution, it is difficult to capture the discriminative information. In this paper, without assuming that the noise is a certain distribution, the proposed method can handle the general noise, and it mainly target a new family of feature-dependent label noise, which is much more general than commonly used i.i.d. label noise and encompasses a broad spectrum of noise patterns. The experimental results show that the proposed method is promising. Meanwhile, the theoretical analysis of the proposed method is well inferred.", "role": "assistant" } ]

[ { "content": "Analyze the given abstract from a computer science paper. Identify and list entities in a structured JSON format. Focus on capturing distinct elements relevant to the research context.\n\nInput Abstract:\n\nThis paper introduces a system for categorizing unknown words. The system is based on a multi-component architecture where each component is responsible for identifying one class of unknown words. The focus of this paper is the components that identify names and spelling errors. Each component uses a decision tree architecture to combine multiple types of evidence about the unknown word. The system is evaluated using data from live closed captions-a genre replete with a wide variety of unknown words.", "role": "user" }, { "content": "Output Entities:\n\n{\"Generic\": [\"system\", \"components\"], \"Material\": [\"live closed captions\"], \"Method\": [\"multi-component architecture\", \"decision tree architecture\"], \"Metric\": [], \"OtherScientificTerm\": [\"unknown words\", \"names\", \"unknown word\", \"spelling errors\"], \"Task\": [\"categorizing unknown words\"]}", "role": "assistant" } ]

[ { "content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nPerformance Comparison of Crowdworkers and NLP Tools onNamed-Entity Recognition and Sentiment Analysis of Political Tweets\n\nWe report results of a comparison of the accuracy of crowdworkers and seven NaturalLanguage Processing (NLP) toolkits in solving two important NLP tasks, named-entity recognition (NER) and entity-level sentiment(ELS) analysis. We here focus on a challenging dataset, 1,000 political tweets that were collected during the U.S. presidential primary election in February 2016. Each tweet refers to at least one of four presidential candidates,i.e., four named entities. The groundtruth, established by experts in political communication, has entity-level sentiment information for each candidate mentioned in the tweet. We tested several commercial and open-source tools. Our experiments show that, for our dataset of political tweets, the most accurate NER system, Google Cloud NL, performed almost on par with crowdworkers, but the most accurate ELS analysis system, TensiStrength, did not match the accuracy of crowdworkers by a large margin of more than 30 percent points.\n\nIntroduction\nAs social media, specially Twitter, takes on an influential role in presidential elections in the U.S., natural language processing of political tweets BIBREF0 has the potential to help with nowcasting and forecasting of election results as well as identifying the main issues with a candidate – tasks of much interest to journalists, political scientists, and campaign organizers BIBREF1. As a methodology to obtain training data for a machine learning system that analyzes political tweets, BIBREF2 devised a crowdsourcing scheme with variable crowdworker numbers based on the difficulty of the annotation task. They provided a dataset of tweets where the sentiments towards political candidates were labeled both by experts in political communication and by crowdworkers who were likely not domain experts. BIBREF2 revealed that crowdworkers can match expert performance relatively accurately and in a budget-efficient manner. Given this result, the authors envisioned future work in which groundtruth labels would be crowdsourced for a large number of tweets and then used to design an automated NLP tool for political tweet analysis.\nThe question we address here is: How accurate are existing NLP tools for political tweet analysis? These tools would provide a baseline performance that any new machine learning system for political tweet analysis would compete against. We here explore whether existing NLP systems can answer the questions \"What sentiment?\" and \"Towards whom?\" accurately for the dataset of political tweets provided by BIBREF2. In our analysis, we include NLP tools with publicly-available APIs, even if the tools were not specifically designed for short texts like tweets, and, in particular, political tweets.\nOur experiments reveal that the task of entity-level sentiment analysis is difficult for existing tools to answer accurately while the recognition of the entity, here, which politician, was easier.\n\nNLP Toolkits\nNLP toolkits typically have the following capabilities: tokenization, part-of-speech (PoS) tagging, chunking, named entity recognition and sentiment analysis. In a study by BIBREF3, it is shown that the well-known NLP toolkits NLTK BIBREF4, Stanford CoreNLP BIBREF5, and TwitterNLP BIBREF6 have tokenization, PoS tagging and NER modules in their pipelines. There are two main approaches for NER: (1) rule-based and (2) statistical or machine learning based. The most ubiquitous algorithms for sequence tagging use Hidden Markov Models BIBREF7, Maximum Entropy Markov Models BIBREF7, BIBREF8, or Conditional Random Fields BIBREF9. Recent works BIBREF10, BIBREF11 have used recurrent neural networks with attention modules for NER.\nSentiment detection tools like SentiStrength BIBREF12 and TensiStrength BIBREF13 are rule-based tools, relying on various dictionaries of emoticons, slangs, idioms, and ironic phrases, and set of rules that can detect the sentiment of a sentence overall or a targeted sentiment. Given a list of keywords, TensiStrength (similar to SentiStrength) reports the sentiment towards selected entities in a sentence, based on five levels of relaxation and five levels of stress.\nAmong commercial NLP toolkits (e.g., BIBREF14, BIBREF15, BIBREF16), we selected BIBREF17 and BIBREF18 for our experiments, which, to the best of our knowledge, are the only publicly accessible commercial APIs for the task of entity-level sentiment analysis that is agnostic to the text domain. We also report results of TensiStrength BIBREF13, TwitterNLP BIBREF6, BIBREF19, CogComp-NLP BIBREF20, and Stanford NLP NER BIBREF21.\n\nDataset and Analysis Methodology\nWe used the 1,000-tweet dataset by BIBREF2 that contains the named-entities labels and entity-level sentiments for each of the four 2016 presidential primary candidates Bernie Sanders, Donald Trump, Hillary Clinton, and Ted Cruz, provided by crowdworkers, and by experts in political communication, whose labels are considered groundtruth. The crowdworkers were located in the US and hired on the BIBREF22 platform. For the task of entity-level sentiment analysis, a 3-scale rating of \"negative,\" \"neutral,\" and \"positive\" was used by the annotators.\nBIBREF2 proposed a decision tree approach for computing the number of crowdworkers who should analyze a tweet based on the difficulty of the task. Tweets are labeled by 2, 3, 5, or 7 workers based on the difficulty of the task and the level of disagreement between the crowdworkers. The model computes the number of workers based on how long a tweet is, the presence of a link in a tweet, and the number of present sarcasm signals. Sarcasm is often used in political tweets and causes disagreement between the crowdworkers. The tweets that are deemed to be sarcastic by the decision tree model, are expected to be more difficult to annotate, and hence are allocated more crowdworkers to work on.\nWe conducted two sets of experiments. In the first set, we used BIBREF23, BIBREF17, and BIBREF18, for entity-level sentiment analysis; in the second set, BIBREF17, BIBREF19, BIBREF24, BIBREF25, and BIBREF26, BIBREF18 for named-entity recognition.\nIn the experiments that we conducted with TwitterNLP for named-entity recognition, we worked with the default values of the model. Furthermore, we selected the 3-class Stanford NER model, which uses the classes “person,” “organization,” and “location” because it resulted in higher accuracy compared to the 7-class model. For CogComp-NLP NER we used Ontonotes 5.0 NER model BIBREF27. For spaCy NER we used the `en_core_web_lg' model.\nWe report the experimental results for our two tasks in terms of the correct classification rate (CCR). For sentiment analysis, we have a three-class problem (positive, negative, and neutral), where the classes are mutually exclusive. The CCR, averaged for a set of tweets, is defined to be the number of correctly-predicted sentiments over the number of groundtruth sentiments in these tweets. For NER, we consider that each tweet may reference up to four candidates, i.e., targeted entities. The CCR, averaged for a set of tweets, is the number of correctly predicted entities (candidates) over the number of groundtruth entities (candidates) in this set.\n\nResults and Discussion\nThe dataset of 1,000 randomly selected tweets contains more than twice as many tweets about Trump than about the other candidates. In the named-entity recognition experiment, the average CCR of crowdworkers was 98.6%, while the CCR of the automated systems ranged from 77.2% to 96.7%. For four of the automated systems, detecting the entity Trump was more difficult than the other entities (e.g., spaCy 72.7% for the entity Trump vs. above 91% for the other entities). An example of incorrect NER is shown in Figure FIGREF1 top. The difficulties the automated tools had in NER may be explained by the fact that the tools were not trained on tweets, except for TwitterNLP, which was not in active development when the data was created BIBREF1.\nIn the sentiment analysis experiments, we found that a tweet may contain multiple sentiments. The groundtruth labels contain 210 positive sentiments, 521 neutral sentiments, and 305 negative sentiments to the candidates. We measured the CCR, across all tweets, to be 31.7% for Rosette Text Analytics, 43.2% for Google Cloud, 44.2% for TensiStrength, and 74.7% for the crowdworkers. This means the difference between the performance of the tools and the crowdworkers is significant – more than 30 percent points.\nCrowdworkers correctly identified 62% of the neutral, 85% of the positive, and 92% of the negative sentiments. Google Cloud correctly identified 88% of the neutral sentiments, but only 3% of the positive, and 19% of the negative sentiments. TensiStrength correctly identified 87.2% of the neutral sentiments, but 10.5% of the positive, and 8.1% of the negative sentiments. Rosette Text Analytics correctly identified 22.7% of neutral sentiments, 38.1% of negative sentiments and 40.9% of positive sentiments. The lowest and highest CCR pertains to tweets about Trump and Sanders for both Google Cloud and TensiStrength, Trump and Clinton for Rosette Text Analytics, and Clinton and Cruz for crowdworkers. An example of incorrect ELS analysis is shown in Figure FIGREF1 bottom.\n\nConclusions and Future Work\nOur results show that existing NLP systems cannot accurately perform sentiment analysis of political tweets in the dataset we experimented with. Labeling by humans, even non-expert crowdworkers, yields accuracy results that are well above the results of existing automated NLP systems. In future work we will therefore use a crowdworker-labeled dataset to train a new machine-learning based NLP system for tweet analysis. We will ensure that the training data is balanced among classes. Our plan is to use state-of-the-art deep neural networks and compare their performance for entity-level sentiment analysis of political tweets.\n\nAcknowledgments\nPartial support of this work by the Hariri Institute for Computing and Computational Science & Engineering at Boston University (to L.G.) and a Google Faculty Research Award (to M.B. and L.G.) is gratefully acknowledged. Additionally, we would like to thank Daniel Khashabi for his help in running the CogComp-NLP Python API and Mike Thelwal for his help with TensiStrength. We are also grateful to the Stanford NLP group for clarifying some of the questions we had with regards to the Stanford NER tool.\n\nFigure 1: Incorrect NER by spaCy (top) and incorrect ELS analysis by Google Cloud (bottom)\n\nTable 1: Average Correct Classification Rate (CCR) for named-entity recognition (NER) of four presidential candidates and entity-level sentiment (ELS) analysis by NLP tools and crowdworkers\n\nQuery: Which sentiment class is the most accurately predicted by ELS systems?\n\nJSON Response:", "role": "user" }, { "content": "{\"answer\": \"neutral sentiment\", \"evidence\": [\"Crowdworkers correctly identified 62% of the neutral, 85% of the positive, and 92% of the negative sentiments. Google Cloud correctly identified 88% of the neutral sentiments, but only 3% of the positive, and 19% of the negative sentiments. TensiStrength correctly identified 87.2% of the neutral sentiments, but 10.5% of the positive, and 8.1% of the negative sentiments. Rosette Text Analytics correctly identified 22.7% of neutral sentiments, 38.1% of negative sentiments and 40.9% of positive sentiments. The lowest and highest CCR pertains to tweets about Trump and Sanders for both Google Cloud and TensiStrength, Trump and Clinton for Rosette Text Analytics, and Clinton and Cruz for crowdworkers. An example of incorrect ELS analysis is shown in Figure FIGREF1 bottom.\"]}", "role": "assistant" } ]

[ { "content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nA deficiency of folate increases blood levels of homocysteine.\n\nResearch Abstract:\nBACKGROUND Lowering serum homocysteine levels with folic acid is expected to reduce mortality from ischemic heart disease.\nHomocysteine reduction is known to be maximal at a folic acid dosage of 1 mg/d, but the effect of lower doses (relevant to food fortification) is unclear.\nMETHODS We randomized 151 patients with ischemic heart disease to 1 of 5 dosages of folic acid (0.2, 0.4, 0.6, 0.8, and 1.0 mg/d) or placebo.\nFasting blood samples for serum homocysteine and serum folate analysis were taken initially, after 3 months of supplementation, and 3 months after folic acid use was discontinued.\nRESULTS Median serum homocysteine level decreased with increasing folic acid dosage, to a maximum at 0.8 mg of folic acid per day, when the homocysteine reduction (placebo adjusted) was 2.7 micromol/L (23%), similar to the known effect of folic acid dosages of 1 mg/d and above.\nThe higher a person's initial serum homocysteine level, the greater was the response to folic acid, but there were statistically significant reductions regardless of the initial level.\nSerum folate level increased approximately linearly (5.5 nmol/L for every 0.1 mg of folic acid).\nWithin-person fluctuations over time in serum homocysteine levels, measured in the placebo group, were large compared with the effect of folic acid, indicating that monitoring of the reduction in an individual is impractical.\nCONCLUSIONS A dosage of folic acid of 0.8 mg/d appears necessary to achieve the maximum reduction in serum homocysteine level across the range of homocysteine levels in the population.\nCurrent US food fortification levels will achieve only a small proportion of the achievable homocysteine reduction.", "role": "user" }, { "content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"RESULTS Median serum homocysteine level decreased with increasing folic acid dosage, to a maximum at 0.8 mg of folic acid per day, when the homocysteine reduction (placebo adjusted) was 2.7 micromol/L (23%), similar to the known effect of folic acid dosages of 1 mg/d and above.\"]}", "role": "assistant" } ]

[ { "content": "| Glass ID | f O | N | E c (kJmol -1) | E b (kJmol -1) | m (GPa) | r d (A) | E s (kJmol -1) | Ex (kJmol -1) | r x , calc (molm -2 s -1) |\n| NaSiO-0Al | 1.00 | 5.1 | 0 | 25.8 | 25.7 | 0.976 | 7.87 | 33.6 | 6.7x10-6 |\n| NaSiO-5Al | 0.89 | 4.8 | 3.0 | 30.4 | 26.0 | 0.975 | 7.99 | 38.4 | 8.2x10-7 |\n| NaSiO-10Al | 0.79 | 4.6 | 6.3 | 35.0 | 26.5 | 0.974 | 8.23 | 43.2 | 9.8x10-8 |\n| NaSiO-15Al | 0.70 | 4.4 | 9.9 | 39.9 | 27.3 | 0.973 | 8.58 | 48.5 | 9.1x10-9 |\n\nCaption: Parameters for calculating activation enthalpy and sodium ion-exchange rate\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"1.00\", \"type\": \"Other\"}\n{\"value\": \"5.1\", \"type\": \"Other\"}\n{\"value\": \"0\", \"type\": \"Other\"}\n{\"value\": \"25.8\", \"type\": \"Other\"}\n{\"value\": \"25.7\", \"type\": \"Other\"}\n{\"value\": \"0.976\", \"type\": \"Other\"}\n{\"value\": \"7.87\", \"type\": \"Other\"}\n{\"value\": \"33.6\", \"type\": \"Other\"}\n{\"value\": \"0.89\", \"type\": \"Other\"}\n{\"value\": \"4.8\", \"type\": \"Other\"}\n{\"value\": \"3.0\", \"type\": \"Other\"}\n{\"value\": \"30.4\", \"type\": \"Other\"}\n{\"value\": \"26.0\", \"type\": \"Other\"}\n{\"value\": \"0.975\", \"type\": \"Other\"}\n{\"value\": \"7.99\", \"type\": \"Other\"}\n{\"value\": \"38.4\", \"type\": \"Other\"}\n{\"value\": \"0.79\", \"type\": \"Other\"}\n{\"value\": \"4.6\", \"type\": \"Other\"}\n{\"value\": \"6.3\", \"type\": \"Other\"}\n{\"value\": \"35.0\", \"type\": \"Other\"}\n{\"value\": \"26.5\", \"type\": \"Other\"}\n{\"value\": \"0.974\", \"type\": \"Other\"}\n{\"value\": \"8.23\", \"type\": \"Other\"}\n{\"value\": \"43.2\", \"type\": \"Other\"}\n{\"value\": \"0.70\", \"type\": \"Other\"}\n{\"value\": \"4.4\", \"type\": \"Other\"}\n{\"value\": \"9.9\", \"type\": \"Other\"}\n{\"value\": \"39.9\", \"type\": \"Other\"}\n{\"value\": \"27.3\", \"type\": \"Other\"}\n{\"value\": \"0.973\", \"type\": \"Other\"}\n{\"value\": \"8.58\", \"type\": \"Other\"}\n{\"value\": \"48.5\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nInformed Temporal Modeling via Logical Specification of Factorial LSTMs\n\nMain Content:\nConsider a world in which events occur that involve various entities . Learning how to predict future events from patterns of past events becomes more difficult as we consider more types of events . Many of the patterns detected in the dataset by an ordinary LSTM will be spurious since the number of potential pairwise correlations , for example , grows quadratically with the number of events . We propose a type of factorial LSTM architecture where different blocks of LSTM cells are responsible for capturing different aspects of the world state . We use Datalog rules to specify how to derive the LSTM structure from a database of facts about the entities in the world . This is analogous to how a probabilistic relational model ( Getoor & Taskar , 2007 ) specifies a recipe for deriving a graphical model structure from a database . In both cases , the goal is to obtain useful inductive biases by encoding informed independence assumptions into the model . We specifically consider the neural Hawkes process , which uses an LSTM to modulate the rate of instantaneous events in continuous time . In both synthetic and real-world domains , we show that we obtain better generalization by using appropriate factorial designs specified by simple Datalog programs . 1 INTRODUCTION . Temporal sequence data is abundant in applied machine learning . A common task is to impute missing events , e.g. , to predict the future from the past . Often this is done by fitting a generative probability model . For evenly spaced sequences , historically popular models have included hidden Markov models and discrete-time linear dynamical systems , with more recent interest in recurrent neural network models such as LSTMs . For irregularly spaced sequences , a good starting point is the Hawkes process , a self-exciting temporal point process ; many variations and enhancements have been published , including neural variants using LSTMs . All of these models can be described schematically by Figure 1a . Events ei , ei+1 , . . . are assumed to be conditionally independent of previous events , given the system state si ( which may or may not be fully known given events e1 , . . . , ei ) . That is , si is enough to determine the joint distribution of the ith event and the updated state si+1 , which is needed to recursively predict all subsequent events . Figure 1a and its caption show the three types of influence in the model . The update , affect , and depend arrows are characterized by parameters of the model . In the case of a recurrent neural network , these are the transition , input , and output matrices . Our main idea in this paper is to inject structural zeros into these weight matrices . Structural zeros are weights that are fixed at zero regardless of the model parameters . In other words , we will remove many connections ( synapses ) from both the recurrent and non-recurrent portions of the neural network . Parameter estimation must use the sparse remaining connections to explain the observed data . Specifically , we partition the neural state si ∈ Rd into a number of node blocks . Different node blocks are intended to capture different aspects of the world ’ s state at step i . By zeroing out rectangular blocks of the weight matrix , we will restrict how these node blocks interact with the events and with one another . An example is depicted in Figures 1b ( affect , depend ) and 1d ( update ) . In addition , by reusing nonzero blocks within a weight matrix , we can stipulate ( for example ) that event e affects node block b in the same way in which event e′ affects node block b′ . Such parameter tying makes it possible to generalize from frequent events to rare events of the same type . Although our present experiments are small , we are motivated by the challenges of scale . Real-world domains may have millions of event types , including many rare types . To model organizational behavior , we might consider a dataset of meetings and emails in a large organization . To model supply chains , we might consider purchases of goods and services around the world . In an unrestricted model , anything in the past could potentially influence anything in the future , making estimation extremely difficult . Structural zeroes and parameter tying , if chosen carefully , should help us avoid overfitting to coincidental patterns in the data . Analogous architectures have been proposed in the world of graphical models and causal models . Indeed , to write down such a model is to explicitly allow specific direct interactions and forbid the rest . For example , the edges of a Gaussian graphical model explicitly indicate which blocks of the inverse covariance matrix are allowed to be nonzero . Some such models reuse blocks ( Hojsgaard & Lauritzen , 2008 ) . As another example , a factorial HMM ( Ghahramani & Jordan , 1997 ) —an HMM whose states are m-tuples—can be regarded as a simple example of our architecture . The state si can be represented using m node blocks , each of which is a 1-hot vector that encodes the value of a different tuple element . The key aspect of a factorial HMM is that the stochastic transition matrix ( update in Figure 1d ) is fully block-diagonal . The affect matrix is 0 , since the HMM graphical model does not feed the output back into the next state ; the depend matrix is unrestricted . But how do we know which interactions to allow and which to forbid ? This is a domain-specific modeling question . In general , we would like to exploit the observation that events are structured objects with participants ( which is why the number of possible event types is often large ) . For example , a travel event involves both a person and a place . We might assume that the probability that Alice travels to Chicago depends only on Alice ’ s state , the states of Alice ’ s family members , and even the state of affairs in Chicago . Given that modeling assumption , parameter estimation can not try to derive this probability ( presumably incorrectly ) from the state of the coal market . These kinds of systematic dependencies can be elegantly written down using Datalog rules , as we will show . Datalog rules can refer to database facts , such as the fact that Alice is a person and that she is related to other people . Given these facts , we use Datalog rules to automatically generate the set of possible events and node blocks , and the ways in which they influence one another . Datalog makes it easy to give structured names to the events and node blocks . The rules can inspect these structures via pattern-matching . In short , our contribution is to show how to use a Datalog program to systematically derive a constrained neural architecture from a database . Datalog is a blend of logic and databases , both of which have previously been used in various formalisms for deriving a graphical model architecture from a database ( Getoor & Taskar , 2007 ) . 2 PRELIMINARIES1 . Our methods could be applied to RNN sequence models . In this setting , each possible event type would derive its unnormalized probability from selected node blocks of state si . Normalizing these probabilities to sum to 1 would yield the model ’ s distribution for event ei . Only the normalizing constant would depend on all node blocks . In this paper , we focus on the even more natural setting of real-time events . Here no normalizing constant is needed : the events are not in competition . As we will see in section 5.1 , it is now even possible for different node blocks to generate completely independent sequences of timestamped events . The observed dataset is formed by taking the union of these sequences . In the real-time setting , event ei has the form ki @ ti where ki ∈ K is the type of the event and ti ∈ R is its time . The probability of an event of type k at any specific instant t is infinitesimal . We will model how this infinitesimal probability depends on selected node blocks of si . There is no danger that two events will ever occur at the same instant , i.e. , the probability of this is 0 . 1Our conventions of mathematical notation mainly follow those given by Mei & Eisner ( 2017 , section 2 ) . We begin by describing our baseline model for this setting , drawn from Mei & Eisner ( 2017 ) . 2.1 BASELINE MODEL : THE NEURAL HAWKES PROCESS . In general , a multivariate point process is a distribution over possible sequences of events e1 = k1 @ t1 , e2 = k2 @ t2 , . . . where 0 < t1 < t2 < . . .. A common paradigm for defining such processes , starting with Hawkes ( 1971 ) , is to describe their temporal evolution as in Figure 1a . Each si is deterministically computed from si−1 ( update ) and ei−1 ( affect ) , according to some formula , so by induction , si is a deterministic summary of the first i − 1 events . ei = ki @ ti is then emitted stochastically from some distribution parameterized by si ( depend ) . The structure of the depend distribution is the interesting part . si is used , for each event type k ∈ K , to define some time-varying intensity function λk : ( ti−1 , ∞ ) → R≥0 . This intensity function is treated as the parameter of an inhomogeneous Poisson process , which stochastically generates a set of future events of type k at various times in ( ti−1 , ∞ ) .2 Thus , all these |K| Poisson processes together give us many events of the form e = k @ t. The first such event—the one with the earliest time t—is taken to be the next event ei . The remaining events are discarded ( or in practice , never generated ) . As our baseline method , we take the neural Hawkes process ( Mei & Eisner , 2017 ) to be our method for computing si and defining the intensity function λk from it . In that work , si actually describes a parametric function of the form h : ( ti−1 , ∞ ) → Rd , which describes how the hidden state of the system evolves following event ei−1 . That function is used to define the intensity functions via λk ( t ) = fk ( v > k h ( t ) ) > 0 ( 1 ) 2Under an inhomogenous Poisson process , disjoint intervals generate events independently , and the number of events on the interval ( a , b ] is Poisson-distributed with mean ∫ b a λk ( t ) dt . Thus , on a sufficiently narrow interval ( t , t+ dt ] , the probability of a single event is approximately λk ( t ) dt and the probability of more than one event is approximately 0 , with an error of O ( dt2 ) in both cases . so the parameters of depend are the vectors vk and the monotonic functions fk . Once ei = ki @ ti has been sampled , the parameters for si+1 are obtained by si+1 ← Ψ ( Uwki + Vh ( ti ) ) ( 2 ) where Ψ is inspired by the structure of an LSTM , the affect parameters are given by matrix U and the event embeddings wk , and the depend parameters are given by matrix V. In this paper , we will show an advantage to introducing structural zeroes into vk , U , and V .", "role": "user" }, { "content": "This paper builds an interesting connection between Datalog rules and temporal point processes. The novelty of the approach is to factorize the latent state of LSTM into different blocks that represent three major interactions between temporal events, including: dependency, affects, and updates. The design of the node blocks within the hidden state allows the modeling of fine-grain structure of a given event type. Based on the Datalog program and the logic rules, the intensity function of the temporal point process can be formulated from facts in a database. The problem of enabling a flexible family of intensity functions is one of the most important topics in point processes, and a paper advancing knowledge in this area is certainly welcome.", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND/AIMS: Food residue is frequently observed in the gastric remnant after distal gastrectomy, despite adequate preparation. We devised a water-intake method to reduce food residue in the gastric remnant by drinking large quantities of water in a short time. The aims of this study were to identify the risk factors for food residue and to study the effectiveness of this new method for endoscopy preparation.\n\nABSTRACT.METHODS: A cohort of 708 patients who underwent distal gastrectomy for gastric cancer was reviewed prospectively. Sixty patients with large amounts of food residue were randomly divided into two groups: a water-intake group (n=40) and a prolonged fasting group (n=20).\n\nABSTRACT.RESULTS: The incidences of a large amount of food residue were 15.7%, 5.8%, 7.5%, and 2.8% at 3, 12, 24, and 36 months, respectively, after distal gastrectomy. Independent risk factors for food residue were endoscopy at 3 months, diabetes mellitus, a body mass index of <19.5, and laparoscopic surgery. The proportion of successful preparations at follow-up endoscopy was higher for the water-intake group (70%) than for the prolonged fasting group (40%, p=0.025).\n\nABSTRACT.CONCLUSIONS: The water-intake method can be recommended as a preparation for endoscopy in patients who have had repetitive food residue or risk factors after distal gastrectomy.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"a water-intake group\", \"a prolonged fasting group\", \"The proportion of successful preparations at follow-up endoscopy\", \"significantly increased\", \"The proportion of successful preparations at follow-up endoscopy was higher for the water-intake group (70%) than for the prolonged fasting group (40%, p=0.025).\"]]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND:: Family-centered empowerment of drug and stimulant users is an effective program for a better response to treatment, prevention of treatment adverse effects, and promotion quality of life (QoL) and lifestyle in the process of discontinuing drug abuse.\n\nABSTRACT.OBJECTIVES:: This study aimed to determine the effects of educational intervention, based on family-centered empowerment and Pender's health promotion models, on health-promoting lifestyle and health-related QoL among methamphetamine users and their families.\n\nABSTRACT.PATIENTS AND METHODS:: In a randomized clinical trial, methamphetamine users, who were admitted to Tehran University of Medical Sciences Substance Abuse Treatment Centers, were randomly allocated to three groups: a group for training of methamphetamine users who were in recovery phase (intervention group 1;95 subjects);a group for training of a family member of methamphetamine users who were in recovery phase (intervention group 2; 95 subjects); and a control group (95 subjects). A demographic checklist and a standard questionnaire covering health-promoting lifestyle, health-related QoL, self-efficacy, perceived affect, perceived social support, and perceived barriers dimensions were used to gather required data. Independent-samples t test, paired-samples t-test, and ANCOVA were used to analyze the data.\n\nABSTRACT.RESULTS:: Analysis of covariance showed that after adjusting for effects of pretest scores, the difference between mean post-test scores of health-promoting lifestyle scale, health-related QoL scale, and all constructs of Pender's health promotion model (self-efficacy, perceived affect, perceived social support, and perceived barriers) in the intervention group 1 and control group were significant (P< 0.0001). In addition, changes in mean scores of lifestyle scale (42.4 ± 13.6), QoL scale (29.1 ± 14.2), self-efficacy (16.1 ± 2.6), perceived affect (16.1 ± 8), social support (35.4 ± 12.4), and barriers (17.2 ± 15.8) before and after intervention were significant in the intervention group 2 (P < 0.0001).\n\nABSTRACT.CONCLUSIONS:: Application of family-centered empowerment model among methamphetamine users and their families is practically feasible and can result in enhancement and improvement of their QoL, lifestyle, and health promotion model constructs.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"a group for training of methamphetamine users who were in recovery phase (intervention group 1) and control\", \"baseline\", \"mean post-test scores of health-promoting lifestyle scale, health-related QoL scale, and all constructs of Pender\\u0027s health promotion model (self-efficacy, perceived affect, perceived social support, and perceived barriers)\", \"significantly increased\", \"Analysis of covariance showed that after adjusting for effects of pretest scores, the difference between mean post-test scores of health-promoting lifestyle scale, health-related QoL scale, and all constructs of Pender\\u0027s health promotion model (self-efficacy, perceived affect, perceived social support, and perceived barriers) in the intervention group 1 and control group were significant (P\\u003c 0.0001). In addition, changes in mean scores of lifestyle scale (42.4 \\u00b1 13.6), QoL scale (29.1 \\u00b1 14.2), self-efficacy (16.1 \\u00b1 2.6), perceived affect (16.1 \\u00b1 8), social support (35.4 \\u00b1 12.4), and barriers (17.2 \\u00b1 15.8) before and after intervention were significant in the intervention group 2 (P \\u003c 0.0001).\"]]", "role": "assistant" } ]

[ { "content": "Analyze the given abstract from a computer science paper. Identify and list entities in a structured JSON format. Focus on capturing distinct elements relevant to the research context.\n\nInput Abstract:\n\nA new approach for Interactive Machine Translation where the author interacts during the creation or the modification of the document is proposed. The explanation of an ambiguity or an error for the purposes of correction does not use any concepts of the underlying linguistic theory: it is a reformulation of the erroneous or ambiguous sentence. The interaction is limited to the analysis step of the translation process. This paper presents a new interactive disambiguation scheme based on the paraphrasing of a parser 's multiple output. Some examples of paraphrasing ambiguous sentences are presented.", "role": "user" }, { "content": "Output Entities:\n\n{\"Generic\": [\"approach\"], \"Material\": [], \"Method\": [\"translation process\", \"paraphrasing\", \"linguistic theory\"], \"Metric\": [], \"OtherScientificTerm\": [], \"Task\": [\"Interactive Machine Translation\"]}", "role": "assistant" } ]

[ { "content": "| Sample no. | DE 1 (eV) | DE 2 (eV) | ln s 1 (Ocm)-1 | ln s 2 (Ocm)-1 | ln s RT (Ocm)-1 |\n| Base | 0.72+-0.004 (373-293K) | 0.37+-0.002 (283-213K) | 4.36+-0.02 | -9.49+-0.05 | -24.15+-0.12 |\n| Second | 0.79+-0.004 (373-273K) | 0.11+-0.0006 (283-153K) | 6.02+-0.03 | -22.62+-0.11 | -25.26+-0.13 |\n| Third | 0.5+-0.003 (373-223K) | 0.08+-0.0004 (213-153K) | -2.05+-0.01 | -22.15+-0.11 | -21.84+-0.11 |\n| Fourth | 0.43+-0.002 (373-223K) | 0.07+-0.0004 (223-153K) | -3.81+-0.02 | -23.25+-0.12 | -20.84+-0.10 |\n\nCaption: The electrical conductivity quantities DE 1,2 (activation energy of conduction), and the pre-exponential factor s 1,2, and s RT (conductivity at room temperature20degC) for investigated bulk LAS samples for un-doped and doped with 0.2, 0.4, and 0.6 (wt%) of Cr2O3 in sequence.\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"0.72\", \"type\": \"Other\"}\n{\"value\": \"0.37\", \"type\": \"Other\"}\n{\"value\": \"4.36\", \"type\": \"Other\"}\n{\"value\": \"0.79\", \"type\": \"Other\"}\n{\"value\": \"0.11\", \"type\": \"Other\"}\n{\"value\": \"6.02\", \"type\": \"Other\"}\n{\"value\": \"0.5\", \"type\": \"Other\"}\n{\"value\": \"0.08\", \"type\": \"Other\"}\n{\"value\": \"0.43\", \"type\": \"Other\"}\n{\"value\": \"0.07\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nCombating Adversarial Misspellings with Robust Word Recognition\n\nTo combat adversarial spelling mistakes, we propose placing a word recognition model in front of the downstream classifier. Our word recognition models build upon the RNN semicharacter architecture, introducing several new backoff strategies for handling rare and unseen words. Trained to recognize words corrupted by random adds, drops, swaps, and keyboard mistakes, our method achieves 32% relative (and 3.3% absolute) error reduction over the vanilla semi-character model. Notably, our pipeline confers robustness on the downstream classifier, outperforming both adversarial training and off-the-shelf spell checkers. Against a BERT model fine-tuned for sentiment analysis, a single adversarially-chosen character attack lowers accuracy from 90.3% to 45.8%. Our defense restores accuracy to 75%\n1 . Surprisingly, better word recognition does not always entail greater robustness. Our analysis reveals that robustness also depends upon a quantity that we denote the sensitivity.\n\nRobust Word Recognition\nTo tackle character-level adversarial attacks, we introduce a simple two-stage solution, placing a word recognition model ( $W$ ) before the downstream classifier ( $C$ ). Under this scheme, all inputs are classified by the composed model $C \\circ W$ . This modular approach, with $W$ and $C$ trained separately, offers several benefits: (i) we can deploy the same word recognition model for multiple downstream classification tasks/models; and (ii) we can train the word recognition model with larger unlabeled corpora.\nAgainst adversarial mistakes, two important factors govern the robustness of this combined model: $W$ 's accuracy in recognizing misspelled words and $W$ 's sensitivity to adversarial perturbations on the same input. We discuss these aspects in detail below.\n\nExperiments and Results\nIn this section, we first discuss our experiments on the word recognition systems.\n\nWord Error Correction\nData: We evaluate the spell correctors from § \"Robust Word Recognition\" on movie reviews from the Stanford Sentiment Treebank (SST) BIBREF24 . The SST dataset consists of 8544 movie reviews, with a vocabulary of over 16K words. As a background corpus, we use the IMDB movie reviews BIBREF25 , which contain 54K movie reviews, and a vocabulary of over 78K words. The two datasets do not share any reviews in common. The spell-correction models are evaluated on their ability to correct misspellings. The test setting consists of reviews where each word (with length $\\ge 4$ , barring stopwords) is attacked by one of the attack types (from swap, add, drop and keyboard attacks). In the all attack setting, we mix all attacks by randomly choosing one for each word. This most closely resembles a real world attack setting.\nIn addition to our word recognition models, we also compare to After The Deadline (ATD), an open-source spell corrector. We found ATD to be the best freely-available corrector. We refer the reader to BIBREF7 for comparisons of ScRNN to other anonymized commercial spell checkers.\nFor the ScRNN model, we use a single-layer Bi-LSTM with a hidden dimension size of 50. The input representation consists of 198 dimensions, which is thrice the number of unique characters (66) in the vocabulary. We cap the vocabulary size to 10K words, whereas we use the entire vocabulary of 78470 words when we backoff to the background model. For training these networks, we corrupt the movie reviews according to all attack types, i.e., applying one of the 4 attack types to each word, and trying to reconstruct the original words via cross entropy loss.\nWe calculate the word error rates (WER) of each of the models for different attacks and present our findings in Table 2 . Note that ATD incorrectly predicts $11.2$ words for every 100 words (in the `all' setting), whereas, all of the backoff variations of the ScRNN reconstruct better. The most accurate variant involves backing off to the background model, resulting in a low error rate of $6.9\\%$ , leading to the best performance on word recognition. This is a $32\\%$ relative error reduction compared to the vanilla ScRNN model with a pass-through backoff strategy. We can attribute the improved performance to the fact that there are $5.25\\%$ words in the test corpus that are unseen in the training corpus, and are thus only recoverable by backing off to a larger corpus. Notably, only training on the larger background corpus does worse, at $8.7\\%$ , since the distribution of word frequencies is different in the background corpus compared to the foreground corpus.\n\nRobustness to adversarial attacks\nWe use sentiment analysis and paraphrase detection as downstream tasks, as for these two tasks, 1-2 character edits do not change the output labels.\nFor sentiment classification, we systematically study the effect of character-level adversarial attacks on two architectures and four different input formats. The first architecture encodes the input sentence into a sequence of embeddings, which are then sequentially processed by a BiLSTM. The first and last states of the BiLSTM are then used by the softmax layer to predict the sentiment of the input. We consider three input formats for this architecture: (1) Word-only: where the input words are encoded using a lookup table; (2) Char-only: where the input words are encoded using a separate single-layered BiLSTM over their characters; and (3) Word $+$ Char: where the input words are encoded using a concatenation of (1) and (2) .\nThe second architecture uses the fine-tuned BERT model BIBREF26 , with an input format of word-piece tokenization. This model has recently set a new state-of-the-art on several NLP benchmarks, including the sentiment analysis task we consider here. All models are trained and evaluated on the binary version of the sentence-level Stanford Sentiment Treebank BIBREF24 dataset with only positive and negative reviews.\nWe also consider the task of paraphrase detection. Here too, we make use of the fine-tuned BERT BIBREF26 , which is trained and evaluated on the Microsoft Research Paraphrase Corpus (MRPC) BIBREF27 .\nTwo common methods for dealing with adversarial examples include: (1) data augmentation (DA) BIBREF28 ; and (2) adversarial training (Adv) BIBREF29 . In DA, the trained model is fine-tuned after augmenting the training set with an equal number of examples randomly attacked with a 1-character edit. In Adv, the trained model is fine-tuned with additional adversarial examples (selected at random) that produce incorrect predictions from the current-state classifier. The process is repeated iteratively, generating and adding newer adversarial examples from the updated classifier model, until the adversarial accuracy on dev set stops improving.\nIn Table 3 , we examine the robustness of the sentiment models under each attack and defense method. In the absence of any attack or defense, BERT (a word-piece model) performs the best ( $90.3\\%$ ) followed by word+char models ( $80.5\\%$ ), word-only models ( $79.2\\%$ ) and then char-only models ( $70.3\\%$ ). However, even single-character attacks (chosen adversarially) can be catastrophic, resulting in a significantly degraded performance of $46\\%$ , $57\\%$ , $59\\%$ and $33\\%$ , respectively under the `all' setting.\nIntuitively, one might suppose that word-piece and character-level models would be more robust to such attacks given they can make use of the remaining context. However, we find that they are the more susceptible. To see why, note that the word `beautiful' can only be altered in a few ways for word-only models, either leading to an UNK or an existing vocabulary word, whereas, word-piece and character-only models treat each unique character combination differently. This provides more variations that an attacker can exploit. Following similar reasoning, add and key attacks pose a greater threat than swap and drop attacks. The robustness of different models can be ordered as word-only $>$ word+char $>$ char-only $\\sim $ word-piece, and the efficacy of different attacks as add $>$ key $>$ drop $>$ swap.\nNext, we scrutinize the effectiveness of defense methods when faced against adversarially chosen attacks. Clearly from table 3 , DA and Adv are not effective in this case. We observed that despite a low training error, these models were not able to generalize to attacks on newer words at test time. ATD spell corrector is the most effective on keyboard attacks, but performs poorly on other attack types, particularly the add attack strategy.\nThe ScRNN model with pass-through backoff offers better protection, bringing back the adversarial accuracy within $5\\%$ range for the swap attack. It is also effective under other attack classes, and can mitigate the adversarial effect in word-piece models by $21\\%$ , character-only models by $19\\%$ , and in word, and word+char models by over $4.5\\%$ . This suggests that the direct training signal of word error correction is more effective than the indirect signal of sentiment classification available to DA and Adv for model robustness.\nWe observe additional gains by using background models as a backoff alternative, because of its lower word error rate (WER), especially, under the swap and drop attacks. However, these gains do not consistently translate in all other settings, as lower WER is necessary but not sufficient. Besides lower error rate, we find that a solid defense should furnish the attacker the fewest options to attack, i.e. it should have a low sensitivity.\nAs we shall see in section § \"Understanding Model Sensitivity\" , the backoff neutral variation has the lowest sensitivity due to mapping UNK predictions to a fixed neutral word. Thus, it results in the highest robustness on most of the attack types for all four model classes.\nTable 4 shows the accuracy of BERT on 200 examples from the dev set of the MRPC paraphrase detection task under various attack and defense settings. We re-trained the ScRNN model variants on the MRPC training set for these experiments. Again, we find that simple 1-2 character attacks can bring down the accuracy of BERT significantly ( $89\\%$ to $31\\%$ ). Word recognition models can provide an effective defense, with both our pass-through and neutral variants recovering most of the accuracy. While the neutral backoff model is effective on 2-char attacks, it hurts performance in the no attack setting, since it incorrectly modifies certain correctly spelled entity names. Since the two variants are already effective, we did not train a background model for this task.\n\nUnderstanding Model Sensitivity\nTo study model sensitivity, for each sentence, we perturb one randomly-chosen word and replace it with all possible perturbations under a given attack type. The resulting set of perturbed sentences is then fed to the word recognizer (whose sensitivity is to be estimated). As described in equation 12 , we count the number of unique predictions from the output sentences. Two corrections are considered unique if they are mapped differently by the downstream classifier.\nThe neutral backoff variant has the lowest sensitivity (Table 5 ). This is expected, as it returns a fixed neutral word whenever the ScRNN predicts an UNK, therefore reducing the number of unique outputs it predicts. Open vocabulary (i.e. char-only, word+char, word-piece) downstream classifiers consider every unique combination of characters differently, whereas word-only classifiers internally treat all out of vocabulary (OOV) words alike. Hence, for char-only, word+char, and word-piece models, the pass-through version is more sensitive than the background variant, as it passes words as is (and each combination is considered uniquely). However, for word-only models, pass-through is less sensitive as all the OOV character combinations are rendered identical.\nIdeally, a preferred defense is one with low sensitivity and word error rate. In practice, however, we see that a low error rate often comes at the cost of sensitivity. We see this trade-off in Figure 2 , where we plot WER and sensitivity on the two axes, and depict the robustness when using different backoff variants. Generally, sensitivity is the more dominant factor out of the two, as the error rates of the considered variants are reasonably low.\nWe verify if the sentiment (of the reviews) is preserved with char-level attacks. In a human study with 50 attacked (and subsequently misclassified), and 50 unchanged reviews, it was noted that 48 and 49, respectively, preserved the sentiment.\n\nFigure 1: A schematic sketch of our proposed word recognition system, consisting of a foreground and a background model. We train the foreground model on the smaller, domain-specific dataset, and the background model on a larger dataset (e.g., the IMDB movie corpus). We train both models to reconstruct the correct word from the orthography and context of the individual words, using synthetically corrupted inputs during training. Subse-\n\nTable 2: Word Error Rates (WER) of ScRNN with each backoff strategy, plus ATD and an ScRNN trained only on the background corpus (78K vocabulary) The error rates include 5.25% OOV words.\n\nFigure 2: Effect of sensitivity and word error rate on robustness (depicted by the bubble sizes) in word-only models (left) and char-only models (right).\n\nQuery: What end tasks do they evaluate on?\n\nJSON Response:", "role": "user" }, { "content": "{\"answer\": \"Sentiment analysis and paraphrase detection under adversarial attacks\", \"evidence\": [\"For sentiment classification, we systematically study the effect of character-level adversarial attacks on two architectures and four different input formats. The first architecture encodes the input sentence into a sequence of embeddings, which are then sequentially processed by a BiLSTM. The first and last states of the BiLSTM are then used by the softmax layer to predict the sentiment of the input. We consider three input formats for this architecture: (1) Word-only: where the input words are encoded using a lookup table; (2) Char-only: where the input words are encoded using a separate single-layered BiLSTM over their characters; and (3) Word $+$ Char: where the input words are encoded using a concatenation of (1) and (2) .\", \"We also consider the task of paraphrase detection. Here too, we make use of the fine-tuned BERT BIBREF26 , which is trained and evaluated on the Microsoft Research Paraphrase Corpus (MRPC) BIBREF27 .\"]}", "role": "assistant" } ]

[ { "content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nIdentification of the BRAF V600E mutation in gastroenteropancreatic neuroendocrine tumors. Genomic profiles of gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are still insufficiently understood, and the genetic alterations associated with drug responses have not been studied. Here, we performed whole exome sequencing of 12 GEP-NETs from patients enrolled in a nonrandomized, open-labeled, single-center phase II study for pazopanib, and integrated our results with previously published results on pancreas (n = 12) and small intestine NETs (n = 50). The mean numbers of somatic mutations in each case varied widely from 20 to 4682. Among 12 GEP-NETs, eight showed mutations of more than one cancer-related gene, including TP53, CNBD1, RB1, APC, BCOR, BRAF, CTNNB1, EGFR, EP300, ERBB3, KDM6A, KRAS, MGA, MLL3, PTEN, RASA1, SMARCB1, SPEN, TBC1D12, and VHL. TP53 was recurrently mutated in three cases, whereas CNBD1 and RB1 mutations were identified in two cases. Three GEP-NET patients with TP53 mutations demonstrated a durable response and one small intestinal grade (G) 1 NET patient with BRAF V600E mutation showed progression after pazopanib treatment. We found BRAF V600E (G1 NET from rectum and two G3 NETs from colon) and BRAF G593S (G2 NET from pancreas) missense mutations (9.1%) in an independent cohort of 44 GEP-NETs from the rectum (n = 26), colon (n = 7), pancreas (n = 4), small intestine (n = 3), stomach (n = 3) and appendix (n = 1) by Sanger sequencing. All tumor specimens were obtained before chemotherapy. In conclusion, BRAF V600E mutation is likely to result in resistance to pazopanib but may be a potentianally actionable mutation in metastatic GEP-NETs patients.", "role": "user" }, { "content": "{\"CellLine\": [], \"Chemical\": [\"pazopanib\"], \"Disease\": [\"gastroenteropancreatic neuroendocrine tumors\", \"intestine NETs\", \"cancer\", \"intestinal grade (G) 1 NET\", \"NET\", \"GEP-NET\", \"GEP-NETs\", \"tumor\", \"NETs\"], \"Gene\": [\"APC\", \"CNBD1\", \"PTEN\", \"RB1\", \"ERBB3\", \"MGA\", \"SMARCB1\", \"BRAF\", \"SPEN\", \"KDM6A\", \"TP53\", \"EGFR\", \"KRAS\", \"EP300\", \"RASA1\", \"VHL\", \"CTNNB1\", \"TBC1D12\", \"BCOR\", \"MLL3\"], \"Species\": [\"patient\", \"patients\"], \"Variant\": [\"V600E\", \"G593S\"]}", "role": "assistant" } ]

[ { "content": "| Standard | r-factor | r (A) | n (atoms) | s 2 (A2) |\n| SrCO 3 | SrCO 3 | SrCO 3 | SrCO 3 | SrCO 3 |\n| Sr-O fit | 0.006 | 2.59 (0.01) | 9.3 (0.8) | 0.0121 (0.0013) |\n| | 0.031 | 2.59 (0.01) | 7.1 (0.6) | 0.0080 |\n| | | | | |\n| Actual [6] | | | | |\n| Inner 5 oxygens | | 2.59 | 5.0 | |\n| Inner 7 oxygens | | 2.61 | 7.0 | |\n| All oxygens | | 2.63 | 9.0 | |\n| | | | | |\n| Haradaite | Haradaite | Haradaite | Haradaite | Haradaite |\n| Sr-O fit | 0.035 | 2.68 (0.01) | 10.2 (2.5) | 0.0180 (0.0047) |\n| | 0.091 | 2.67 (0.01) | 6.0 (1.0) | 0.0090 |\n| | | | | |\n| Actual [7] | | | | |\n| Inner 4 oxygens | | 2.69 | 4.0 | |\n| Inner 8 oxygens | | 2.75 | 8.0 | |\n| All oxygens | | 2.79 | 11.0 | |\n| | | | | |\n| Sr-Si fit | | 3.38 (0.01) | 10.2 (2.1) | 0.0142 (0.0018) |\n| | | 3.38 (0.02) | 8.2 (1.5) | 0.0120 |\n| Actual [7] | | 3.39 | 6.0 | |\n\nCaption: Fitting results for the standards\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:", "role": "user" }, { "content": "{\"value\": \"0.006\", \"type\": \"Other\"}\n{\"value\": \"2.59\", \"type\": \"Other\"}\n{\"value\": \"9.3\", \"type\": \"Other\"}\n{\"value\": \"0.0121\", \"type\": \"Other\"}\n{\"value\": \"0.031\", \"type\": \"Other\"}\n{\"value\": \"2.59\", \"type\": \"Other\"}\n{\"value\": \"7.1\", \"type\": \"Other\"}\n{\"value\": \"0.0080\", \"type\": \"Other\"}\n{\"value\": \"2.59\", \"type\": \"Other\"}\n{\"value\": \"5.0\", \"type\": \"Other\"}\n{\"value\": \"2.61\", \"type\": \"Other\"}\n{\"value\": \"7.0\", \"type\": \"Other\"}\n{\"value\": \"2.63\", \"type\": \"Other\"}\n{\"value\": \"9.0\", \"type\": \"Other\"}\n{\"value\": \"0.035\", \"type\": \"Other\"}\n{\"value\": \"2.68\", \"type\": \"Other\"}\n{\"value\": \"10.2\", \"type\": \"Other\"}\n{\"value\": \"0.0180\", \"type\": \"Other\"}\n{\"value\": \"0.091\", \"type\": \"Other\"}\n{\"value\": \"2.67\", \"type\": \"Other\"}\n{\"value\": \"6.0\", \"type\": \"Other\"}\n{\"value\": \"0.0090\", \"type\": \"Other\"}\n{\"value\": \"2.69\", \"type\": \"Other\"}\n{\"value\": \"4.0\", \"type\": \"Other\"}\n{\"value\": \"2.75\", \"type\": \"Other\"}\n{\"value\": \"8.0\", \"type\": \"Other\"}\n{\"value\": \"2.79\", \"type\": \"Other\"}\n{\"value\": \"11.0\", \"type\": \"Other\"}\n{\"value\": \"3.38\", \"type\": \"Other\"}\n{\"value\": \"10.2\", \"type\": \"Other\"}\n{\"value\": \"0.0142\", \"type\": \"Other\"}\n{\"value\": \"3.38\", \"type\": \"Other\"}\n{\"value\": \"8.2\", \"type\": \"Other\"}\n{\"value\": \"0.0120\", \"type\": \"Other\"}\n{\"value\": \"3.39\", \"type\": \"Other\"}\n{\"value\": \"6.0\", \"type\": \"Other\"}", "role": "assistant" } ]

[ { "content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nMutational analysis of CYP2C8 in hypertensive patients using denaturing high performance liquid chromatography. WHAT IS KNOWN AND OBJECTIVE: CYP2C8 is involved in the cytochrome P450 (CYP) epoxygenase pathway. Arachidonic acid metabolites such as epoxyeicosatrienenoic acids and hydroxyeicosatetrenoic acids, produced may have a role in hypertension. We aimed to develop a medium through-put method for screening samples of known and new mutations of CYP2C8 using denaturing high performance liquid chromatography (DHPLC). METHODS: DNA samples from 200 subjects (hypertensive patients and healthy controls) were screened for SNPs in CYP2C8 using DHPLC. Genotypes and allelic frequencies of CYP2C8 between the healthy controls and patients with hypertension were compared. RESULTS AND DISCUSSIONS: Six variants were detected and two were new; T deletion at 5063 and substitution of C to T at 33468 in exon 8. Differences in variant frequencies were detected between the controls and hypertensive patients. The controls have significantly higher prevalence of C35322C compared to the patients. The functional significance of the SNP at 35322 requires further study. Having homozygous C35322C could be a protective factor for hypertension. WHAT IS NEW AND CONCLUSION: Denaturing high performance liquid chromatography is useful for population screening to identify new and existing SNPs. A higher frequency of the C35322T SNP was observed among hypertensive patients than control subjects. This potentially important observation requires confirmation and the clinical significance assessed.", "role": "user" }, { "content": "{\"CellLine\": [], \"Chemical\": [\"Arachidonic acid\", \"epoxyeicosatrienenoic acids\", \"hydroxyeicosatetrenoic acids\"], \"Disease\": [\"hypertensive\", \"hypertension\"], \"Gene\": [\"cytochrome P450 (CYP) epoxygenase\", \"CYP2C8\"], \"Species\": [\"patients\"], \"Variant\": [\"C35322T\", \"T deletion at 5063\", \"C to T at 33468\", \"C35322C\"]}", "role": "assistant" } ]

[ { "content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nAccelerating Robotic Reinforcement Learning via Parameterized Action Primitives\n\nMain Content:\n1 Introduction . Meaningful exploration remains a challenge for robotic reinforcement learning systems . For example , in the manipulation tasks shown in Figure 1 , useful exploration might correspond to picking up and placing objects in different configurations . However , random motions in the robot ’ s joint space will rarely , if ever , result in the robot touching the objects , let alone pick them up . Recent work , on the other hand , has demonstrated remarkable success in training RL agents to solve manipulation tasks [ 4 , 24 , 26 ] by sidestepping the exploration problem with careful engineering . Levine et al . [ 26 ] use densely shaped rewards , while Kalashnikov et al . [ 24 ] leverage a large scale robot infrastructure and Andrychowicz et al . [ 4 ] require training in simulation with engineered reward functions in order to transfer to the real world . In general , RL methods can be prohibitively data inefficient , require careful reward development to learn , and struggle to scale to more complex tasks without the aid of human demonstrations or carefully designed simulation setups . An alternative view on why RL is difficult for robotics is that it requires the agent to learn both what to do in order to achieve the task and how to control the robot to execute the desired motions . For example , in the kitchen environment featured at the bottom of Figure 1 , the agent would have to learn how to accurately manipulate the arm to reach different locations as well as how to grasp different objects , while also ascertaining what object it has to grasp and where to move it . Considered independently , the problems of controlling a robot arm to execute particular motions and figuring out the desired task from scalar reward feedback , then achieving it , are non-trivial . Jointly learning to solve both problems makes the task significantly more difficult . †Equal advising 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . s * robot Sample Primitive Select Argument Parameters Mean Variance Sample Arguments Feedback Control Loop srobot s′ robot ek ( s , s * ) Ck ( e , s ) Primitives are re-used across robots , environments , and tasks π Grasp : Lift : Push : Twist : Image Input Sparse Rewards 0 Hk.66Hk.33Hk In contrast to training RL agents on raw actions such as torques or delta positions , a common strategy is to decompose the agent action space into higher ( i.e. , what ) and lower ( i.e. , how ) level structures . A number of existing methods have focused on designing or learning this structure , from manually architecting and fine-tuning action hierarchies [ 14 , 27 , 32 , 47 ] , to organizing agent trajectories into distinct skills [ 3 , 20 , 41 , 50 ] to more recent work on leveraging large offline datasets in order to learn skill libraries [ 29 , 40 ] . While these methods have shown success in certain settings , many of them are either too sample inefficient , do not scale well to more complex domains , or lack generality due to dependence on task relevant data . In this work , we investigate the following question : instead of learning low-level primitives , what if we were to design primitives with minimal human effort , enable their expressiveness by parameterizing them with arguments and learn to control them with a high-level policy ? Such primitives have been studied extensively in task and motion planning ( TAMP ) literature [ 22 ] and implemented as parameterized actions [ 19 ] in RL . We apply primitive robot motions to redefine the policy-robot interface in the context of robotic reinforcement learning . These primitives include manually defined behaviors such as lift , push , top-grasp , and many others . The behavior of these primitives is parameterized by arguments that are the learned outputs of a policy network . For instance , top-grasp is parameterized by four scalar values : grasp position ( x , y ) , how much to move down ( z ) and the degree to which the gripper should close . We call this application of parameterized behaviors , Robot Action Primitives for RL ( RAPS ) . A crucial point to note is that these parameterized actions are easy to design , need only be defined once and can be re-used without modification across tasks . The main contribution of this work is to support the effectiveness of RAPS via a thorough empirical evaluation across several dimensions : • How do parameterized primitives compare to other forms of action parameterization ? • How does RAPS compare to prior methods that learn skills from offline expert data ? • Is RAPS agnostic to the underlying RL algorithm ? • Can we stitch the primitives to perform multiple complex manipulation tasks in sequence ? • Does RAPS accelerate exploration even in the absence of extrinsic rewards ? We investigate these questions across complex manipulation environments including Kitchen Suite , Metaworld and Robosuite domains . We find that a simple parameterized action based approach outperforms prior state-of-the-art by a significant margin across most of these settings2 . 2Please view our website for performance videos and links to our code : https : //mihdalal.github.io/raps/ 2 Related Work . Higher Level Action and Policy Spaces in Robotics In robotics literature , decision making over primitive actions that execute well-defined behaviors has been explored in the context of task and motion planning [ 9 , 22 , 23 , 43 ] . However , such methods are dependent on accurate state estimation pipelines to enable planning over the argument space of primitives . One advantage of using reinforcement learning methods instead is that the agent can learn to adjust its implicit state estimates through trial and error experience . Dynamic Movement Primitive and ensuing policy search approaches [ 11 , 21 , 25 , 36 , 37 ] leverage dynamical systems to learn flexible , parameterized skills , but are sensitive to hyper-parameter tuning and often limited to the behavior cloning regime . Neural Dynamic Policies [ 6 ] incorporate dynamical structure into neural network policies for RL , but evaluate in the state based regime with dense rewards , while we show that parameterized actions can enable RL agents to efficiently explore in sparse reward settings from image input . Hierarchical RL and Skill Learning Enabling RL agents to act effectively over temporally extended horizons is a longstanding research goal in the field of hierarchical RL . Prior work introduced the options framework [ 45 ] , which outlines how to leverage lower level policies as actions for a higher level policy . In this framework , parameterized action primitives can be viewed as a particular type of fixed option with an initiation set that corresponds to the arguments of the primitive . Prior work on options has focused on discovering [ 1 , 12 , 41 ] or fine-tuning options [ 5 , 14 , 27 ] in addition to learning higher level policies . Many of these methods have not been extended beyond carefully engineered state based settings . More recently , research has focused on extracting useful skills from large offline datasets of interaction data ranging from unstructured interaction data [ 49 ] , play [ 28 , 29 ] to demonstration data [ 2 , 35 , 39 , 40 , 44 , 46 , 53 ] . While these methods have been shown to be successful on certain tasks , the learned skills are only relevant for the environment they are trained on . New demonstration data must be collected to use learned skills for a new robot , a new task , or even a new camera viewpoint . Since RAPS uses manually specified primitives dependent only on the robot state , RAPS can re-use the same implementation details across robots , tasks and domains . Parameterized Actions in RL The parameterized action Markov decision process ( PAMDP ) formalism was first introduced in Masson et al . [ 31 ] , though there is a large body of earlier work in the area of hybrid discrete-continuous control , surveyed in [ 7 , 8 ] . Most recent research on PAMDPs has focused on better aligning policy architectures and RL updates with the nature of parameterized actions and has largely been limited to state based domains [ 13 , 51 ] . A number of papers in this area have focused on solving a simulated robot soccer domain modeled as either a single-agent [ 19 , 31 , 48 ] or multi-agent [ 15 ] problem . In this paper , we consider more realistic robotics tasks that involve interaction with and manipulation of common household objects . While prior work [ 42 ] has trained RL policies to select hand-designed behaviors for simultaneous execution , we instead train RL policies to leverage more expressive , parameterized behaviors to solve a wide variety of tasks . Closely related to this work is Chitnis et al . [ 10 ] , which develops a specific architecture for training policies over parameterized actions from state input and sparse rewards in the context of bi-manual robotic manipulation . Our work is orthogonal in that we demonstrate that a higher level policy architecture is sufficient to solve a large suite of manipulation tasks from image input . We additionally note that there is concurrent work [ 34 ] that also applies engineered primitives in the context of RL , however , we consider learning from image input and sparse terminal rewards . 3 Robot Action Primitives in RL . To address the challenge of exploration and behavior learning in continuous action spaces , we decompose a desired task into the what ( high level task ) and the how ( control motion ) . The what is handled by the environment-centric RL policy while the how is handled by a fixed , manually defined set of agent-centric primitives parameterized by continuous arguments . This enables the high level policy to reason about the task at a high level by choosing primitives and their arguments while leaving the low-level control to the parameterized actions themselves . 3.1 Background . Let the Markov decision process ( MDP ) be defined as ( S , A , R ( s , a , s′ ) , T ( s′|s , a ) , p ( s0 ) , γ , ) in which S is the set of true states , A is the set of possible actions , R ( s , a , s′ ) is the reward function , T ( s′|s , a ) is the transition probability distribution , p ( s0 ) defines the initial state distribution , and γ is the discount factor . The agent executes actions in the environment using a policy π ( a|s ) with a corresponding trajectory distribution p ( τ = ( s0 , a0 , ... at−1 , sT ) ) = p ( s0 ) Πtπ ( at|st ) T ( st+1|st , at ) . The goal of the RL agent is to maximize the expected sum of rewards with respect to the policy : Es0 , a0 , ... at−1 , sT , ∼p ( τ ) [ ∑ t γ tR ( st , at ) ] . In the case of vision-based RL , the setup is now a partially observed Markov decision process ( POMDP ) ; we have access to the true state via image observations . In this case , we include an observation space O which corresponds to the set of visual observations that the environment may emit , an observation model p ( o|s ) which defines the probability of emission and policy π ( a|o ) which operates over observations . In this work , we consider various modifications to the action space A while keeping all other components of the MDP or POMDP the same .", "role": "user" }, { "content": "This paper proposes a novel action space for efficient learning of robotic manipulation tasks. In contrast to typical joint space and task space control, the proposed action space consists of a set of hand-designed agent-centric primitive skills (e.g. lift arm for $d$ cm, close grippers to $d$ cm, and rotate wrist for $\\theta$) with continuous parameters (e.g. $d$ and $\\theta$). Since the primitive skills are agent-centric (i.e. conditioned only on a robot state), the primitive skills can be used across diverse environments and tasks. With this appropriately designed action space, a high-level policy learns to choose which primitive skill and corresponding parameters to execute from visual inputs and sparse rewards. The exhaustive experiments on three robotic manipulation benchmarks and three different RL algorithms demonstrate improved sample efficiency of the proposed action space. The proposed method outperforms baselines with joint space control, task space control, and learned skills from offline data or random interactions.", "role": "assistant" } ]

[ { "content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nOne common approach to measuring compositionality is to compare distributional representations of an MWE and its component words (e.g., Schone and Jurafsky, 2001; Baldwin et al., 2003; Katz and Giesbrecht, 2006; Reddy et al., 2011; Schulte im Walde et al., 2013; <cite>Salehi et al., 2015</cite>) .", "role": "user" }, { "content": "[\"Background\"]", "role": "assistant" } ]

[ { "content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND:: Postoperative sore throat is a common complication of endotracheal intubation and can lead to dissatisfaction after surgery. Airway management has the strongest influence on the incidence of sore throat and improving endotracheal intubating conditions can reduce this complaint. Type of induction agent used during anesthesia can contribute to variances in the degree of post-operative sore throat.\n\nABSTRACT.OBJECTIVES:: We aimed to compare the incidence of postoperative sore throat after rapid sequence induction with Succinylcholine and high dose Cisatracurium.\n\nABSTRACT.PATIENTS AND METHODS:: The study was carried out on patients admitted to Shohada-e-Tajrish hospital for emergent abdominal surgery. Of the 80 patients who were enrolled in the study, 40 were randomly assigned to receive Succinylcholine while the remaining patients received Cistracurium during induction. Sore throat, muscle ache, hoarseness, dry throat and pain were assessed in each patient at baseline in recovery and at 2, 4, 12 and 24 hours post-operation.\n\nABSTRACT.RESULTS:: Number of patients who developed sore throat was significantly higher in the Succinylcholine group (75%) compared to Cisatracurium group (27.5%) at the time of entrance to the recovery room (P = 0.001). These numbers decreased at 2 hours post–operation (42% versus 17.5%) but the difference was still statistically significant (P < 0.05). At 12 (P = 0.062) and 24 (P = 0.14) hours post operation, the difference was no longer significant.\n\nABSTRACT.CONCLUSIONS:: Use of high dose Cisatracurium for induction during rapid sequence intubation carries a lower chance of developing sore throat compared to Succinylcholine. Studies comparing other adverse effects of these two agents are required to guide physician's choice of induction agent.\n\nExtracted Data:", "role": "user" }, { "content": "[[\"Succinylcholine group\", \"Cisatracurium group\", \"sore throat 2 hours post\\u2013operation\", \"significantly increased\", \"Number of patients who developed sore throat was significantly higher in the Succinylcholine group (75%) compared to Cisatracurium group (27.5%) at the time of entrance to the recovery room (P = 0.001). These numbers decreased at 2 hours post\\u2013operation (42% versus 17.5%) but the difference was still statistically significant (P \\u003c 0.05).\"]]", "role": "assistant" } ]