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" } ]