wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our experimental results show that adapting the node setup to the workload at runtime can save up to 50\% power consumption and a quantized neural network reaches an accuracy value higher than 98 \% for arrhythmia disorders detection on MIT-BIH Arrhythmia dataset.
Revised:  Our experimental results show that adapting the node setup to the workload at runtime can save up to 50\% power consumption and a quantized neural network reaches an accuracy value higher than 97 \% for arrhythmia disorders detection on MIT-BIH Arrhythmia dataset.
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Original:  The validity of our implementation is proven by finding the efficient frontier , using D-Wave Hybrid and its Advantage quantum processor, on static portfolios taking assets from the S& P500. We use three different subsets of this index. First, the S P100 which consists of 100 of the largest companies of the S P500;
Revised:  The validity of our implementation is proven by finding the efficient frontier , using D-Wave Hybrid and its Advantage quantum processor, on static portfolios taking assets from the S& P100 which consists of 100 of the largest companies of the S P500;
Edit intent: coherence
Original:  We show that our algorithm is able to steer the user along paths that result in significantly fewer red resets than existing state-of-the-art algorithms in both static and dynamic scenes. 
Revised:  We show that our algorithm is able to steer the user along paths that result in significantly fewer red resets than existing state-of-the-art algorithms in both static and dynamic scenes. Our project website is available at URL
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Original:  Our objective is to determine a rule which identifies the best population with a fixed minimum confidence using as few observations as possible, i.e. fixed-confidence (FC) best arm identification (BAI) in multi-armed bandits.
Revised:  Our objective is to determine a rule which identifies the best arm with a fixed minimum confidence using as few observations as possible, i.e. fixed-confidence (FC) best arm identification (BAI) in multi-armed bandits.
Edit intent: clarity
Original:  Our objective is to determine a rule which identifies the best arm with a fixed minimum confidence using as few observations as possible, i.e.  fixed-confidence (FC) best arm identification (BAI) in multi-armed bandits.
Revised:  Our objective is to determine a rule which identifies the best arm with a fixed minimum confidence using as few observations as possible, i.e. this is a fixed-confidence (FC) best arm identification (BAI) in multi-armed bandits.
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Original:  This will be done by introducing a new data structure called Wise Red-Black Trees, a variation of classical Red-Black Trees.
Revised:  The goal is to improve existing solutions, by optimizing the average-case time complexity and getting rid of heavy multiplicative constants in the worst-case, without sacrificing space complexity. This is an high-impact operation in practical applications, and will be performed by introducing a new data structure called Wise Red-Black Trees, a variation of classical Red-Black Trees.
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Original:  This will be done by introducing a new data structure called Wise Red-Black Trees, a variation of classical Red-Black Trees. The goal is to improve time complexity, while also keeping spatial complexity to a minimum. The optimization will be visible both at the asymptote and in terms of multiplicative constants, affecting not only the worst-case, but also the average one .
Revised:  This will be done by introducing a new data structure called Wise Red-Black Trees, a variation of classical Red-Black Tree .
Edit intent: coherence
Original: Order-agnostic autoregressive distribution  estimation (OADE), i.e., autoregressive distribution estimation where the features can occur in an arbitrary order, is a challenging problem in generative machine learning.
Revised: Order-agnostic autoregressive distribution (density) estimation (OADE), i.e., autoregressive distribution estimation where the features can occur in an arbitrary order, is a challenging problem in generative machine learning.
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Original:  As a proof of concept, we show that a Transformer trained on this input (which we refer to as "the DEformer", i.e., the distribution estimating Transformer) can effectively model binarized-MNIST, approaching the average negative log-likelihood of fixed order autoregressive distribution estimating algorithms while still being entirely order-agnostic  .
Revised:  As a proof of concept, we show that a Transformer trained on this input (which we refer to as "the DEformer", i.e., the distribution estimating Transformer) can effectively model binarized-MNIST, approaching the performance of fixed-order autoregressive distribution estimating algorithms while still being entirely order-agnostic .
Edit intent: clarity
Original:  As a proof of concept, we show that a Transformer trained on this input (which we refer to as "the DEformer", i.e., the distribution estimating Transformer) can effectively model binarized-MNIST, approaching the average negative log-likelihood of fixed order autoregressive distribution estimating algorithms while still being entirely order-agnostic  .
Revised:  As a proof of concept, we show that a Transformer trained on this input (which we refer to as "the DEformer", i.e., the distribution estimating Transformer) can effectively model binarized-MNIST, approaching the average negative log-likelihood of fixed order autoregressive distribution estimating algorithms while still being entirely order-agnostic . Additionally, we find that the DEformer surpasses the performance of recent flow-based architectures when modeling a tabular dataset .
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Original:  We also both achieve new state-of-the-art with FID 20.11, IS 10.16 , and FID 3.66, respectively on STL-10 and CelebA.
Revised:  We also both achieve new state-of-the-art with FID 15.17, IS 11.01 , and FID 3.66, respectively on STL-10 and CelebA.
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Original:  We share a dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million `` in-stock '' molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome.
Revised:  We share a dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million " in-stock '' molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome.
Edit intent: fluency
Original:  We share a dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million `` in-stock '' molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome.
Revised:  We share a dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million `` in-stock " molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome.
Edit intent: fluency
Original:  Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA], while the artificial ones are frequently used in self-supervised learning (SSL) paradigms, e.g., SimCLR andBarlow Twins .
Revised:  Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA], while the artificial ones are frequently used in self-supervised learning (SSL) paradigms, e.g., andBarlow Twins .
Edit intent: clarity
Original:  Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA], while the artificial ones are frequently used in self-supervised learning (SSL) paradigms, e.g., SimCLR andBarlow Twins .
Revised:  Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA], while the artificial ones are frequently used in self-supervised learning (SSL) paradigms, e.g., SimCLR Barlow Twins .
Edit intent: fluency
Original:  Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA], while the artificial ones are frequently used in self-supervised learning (SSL) paradigms, e.g., SimCLR andBarlow Twins .
Revised:  Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA], while the artificial ones are frequently used in self-supervised learning (SSL) paradigms, e.g., SimCLR andSimCLR and Barlow Twins .
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Original:  In A/A tests, for a set of 48 outcome metrics commonly monitored in Facebook experiments the estimator has over 70 %DIFDELCMD < \\%%% %DIF < lower variance than the simple difference-in-means estimator, and about 19\\% lower variance than the common univariate procedure which adjusts only for pre-experiment values of the outcome.abstract 
Revised:  In A/A tests, for a set of 48 outcome metrics commonly monitored in Facebook experiments the estimator has over 70 %DIFDELCMD < \\%%% %DIF < lower variance than the simple difference-in-means estimator, and about 19\\% lower variance than the common univariate procedure which adjusts only for pre-experiment values of the outcome.abstract \% lower variance than the simple difference-in-means estimator, and about 19\% lower variance than the common univariate procedure which adjusts only for pre-experiment values of the outcome.
Edit intent: fluency
Original:  Results of our experiments significantly demonstrate our proposed framework for transfer learning is a potential and effective method for brain tumor multi-classification tasks.abstract 
Revised:  Results of our experiments significantly demonstrate our proposed framework for transfer learning is a potential and effective method for brain tumor multi-classification tasks.abstract \% on the curated dataset, 93.80\% on the Harvard Whole Brain Atlas dataset, and 97.05\% accuracy on the School of Biomedical Engineering dataset. Results of our experiments significantly demonstrate our proposed framework for transfer learning is a potential and effective method for brain tumor multi-classification tasks.
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Original: Short videos have many applications on fashion trends, hot spots, street interviews, public education, and creative advertising. We propose an Edge-Aware Network (EANet) that uses edge information to refine the segmentation edge.
Revised: This is a short technical report introducing the solution of Team Rat for Short-video Parsing Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR 2021. In this report, we propose an Edge-Aware Network (EANet) that uses edge information to refine the segmentation edge.
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Original:  And experiments show our proposed EANet boots up the facial parsing results. We also use post-process like grab cut to refine and merge the parsing results.
Revised:  To further obtain the finer edge results, we introduce edge attention loss that only compute cross entropy on the edges, it can effectively reduce the classification error around edge and get more smooth boundary. Benefiting from the edge information and edge attention loss, the proposed EANet achieves 86.16
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Original:  Our models RevGNN-Deep (1001 layers with 80 channels each) and RevGNN-Wide (448 layers with 224 channels each) were both trained on a single commodity GPU and achieve an ROC-AUC of 87.74 \pm 0.13 and 88.14 \pm 0.15 on the ogbn-proteins dataset.
Revised:  Our models RevGNN-Deep (1001 layers with 80 channels each) and RevGNN-Wide (448 layers with 224 channels each) were both trained on a single commodity GPU and achieve an ROC-AUC of 87.74 \pm 0.13 and 88.24 \pm 0.15 on the ogbn-proteins dataset.
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Original: Recently, visual Transformer (ViT) and its following works abandon the convolution and exploit the self-attention operation, attaining a comparable or even higher accuracy than CNN . More recently, MLP-Mixer abandons both the convolution and the self-attention operation, proposing an architecture containing only MLP layers.
Revised: Recently, visual Transformer (ViT) and its following works abandon the convolution and exploit the self-attention operation, attaining a comparable or even higher accuracy than CNNs . More recently, MLP-Mixer abandons both the convolution and the self-attention operation, proposing an architecture containing only MLP layers.
Edit intent: clarity
Original: RGB-Infrared (IR) person re-identification aims to retrieve person-of-interest between heterogeneous modalities, suffering from large  modality discrepancy caused by different sensory devices. Existing methods mainly focus on global-level modality alignment, whereas neglect sample-level modality divergence to some extent , leading to performance degradation.
Revised: RGB-Infrared (IR) person re-identification aims to retrieve person-of-interest between heterogeneous modalities, suffering from large modality discrepancy caused by different sensing wavelength ranges. Existing work usually minimizes such discrepancy by aligning domain distribution of global features, while neglecting the intra-modality structural relations between semantic parts. This could result in the network overly focusing on local cues, without considering long-range body part dependencies , leading to performance degradation.
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Original:  Existing methods mainly focus on global-level modality alignment, whereas neglect sample-level modality divergence to some extent , leading to performance degradation. This paperattempts to find RGB-IR ReID solutions from tackling sample-level modality difference, and presents a Geometry-Guided Dual-Alignment learning framework (G^2DA)
Revised:  Existing methods mainly focus on global-level modality alignment, whereas neglect sample-level modality divergence to some extent , leading to meaningless region representations. In this paper, we propose a graph-enabled distribution matching solution, dubbed Geometry-Guided Dual-Alignment learning framework (G^2DA)
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Original:  This paperattempts to find RGB-IR ReID solutions from tackling sample-level modality difference, and presents a Geometry-Guided Dual-Alignment learning framework (G^2DA) , which jointly enhances modality-invariance and reinforces discriminability with human topological structure in features to boost the overall matching performance . Specifically, G^2DA extracts accurate body part features with a pose estimator, serving as a semantic bridge complementing the missing local details in global descriptor.
Revised:  This paperattempts to find RGB-IR ReID solutions from tackling sample-level modality difference, and presents a Geometry-Guided Dual-Alignment (G2DA) learning, for RGB-IR ReID. It can jointly encourage the cross-modal consistency between part semantics and structural relations for fine-grained modality alignment by solving a graph matching task within a multi-scale skeleton graph that embeds human topology information . Specifically, G^2DA extracts accurate body part features with a pose estimator, serving as a semantic bridge complementing the missing local details in global descriptor.
Edit intent: coherence
Original:  , which jointly enhances modality-invariance and reinforces discriminability with human topological structure in features to boost the overall matching performance . Specifically, G^2DA extracts accurate body part features with a pose estimator, serving as a semantic bridge complementing the missing local details in global descriptor. Based on extracted local and global features , a novel distribution constraint derived from optimal transport is introduced to mitigate the modality gap in a fine-grained sample-level manner. Beyond pair-wise relations across two modalities, it additionally measures the structural similarity of different parts, thus both multi-level features and their relations are kept consistent in the common feature space. Considering the inherent human-topology information, we further advance a geometry-guided graph learning module to refine each part features, where relevant regions can be emphasized while meaningless ones are suppressed, effectively facilitating robust feature learning. Extensive experiments on two standard benchmark datasets validate the superiority of our proposed method, yielding competitive performance over the state-of-the-art approaches .
Revised:  , which jointly enhances modality-invariance and reinforces discriminability with human topological structure in features to boost the overall matching performance . Specifically, we propose to build a semantic-aligned complete graph into which all cross-modality images can be mapped via a pose-adaptive graph construction mechanism. This graph represents extracted whole-part features by nodes and expresses the node-wise similarities with associated edges. To achieve the graph-based dual-alignment learning, an Optimal Transport (OT) based structured metric is further introduced to simultaneously measure point-wise relations and group-wise structural similarities across modalities. By minimizing the cost of an inter-modality transport plan, G2DA can learn a consistent and discriminative feature subspace for cross-modality image retrieval. Furthermore, we advance a Message Fusion Attention (MFA) mechanism to adaptively reweight the information flow of semantic propagation, effectively strengthening the discriminability of extracted semantic features .
Edit intent: clarity
Original:  Our model achieved performance of Macro-aver-aged F1: 0.62898 and Micro-averaged F1: 0.68555 , and is performing better than a performance of the previous state-of-the-art model  .
Revised:  Our model achieved performance of macro-averaged F1: 0.62898 and Micro-averaged F1: 0.68555 , and is performing better than a performance of the previous state-of-the-art model .
Edit intent: fluency
Original:  Our model achieved performance of Macro-aver-aged F1: 0.62898 and Micro-averaged F1: 0.68555 , and is performing better than a performance of the previous state-of-the-art model  .
Revised:  Our model achieved performance of Macro-aver-aged F1: 0.62898 and micro-averaged F1: 0.68555 , and is performing better than a performance of the previous state-of-the-art model .
Edit intent: fluency
Original:  Our model achieved performance of Macro-aver-aged F1: 0.62898 and Micro-averaged F1: 0.68555 , and is performing better than a performance of the previous state-of-the-art model  .
Revised:  Our model achieved performance of Macro-aver-aged F1: 0.62898 and Micro-averaged F1: 0.68555 and is performing better than a performance of the previous state-of-the-art model .
Edit intent: fluency
Original:  Our model achieved performance of Macro-aver-aged F1: 0.62898 and Micro-averaged F1: 0.68555 , and is performing better than a performance of the previous state-of-the-art model  .
Revised:  Our model achieved performance of Macro-aver-aged F1: 0.62898 and Micro-averaged F1: 0.68555 , and is performing better than a performance of the state-of-the-art model .
Edit intent: clarity
Original:  Our model achieved performance of Macro-aver-aged F1: 0.62898 and Micro-averaged F1: 0.68555 , and is performing better than a performance of the previous state-of-the-art model  .
Revised:  Our model achieved performance of Macro-aver-aged F1: 0.62898 and Micro-averaged F1: 0.68555 , and is performing better than a performance of the previous state-of-the-art model using the MIMIC-III dataset .
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Original:  In order to make  best use of the important features in the dataset, we propose a technique named "synthetic generation" to develop more combined features based on arithmetic operation, which ends up improving the accuracy and AUC of  original CatBoost model  .
Revised:  In order to make the best use of the important features in the dataset, we propose a technique named "synthetic generation" to develop more combined features based on arithmetic operation, which ends up improving the accuracy and AUC of original CatBoost model .
Edit intent: fluency
Original:  In order to make  best use of the important features in the dataset, we propose a technique named "synthetic generation" to develop more combined features based on arithmetic operation, which ends up improving the accuracy and AUC of  original CatBoost model  .
Revised:  In order to make best use of the important features in the dataset, we propose a technique named "synthetic generation" to develop more combined features based on arithmetic operation, which ends up improving the accuracy and AUC of the original CatBoost model .
Edit intent: fluency
Original:  In order to make  best use of the important features in the dataset, we propose a technique named "synthetic generation" to develop more combined features based on arithmetic operation, which ends up improving the accuracy and AUC of  original CatBoost model  .
Revised:  In order to make best use of the important features in the dataset, we propose a technique named "synthetic generation" to develop more combined features based on arithmetic operation, which ends up improving the accuracy and AUC of original CatBoost model . We obtain a high accuracy of 95.84\% and well-performed AUC of 98.80\% compared with the existent literature of related research .
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Original: Automated Program Repair (APR) helps improve the efficiency of software development and maintenance. Recent APR techniques use deep learning, particularly the encoder-decoder architecture, to generate patches. Though existing DL-based APR approaches have proposed different encoder architectures, the decoder remains to be the standard one, which generates a sequence of tokens one by one to replace the faulty statement. This decoder has multiple limitations: 1) allowing to generate syntactically incorrect programs, 2) inefficiently representing small edits, and 3) not being able to generate project-specific identifiers.
Revised: Automatic software development has been a research hot spot in the field of software engineering (SE) in the past decade. In particular, deep learning (DL) has been applied and achieved a lot of progress in various SE tasks. Among all applications, automatic code generation by machines as a general concept, including code completion and code synthesis, is a common expectation in the field of SE, which may greatly reduce the development burden of the software developers and improves the efficiency and quality of the software development process to a certain extent. Code completion is an important part of modern integrated development environments (IDEs). Code completion technology effectively helps programmers complete code class names, method names, and 3) not being able to generate project-specific identifiers.
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