wanyu/IteraTeR_v2 huggingface dataset:
Original:  Recently, a new problem was introduced in the field to predict the 3D human poses from an observed 2D poses.
Revised:  Recently, a new problem was introduced in the field to predict the 3D human poses from observed 2D poses.
Edit intent: fluency
Original:  By the design, Skeleton-Graph predicts the future 3D poses without divergence on the long-term  unlike prior works.
Revised:  By the design, Skeleton-Graph predicts the future 3D poses without divergence in the long-term unlike prior works.
Edit intent: clarity
Original:  By the design, Skeleton-Graph predicts the future 3D poses without divergence on the long-term  unlike prior works.
Revised:  By the design, Skeleton-Graph predicts the future 3D poses without divergence on the long-term , unlike prior works.
Edit intent: fluency
Original:  We also introduce a new metric that measures the divergence of predictions on the long-term .
Revised:  We also introduce a new metric that measures the divergence of predictions in the long term .
Edit intent: fluency
Original:  As op-posed to earlier architectures, 5G Networks provide low latency access to ser-vices with high reliability.
Revised:  As opposed to earlier architectures, 5G Networks provide low latency access to ser-vices with high reliability.
Edit intent: fluency
Original:  As op-posed to earlier architectures, 5G Networks provide low latency access to ser-vices with high reliability.
Revised:  As op-posed to earlier architectures, 5G Networks provide low latency access to services with high reliability.
Edit intent: fluency
Original:  This paper analyzes the feasibility of a real-time Computer Vi-sion use case model in small devices using a fully deployed 5G Network.
Revised:  This paper analyzes the feasibility of a real-time Computer Vision use case model in small devices using a fully deployed 5G Network.
Edit intent: fluency
Original:  The results show an improvement in Latency and Throughput over previous genera-tions , and a high percentage of Availability and Reliability in the analyzed use case.
Revised:  The results show an improvement in Latency and Throughput over previous generations , and a high percentage of Availability and Reliability in the analyzed use case.
Edit intent: fluency
Original:  However, at present, most network analysis ignores the direction.
Edit intent: coherence
Original:  By considering a sparsity parameter, under some mild conditions, we show the proposed approach can consistently recover hidden row and column communities for different scaling of degrees.
Revised:  By considering a sparsity parameter, under mild conditions, we show the proposed approach can consistently recover hidden row and column communities for different scaling of degrees.
Edit intent: clarity
Original:  Furthermore, our theoretical results under DiDCSBM are consistent with those under DiSBM when DiDCSBM degenerates to DiSBM.
Revised:  Furthermore, the theoretical results under DiDCSBM are consistent with those under DiSBM when DiDCSBM degenerates to DiSBM.
Edit intent: clarity
Original:  Furthermore, our theoretical results under DiDCSBM are consistent with those under DiSBM when DiDCSBM degenerates to DiSBM.
Revised:  Furthermore, our theoretical results under DiDCSBM are consistent with those under DiSBM.
Edit intent: clarity
Original:  Cardiac mapping techniques acquire numerous local temporal measurements and combine them to visualize the spread of electrophysiological wave phenomena across the heart surface.
Revised:  Cardiac mapping techniques acquire local temporal measurements and combine them to visualize the spread of electrophysiological wave phenomena across the heart surface.
Edit intent: clarity
Original:  However, low spatial resolutions , sparse measurement locations, noise and other artifacts make it challenging to accurately visualize spatio-temporal activity.
Revised:  However, low spatial resolution , sparse measurement locations, noise and other artifacts make it challenging to accurately visualize spatio-temporal activity.
Edit intent: fluency
Original:  For instance, electro-anatomical catheter mapping is severely limited by the sparsity of the measurements  and optical mapping is prone to noise and motion artifacts.
Revised:  For instance, electro-anatomical catheter mapping is severely limited by the sparsity of the measurements , and optical mapping is prone to noise and motion artifacts.
Edit intent: fluency
Original:  Rather than encoding a phase signal from time-series data, the network instead learns to directly associate short spatio-temporal sequences of electrical data with phase maps and the positions of phase singularities . Using this method, we were able to accurately compute phase maps and locate rotor cores even from extremely sparse and noisy data, generated from both optical mapping experiments and computer simulations.
Revised:  Rather than encoding a phase signal from time-series data, the network instead learns to directly associate phase maps and the positions of phase singularities . Using this method, we were able to accurately compute phase maps and locate rotor cores even from extremely sparse and noisy data, generated from both optical mapping experiments and computer simulations.
Edit intent: clarity
Original:  Neural networks are a promising alternative to conventional phase mapping and rotor core localization methods, that could be used in optical mapping studies in basic cardiovascular research  as well as in the clinical setting for the analysis of atrial fibrillation  .
Revised:  Neural networks are a promising alternative to conventional phase mapping and rotor core localization methods, that could be used in optical mapping studies in basic cardiovascular research , as well as in the clinical setting for the analysis of atrial fibrillation .
Edit intent: fluency
Original:  Neural networks are a promising alternative to conventional phase mapping and rotor core localization methods, that could be used in optical mapping studies in basic cardiovascular research  as well as in the clinical setting for the analysis of atrial fibrillation  .
Revised:  Neural networks are a promising alternative to conventional phase mapping and rotor core localization methods, that could be used in optical mapping studies in basic cardiovascular research as well as the mapping of atrial fibrillation .
Edit intent: clarity
Original:  Recently, learning from synthetic data, which benefits from the popularity of synthetic data engine, has attracted attention from both academia and the public eye .
Revised:  Recently, learning from synthetic data, which benefits from the popularity of synthetic data engine, has attracted great attention from the public eyes .
Edit intent: clarity
Original:  However, existing synthetic datasets are limited in quantity, diversity and realisticity, and cannot be efficiently used for generalizable re-ID problem.
Revised:  However, existing datasets are limited in quantity, diversity and realisticity, and cannot be efficiently used for generalizable re-ID problem.
Edit intent: clarity
Original:  However, existing synthetic datasets are limited in quantity, diversity and realisticity, and cannot be efficiently used for generalizable re-ID problem.
Revised:  However, existing synthetic datasets are limited in quantity, diversity and realisticity, and cannot be efficiently used for re-ID problem.
Edit intent: clarity
Original:  To address this challenge, we construct and label a large-scale synthetic person dataset named FineGPR with fine-grained attribute distribution .
Revised:  To address this challenge, we manually construct a large-scale synthetic person dataset named FineGPR with fine-grained attribute distribution .
Edit intent: clarity
Original:  To address this challenge, we construct and label a large-scale synthetic person dataset named FineGPR with fine-grained attribute distribution .
Revised:  To address this challenge, we construct and label a large-scale person dataset named FineGPR with fine-grained attribute distribution .
Edit intent: clarity
Original:  To address this challenge, we construct and label a large-scale synthetic person dataset named FineGPR with fine-grained attribute distribution .
Revised:  To address this challenge, we construct and label a large-scale synthetic person dataset named FineGPR with fine-grained attribute annotations .
Edit intent: clarity
Original:  Moreover, aiming to fully exploit the potential of FineGPR and promote the efficient training from millions of synthetic data, we propose an attribute analysis pipeline AOST to learn attribute distribution in target domain, then apply style transfer network to eliminate the gap between synthetic and real-world data and thus is freely deployed to new scenarios.
Revised:  Moreover, aiming to fully exploit the potential of FineGPR and promote the efficient training from millions of synthetic data, we propose an attribute analysis pipeline AOST to learn attribute distribution in real domain, then apply style transfer network to eliminate the gap between synthetic and real-world data and thus is freely deployed to new scenarios.
Edit intent: clarity
Original:  Moreover, aiming to fully exploit the potential of FineGPR and promote the efficient training from millions of synthetic data, we propose an attribute analysis pipeline AOST to learn attribute distribution in target domain, then apply style transfer network to eliminate the gap between synthetic and real-world data and thus is freely deployed to new scenarios.
Revised:  Moreover, aiming to fully exploit the potential of FineGPR and promote the efficient training from millions of synthetic data, we propose an attribute analysis pipeline AOST to learn attribute distribution in target domain, then eliminates the gap between synthetic and real-world data and thus is freely deployed to new scenarios.
Edit intent: coherence
Original:  Experiments conducted on benchmarks demonstrate that FineGPR with AOST outperforms (or is on par with) existing real and synthetic datasets, which suggests its feasibility for re-ID  and proves the proverbial less-is-more principle.
Revised:  Experiments conducted on benchmarks demonstrate that FineGPR with AOST outperforms (or is on par with) existing real and synthetic datasets, which suggests its feasibility for re-ID task and proves the proverbial less-is-more principle.
Edit intent: clarity
Original:  In order to make full use of the overall 3D context, we develop a whole-volumebased coarse-to-fine framework for efficient and effective abdominal multi-organ segmentation.
Revised:  In order to make full use of the overall 3D context, we develop a whole-volume-based coarse-to-fine framework for efficient and effective abdominal multi-organ segmentation.
Edit intent: fluency
Original:  When we ask a question (e.g. A bat and ball cost 1.10 \1 . The bat costs 1 \1 more than the ball.
Revised:  When we ask a question (e.g. A bat and ball cost 1.10 \1 . The bat costs \1 more than the ball.
Edit intent: fluency
Original:  How much does the ball cost?), with prior, we can identify fast/slow thinking ( .10 / .05 ).
Revised:  How much does the ball cost?), with prior, we can identify fast/slow thinking ( \.10 / .05 ).
Edit intent: fluency
Original:  How much does the ball cost?), with prior, we can identify fast/slow thinking ( .10 / .05 ).
Revised:  How much does the ball cost?), with prior, we can identify fast/slow thinking ( .10 / \.05 ).
Edit intent: fluency
Original:  A very clever method, surprisingly popular, additionally asks what percentage of other people answer .10 / .05 and selects the answer that is more popular than people predict.
Revised:  A very clever method, surprisingly popular, additionally asks what percentage of other people answer \.10 / .05 and selects the answer that is more popular than people predict.
Edit intent: fluency
Original:  A very clever method, surprisingly popular, additionally asks what percentage of other people answer .10 / .05 and selects the answer that is more popular than people predict.
Revised:  A very clever method, surprisingly popular, additionally asks what percentage of other people answer .10 / \.05 and selects the answer that is more popular than people predict.
Edit intent: fluency
Original:  Here we propose a simple minimal paradigm that elicits the full hierarchy of the collected answers: we ask a single open response question and elicit each respondent's answer (e.g. .05 ) and guess(es) for other people's answers (e.g. .10 ).
Revised:  Here we propose a simple minimal paradigm that elicits the full hierarchy of the collected answers: we ask a single open response question and elicit each respondent's answer (e.g. \.05 ) and guess(es) for other people's answers (e.g. .10 ).
Edit intent: fluency
Original:  Here we propose a simple minimal paradigm that elicits the full hierarchy of the collected answers: we ask a single open response question and elicit each respondent's answer (e.g. .05 ) and guess(es) for other people's answers (e.g. .10 ).
Revised:  Here we propose a simple minimal paradigm that elicits the full hierarchy of the collected answers: we ask a single open response question and elicit each respondent's answer (e.g. .05 ) and guess(es) for other people's answers (e.g. \.10 ).
Edit intent: fluency
Original:  How much does the ball cost? ), with prior, we can identify fast/slow thinking ( \.10 /\.05).
Revised:  How much does the ball cost? , the majority may answer \.10 /\.05).
Edit intent: clarity
Original:   Here we propose a simple minimal paradigm that elicits the full hierarchy of the collected answers: we ask a single open response question and elicit each respondent's answer (e.g. \.05) and guess(es) for other people's answers (e.g. \.10) .
Revised:  Here we focus on eliciting the full thinking hierarchy without any prior. By asking a single open response question and elicit each respondent's answer (e.g. \.05) and guess(es) for other people's answers (e.g. \.10) .
Edit intent: clarity
Original:   Here we propose a simple minimal paradigm that elicits the full hierarchy of the collected answers: we ask a single open response question and elicit each respondent's answer (e.g. \.05) and guess(es) for other people's answers (e.g. \.10) .
Revised:  Here we propose a simple minimal paradigm that elicits the full hierarchy of the collected answers: we ask a single open response question and eliciting both of each respondent's answer (e.g. \.05) and guess(es) for other people's answers (e.g. \.10) .
Edit intent: clarity
Original:  We record the number of people who report a specific answer-guess pair (e.g. 10 people answer \.05 and guess \.10) by an answer-guess matrix .
Revised:  We record the number of people who report a specific answer-guess pair (e.g. 10 people answer \.05 and guess \.10) .
Edit intent: clarity
Original:  Our method has been integrated into commercial products and its effectiveness has been verified with unconstrained user inputs.
Edit intent: coherence
Original:  The extracted moments reveal successive degrees of regularization of the weight potential field around the local neighborhood of the model output.
Revised:  The extracted moments reveal successive degrees of regularization of the weight PDF around the local neighborhood of the model output.
Edit intent: clarity
Original:  Such localized moments represent well the PDF tails and provide significantly greater accuracy of the model 's predictive uncertainty than the central moments characterized by Bayesian and ensemble methods or their variants .
Revised:  Such localized moments represent well the PDF tails and provide significantly greater accuracy of model predictive uncertainty than the central moments characterized by Bayesian and ensemble methods or their variants .
Edit intent: fluency
Original:  Such localized moments represent well the PDF tails and provide significantly greater accuracy of the model 's predictive uncertainty than the central moments characterized by Bayesian and ensemble methods or their variants .
Revised:  Such localized moments represent well the PDF tails and provide significantly greater accuracy of the model 's predictive uncertainty than the central moments characterized by Bayesian and ensemble methods .
Edit intent: clarity
Original: Evolutionary generative adversarial networks (E-GAN) tries to alleviate mode collapse and gradient vanish that plague generative adversarial networks by introducing evolutionary computation.
Revised: Evolutionary generative adversarial networks (E-GAN) attempts to alleviate mode collapse and gradient vanish that plague generative adversarial networks by introducing evolutionary computation.
Edit intent: fluency
Original: Evolutionary generative adversarial networks (E-GAN) tries to alleviate mode collapse and gradient vanish that plague generative adversarial networks by introducing evolutionary computation.
Revised: Evolutionary generative adversarial networks (E-GAN) tries to alleviate mode collapse and vanishing gradient that plague generative adversarial networks by introducing evolutionary computation.
Edit intent: clarity
Original:  Experiments on various datasets demonstrate the effectiveness of IE-GAN  and show that our method is competitive in terms of generated samples quality and time efficiency.
Revised:  Experiments on various datasets demonstrate the effectiveness of IE-GAN , and show that our method is competitive in terms of generated samples quality and time efficiency.
Edit intent: fluency
Original:  Experiments on various datasets demonstrate the effectiveness of IE-GAN  and show that our method is competitive in terms of generated samples quality and time efficiency.
Revised:  Experiments on various datasets demonstrate the effectiveness of IE-GAN and show that our framework is competitive in terms of generated samples quality and time efficiency.
Edit intent: clarity
Original:  Experiments on various datasets demonstrate the effectiveness of IE-GAN  and show that our method is competitive in terms of generated samples quality and time efficiency.
Revised:  Experiments on various datasets demonstrate the effectiveness of IE-GAN and show that our method is competitive in terms of the quality of generated samples and time efficiency.
Edit intent: fluency
Original: Peg solitaire is traditionally a one-player game played on a grid board filled with pegs.
Revised: Peg solitaire is classically a one-player game played on a grid board filled with pegs.
Edit intent: clarity
Original: Peg solitaire is traditionally a one-player game played on a grid board filled with pegs.
Revised: Peg solitaire is traditionally a one-player game played on a square grid board containing pegs.
Edit intent: clarity
Original:  In addition, we generalize peg solitaire on finite graphs as introduced by Beeler and Hoilman (2011) to an infinite game played on countably infinite graphs.
Revised:  In addition, we generalize peg solitaire on finite graphs as introduced by Beeler and Hoilman (2011) to an infinite game played on countable graphs.
Edit intent: clarity
Original: For the model-free deep reinforcement learning of quadruped fall recovery , the initialization of robot configurations is crucial to the data efficiency and robustness.
Revised: For model-free deep reinforcement learning of quadruped fall recovery , the initialization of robot configurations is crucial to the data efficiency and robustness.
Edit intent: fluency
Original: For the model-free deep reinforcement learning of quadruped fall recovery , the initialization of robot configurations is crucial to the data efficiency and robustness.
Revised: For the model-free deep reinforcement learning of quadruped locomotion , the initialization of robot configurations is crucial to the data efficiency and robustness.
Edit intent: clarity
Original: For the model-free deep reinforcement learning of quadruped fall recovery , the initialization of robot configurations is crucial to the data efficiency and robustness.
Revised: For the model-free deep reinforcement learning of quadruped fall recovery , the initialization of robot configurations is crucial for data efficiency and robustness.
Edit intent: fluency
Original:  By using the discovered centroidal static poses as  initial states, we improve the data efficiency by reducing the redundant exploration , and enhance the robustness by easy explorations from the centroids to sampled static poses.
Revised:  By using the discovered centroidal static poses as the initial states, we improve the data efficiency by reducing the redundant exploration , and enhance the robustness by easy explorations from the centroids to sampled static poses.
Edit intent: fluency
Original:  By using the discovered centroidal static poses as  initial states, we improve the data efficiency by reducing the redundant exploration , and enhance the robustness by easy explorations from the centroids to sampled static poses.
Revised:  By using the discovered centroidal static poses as initial states, we can improve data efficiency by reducing the redundant exploration , and enhance the robustness by easy explorations from the centroids to sampled static poses.
Edit intent: clarity
Original:  By using the discovered centroidal static poses as  initial states, we improve the data efficiency by reducing the redundant exploration , and enhance the robustness by easy explorations from the centroids to sampled static poses.
Revised:  By using the discovered centroidal static poses as initial states, we improve the data efficiency by reducing redundant explorations , and enhance the robustness by easy explorations from the centroids to sampled static poses.
Edit intent: fluency
Original:  By using the discovered centroidal static poses as  initial states, we improve the data efficiency by reducing the redundant exploration , and enhance the robustness by easy explorations from the centroids to sampled static poses.
Revised:  By using the discovered centroidal static poses as initial states, we improve the data efficiency by reducing the redundant exploration , and enhance the robustness by more effective explorations from the centroids to sampled static poses.
Edit intent: clarity
Original:  Compared to random initialization , the learning curve of our proposed method converges much faster, requiring only around 60\% of training episodes.
Revised:  Compared to random initialization , the learning curve of our method converges much faster, requiring only around 60\% of training episodes.
Edit intent: clarity
Original:  Compared to random initialization , the learning curve of our proposed method converges much faster, requiring only around 60\% of training episodes.
Revised:  Compared to random initialization , the learning curve of our proposed method converges much faster, requiring only 60\% of training episodes.
Edit intent: clarity
Original:  In this paper we investigate the vulnerability of BERT models to variation in input data for Named Entity Recognition (NER ) through adversarial attack.
Revised:  In this paper we investigate the vulnerability of BERT models to variation in input data for NER through adversarial attack.
Edit intent: clarity
Original:  Efficient and accurate numerical methods for pre-calculating acoustics have been developed over the last decade , however, pre-calculating acoustics makes handling dynamic scenes with moving sources challenging and requires intractable memory storage.
Revised:  Efficient and accurate numerical methods for pre-calculating acoustics have been developed over the last decade ; however, pre-calculating acoustics makes handling dynamic scenes with moving sources challenging and requires intractable memory storage.
Edit intent: fluency
Original:  Efficient and accurate numerical methods for pre-calculating acoustics have been developed over the last decade , however, pre-calculating acoustics makes handling dynamic scenes with moving sources challenging and requires intractable memory storage.
Revised:  Efficient and accurate numerical methods for pre-calculating acoustics have been developed over the last decade , however, pre-calculating acoustics makes handling dynamic scenes with moving sources challenging , requiring intractable memory storage.
Edit intent: coherence