wanyu/IteraTeR_v2 huggingface dataset:
Original:  We show that, in these cases, the K-class MARL problem can be approximated by MFC with errors given as e_1=O( \frac{\sqrt{|\mathcal{X|}+|\mathcal{U|}}{N_{pop}}} kN_k), e_2=O( \sqrt{|\mathcal{X \left[|}+|\mathcal{U|}}\right]
Revised:  We show that, in these cases, the K-class MARL problem can be approximated by MFC with errors given as e_1=O( \frac{\sqrt{|\mathcal{X|}+|\mathcal{U|}}{N_{pop}}} kN_k), e_2=O( \sqrt{|\mathcal{X \left[\sqrt{|\mathcal{X|}+|\mathcal{U|}}\right]
Edit intent: fluency
Original:  k\frac{1}{\sqrt{N_k}}) and e_3=\mathcal{O}\left( \sqrt{|\mathcal{X||\mathcal{U}|}} \left[|}+\sqrt{|\mathcal{U}|}}\right] \left[\frac{A}{N_{\mathrm{pop}}}\sum_{k\in[K]}\sqrt{N_k}+\frac{B}{\sqrt{N_{\mathrm{pop}}}}\right]\right), respectively, where A, B are some constants and |\mathcal{X}|,|\mathcal{U}| are the sizes of state and action spaces of each agent.
Revised:  k\frac{1}{\sqrt{N_k}}) and e_3=\mathcal{O}\left( ||\mathcal{U}|}} \left[|}+\sqrt{|\mathcal{U}|}}\right] \left[\frac{A}{N_{\mathrm{pop}}}\sum_{k\in[K]}\sqrt{N_k}+\frac{B}{\sqrt{N_{\mathrm{pop}}}}\right]\right), respectively, where A, B are some constants and |\mathcal{X}|,|\mathcal{U}| are the sizes of state and action spaces of each agent.
Edit intent: coherence
Original:  k\frac{1}{\sqrt{N_k}}) and e_3=\mathcal{O}\left( \sqrt{|\mathcal{X||\mathcal{U}|}} \left[|}+\sqrt{|\mathcal{U}|}}\right] \left[\frac{A}{N_{\mathrm{pop}}}\sum_{k\in[K]}\sqrt{N_k}+\frac{B}{\sqrt{N_{\mathrm{pop}}}}\right]\right), respectively, where A, B are some constants and |\mathcal{X}|,|\mathcal{U}| are the sizes of state and action spaces of each agent.
Revised:  k\frac{1}{\sqrt{N_k}}) and e_3=\mathcal{O}\left( \sqrt{|\mathcal{X||\mathcal{U}|}} \left[\sqrt{|\mathcal{X|}+\sqrt{|\mathcal{U}|}}\right] \left[\frac{A}{N_{\mathrm{pop}}}\sum_{k\in[K]}\sqrt{N_k}+\frac{B}{\sqrt{N_{\mathrm{pop}}}}\right]\right), respectively, where A, B are some constants and |\mathcal{X}|,|\mathcal{U}| are the sizes of state and action spaces of each agent.
Edit intent: coherence
Original:  Audio-visual biometrics are getting popular due to the usability and also it will be challenging to spoof because of multi-modal nature.
Revised:  Audio-visual biometrics are getting popular due to their usability, and also it will be challenging to spoof because of multi-modal nature.
Edit intent: fluency
Original:  Audio-visual biometrics are getting popular due to the usability and also it will be challenging to spoof because of multi-modal nature.
Revised:  Audio-visual biometrics are getting popular due to the usability and also it will be challenging to spoof because of their multimodal nature.
Edit intent: fluency
Original:  (b) how the models can be used to assess the impact of storage capacities  at the cold chain points, transportation vehicle capacities , and manufacturer capacities on the optimal vaccine distribution pattern;
Revised:  (b) how the models can be used to assess the impact of storage capacities ( at the cold chain points, transportation vehicle capacities , and manufacturer capacities on the optimal vaccine distribution pattern;
Edit intent: fluency
Original:  (b) how the models can be used to assess the impact of storage capacities  at the cold chain points, transportation vehicle capacities , and manufacturer capacities on the optimal vaccine distribution pattern;
Revised:  (b) how the models can be used to assess the impact of storage capacities at the cold chain points, transportation vehicle capacities ) and manufacturer capacities on the optimal vaccine distribution pattern;
Edit intent: fluency
Original:  We will release our code fully open-sourced upon acceptance.
Edit intent: coherence
Original:  Methodology: We reviewed the reported DI anti-patterns in order to analyze their completeness and propose a catalog containing 12 Java DI anti-patterns.
Revised:  Methodology: We reviewed existing reported DI anti-patterns in order to analyze their completeness and propose a catalog containing 12 Java DI anti-patterns.
Edit intent: clarity
Original:  Methodology: We reviewed the reported DI anti-patterns in order to analyze their completeness and propose a catalog containing 12 Java DI anti-patterns.
Revised:  Methodology: We reviewed the reported DI anti-patterns in order to analyze their completeness and propose a catalog containing 12 DI anti-patterns.
Edit intent: clarity
Original:  We developed a tool to statically analyze the occurrence level of the candidate DI anti-patterns in  open-source and industry projects.
Revised:  We developed a tool to statically analyze the occurrence level of the candidate DI anti-patterns in both open-source and industry projects.
Edit intent: coherence
Original:  However, the existing methods have a low malware embedding rate and a high impact on the model performance, making it not practical .
Revised:  However, the existing methods have a low malware embedding rate , the high model performance degradation and the extra efforts .
Edit intent: clarity
Original:  We used 19 malware samples and 10 mainstream modelsto build 550 malware-embedded models and analyzed the models' performance on ImageNet dataset.
Revised:  We used 19 malware samples and 10 mainstream modelsto build 550 malware-embedded models , and analyze these models' performance on ImageNet dataset.
Edit intent: clarity
Original:  With the widespread application of artificial intelligence, utilizing neural networks for attacks is becoming a forwarding trend.
Edit intent: coherence
Original:  We hope this work can provide a reference scenario for the defense of neural network-assisted attacks .
Revised:  We hope this work can provide a comprehensive understanding of such a novel AI-powered threat, and recommend to defense it in advance .
Edit intent: clarity
Original:  A truncation criterion is established for a multi-sensor measurement-generated labeled multi-Bernoulli random finite set that provably minimizes the L1-truncation error in the generalized labeled multi-Bernoulli posterior distribution .
Revised:  A truncation criterion is established for a labeled multi-Bernoulli random finite set that provably minimizes the L1-truncation error in the generalized labeled multi-Bernoulli posterior distribution .
Edit intent: clarity
Original:  A truncation criterion is established for a multi-sensor measurement-generated labeled multi-Bernoulli random finite set that provably minimizes the L1-truncation error in the generalized labeled multi-Bernoulli posterior distribution .
Revised:  A truncation criterion is established for a multi-sensor measurement-generated labeled multi-Bernoulli random finite set that provably minimizes the L1-truncation error in the generalized labeled multi-Bernoulli posterior density .
Edit intent: clarity
Original:  A closed form solution of the conditional sampling distribution assuming linear (or linearized) Gaussian likelihoods is provided, alongside an approximate solution using Monte Carlo importance sampling.
Revised:  A closed-form solution of the conditional sampling distribution assuming linear (or linearized) Gaussian likelihoods is provided, alongside an approximate solution using Monte Carlo importance sampling.
Edit intent: fluency
Original: In this paper we propose a novel millimeter wave (mmW) multiple access method that exploits unique frequency dependent beamforming capabilities of True Time Delay (TTD) array architecture.
Revised: The millimeter-wave (mmW) multiple access method that exploits unique frequency dependent beamforming capabilities of True Time Delay (TTD) array architecture.
Edit intent: clarity
Original: Surface reconstruction from noisy, non-uniformly , and unoriented point clouds is a fascinating yet difficult problem in computer vision and computer graphics.
Revised: Surface reconstruction from noisy, non-uniform , and unoriented point clouds is a fascinating yet difficult problem in computer vision and computer graphics.
Edit intent: fluency
Original: Surface reconstruction from noisy, non-uniformly , and unoriented point clouds is a fascinating yet difficult problem in computer vision and computer graphics.
Revised: Surface reconstruction from noisy, non-uniformly , and unoriented point clouds is a fascinating yet challenging problem in computer vision and computer graphics.
Edit intent: clarity
Original:  In this paper, we propose Neural-IMLS, a novel approach that learning noise-resistant signed distance function (SDF) for reconstruction .
Revised:  In this paper, we introduce Neural-IMLS, a novel approach that learning noise-resistant signed distance function (SDF) for reconstruction .
Edit intent: coherence
Original:  In this paper, we propose Neural-IMLS, a novel approach that learning noise-resistant signed distance function (SDF) for reconstruction .
Revised:  In this paper, we propose Neural-IMLS, a novel approach that learns the noise-resistant signed distance function (SDF) for reconstruction .
Edit intent: fluency
Original:  Instead of explicitly learning priors with the ground-truth signed distance values, our method learns the  SDF from raw point clouds directly in a self-supervised fashion by minimizing the loss between the couple of SDFs, one obtained by the implicit moving least-square function (IMLS) and the other by our network .
Revised:  Instead of explicitly learning priors with the ground-truth signed distance values, our method learns the underlying SDF from raw point clouds directly in a self-supervised fashion by minimizing the loss between the couple of SDFs, one obtained by the implicit moving least-square function (IMLS) and the other by our network .
Edit intent: clarity
Original:  Instead of explicitly learning priors with the ground-truth signed distance values, our method learns the  SDF from raw point clouds directly in a self-supervised fashion by minimizing the loss between the couple of SDFs, one obtained by the implicit moving least-square function (IMLS) and the other by our network .
Revised:  Instead of explicitly learning priors with the ground-truth signed distance values, our method learns the SDF from raw point clouds in a self-supervised fashion by minimizing the loss between the couple of SDFs, one obtained by the implicit moving least-square function (IMLS) and the other by our network .
Edit intent: coherence
Original:  Instead of explicitly learning priors with the ground-truth signed distance values, our method learns the  SDF from raw point clouds directly in a self-supervised fashion by minimizing the loss between the couple of SDFs, one obtained by the implicit moving least-square function (IMLS) and the other by our network .
Revised:  Instead of explicitly learning priors with the ground-truth signed distance values, our method learns the SDF from raw point clouds directly in a self-supervised fashion by minimizing the loss between a couple of SDFs, one obtained by the implicit moving least-square function (IMLS) and the other by our network .
Edit intent: fluency
Original:  Finally, a watertight and smooth 2-manifold triangle mesh is yielded by running Marching Cubes.
Edit intent: coherence
Original: In this letter, an accelerated robust model predictive control ( RMPC ) algorithm for linear systems with additive disturbance is proposed based on the tube technique and the proximal gradient method.
Revised: In this letter, an accelerated model predictive control ( RMPC ) algorithm for linear systems with additive disturbance is proposed based on the tube technique and the proximal gradient method.
Edit intent: clarity
Original: In this letter, an accelerated robust model predictive control ( RMPC ) algorithm for linear systems with additive disturbance is proposed based on the tube technique and the proximal gradient method.
Revised: In this letter, an accelerated robust model predictive control ( RMPC ) algorithm for linear systems is proposed based on the tube technique and the proximal gradient method.
Edit intent: clarity
Original: In this letter, an accelerated robust model predictive control ( RMPC ) algorithm for linear systems with additive disturbance is proposed based on the tube technique and the proximal gradient method.
Revised: In this letter, an accelerated robust model predictive control ( RMPC ) algorithm for linear systems with additive disturbance is proposed based on the proximal gradient method.
Edit intent: coherence
Original:  A case study and several numerical experiments are provided to show the effectiveness of the proposed approach.
Revised:  Several numerical experiments are provided to show the effectiveness of the proposed approach.
Edit intent: clarity
Original:  We explore new label annotation distribution schemes, assigning multiple labels per example for a small subset of training examples.
Revised:  We explore new annotation distribution schemes, assigning multiple labels per example for a small subset of training examples.
Edit intent: fluency
Original:  Together, our method achieves consistent gains in both accuracy and label distribution metrics in two tasks, suggesting training with uneven training data can be beneficial for many NLP tasks.
Revised:  Together, our method achieves consistent gains in two tasks, suggesting training with uneven training data can be beneficial for many NLP tasks.
Edit intent: clarity
Original:  Together, our method achieves consistent gains in both accuracy and label distribution metrics in two tasks, suggesting training with uneven training data can be beneficial for many NLP tasks.
Revised:  Together, our method achieves consistent gains in both accuracy and label distribution metrics in two tasks, suggesting distributing labels unevenly among training examples can be beneficial for many NLP tasks.
Edit intent: clarity
Original: Our automatic portrait video matting method does not require extra inputs .
Revised: Automatic portrait video matting method does not require extra inputs .
Edit intent: fluency
Original: Our automatic portrait video matting method does not require extra inputs .
Revised: Our automatic portrait video matting is an under-constrained problem .
Edit intent: clarity
Original:  As a result, efficiently identifying the tasks that would benefit from co-training remains a challenging design question without a clear solution.
Revised:  As a result, efficiently identifying the tasks that would benefit from training together remains a challenging design question without a clear solution.
Edit intent: fluency
Original:  Our method determines task groupings in a single training run by co-training all tasks together and quantifying the effect to which one task's gradient would affect another task's loss.
Revised:  Our method determines task groupings in a single run by training all tasks together and quantifying the effect to which one task's gradient would affect another task's loss.
Edit intent: clarity
Original: In the paper [Hainaut, D. and Colwell, D.B., A structural model for credit risk with switchingprocesses and synchronous jumps, The European Journal of Finance44(33) (4238):3262-3284], the authors exploit a synchronous-jump regime-switching model to compute the default probabilityof a publicly traded company.
Revised: In the paper [Hainaut, D. and Colwell, D.B., A structural model for credit risk with switching processes and synchronous jumps, The European Journal of Finance44(33) (4238):3262-3284], the authors exploit a synchronous-jump regime-switching model to compute the default probabilityof a publicly traded company.
Edit intent: fluency
Original: In the paper [Hainaut, D. and Colwell, D.B., A structural model for credit risk with switchingprocesses and synchronous jumps, The European Journal of Finance44(33) (4238):3262-3284], the authors exploit a synchronous-jump regime-switching model to compute the default probabilityof a publicly traded company.
Revised: In the paper [Hainaut, D. and Colwell, D.B., A structural model for credit risk with switchingprocesses and synchronous jumps, The European Journal of Finance44(33) (4238):3262-3284], the authors exploit a synchronous-jump regime-switching model to compute the default probability of a publicly-traded company.
Edit intent: fluency
Original:  Here, we first generalize the proposed L\'evy model to more generalsetting of tempered stable processes recently introduced into the finance literature.
Revised:  Here, we first generalize the proposed L\'evy model to a more general setting of tempered stable processes recently introduced into the finance literature.
Edit intent: fluency
Original:  Based on thesingularity of the resulting partial integro-differential operator, we propose a general frameworkbased on strictly positive-definite functions to de-singularize the operator.
Revised:  Based on the singularity of the resulting partial integro-differential operator, we propose a general frameworkbased on strictly positive-definite functions to de-singularize the operator.
Edit intent: fluency
Original:  Based on thesingularity of the resulting partial integro-differential operator, we propose a general frameworkbased on strictly positive-definite functions to de-singularize the operator.
Revised:  Based on thesingularity of the resulting partial integro-differential operator, we propose a general framework based on strictly positive-definite functions to de-singularize the operator.
Edit intent: fluency
Original:  We then analyze anefficient meshfree collocation method based on radial basis functions to approximate the solution ofthe corresponding system of partial integro-differential equations arising from the structural creditrisk model.
Revised:  We then analyze an efficient meshfree collocation method based on radial basis functions to approximate the solution ofthe corresponding system of partial integro-differential equations arising from the structural creditrisk model.
Edit intent: fluency
Original:  We then analyze anefficient meshfree collocation method based on radial basis functions to approximate the solution ofthe corresponding system of partial integro-differential equations arising from the structural creditrisk model.
Revised:  We then analyze anefficient meshfree collocation method based on radial basis functions to approximate the solution of the corresponding system of partial integro-differential equations arising from the structural creditrisk model.
Edit intent: fluency
Original:  We then analyze anefficient meshfree collocation method based on radial basis functions to approximate the solution ofthe corresponding system of partial integro-differential equations arising from the structural creditrisk model.
Revised:  We then analyze anefficient meshfree collocation method based on radial basis functions to approximate the solution ofthe corresponding system of partial integro-differential equations arising from the structural credit risk model.
Edit intent: fluency
Original:  We show that under some regularity assumptions, our proposed method naturallyde-sinularizes the problem in the tempered stable case.
Revised:  We show that under some regularity assumptions, our proposed method naturally de-sinularizes the problem in the tempered stable case.
Edit intent: fluency
Original:  Numerical results of applying the methodon some standard examples from the literature confirms the accuracy of our theoretical results andnumerical algorithm.
Revised:  Numerical results of applying the method on some standard examples from the literature confirms the accuracy of our theoretical results andnumerical algorithm.
Edit intent: fluency
Original:  Numerical results of applying the methodon some standard examples from the literature confirms the accuracy of our theoretical results andnumerical algorithm.
Revised:  Numerical results of applying the methodon some standard examples from the literature confirm the accuracy of our theoretical results andnumerical algorithm.
Edit intent: fluency
Original:  Numerical results of applying the methodon some standard examples from the literature confirms the accuracy of our theoretical results andnumerical algorithm.
Revised:  Numerical results of applying the methodon some standard examples from the literature confirms the accuracy of our theoretical results and numerical algorithm.
Edit intent: fluency
Original: To align advanced artificial intelligence (AI) with human values and promote safe AI, it is important for AI to predict the outcome of physical interactions.
Revised: Accurate prediction of physical interaction outcomes is a crucial component of human intelligence and is important for AI to predict the outcome of physical interactions.
Edit intent: clarity
Original:  However, there is still a lack of AI approaches that mimic the mental imagery humans use to predict physical interactions in the real world.
Revised:  However, there is a lack of AI approaches that mimic the mental imagery humans use to predict physical interactions in the real world.
Edit intent: coherence
Original:  In this work , we propose a novel PIP scheme: Physical Interaction Prediction via Mental Imagery with Span Selection . PIP utilizes a deep generative model to output future frames of physical interactions among objects before extracting crucial information for predicting physical interactions by focusing on salient frames using span selection .
Revised:  In this work , we propose a novel scheme: Physical Interaction Prediction via Mental Imagery with Span Selection . PIP utilizes a deep generative model to output future frames of physical interactions among objects before extracting crucial information for predicting physical interactions by focusing on salient frames using span selection .
Edit intent: fluency
Original:  In this work , we propose a novel PIP scheme: Physical Interaction Prediction via Mental Imagery with Span Selection . PIP utilizes a deep generative model to output future frames of physical interactions among objects before extracting crucial information for predicting physical interactions by focusing on salient frames using span selection .
Revised:  In this work , we propose a novel PIP scheme: Physical Interaction Prediction via Mental Simulation with Span Selection . PIP utilizes a deep generative model to output future frames of physical interactions among objects before extracting crucial information for predicting physical interactions by focusing on salient frames using span selection .
Edit intent: clarity
Original:  In this work , we propose a novel PIP scheme: Physical Interaction Prediction via Mental Imagery with Span Selection . PIP utilizes a deep generative model to output future frames of physical interactions among objects before extracting crucial information for predicting physical interactions by focusing on salient frames using span selection .
Revised:  In this work , we propose a novel PIP scheme: Physical Interaction Prediction via Mental Imagery with Span Selection (PIP). It utilizes a deep generative model to output future frames of physical interactions among objects before extracting crucial information for predicting physical interactions by focusing on salient frames using span selection .
Edit intent: clarity
Original:  In this work , we propose a novel PIP scheme: Physical Interaction Prediction via Mental Imagery with Span Selection . PIP utilizes a deep generative model to output future frames of physical interactions among objects before extracting crucial information for predicting physical interactions by focusing on salient frames using span selection .
Revised:  In this work , we propose a novel PIP scheme: Physical Interaction Prediction via Mental Imagery with Span Selection . PIP utilizes a deep generative model to output future frames of physical interactions before employing selective temporal attention in the form of span selection for predicting physical interactions by focusing on salient frames using span selection .
Edit intent: clarity