wanyu/IteraTeR_v2 huggingface dataset:
Original:  Throughout this paper, we observe the critical decisions that are often overlooked when deploying AI, argue for the use of fairnessforensics to discover bias and fairness issues in systems, assert the need for a responsible human-over-the-loop  to bring accountability into the deployed system , and finally, reflect on the need to explore research agendas that have harmful societal impacts.
Revised:  Throughout this paper, we observe the critical decisions that are often overlooked when deploying AI, argue for the use of fairnessforensics to discover bias and fairness issues in systems, assert the need for a responsible human-over-the-loop methodology to bring accountability into the deployed system , and finally, reflect on the need to explore research agendas that have harmful societal impacts.
Edit intent: clarity
Original:  We examine visual privacy research and draw lessons that can apply broadly to Artificial Intelligence .
Revised:  We examine visual privacy research and draw lessons that can apply broadly to artificial intelligence .
Edit intent: fluency
Original:  Our goal is to provide a systematic analysis of the machine learning pipeline for visual privacy and bias issues.
Revised:  Our goal is to systematically analyze the machine learning pipeline for visual privacy and bias issues.
Edit intent: clarity
Original:  With this pipeline, we hope to raise stakeholder (e.g., researchers, modelers, corporations) awareness as these issues propagate in the various machine learning phases.
Revised:  We hope to raise stakeholder (e.g., researchers, modelers, corporations) awareness as these issues propagate in the various machine learning phases.
Edit intent: coherence
Original:  With this pipeline, we hope to raise stakeholder (e.g., researchers, modelers, corporations) awareness as these issues propagate in the various machine learning phases.
Revised:  With this pipeline, we hope to raise stakeholder (e.g., researchers, modelers, corporations) awareness as these issues propagate in this pipeline's various machine learning phases.
Edit intent: clarity
Original:  Even in scenarios where hyper-cluster resources are available, Varuna improves end-to-end training time by 20-78 \% compared to alternative approaches.
Revised:  Varuna improves end-to-end training time by 20-78 \% compared to alternative approaches.
Edit intent: clarity
Original:  During this period, we have witnessed a paradigm shift from manual feature extraction to the CNN with local receptive fields , and further to the Transform with global receptive fields based on self-attention mechanism.
Revised:  During this period, we have witnessed a paradigm shift from manual feature extraction to the CNN with local receptive field , and further to the Transform with global receptive fields based on self-attention mechanism.
Edit intent: fluency
Original:  During this period, we have witnessed a paradigm shift from manual feature extraction to the CNN with local receptive fields , and further to the Transform with global receptive fields based on self-attention mechanism.
Revised:  During this period, we have witnessed a paradigm shift from manual feature extraction to the CNN with local receptive fields , and further to the Transformer with global receptive fields based on self-attention mechanism.
Edit intent: fluency
Original:  During this period, we have witnessed a paradigm shift from manual feature extraction to the CNN with local receptive fields , and further to the Transform with global receptive fields based on self-attention mechanism.
Revised:  During this period, we have witnessed a paradigm shift from manual feature extraction to the CNN with local receptive fields , and further to the Transform with global receptive field based on self-attention mechanism.
Edit intent: fluency
Original:  the community can't help but wonder, Will MLP, the simplest structure with global receptive fields but no attention, become a new computer vision paradigm \emph{ ? To answer this question, this survey aims to provide a comprehensive overview of the recent development of vision deep MLP models  . Specifically, we review these vision deep MLPs detailedly , from the subtle sub-module design to the global network structure.
Revised:  the community can't help but wonder, \emph{ ? To answer this question, this survey aims to provide a comprehensive overview of the recent development of vision deep MLP models . Specifically, we review these vision deep MLPs detailedly , from the subtle sub-module design to the global network structure.
Edit intent: coherence
Original:  the community can't help but wonder, Will MLP, the simplest structure with global receptive fields but no attention, become a new computer vision paradigm \emph{ ? To answer this question, this survey aims to provide a comprehensive overview of the recent development of vision deep MLP models  . Specifically, we review these vision deep MLPs detailedly , from the subtle sub-module design to the global network structure.
Revised:  the community can't help but wonder, Will MLP, the simplest structure with global receptive fields but no attention, become a new computer vision paradigm \emph{Will deep MLP, the simplest structure with global receptive field but no attention, become a new computer vision paradigm ? To answer this question, this survey aims to provide a comprehensive overview of the recent development of vision deep MLP models . Specifically, we review these vision deep MLPs detailedly , from the subtle sub-module design to the global network structure.
Edit intent: fluency
Original:  the community can't help but wonder, Will MLP, the simplest structure with global receptive fields but no attention, become a new computer vision paradigm \emph{ ? To answer this question, this survey aims to provide a comprehensive overview of the recent development of vision deep MLP models  . Specifically, we review these vision deep MLPs detailedly , from the subtle sub-module design to the global network structure.
Revised:  the community can't help but wonder, Will MLP, the simplest structure with global receptive fields but no attention, become a new computer vision paradigm \emph{ ? To answer this question, this survey aims to provide a comprehensive overview of the recent development of deep MLP models . Specifically, we review these vision deep MLPs detailedly , from the subtle sub-module design to the global network structure.
Edit intent: fluency
Original:  the community can't help but wonder, Will MLP, the simplest structure with global receptive fields but no attention, become a new computer vision paradigm \emph{ ? To answer this question, this survey aims to provide a comprehensive overview of the recent development of vision deep MLP models  . Specifically, we review these vision deep MLPs detailedly , from the subtle sub-module design to the global network structure.
Revised:  the community can't help but wonder, Will MLP, the simplest structure with global receptive fields but no attention, become a new computer vision paradigm \emph{ ? To answer this question, this survey aims to provide a comprehensive overview of the recent development of vision deep MLP models in vision . Specifically, we review these vision deep MLPs detailedly , from the subtle sub-module design to the global network structure.
Edit intent: clarity
Original:  the community can't help but wonder, Will MLP, the simplest structure with global receptive fields but no attention, become a new computer vision paradigm \emph{ ? To answer this question, this survey aims to provide a comprehensive overview of the recent development of vision deep MLP models  . Specifically, we review these vision deep MLPs detailedly , from the subtle sub-module design to the global network structure.
Revised:  the community can't help but wonder, Will MLP, the simplest structure with global receptive fields but no attention, become a new computer vision paradigm \emph{ ? To answer this question, this survey aims to provide a comprehensive overview of the recent development of vision deep MLP models . Specifically, we review these MLPs in detail , from the subtle sub-module design to the global network structure.
Edit intent: fluency
Original:  We compare the receptive field, computational complexity, and other properties of different network designs in order to have a clear understanding of the development path of MLPs  .
Revised:  We compare the receptive field, computational complexity, and other properties of different network designs in order to understand the development path of MLPs .
Edit intent: clarity
Original:  Finally, we provide an analysis of open research directions and possible future works.
Revised:  Finally, we provide our viewpoint about open research directions and possible future works.
Edit intent: clarity
Original:  Finally, we provide an analysis of open research directions and possible future works.
Revised:  Finally, we provide an analysis of open research directions and potential future works.
Edit intent: fluency
Original:  We hope this effort will ignite further interest in the community and encourage better visual tailored design for the neural network at the moment .
Revised:  We hope this effort will ignite further interest in the community and encourage better visual tailored design for the neural network in the future .
Edit intent: clarity
Original:  Experimental results on both benchmark datasets and a real robot in our own experimental environments show that our proposed method improves the SLAM reconstruction accuracy.
Revised:  Experimental results on both benchmark datasets and a real robot in our own experimental environments show that our proposed method improves the overall SLAM accuracy.
Edit intent: clarity
Original:  In this class of games, players are split in two teams with payoffs equal within the same team and of opposite sign across the opponent team.
Revised:  In this class of games, players are split into two teams with payoffs equal within the same team and of opposite sign across the opponent team.
Edit intent: fluency
Original:  Unlike the textbook two-player zero-sum games, finding a Nash equilibrium in our class can be shown to be CLS-hard, i.e., it is unlikely to have a polynomial time algorithm for computing Nash equilibria.
Revised:  Unlike the textbook two-player zero-sum games, finding a Nash equilibrium in our class can be shown to be CLS-hard, i.e., it is unlikely to have a polynomial-time algorithm for computing Nash equilibria.
Edit intent: fluency
Original:  Moreover  in this generalized framework, we establish that even asymptotic last iterate or time average convergence to a Nash Equilibrium is not possible using Gradient Descent Ascent (GDA), its optimistic variant  and extra gradient.
Revised:  Moreover , in this generalized framework, we establish that even asymptotic last iterate or time average convergence to a Nash Equilibrium is not possible using Gradient Descent Ascent (GDA), its optimistic variant and extra gradient.
Edit intent: fluency
Original:  Moreover  in this generalized framework, we establish that even asymptotic last iterate or time average convergence to a Nash Equilibrium is not possible using Gradient Descent Ascent (GDA), its optimistic variant  and extra gradient.
Revised:  Moreover in this generalized framework, we establish that even asymptotic last iterate or time average convergence to a Nash Equilibrium is not possible using Gradient Descent Ascent (GDA), its optimistic variant , and extra gradient.
Edit intent: fluency
Original:  Finally, we discuss connections of our framework with AI architectures with team competition structure like multi-agent generative adversarial networks.
Revised:  Finally, we discuss connections of our framework with AI architectures with team competition structures like multi-agent generative adversarial networks.
Edit intent: fluency
Original:  With this purpose is shown that the use of four classic notions, namely, those from Leibniz & Kant (1768), Maynard Smith (1970), Einstein & Infeld (1961), and Anfinsen (1973), are sufficient to understand the origin of the main factor controlling the protein evolvability .
Revised:  With this purpose , we show that the use of four classic notions, namely, those from Leibniz & Kant (1768), Maynard Smith (1970), Einstein & Infeld (1961), and Anfinsen (1973), are sufficient to understand the origin of the main factor controlling the protein evolvability .
Edit intent: clarity
Original:  With this purpose is shown that the use of four classic notions, namely, those from Leibniz & Kant (1768), Maynard Smith (1970), Einstein & Infeld (1961), and Anfinsen (1973), are sufficient to understand the origin of the main factor controlling the protein evolvability .
Revised:  With this purpose is shown that the use of four classic notions, namely, those from Leibniz & Kant (1768), Maynard Smith (1970), Einstein & Infeld (1961), and Anfinsen (1973), is sufficient for a better understanding of the protein-evolution and, consequently, to determine the factors that could control it .
Edit intent: clarity
Original:  We then consider hybrid quantum-classical architecture and define a large width limit for hybrid kernels, showing that a hybrid quantum-classical neural network can be approximately Gaussian.
Revised:  We then consider hybrid quantum-classical architecture and define a large-width limit for hybrid kernels, showing that a hybrid quantum-classical neural network can be approximately Gaussian.
Edit intent: fluency
Original: The paper Zhao et al. (2015) shows that mean-CVaR-skewness portfolio optimization prob- lems based on asymetric Laplace (AL) distributions can be transformed into quadratic optimiza- tion problems under which closed form solutions can be found.
Revised: The paper Zhao et al. (2015) shows that mean-CVaR-skewness portfolio optimization problems based on asymetric Laplace (AL) distributions can be transformed into quadratic optimiza- tion problems under which closed form solutions can be found.
Edit intent: fluency
Original: The paper Zhao et al. (2015) shows that mean-CVaR-skewness portfolio optimization prob- lems based on asymetric Laplace (AL) distributions can be transformed into quadratic optimiza- tion problems under which closed form solutions can be found.
Revised: The paper Zhao et al. (2015) shows that mean-CVaR-skewness portfolio optimization prob- lems based on asymetric Laplace (AL) distributions can be transformed into quadratic optimization problems under which closed form solutions can be found.
Edit intent: fluency
Original:  They have closed form expres- sions for portfolios of normal and more generally elliptically distributed returns as discussed in Rockafellar & Uryasev (2000) and in Landsman & Valdez (2003).
Revised:  They have closed form expressions for portfolios of normal and more generally elliptically distributed returns as discussed in Rockafellar & Uryasev (2000) and in Landsman & Valdez (2003).
Edit intent: fluency
Original:  When the returns have gen- eral NMVM distributions, these risk measures do not give closed form expressions.
Revised:  When the returns have general NMVM distributions, these risk measures do not give closed form expressions.
Edit intent: fluency
Original:  These forms of money have novel properties that have not been studied extensively within the economics literature, but may be important determinants of the monetary equilibrium that emerges in  forthcoming era of heightened currency competition.
Revised:  These forms of money have novel properties that have not been studied extensively within the economics literature, but may be important determinants of the monetary equilibrium that emerges in the forthcoming era of heightened currency competition.
Edit intent: fluency
Original:  Importantly, the PINN results also show that the inferred values of the empirical model's parameters vary with the shear P\'eclet number and the particle bulk volume fraction of the suspension, instead of being constant as assumed in  previous literature.
Revised:  Importantly, the PINN results also show that the inferred values of the empirical model's parameters vary with the shear P\'eclet number and the particle bulk volume fraction of the suspension, instead of being constant as assumed in some previous literature.
Edit intent: clarity
Original: In clinical practice, radiologists are reliant on the lesion size when distinguishing metastatic from non-metastaticlesions . A prerequisite for lesion sizing is their detection, as it promotes the downstream assessment of tumorspread .
Revised: In clinical practice, radiologists are reliant on the lesion size when distinguishing metastatic from non-metastatic lesions . A prerequisite for lesion sizing is their detection, as it promotes the downstream assessment of tumorspread .
Edit intent: fluency
Original: In clinical practice, radiologists are reliant on the lesion size when distinguishing metastatic from non-metastaticlesions . A prerequisite for lesion sizing is their detection, as it promotes the downstream assessment of tumorspread .
Revised: In clinical practice, radiologists are reliant on the lesion size when distinguishing metastatic from non-metastaticlesions . A prerequisite for lesion sizing is their detection, as it promotes the downstream assessment of tumor spread .
Edit intent: fluency
Original:  However, lesions vary in their size and appearance in CT scans, and radiologists often miss small lesionsduring a busy clinical day.
Revised:  However, lesions vary in their size and appearance in CT scans, and radiologists often miss small lesions during a busy clinical day.
Edit intent: fluency
Original:  To overcome these challenges, we propose the use of state-of-the-art detection neuralnetworks to flag suspicious lesions present in the NIH DeepLesion dataset for sizing.
Revised:  To overcome these challenges, we propose the use of state-of-the-art detection neural networks to flag suspicious lesions present in the NIH DeepLesion dataset for sizing.
Edit intent: fluency
Original:  Additionally, we incorporatea bounding box fusion technique to minimize false positives (FP) and improve detection accuracy.
Revised:  Additionally, we incorporate a bounding box fusion technique to minimize false positives (FP) and improve detection accuracy.
Edit intent: fluency
Original:  Finally, toresemble clinical usage, we constructed an ensemble of the best detection models to localize lesions for sizingwith a precision of 65.17\% and sensitivity of 91.67\% at 4 FP per image.
Revised:  Finally, to resemble clinical usage, we constructed an ensemble of the best detection models to localize lesions for sizingwith a precision of 65.17\% and sensitivity of 91.67\% at 4 FP per image.
Edit intent: fluency
Original:  Finally, toresemble clinical usage, we constructed an ensemble of the best detection models to localize lesions for sizingwith a precision of 65.17\% and sensitivity of 91.67\% at 4 FP per image.
Revised:  Finally, toresemble clinical usage, we constructed an ensemble of the best detection models to localize lesions for sizing with a precision of 65.17\% and sensitivity of 91.67\% at 4 FP per image.
Edit intent: fluency
Original:  Our results improve upon or maintainthe performance of current state-of-the-art methods for lesion detection in challenging CT scans.
Revised:  Our results improve upon or maintain the performance of current state-of-the-art methods for lesion detection in challenging CT scans.
Edit intent: fluency
Original: We study the deep ReLU neural network collocation approximation of the solution u to elliptic PDEs with lognormal inputs, parametrized by y from the non-compact set R^\infty.
Revised: We study the deep ReLU neural networks of the solution u to elliptic PDEs with lognormal inputs, parametrized by y from the non-compact set R^\infty.
Edit intent: clarity
Original:  With the additional assumption that M is self-adjoint, the global small-ball probability bound can be replaced with a weaker version.
Revised:  With the additional assumption that M is self-adjoint, the global small-ball probability bound can be replaced by a weaker version.
Edit intent: fluency
Original: The advancement of Natural Language Processing (NLP) is spreading through various domains in forms of practical applications and academic interests.
Edit intent: coherence
Original:  Therefore it requires the application of NLP to cater to the analytically demanding needs of the domain.
Revised:  Therefore it requires the application of Natural Language Processing (NLP) to cater to the analytically demanding needs of the domain.
Edit intent: clarity
Original:  Identifying important sentences, facts and arguments in a legal case is such a tedious task for legal professionals.
Revised:  Identifying important sentences, facts and arguments in a legal case is a tedious task for legal professionals.
Edit intent: coherence
Original:  In this research we explore the usage of sentence embeddings for multi-class classification to identify important sentences in a legal case, in the perspective of the main parties present in the case.
Revised:  In this research we explore the usage of sentence embeddings for multi-class classification to identify critical sentences in a legal case, in the perspective of the main parties present in the case.
Edit intent: clarity
Original:  Consequently, the produced features do not guarantee to be easily classified by the class weights learned from labeled dataalthough they are information-rich .
Revised:  Consequently, the produced features to be clustered around the class weights learned from labeled dataalthough they are information-rich .
Edit intent: clarity
Original:  Consequently, the produced features do not guarantee to be easily classified by the class weights learned from labeled dataalthough they are information-rich .
Revised:  Consequently, the produced features do not guarantee to be easily classified by the class weights learned from source data .
Edit intent: clarity
Original:  To tackle this issue, we propose a novel probability contrastive learning (PCL) in this paper, which not only produces rich features but also enforces them to be distributed around the class prototypes . Specifically, we propose to use the output probabilities after softmax to perform contrastive learning instead of the extracted features in FCL.
Revised:  To tackle this issue, we propose a novel probability contrastive learning (PCL) in this paper, which not only produces rich features but also enforces them to be distributed around the class weights . Specifically, we propose to use the output probabilities after softmax to perform contrastive learning instead of the extracted features in FCL.
Edit intent: clarity
Original:  We conduct extensive experiments on three close-set image classification tasks, i.e., unsupervised domain adaptation , semi-supervised learning, and semi-supervised domain adaptation.
Revised:  We conduct extensive experiments on three close-set image classification tasks, i.e., unsupervised domain adaptation and semi-supervised domain adaptation.
Edit intent: clarity
Original:  We show that a generalization of the binary search algorithm achieves an optimal \theta(n ^{d-1 ) worst-case run time for d\geq 2.
Revised:  We show that a generalization of the binary search algorithm achieves an optimal \theta(n ) worst-case run time for d\geq 2.
Edit intent: clarity
Original:  We show that a generalization of the binary search algorithm achieves an optimal \theta(n ^{d-1 ) worst-case run time for d\geq 2.
Revised:  We show that a generalization of the binary search algorithm achieves an optimal \theta(n ^{d-1 ) worst-case run time .
Edit intent: coherence
Original: AlphaFold developed by DeepMind predicts protein structures from the amino acid sequence at or near experimental resolution, solving the 50-year-old protein folding challenge, leading to progress by transforming large-scale genomics data into protein structures.
Revised: AlphaFold predicts protein structures from the amino acid sequence at or near experimental resolution, solving the 50-year-old protein folding challenge, leading to progress by transforming large-scale genomics data into protein structures.
Edit intent: coherence