wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We here perform an energy stability analysis of turbulent incompressible flow, which suggests a scenario where  any exponentially unstable irrotational  flow structures rapidly evolve towards linearly unstable shear flow and stable rigid body rotational flow , which dissipates to heat .
Revised:  We here perform an energy stability analysis of turbulent incompressible flow, which suggests a scenario where any exponentially unstable irrotational flow structures rapidly evolve towards linearly unstable shear flow and stable rigid body rotational flow . This scenario does not rule out irrotational straining flow close to the Kolmogorov microscales, since there viscous dissipation stabilizes the unstable flow structures .
Edit intent: meaning-changed
Original:  The only work that deals with bounds on both the sides violates both the bounds \mbox{%DIFAUXCMD friggstad_et_al_LBUFL . In this paper, we present the first (constant factor) approximation for the problem violating the upper bound by a factor of \%\textcolor{magenta{(2 + \epsilon)} } (5/2) without violating the lower bounds when both the lower and the upper bounds are uniform.
Revised:  The only work that deals with bounds on both the sides violates both the bounds 8 . In this paper, we present the first (constant factor) approximation for the problem violating the upper bound by a factor of \%\textcolor{magenta{(2 + \epsilon)} } (5/2) without violating the lower bounds when both the lower and the upper bounds are uniform.
Edit intent: meaning-changed
Original:  The only work that deals with bounds on both the sides violates both the bounds \mbox{%DIFAUXCMD friggstad_et_al_LBUFL . In this paper, we present the first (constant factor) approximation for the problem violating the upper bound by a factor of \%\textcolor{magenta{(2 + \epsilon)} } (5/2) without violating the lower bounds when both the lower and the upper bounds are uniform.
Revised:  The only work that deals with bounds on both the sides violates both the bounds \mbox{%DIFAUXCMD friggstad_et_al_LBUFL . In this paper, we present the first (constant factor) approximation for the problem violating the upper bound by a factor of {(2 + \epsilon)} } (5/2) without violating the lower bounds when both the lower and the upper bounds are uniform.
Edit intent: meaning-changed
Original:  In this paper, we investigate ``estimating the Fourier Transform of the input image" as a potential alternative auxiliary task , in the hope that it may further boost the performances on the primary task or introduce novel constraints not well covered by image reconstruction.
Revised:  In this paper, we investigate "approximating the Fourier Transform of the input image" as a potential alternative auxiliary task , in the hope that it may further boost the performances on the primary task or introduce novel constraints not well covered by image reconstruction.
Edit intent: fluency
Original:  In this paper, we investigate ``estimating the Fourier Transform of the input image" as a potential alternative auxiliary task , in the hope that it may further boost the performances on the primary task or introduce novel constraints not well covered by image reconstruction.
Revised:  In this paper, we investigate ``estimating the Fourier Transform of the input image" as a potential alternative , in the hope that it may further boost the performances on the primary task or introduce novel constraints not well covered by image reconstruction.
Edit intent: coherence
Original:  In this paper, we investigate "approximating the Fourier Transform of the input image" as a potential alternative, in the hope that it may further boost the performances on the primary task or introduce novel constraints not well covered by image reconstruction.
Revised:  In this paper, we investigated "approximating the Fourier Transform of the input image" as a potential alternative, in the hope that it may further boost the performances on the primary task or introduce novel constraints not well covered by image reconstruction.
Edit intent: fluency
Original:  The inverse dynamics of the vehicle is approximated using a feedback error learning mechanism that utilizes a dynamic Radial Basis Function neural network, referred to as the Extended Minimal Resource Allocating Network (EMRAN).
Revised:  The inverse dynamics of the vehicle are approximated using a feedback error learning mechanism that utilizes a dynamic Radial Basis Function neural network, referred to as the Extended Minimal Resource Allocating Network (EMRAN).
Edit intent: fluency
Original:  Performances of both the longitudinal and lateral controllers are compared with existing control methods and the  results clearly indicate that the proposed control scheme handles the disturbances and parametric uncertainties better, and also provides better tracking performance in autonomous vehicles.
Revised:  Performances of both the longitudinal and lateral controllers are compared with existing control methods and the simulation results clearly indicate that the proposed control scheme handles the disturbances and parametric uncertainties better, and also provides better tracking performance in autonomous vehicles.
Edit intent: meaning-changed
Original: Dietary-related problems such as obesity are a growing concern in todays modern world. If the current trend continues, it is most likely that the quality of life, in general, is significantly affected since obesity is associated with other chronic diseases such as hypertension, irregular blood sugar levels, and increased risk of heart attacks.
Revised: Dietary-related problems such as obesity is associated with other chronic diseases such as hypertension, irregular blood sugar levels, and increased risk of heart attacks.
Edit intent: coherence
Original:  If the current trend continues, it is most likely that the quality of life, in general, is significantly affected since obesity is associated with other chronic diseases such as hypertension, irregular blood sugar levels, and increased risk of heart attacks.
Revised:  If the current trend continues, it is most likely that the quality of life, in general, is significantly affected since obesity is associated with other chronic diseases like hypertension, irregular blood sugar levels, and increased risk of heart attacks.
Edit intent: fluency
Original:  The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, with emphasis on a select few food groups such as sugars, fats, and carbohydrates. In this regard, computer-based food recognition offers automatic visual-based methods to assess dietary intake and help people make healthier choices.
Revised:  The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, which are manageable by using interactive mHealth apps that use automatic visual-based methods to assess dietary intake and help people make healthier choices.
Edit intent: coherence
Original:  In this regard, computer-based food recognition offers automatic visual-based methods to assess dietary intake and help people make healthier choices. Thus, the following paper presents a brief review of visual-based methods for food recognition , including their accuracy, performance, and the use of popular food databases to evaluate existing models.
Revised:  In this regard, computer-based food recognition offers automatic visual-based methods to assess dietary intake . This review discusses the most performing methodologies that have been developed so far for automatic food recognition. First, we will present the rationale of visual-based methods for food recognition , including their accuracy, performance, and the use of popular food databases to evaluate existing models.
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Original:  Thus, the following paper presents a brief review of visual-based methods for food recognition , including their accuracy, performance, and the use of popular food databases to evaluate existing models. The work further aims to highlight future challenges in this area .
Revised:  Thus, the following paper presents a brief review of visual-based methods for food recognition . The core of the paper is the presentation, discussion and evaluation of these methods on popular food image databases. We also discussed the mobile applications that are implementing these methods. The review ends with a discussion of research gaps and future challenges in this area .
Edit intent: meaning-changed
Original:  To solve this problem, this work develops a novel physics-informed deep reversible regression models for temperature field reconstruction of heat-source systems (TFR-HSS), which can better reconstruct the temperature field with the given limited monitoring points unsupervisedly.
Revised:  To solve this problem, this work develops a novel physics-informed deep reversible regression models for temperature field reconstruction of heat-source systems (TFR-HSS), which can better reconstruct the temperature field with limited monitoring points unsupervisedly.
Edit intent: clarity
Original:  Finally, considering the physical characteristics of heat conduction as well as the boundary conditions, this work proposes the physics-informed reconstruction loss including four training losses and joint learns the deep surrogate model with these losses unsupervisedly.
Revised:  Finally, considering the physical characteristics of heat conduction as well as the boundary conditions, this work proposes the physics-informed reconstruction loss including four training losses and jointly learns the deep surrogate model with these losses unsupervisedly.
Edit intent: fluency
Original:  To this end, we note that pyramid pooling has been demonstrated to be effective in various vision tasks owing to its powerful  context abstraction, and its natural property of spatial invariance is  suitable to address the loss of structural information (problem ii)).
Revised:  To this end, we note that pyramid pooling has been demonstrated to be effective in various vision tasks owing to its powerful ability in context abstraction, and its natural property of spatial invariance is suitable to address the loss of structural information (problem ii)).
Edit intent: clarity
Original:  To this end, we note that pyramid pooling has been demonstrated to be effective in various vision tasks owing to its powerful  context abstraction, and its natural property of spatial invariance is  suitable to address the loss of structural information (problem ii)).
Revised:  To this end, we note that pyramid pooling has been demonstrated to be effective in various vision tasks owing to its powerful context abstraction, and its natural property of spatial invariance is also suitable to address the loss of structural information (problem ii)).
Edit intent: coherence
Original:  We investigate the importance of the value of ReLU  (0) for several precision levels (16, 32, 64 bits), on various networks (fully connected, VGG, ResNet) and datasets (MNIST, CIFAR10, SVHN).
Revised:  We investigate the importance of the value of ReLU ' (0) for several precision levels (16, 32, 64 bits), on various networks (fully connected, VGG, ResNet) and datasets (MNIST, CIFAR10, SVHN).
Edit intent: fluency
Original:  In this manuscript, I review and extend recent work on the  relationship between predictive coding and backpropagation for training  artificial neural networks on supervised learning tasks.
Revised: Artificial neural networks are often interpreted as abstract models of biological neuronal networks, but they are typically trained using the biologically unrealistic backpropagation algorithm and its variants. Predictive coding has been offered as a potentially more biologically realistic alternative to backpropagation for training neural networks. In this manuscript, I review and extend recent work on the relationship between predictive coding and backpropagation for training artificial neural networks on supervised learning tasks.
Edit intent: meaning-changed
Original:  I also discuss some implications of these results for the interpretation of predictive coding and deep neural networks as models of biological learning and I describe a repository of functions, Torch2PC, for performing predictive coding with PyTorch neural network models.
Revised:  I discuss some implications of these results for the interpretation of predictive coding and deep neural networks as models of biological learning and I describe a repository of functions, Torch2PC, for performing predictive coding with PyTorch neural network models.
Edit intent: clarity
Original:  This paper introduces a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FF-NSL), that integrates state-of-the-art ILP systems  based on the Answer Set semantics, with neural networks , in order to learn interpretable hypotheses from labelled unstructured data.
Revised:  To address this problem, this paper proposes a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FF-NSL), that integrates state-of-the-art ILP systems based on the Answer Set semantics, with neural networks , in order to learn interpretable hypotheses from labelled unstructured data.
Edit intent: coherence
Original:  This paper introduces a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FF-NSL), that integrates state-of-the-art ILP systems  based on the Answer Set semantics, with neural networks , in order to learn interpretable hypotheses from labelled unstructured data.
Revised:  This paper introduces a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FF-NSL), that integrates state-of-the-art ILP systems , based on the Answer Set semantics, with neural networks , in order to learn interpretable hypotheses from labelled unstructured data.
Edit intent: fluency
Original:  This paper introduces a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FF-NSL), that integrates state-of-the-art ILP systems  based on the Answer Set semantics, with neural networks , in order to learn interpretable hypotheses from labelled unstructured data.
Revised:  This paper introduces a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FF-NSL), that integrates state-of-the-art ILP systems based on the Answer Set semantics, with Neural Networks (NNs) , in order to learn interpretable hypotheses from labelled unstructured data.
Edit intent: clarity
Original:  FF-NSL uses a pre-trained neural network to extract symbolic facts from unstructured data and an ILP system to learn a hypothesis that performs a downstream classification task. In order to evaluate the applicability of our approach to real-world applications, the framework is evaluated on tasks where distributional shiftsare introduced to unstructured input data , for which pre-trained neural networks are likely to predict incorrectly and with high confidence.
Revised:  To demonstrate the generality and robustness of FF-NSL, we use two datasets subject to distributional shifts , for which pre-trained neural networks are likely to predict incorrectly and with high confidence.
Edit intent: clarity
Original:  In order to evaluate the applicability of our approach to real-world applications, the framework is evaluated on tasks where distributional shiftsare introduced to unstructured input data , for which pre-trained neural networks are likely to predict incorrectly and with high confidence.
Revised:  In order to evaluate the applicability of our approach to real-world applications, the framework is evaluated on tasks where distributional shiftsare introduced to unstructured input data , for which pre-trained NNs may give incorrect predictions with high confidence.
Edit intent: clarity
Original:  Though recently the pre-vailing vision transformers (ViTs) have shown great poten-tial of self-attention based models in ImageNet classifica-tion , their performance is still inferior to latest SOTA CNNsif no extra data are provided.
Revised:  Though recently the prevailing vision transformers (ViTs) have shown great poten-tial of self-attention based models in ImageNet classifica-tion , their performance is still inferior to latest SOTA CNNsif no extra data are provided.
Edit intent: clarity
Original:  Though recently the pre-vailing vision transformers (ViTs) have shown great poten-tial of self-attention based models in ImageNet classifica-tion , their performance is still inferior to latest SOTA CNNsif no extra data are provided.
Revised:  Though recently the pre-vailing vision transformers (ViTs) have shown great potential of self-attention based models in ImageNet classifica-tion , their performance is still inferior to latest SOTA CNNsif no extra data are provided.
Edit intent: fluency
Original:  Though recently the pre-vailing vision transformers (ViTs) have shown great poten-tial of self-attention based models in ImageNet classifica-tion , their performance is still inferior to latest SOTA CNNsif no extra data are provided.
Revised:  Though recently the pre-vailing vision transformers (ViTs) have shown great poten-tial of self-attention based models in ImageNet classification , their performance is still inferior to latest SOTA CNNsif no extra data are provided.
Edit intent: fluency
Original:  Though recently the pre-vailing vision transformers (ViTs) have shown great poten-tial of self-attention based models in ImageNet classifica-tion , their performance is still inferior to latest SOTA CNNsif no extra data are provided.
Revised:  Though recently the pre-vailing vision transformers (ViTs) have shown great poten-tial of self-attention based models in ImageNet classifica-tion , their performance is still inferior to that of the latest SOTA CNNs if no extra data are provided.
Edit intent: clarity
Original:  Unlike self-attention that focuses on global depen-dency modeling at a coarse level, the outlook attention aimsto efficiently encode finer-level features and contexts intotokens, which are shown to be critical for recognition per-formance but largely ignored by the self-attention.
Revised:  Unlike self-attention that focuses on global dependency modeling at a coarse level, the outlook attention aimsto efficiently encode finer-level features and contexts intotokens, which are shown to be critical for recognition per-formance but largely ignored by the self-attention.
Edit intent: fluency
Original:  Unlike self-attention that focuses on global depen-dency modeling at a coarse level, the outlook attention aimsto efficiently encode finer-level features and contexts intotokens, which are shown to be critical for recognition per-formance but largely ignored by the self-attention.
Revised:  Unlike self-attention that focuses on global depen-dency modeling at a coarse level, the outlook attention efficiently encodes finer-level features and contexts intotokens, which are shown to be critical for recognition per-formance but largely ignored by the self-attention.
Edit intent: fluency
Original:  Unlike self-attention that focuses on global depen-dency modeling at a coarse level, the outlook attention aimsto efficiently encode finer-level features and contexts intotokens, which are shown to be critical for recognition per-formance but largely ignored by the self-attention.
Revised:  Unlike self-attention that focuses on global depen-dency modeling at a coarse level, the outlook attention aimsto efficiently encode finer-level features and contexts into tokens, which is shown to be critical for recognition per-formance but largely ignored by the self-attention.
Edit intent: fluency
Original:  Unlike self-attention that focuses on global depen-dency modeling at a coarse level, the outlook attention aimsto efficiently encode finer-level features and contexts intotokens, which are shown to be critical for recognition per-formance but largely ignored by the self-attention.
Revised:  Unlike self-attention that focuses on global depen-dency modeling at a coarse level, the outlook attention aimsto efficiently encode finer-level features and contexts intotokens, which are shown to be critically beneficial to recognition performance but largely ignored by the self-attention.
Edit intent: clarity
Original:  Experi-ments show that our VOLO achieves 87.1\% top-1 accuracyon ImageNet-1K classification, being the first model exceed-ing 87\% accuracy on this competitive benchmark, withoutusing any extra training data .
Revised:  Experiments show that our VOLO achieves 87.1\% top-1 accuracyon ImageNet-1K classification, being the first model exceed-ing 87\% accuracy on this competitive benchmark, withoutusing any extra training data .
Edit intent: fluency
Original:  Experi-ments show that our VOLO achieves 87.1\% top-1 accuracyon ImageNet-1K classification, being the first model exceed-ing 87\% accuracy on this competitive benchmark, withoutusing any extra training data .
Revised:  Experi-ments show that our VOLO achieves 87.1\% top-1 accuracy on ImageNet-1K classification, being the first model exceed-ing 87\% accuracy on this competitive benchmark, withoutusing any extra training data .
Edit intent: fluency
Original:  Experi-ments show that our VOLO achieves 87.1\% top-1 accuracyon ImageNet-1K classification, being the first model exceed-ing 87\% accuracy on this competitive benchmark, withoutusing any extra training data .
Revised:  Experi-ments show that our VOLO achieves 87.1\% top-1 accuracyon ImageNet-1K classification, which is the first model exceed-ing 87\% accuracy on this competitive benchmark, withoutusing any extra training data .
Edit intent: coherence
Original:  Experi-ments show that our VOLO achieves 87.1\% top-1 accuracyon ImageNet-1K classification, being the first model exceed-ing 87\% accuracy on this competitive benchmark, withoutusing any extra training data .
Revised:  Experi-ments show that our VOLO achieves 87.1\% top-1 accuracyon ImageNet-1K classification, being the first model exceeding 87\% accuracy on this competitive benchmark, withoutusing any extra training data .
Edit intent: fluency
Original:  Experi-ments show that our VOLO achieves 87.1\% top-1 accuracyon ImageNet-1K classification, being the first model exceed-ing 87\% accuracy on this competitive benchmark, withoutusing any extra training data .
Revised:  Experi-ments show that our VOLO achieves 87.1\% top-1 accuracyon ImageNet-1K classification, being the first model exceed-ing 87\% accuracy on this competitive benchmark, without using any extra training data .
Edit intent: fluency
Original:  Experi-ments show that our VOLO achieves 87.1\% top-1 accuracyon ImageNet-1K classification, being the first model exceed-ing 87\% accuracy on this competitive benchmark, withoutusing any extra training data .
Revised:  Experi-ments show that our VOLO achieves 87.1\% top-1 accuracyon ImageNet-1K classification, being the first model exceed-ing 87\% accuracy on this competitive benchmark, withoutusing any extra training data 
Edit intent: fluency
Original: With high prevalence of offensive language against the minorities in social media, counter hate speech generation is considered as an automatic way to tackle this challenge. The counter hate speeches are supposed to appear as a third voice to educate people and keep the social red lines bold without limiting the freedom of speech principles. The counter hate speechgeneration is based on the optimistic assumption that, any attempt to intervene the hate speeches in social media can play a positive role in this context. Beyond that, previous works ignored to investigate the sequence of comments before and after counter speech.
Revised: With the rise of voice chat rooms, a gigantic resource of data can be exposed to the research community for natural language processing tasks. Moderators in voice chat rooms actively monitor the discussions and remove the participants with offensive language. However, it makes the hate speech detection even more difficult since some participants try to find creative ways to articulate hate speech. This makes the hate speech detection challenging in new social media like Clubhouse.
Edit intent: meaning-changed