wanyu/IteraTeR_v2 huggingface dataset:
Original:  We show that our proposed scheme yields good numerical approximation accuracywithout being affected by the stiffness , thus outperforming in same cases the \texttt{ode45} and \texttt{ode15s} functions.
Revised:  We show that our proposed scheme yields good approximation accuracy , thus outperforming in same cases the \texttt{ode45} and \texttt{ode15s} functions.
Edit intent: clarity
Original:  We show that our proposed scheme yields good numerical approximation accuracywithout being affected by the stiffness , thus outperforming in same cases the \texttt{ode45} and \texttt{ode15s} functions.
Revised:  We show that our proposed scheme yields good numerical approximation accuracywithout being affected by the stiffness , thus outperforming \texttt{ode45} and \texttt{ode15s} functions.
Edit intent: clarity
Original: In material science, models are derived to describe emergent properties (e.g. elasticity, strength, conductivity , ... ) and their relations to the material and processing conditions.
Revised: In material science, models are derived to predict emergent material properties (e.g. elasticity, strength, conductivity , ... ) and their relations to the material and processing conditions.
Edit intent: clarity
Original: In material science, models are derived to describe emergent properties (e.g. elasticity, strength, conductivity , ... ) and their relations to the material and processing conditions.
Revised: In material science, models are derived to describe emergent properties (e.g. elasticity, strength, conductivity ) and their relations to the material and processing conditions.
Edit intent: fluency
Original: In material science, models are derived to describe emergent properties (e.g. elasticity, strength, conductivity , ... ) and their relations to the material and processing conditions.
Revised: In material science, models are derived to describe emergent properties (e.g. elasticity, strength, conductivity , ... ) and their relations to processing conditions.
Edit intent: clarity
Original:  Constitutive models are models that describe the behaviour of materials for instance deformation processes through applied forces.
Revised:  In deformation processes, constitutive equations are used to describe the materials response to applied forces.
Edit intent: clarity
Original:  This model operates with internal material variables such  a dislocation density and contains a number of parameters . Among them there are three calibration parameters , which are usually fit to measured data since the relations to the processing conditions (e.g. deformation temperature, strain rate) are not fully understood.
Revised:  This model operates with internal material variables such as a dislocation density and contains a number of parameters . Among them there are three calibration parameters , which are usually fit to measured data since the relations to the processing conditions (e.g. deformation temperature, strain rate) are not fully understood.
Edit intent: fluency
Original:  This model operates with internal material variables such  a dislocation density and contains a number of parameters . Among them there are three calibration parameters , which are usually fit to measured data since the relations to the processing conditions (e.g. deformation temperature, strain rate) are not fully understood.
Revised:  This model operates with internal material variables such a dislocation density . The model contains a number of parameters . Among them there are three calibration parameters , which are usually fit to measured data since the relations to the processing conditions (e.g. deformation temperature, strain rate) are not fully understood.
Edit intent: coherence
Original:  This model operates with internal material variables such  a dislocation density and contains a number of parameters . Among them there are three calibration parameters , which are usually fit to measured data since the relations to the processing conditions (e.g. deformation temperature, strain rate) are not fully understood.
Revised:  This model operates with internal material variables such a dislocation density and contains a number of parameters , among them three calibration parameters , which are usually fit to measured data since the relations to the processing conditions (e.g. deformation temperature, strain rate) are not fully understood.
Edit intent: fluency
Original:  This model operates with internal material variables such  a dislocation density and contains a number of parameters . Among them there are three calibration parameters , which are usually fit to measured data since the relations to the processing conditions (e.g. deformation temperature, strain rate) are not fully understood.
Revised:  This model operates with internal material variables such a dislocation density and contains a number of parameters . Among them there are three calibration parameters . Currently, these must be fit to measured data since the relations to the processing conditions (e.g. deformation temperature, strain rate) are not fully understood.
Edit intent: coherence
Original:  Our proposed GP-based method identifies these relations and generates short expressions which can be plugged into the constitutive model instead of the calibration parameters .
Revised:  Our proposed GP-based methods to identify these relations and generates short expressions which can be plugged into the constitutive model instead of the calibration parameters .
Edit intent: style
Original:  One of the major shortcomings is fitting of model parameters due to unknown relations to processing conditions.
Revised:  One of the major shortcomings is fitting of model parameters that depend on processing conditions.
Edit intent: clarity
Original:  Currently, these must be fit to measured data since the relations to the processing conditions (e.g. deformation temperature, strain rate) are not fully understood.
Revised:  Currently, these parameters must be optimized to fit measured data since the relations to the processing conditions (e.g. deformation temperature, strain rate) are not fully understood.
Edit intent: clarity
Original:  Currently, these must be fit to measured data since the relations to the processing conditions (e.g. deformation temperature, strain rate) are not fully understood.
Revised:  Currently, these must be fit to measured data since their relations to processing conditions (e.g. deformation temperature, strain rate) are not fully understood.
Edit intent: fluency
Original:  We  propose two (explicit and implicit) GP-based methods to identify these relations and generate short but accurate expressions.
Revised:  We propose two (explicit and implicit) methods to identify these relations and generate short but accurate expressions.
Edit intent: clarity
Original:  We  propose two (explicit and implicit) GP-based methods to identify these relations and generate short but accurate expressions.
Revised:  We propose two (explicit and implicit) GP-based methods to identify these dependencies and generate short but accurate expressions.
Edit intent: clarity
Original:  We  propose two (explicit and implicit) GP-based methods to identify these relations and generate short but accurate expressions.
Revised:  We propose two (explicit and implicit) GP-based methods to identify these relations and generate short interpretable expressions.
Edit intent: clarity
Original:  The derived expressions can be plugged into the constitutive model instead of the calibration parameters and allow the interpolation between the processing parameters .
Revised:  The derived expressions can be plugged into the constitutive model and replace the calibration parameters and allow the interpolation between the processing parameters .
Edit intent: clarity
Original:  Adding to this uncertainty is the impact of the COVID-19 pandemic that has triggered profound changes in mobility behaviors as well as accelerated the adoption of new technologies at an unprecedented rate.
Revised:  Adding to this uncertainty is the impact of the COVID-19 pandemic that has triggered profound changes in mobility behaviors as well as accelerated adoption of new technologies at an unprecedented rate.
Edit intent: fluency
Original:  This study examines the impact of the COVID-19 pandemic on willingness to adopt the emerging new technology of self-driving vehicles . Using data from a survey disseminated in June 2020 to 700 respondents in contiguous United States, we perform a difference-in-difference regression to analyze the shift in willingness to use autonomous vehicles as part of a shared fleet before and during the pandemic.
Revised:  Accordingly, this study examines the impact of the COVID-19 pandemic on willingness to adopt the emerging new technology of self-driving vehicles . Using data from a survey disseminated in June 2020 to 700 respondents in contiguous United States, we perform a difference-in-difference regression to analyze the shift in willingness to use autonomous vehicles as part of a shared fleet before and during the pandemic.
Edit intent: coherence
Original:  This study examines the impact of the COVID-19 pandemic on willingness to adopt the emerging new technology of self-driving vehicles . Using data from a survey disseminated in June 2020 to 700 respondents in contiguous United States, we perform a difference-in-difference regression to analyze the shift in willingness to use autonomous vehicles as part of a shared fleet before and during the pandemic.
Revised:  This study examines the impact of the COVID-19 pandemic on willingness to adopt the emerging technology of autonomous vehicles (AVs) . Using data from a survey disseminated in June 2020 to 700 respondents in contiguous United States, we perform a difference-in-difference regression to analyze the shift in willingness to use autonomous vehicles as part of a shared fleet before and during the pandemic.
Edit intent: clarity
Original:  This study examines the impact of the COVID-19 pandemic on willingness to adopt the emerging new technology of self-driving vehicles . Using data from a survey disseminated in June 2020 to 700 respondents in contiguous United States, we perform a difference-in-difference regression to analyze the shift in willingness to use autonomous vehicles as part of a shared fleet before and during the pandemic.
Revised:  This study examines the impact of the COVID-19 pandemic on willingness to adopt the emerging new technology of self-driving vehicles . Using data from a survey disseminated in June 2020 to 700 respondents in the United States, we perform a difference-in-difference regression to analyze the shift in willingness to use autonomous vehicles as part of a shared fleet before and during the pandemic.
Edit intent: fluency
Original:  The results reveal that the COVID-19 pandemic has a positive and highly significant impact on consideration of autonomous vehicles.
Revised:  The results reveal that the COVID-19 pandemic has a positive and highly significant impact on the consideration of using autonomous vehicles.
Edit intent: clarity
Original:  This shift is present regardless of techsavviness , gender or political views .
Revised:  This shift is present regardless of tech-savviness , gender or political views .
Edit intent: fluency
Original:  Here we re-establish that experimental results with the help of our exact analytical calculations for a stochastic model with non-competitive transcriptional regulatory architecture and find out some properties of Noise strength ( like sub-Poissonian fano factor) and mean expression as we found in a two state model earlier . Along with those aforesaid properties we also observe some anomalous characteristics in noise strength of mRNA and in variance of protein at lower activator concentrations.
Revised:  Here we construct a stochastic model with non-competitive transcriptional regulatory architecture and find out some properties of Noise strength ( like sub-Poissonian fano factor) and mean expression as we found in a two state model earlier . Along with those aforesaid properties we also observe some anomalous characteristics in noise strength of mRNA and in variance of protein at lower activator concentrations.
Edit intent: coherence
Original:  Here we re-establish that experimental results with the help of our exact analytical calculations for a stochastic model with non-competitive transcriptional regulatory architecture and find out some properties of Noise strength ( like sub-Poissonian fano factor) and mean expression as we found in a two state model earlier . Along with those aforesaid properties we also observe some anomalous characteristics in noise strength of mRNA and in variance of protein at lower activator concentrations.
Revised:  Here we re-establish that experimental results with the help of our exact analytical calculations for a stochastic model with non-competitive transcriptional regulatory architecture and find out some properties of Noise strength ( like sub-Poissonian regime to super-Poissonian regime . Along with those aforesaid properties we also observe some anomalous characteristics in noise strength of mRNA and in variance of protein at lower activator concentrations.
Edit intent: clarity
Original:  Here we re-establish that experimental results with the help of our exact analytical calculations for a stochastic model with non-competitive transcriptional regulatory architecture and find out some properties of Noise strength ( like sub-Poissonian fano factor) and mean expression as we found in a two state model earlier . Along with those aforesaid properties we also observe some anomalous characteristics in noise strength of mRNA and in variance of protein at lower activator concentrations.
Revised:  Here we re-establish that experimental results with the help of our exact analytical calculations for a stochastic model with non-competitive transcriptional regulatory architecture and find out some properties of Noise strength ( like sub-Poissonian fano factor) and mean expression as we found in a two state model earlier . Along with those aforesaid properties we observe some anomalous characteristics in noise strength of mRNA and in variance of protein at lower activator concentrations.
Edit intent: clarity
Original:  \%%DIFDELCMD < \sout{The %DIFDELCMD < results show that our measure has highest accuracy in general.
Revised:  %DIFDELCMD < \sout{The %DIFDELCMD < results show that our measure has highest accuracy in general.
Edit intent: fluency
Original:  This object, that we call Test-or -Set (ToS) , allows a single distinguished process to apply the set operation once, and a different distinguished process to apply the test operation also once.
Revised:  This object, that we call or -Set (ToS) , allows a single distinguished process to apply the set operation once, and a different distinguished process to apply the test operation also once.
Edit intent: clarity
Original:  This object, that we call Test-or -Set (ToS) , allows a single distinguished process to apply the set operation once, and a different distinguished process to apply the test operation also once.
Revised:  This object, that we call Test- -Set (ToS) , allows a single distinguished process to apply the set operation once, and a different distinguished process to apply the test operation also once.
Edit intent: fluency
Original:  This object, that we call Test-or -Set (ToS) , allows a single distinguished process to apply the set operation once, and a different distinguished process to apply the test operation also once.
Revised:  This object, that we call Test-or -Set (ToS) . This object allows a single distinguished process to apply the set operation once, and a different distinguished process to apply the test operation also once.
Edit intent: coherence
Original:  This object, that we call Test-or -Set (ToS) , allows a single distinguished process to apply the set operation once, and a different distinguished process to apply the test operation also once.
Revised:  This object, that we call Test-or -Set (ToS) , allows a single distinguished process to apply the set operation once, and a different distinguished process to apply the test operation also once.
Edit intent: fluency
Original:  Since this weak object can be directly implemented by a single-writer single-reader (SWSR) register (and other common objects such as max-register, snapshot , counter, test-and-set, queue, stack, etc... ), this result implies that there is no 1-resilient  strongly linearizable implementation of a SWSR register (and of these other objects) in   message-passing systems.
Revised:  Since this weak object can be directly implemented by a single-writer single-reader (SWSR) register (and other common objects such as max-register, snapshot and counter ), this result implies that there is no 1-resilient strongly linearizable implementation of a SWSR register (and of these other objects) in message-passing systems.
Edit intent: coherence
Original: The Sinc-Nystr\"{o}m method in time is a high-order spectral method for solving evolutionary differential equations and it has wide applications in scientific computation .
Revised: The Sinc-Nystr\"{o}m method is a high-order spectral method for solving evolutionary differential equations and it has wide applications in scientific computation .
Edit intent: coherence
Original: The Sinc-Nystr\"{o}m method in time is a high-order spectral method for solving evolutionary differential equations and it has wide applications in scientific computation .
Revised: The Sinc-Nystr\"{o}m method in time is a high-order spectral method for solving evolutionary differential equations in time .
Edit intent: clarity
Original:  But in this method we have to solve all the time steps implicitly at one-shot , which may results in a large-scale nonsymmetric dense system that is expensive to solve .
Revised:  But in this method we have to solve all the time steps in one-shot , which may results in a large-scale nonsymmetric dense system that is expensive to solve .
Edit intent: clarity
Original:  But in this method we have to solve all the time steps implicitly at one-shot , which may results in a large-scale nonsymmetric dense system that is expensive to solve .
Revised:  But in this method we have to solve all the time steps implicitly at one-shot , which may results in a large-scale nonsymmetric dense system that is expensive to handle .
Edit intent: clarity
Original:  In this paper, we propose and analyze a parallel-in-time (PinT) preconditioner for solving such Sinc-Nystr\"{o both the parabolic and hyperbolic PDEs are investigated.
Revised:  In this paper, we propose and analyze preconditioner for such dense system arising from both the parabolic and hyperbolic PDEs are investigated.
Edit intent: clarity
Original:  Attributed to the special Toeplitz-like structure of the Sinc-Nystr\"{o a low-rank perturbation of the system matrix and we show that the spectrum of the preconditioned system is highly clustered around one, especially when the time step size is refined .
Revised:  Attributed to the special Toeplitz-like structure of the Sinc-Nystr\"{o a low-rank perturbation of the system matrix and we show that the eigenvalues of the preconditioned system is highly clustered around one, especially when the time step size is refined .
Edit intent: clarity
Original:  We also release our codes %DIFDELCMD < \footnote{%%%  Scripts are available at URL for contributing to the research community in the leaf classification problem.
Revised:  We also release our codes %DIFDELCMD < \footnote{%%% ( Scripts are available at URL for contributing to the research community in the leaf classification problem.
Edit intent: fluency
Original:  GenNAS does not use task-specific labels but instead adopts regression on a set of manually designed synthetic signal bases for architecture evaluation.
Revised:  GenNAS does not use task-specific labels but instead adopts regression on a set of manually designed synthetic signal bases for architecture evaluation.
Edit intent: fluency
Original:  Unlike previous methods that use optical flow for cross-frame context propagation to inpaint unknown regions, we show that this can be achieved implicitly by fitting a convolutional neural network to the known region .
Revised:  Unlike previous methods that use optical flow for cross-frame context propagation to inpaint unknown regions, we show that this can be achieved implicitly by fitting a convolutional neural network to known regions .
Edit intent: fluency
Original:  As a side-product , object categories, as well as edge information, are also provided to facilitate other related works . Extensive experiments with state-of-the-art models are conducted to demonstrate the superiority of our dataset.
Revised:  As a side-product , object categories, and edge maps . Extensive experiments with state-of-the-art models are conducted to demonstrate the superiority of our dataset.
Edit intent: clarity
Original:  We hope that the scale, diversity, and quality of CAT/CAT+ can benefit researchers in this area and beyond.
Revised:  We hope that the scale, diversity, and quality of our dataset can benefit researchers in this area and beyond.
Edit intent: clarity
Original:  The dataset and benchmark toolkit will be  accessible through our project page.
Revised:  The dataset and benchmark toolkit will be publicly accessible through our project page.
Edit intent: clarity
Original:  To tackle this, we introduce the idea of counterfactual training through context adjustment , and propose a "cost-free" group-cut-paste (GCP) procedure to leverage images from off-the-shelf saliency detection datasets and synthesize new samples.
Revised:  To tackle this, we introduce the idea of counterfactual training through context adjustment and propose a "cost-free" group-cut-paste (GCP) procedure to leverage images from off-the-shelf saliency detection datasets and synthesize new samples.
Edit intent: fluency
Original:  To tackle this, we introduce the idea of counterfactual training through context adjustment , and propose a "cost-free" group-cut-paste (GCP) procedure to leverage images from off-the-shelf saliency detection datasets and synthesize new samples.
Revised:  To tackle this, we introduce the idea of counterfactual training through context adjustment , and propose a "cost-free" group-cut-paste (GCP) procedure to leverage off-the-shelf saliency detection datasets and synthesize new samples.
Edit intent: clarity
Original:  To tackle this, we introduce the idea of counterfactual training through context adjustment , and propose a "cost-free" group-cut-paste (GCP) procedure to leverage images from off-the-shelf saliency detection datasets and synthesize new samples.
Revised:  To tackle this, we introduce the idea of counterfactual training through context adjustment , and propose a "cost-free" group-cut-paste (GCP) procedure to leverage images from off-the-shelf images and synthesize new samples.
Edit intent: clarity
Original:  CAT consists of 33,500 images, making it four times larger than the current co-saliency detection datasets.
Revised:  CAT consists of 33,500 images, which is four times larger than the current co-saliency detection datasets.
Edit intent: clarity
Original:  All images are automatically annotated with high-quality mask annotations, object categories, and edge maps.
Revised:  All samples are automatically annotated with high-quality mask annotations, object categories, and edge maps.
Edit intent: clarity
Original:  The dataset and benchmark toolkit will be publicly accessible through our project page.
Revised:  Our dataset will be publicly accessible through our project page.
Edit intent: clarity
Original: Simulating quantum systems is one of the most important potential applications of quantum computers to demonstrate its advantages over classical algorithms .
Revised: Simulating quantum systems is one of the most important potential applications of quantum computers .
Edit intent: clarity