wanyu/IteraTeR_v2 huggingface dataset:
Original:  To solve the stationary nonlinear Navier-Stokes  equation, we introduce the idea of linearization of Navier-Stokes equation and iterative methods to treat the nonlinear convection term .
Revised:  To solve the stationary nonlinear Navier-Stokes equation, we introduce the idea of linearization of the nonlinear convection term .
Edit intent: clarity
Original:  Previous work on this problem has only addressed the extreme cases with respect to the prediction error, and has relied on overly powerful and error-free prediction oracles  .
Revised:  Previous work on this problem has only addressed the extreme cases with respect to the prediction error, has relied on overly powerful and error-free prediction oracles .
Edit intent: coherence
Original: Genomic data provides researchers with an invaluable source of information to advance progress in biomedical research, personalized medicine, and drug development.
Revised: The availability of genomic data is often essential to progress in biomedical research, personalized medicine, and drug development.
Edit intent: clarity
Original:  As a result, organizations have begun to experiment with sharing synthetic data, which should mirror the real data's salient characteristics , without exposing it.
Revised:  As a result, several initiatives have been launched to experiment with sharing synthetic data, which should mirror the real data's salient characteristics , without exposing it.
Edit intent: clarity
Original:  In this paper , we provide the first evaluation of the utility and the privacy protection of five state-of-the-art models for generating synthetic genomic data.
Revised:  This paper provides the first evaluation of the utility and the privacy protection of five state-of-the-art models for generating synthetic genomic data.
Edit intent: fluency
Original:  First, we assess the performance of the synthetic data on a number of common tasks, such as allele and population statistics as well as linkage disequilibriumand principal component analysis.
Revised:  We assess the performance of the synthetic data on a number of common tasks, such as allele and population statistics as well as linkage disequilibriumand principal component analysis.
Edit intent: coherence
Original:  First, we assess the performance of the synthetic data on a number of common tasks, such as allele and population statistics as well as linkage disequilibriumand principal component analysis.
Revised:  First, we assess the performance of the synthetic data on several common tasks, such as allele and population statistics as well as linkage disequilibriumand principal component analysis.
Edit intent: fluency
Original:  Then, we study the susceptibility of the data to membership inference attacks, i.e., inferring whether a target record was part of the data used to train the model producing the synthetic dataset.
Revised:  Then, we study the susceptibility of the data to membership inference attacks, i.e., inferring whether a record was part of the data used to train the model producing the synthetic dataset.
Edit intent: clarity
Original:  Overall, there is no single approach for generating synthetic genomic data that performs well across the board.
Revised:  Overall, there is no single approach to generate synthetic genomic data that performs well across the board.
Edit intent: fluency
Original:  We show how the size and the nature of the training dataset matter , especially in the case of generative models.
Revised:  Also, the size and the nature of the training dataset matter , especially in the case of generative models.
Edit intent: coherence
Original:  We show how the size and the nature of the training dataset matter , especially in the case of generative models.
Revised:  We show how the size and nature of the training dataset matter , especially in the case of generative models.
Edit intent: fluency
Original:  Our measurement framework can be used by practitioners to assess the risks of deploying synthetic genomic data in the wild , and will serve as a benchmark tool for researchers and practitioners in the future .
Revised:  Looking forward, our techniques can be used by practitioners to assess the risks of deploying synthetic genomic data in the wild , and will serve as a benchmark tool for researchers and practitioners in the future .
Edit intent: clarity
Original:  Our measurement framework can be used by practitioners to assess the risks of deploying synthetic genomic data in the wild , and will serve as a benchmark tool for researchers and practitioners in the future .
Revised:  Our measurement framework can be used by practitioners to assess the risks of deploying synthetic genomic data in the wild and serve as a benchmark tool for researchers and practitioners in the future .
Edit intent: coherence
Original:  Our measurement framework can be used by practitioners to assess the risks of deploying synthetic genomic data in the wild , and will serve as a benchmark tool for researchers and practitioners in the future .
Revised:  Our measurement framework can be used by practitioners to assess the risks of deploying synthetic genomic data in the wild , and will serve as a benchmark for future work .
Edit intent: clarity
Original:  In this article, we suggest a classification of different types of reproducibility  that refines the badge system of the Association of Computing Machinery (ACM) adopted by TELO.
Revised:  In this article, we suggest a classification of different types of reproducibility and suggest a classification that refines the badge system of the Association of Computing Machinery (ACM) adopted by TELO.
Edit intent: coherence
Original: We extend the result of our earlier study Angoshtari, Bayraktar, and Young;
Revised: We Angoshtari, Bayraktar, and Young;
Edit intent: clarity
Original:  In this paper, we study the k-Happiness Maximizing Sets ( k-HMS) and Average Happiness Maximizing Sets ( AHMS ) problems.
Revised:  In this paper, we study the Average Happiness Maximizing Sets ( k-HMS) and Average Happiness Maximizing Sets ( AHMS ) problems.
Edit intent: fluency
Original:  In this paper, we study the k-Happiness Maximizing Sets ( k-HMS) and Average Happiness Maximizing Sets ( AHMS ) problems.
Revised:  In this paper, we study the k-Happiness Maximizing Sets ( AHMS) and k-Happiness Maximizing Sets ( AHMS ) problems.
Edit intent: clarity
Original:  In this paper, we study the k-Happiness Maximizing Sets ( k-HMS) and Average Happiness Maximizing Sets ( AHMS ) problems.
Revised:  In this paper, we study the k-Happiness Maximizing Sets ( k-HMS) and Average Happiness Maximizing Sets ( k-HMS ) problems.
Edit intent: clarity
Original:  Meanwhile, AHMS maximizes the average of this ratio within a distribution of utility functions. k-HMS and AHMS are equivalent to the more established k-Regret Minimizing Sets ( k-RMS) and Average Regret Minimizing Sets ( ARMS ) problems, but allow for the derivation of stronger theoretical results and more natural approximation schemes.
Revised:  AHMS and k-HMS seek the same optimal solutions as the more established Average Regret Minimizing Sets ( k-RMS) and Average Regret Minimizing Sets ( ARMS ) problems, but allow for the derivation of stronger theoretical results and more natural approximation schemes.
Edit intent: clarity
Original:  Meanwhile, AHMS maximizes the average of this ratio within a distribution of utility functions. k-HMS and AHMS are equivalent to the more established k-Regret Minimizing Sets ( k-RMS) and Average Regret Minimizing Sets ( ARMS ) problems, but allow for the derivation of stronger theoretical results and more natural approximation schemes.
Revised:  Meanwhile, AHMS maximizes the average of this ratio within a distribution of utility functions. k-HMS and AHMS are equivalent to the more established k-Regret Minimizing Sets ( ARMS) and k-Regret Minimizing Sets ( ARMS ) problems, but allow for the derivation of stronger theoretical results and more natural approximation schemes.
Edit intent: clarity
Original:  Meanwhile, AHMS maximizes the average of this ratio within a distribution of utility functions. k-HMS and AHMS are equivalent to the more established k-Regret Minimizing Sets ( k-RMS) and Average Regret Minimizing Sets ( ARMS ) problems, but allow for the derivation of stronger theoretical results and more natural approximation schemes.
Revised:  Meanwhile, AHMS maximizes the average of this ratio within a distribution of utility functions. k-HMS and AHMS are equivalent to the more established k-Regret Minimizing Sets ( k-RMS) and Average Regret Minimizing Sets ( k-RMS ) problems, but allow for the derivation of stronger theoretical results and more natural approximation schemes.
Edit intent: clarity
Original:  In this paper ,
Revised:  We provide approximation algorithms for AHMS with better approximation ratios and time complexities than known algorithms for ARMS. Next ,
Edit intent: clarity
Original:  we provide dataset reduction schemes which can be used to reduce the runtime of existing heuristic based algorithms, as well as to derive polynomial-time approximation schemes for both k-HMS and AHMS when dimensionality is fixed.
Revised:  we provide dataset reduction schemes which can be used to reduce the runtime of existing heuristic based algorithms, as well as to derive polynomial-time approximation schemes for both k-HMS when dimensionality is fixed.
Edit intent: clarity
Original:  Finally, we provide experimental validation showing that our AHMS algorithm achieves the same happiness as the existing Greedy Shrink FAM algorithm while running faster by over 2 orders of magnitude on even a small dataset of 17265 data points while our reduction scheme was able to reduce runtimes  by up to 93\% (from 4.2 hours to 16.7 minutes) while keeping happiness within 90 \\%DIF < of the original on the largest tested settings.
Revised:  We further provide experimental validation showing that our AHMS algorithm achieves the same happiness as the existing Greedy Shrink FAM algorithm while running faster by over 2 orders of magnitude on even a small dataset of 17265 data points while our reduction scheme was able to reduce runtimes by up to 93\% (from 4.2 hours to 16.7 minutes) while keeping happiness within 90 \\%DIF < of the original on the largest tested settings.
Edit intent: clarity
Original:  Finally, we provide experimental validation showing that our AHMS algorithm achieves the same happiness as the existing Greedy Shrink FAM algorithm while running faster by over 2 orders of magnitude on even a small dataset of 17265 data points while our reduction scheme was able to reduce runtimes  by up to 93\% (from 4.2 hours to 16.7 minutes) while keeping happiness within 90 \\%DIF < of the original on the largest tested settings.
Revised:  Finally, we provide experimental validation showing that our AHMS algorithm achieves the same happiness as the existing Greedy Shrink FAM algorithm while running faster by over 2 orders of magnitude on even small datasets while our reduction scheme was able to reduce runtimes by up to 93\% (from 4.2 hours to 16.7 minutes) while keeping happiness within 90 \\%DIF < of the original on the largest tested settings.
Edit intent: clarity
Original: Linear recurrent sequences are those whose elements are defined as linear combinations of preceding elements, and finding relations of recurrences is a fundamental problem in computer algebra.
Revised: Linear recurrent sequences are those whose elements are defined as linear combinations of preceding elements, and finding recurrence relations is a fundamental problem in computer algebra.
Edit intent: clarity
Original:   We present three methods for finding the idealof canceling polynomials : a Berlekamp-Massey-like approach due to Kurakin, one which computes the kernel of some block-Hankel matrix over A via a minimal approximant basis, and one based on bivariate Pad\'e approximation.
Revised:  We present three methods for finding this ideal : a Berlekamp-Massey-like approach due to Kurakin, one which computes the kernel of some block-Hankel matrix over A via a minimal approximant basis, and one based on bivariate Pad\'e approximation.
Edit intent: clarity
Original:  We propose complexity improvements for the first two methods, respectively by avoiding the computation of redundant sequence generators and by exploiting the Hankel structure to compress the approximation instance.
Revised:  We propose complexity improvements for the first two methods, respectively by avoiding the computation of redundant relations and by exploiting the Hankel structure to compress the approximation instance.
Edit intent: clarity
Original:  The review covers the historical background and the main legislations , administration and enforcement of the policy , standardization and harmonization, legislations enacted for specific products, notifications delivered by national safety authorities to the EU's alert system , recalls of dangerous consumer products, and the liability of these.
Revised:  The review covers the historical background and the main laws , administration and enforcement of the policy , standardization and harmonization, legislations enacted for specific products, notifications delivered by national safety authorities to the EU's alert system , recalls of dangerous consumer products, and the liability of these.
Edit intent: clarity
Original:  The review covers the historical background and the main legislations , administration and enforcement of the policy , standardization and harmonization, legislations enacted for specific products, notifications delivered by national safety authorities to the EU's alert system , recalls of dangerous consumer products, and the liability of these.
Revised:  The review covers the historical background and the main legislations , administration and enforcement , standardization and harmonization, legislations enacted for specific products, notifications delivered by national safety authorities to the EU's alert system , recalls of dangerous consumer products, and the liability of these.
Edit intent: clarity
Original:  The review covers the historical background and the main legislations , administration and enforcement of the policy , standardization and harmonization, legislations enacted for specific products, notifications delivered by national safety authorities to the EU's alert system , recalls of dangerous consumer products, and the liability of these.
Revised:  The review covers the historical background and the main legislations , administration and enforcement of the policy , standardization and harmonization, laws enacted for specific products, notifications delivered by national safety authorities to the EU's alert system , recalls of dangerous consumer products, and the liability of these.
Edit intent: clarity
Original:  The review covers the historical background and the main legislations , administration and enforcement of the policy , standardization and harmonization, legislations enacted for specific products, notifications delivered by national safety authorities to the EU's alert system , recalls of dangerous consumer products, and the liability of these.
Revised:  The review covers the historical background and the main legislations , administration and enforcement of the policy , standardization and harmonization, legislations enacted for specific products, notifications delivered by national safety authorities , recalls of dangerous consumer products, and the liability of these.
Edit intent: clarity
Original:  The review covers the historical background and the main legislations , administration and enforcement of the policy , standardization and harmonization, legislations enacted for specific products, notifications delivered by national safety authorities to the EU's alert system , recalls of dangerous consumer products, and the liability of these.
Revised:  The review covers the historical background and the main legislations , administration and enforcement of the policy , standardization and harmonization, legislations enacted for specific products, notifications delivered by national safety authorities to the EU's alert system , recalls of dangerous products, and the liability of these.
Edit intent: clarity
Original:  Based on the review and analysis of these themes and the associated literature, the current policy challenges are further discussed.
Revised:  Based on the review and analysis of these themes and the associated literature, some current policy challenges are further discussed.
Edit intent: clarity
Original:  Most current EEG studies are working on handcrafted features with a supervised modeling , which would be limited by experience and human feedbacks to a great extent.
Revised:  Most current EEG studies are working on handcrafted features with a supervised model , which would be limited by experience and human feedbacks to a great extent.
Edit intent: fluency
Original:  Most current EEG studies are working on handcrafted features with a supervised modeling , which would be limited by experience and human feedbacks to a great extent.
Revised:  Most current EEG studies are working on handcrafted features with a supervised modeling , which would be limited by the given labels to a great extent.
Edit intent: clarity
Original:  EEGFuseNet is trained in an unsupervised manner, and deep EEG features covering  spatial and temporal dynamics are automatically characterized.
Revised:  EEGFuseNet is trained in an unsupervised manner, and deep EEG features covering both spatial and temporal dynamics are automatically characterized.
Edit intent: clarity
Original:  Comparing to the handcrafted features, the  deep EEG features could be considered to be more generic and independent of any specific EEG task.
Revised:  Comparing to the existing features, the deep EEG features could be considered to be more generic and independent of any specific EEG task.
Edit intent: clarity
Original:  The performance of the extracted deep and low-dimensional features by EEGFuseNet is carefully evaluated in an unsupervised emotion recognition application based on a famous public emotion database .
Revised:  The performance of the extracted deep and low-dimensional features by EEGFuseNet is carefully evaluated in an unsupervised emotion recognition application based on three public emotion databases .
Edit intent: clarity
Original:  The results demonstrate the proposed EEGFuseNet is a robust and reliable model, which is easy to train and manage and perform efficiently in the representation and fusion of dynamic EEG features.
Revised:  The results demonstrate the proposed EEGFuseNet is a robust and reliable model, which is easy to train and performs efficiently in the representation and fusion of dynamic EEG features.
Edit intent: coherence
Original: We introduce a new representation of the rotating shallow water equations based on a stochastic transport principle .
Revised: We introduce a physically relevant stochastic representation of the rotating shallow water equations based on a stochastic transport principle .
Edit intent: clarity
Original: We introduce a new representation of the rotating shallow water equations based on a stochastic transport principle .
Revised: We introduce a new representation of the rotating shallow water equations . The derivation relies mainly on a stochastic transport principle .
Edit intent: coherence
Original:  Moreover, using an inhomogenous noise, we show for a barotropically unstable jet on the sphere that the proposed random model better captures the structure of a large-scale flow than a comparable deterministic model .
Revised:  Moreover, using an inhomogenous noise, we show for a barotropically unstable jet on the sphere .
Edit intent: clarity
Original: We introduce a novel approach to the automated termination analysis of computer programs: we train neural networks to act as ranking functions.
Revised: We introduce a novel approach to the automated termination analysis of computer programs: we train neural networks to behave as ranking functions.
Edit intent: fluency
Original:  While in the past ranking functions were usually constructed using static analysis, our method learns them from sampled executions .
Revised:  While in the past ranking functions were usually constructed using static analysis, our method learns them from executions traces .
Edit intent: clarity
Original:  We train a neural network so that its output decreases along execution traces as a ranking function would;
Revised:  We train a neural network so that its output decreases along sampled executions as a ranking function would;
Edit intent: clarity
Original:  Depending on such a belief, we construct pairwise constraints to train twin deep learning architectures with noise augmentation policies, that generate speaker embeddings.
Revised:  Depending on such a belief, a pairwise constraint is constructed with noise augmentation policies, that generate speaker embeddings.
Edit intent: clarity
Original:  Without relying on domain adaption policy, the process unsupervisely produces clusterable speaker embeddings, and we name it unsupervised vectors (u-vectors).
Revised:  Without relying on domain adaption policy, the process unsupervisely produces clusterable speaker embeddings, termed unsupervised vectors (u-vectors).
Edit intent: clarity
Original:  Also, we include a Bengali dataset , Bengali ASR, to illustrate the diversity of the domain shifts for speaker recognition systems.
Revised:  Also, a Bengali dataset , Bengali ASR, to illustrate the diversity of the domain shifts for speaker recognition systems.
Edit intent: coherence
Original:  Also, we include a Bengali dataset , Bengali ASR, to illustrate the diversity of the domain shifts for speaker recognition systems.
Revised:  Also, we include a Bengali dataset is included to illustrate the diversity of the domain shifts for speaker recognition systems.
Edit intent: clarity
Original: Black-box optimization (BBO) algorithms are concerned with finding the best solutions for the problems with missing analytical details.
Revised: Black-box optimization (BBO) algorithms are concerned with finding the best solutions for problems with missing analytical details.
Edit intent: fluency
Original:  Most classical methods for such problems are based on strong and fixed a priori assumptions  such as Gaussian distribution.
Revised:  Most classical methods for such problems are based on strong and fixed a priori assumptions , such as Gaussian distribution.
Edit intent: fluency
Original:  However, lots of complex real-world problems are far from the a priori distribution , bringing some unexpected obstacles to these methods.
Revised:  However, the complex real-world problems are far from the a priori distribution , bringing some unexpected obstacles to these methods.
Edit intent: coherence
Original:  In this paper, we present an optimizer using generative adversarial nets (OPT-GAN) to guide search on black-box problems via estimating the distribution of optima.
Revised:  In this paper, we present an optimizer using generative adversarial nets (OPT-GAN) to adapt to diverse black-box problems via estimating the distribution of optima.
Edit intent: clarity
Original:  Most classical methods for such problems are based on strong and fixed a priori assumptions, such as Gaussian distribution .
Revised:  Most classical methods for such problems are based on strong and fixed a priori assumptions, such as Gaussian distribution .
Edit intent: fluency
Original:  Most classical methods for such problems are based on strong and fixed a priori assumptions, such as Gaussian distribution .
Revised:  Most classical methods for such problems are based on strong and fixed a priori assumptions, such as Gaussianity .
Edit intent: clarity
Original:  However, the complex real-world problems, especially when the global optimum is desired, could be very far from the a priori assumptions because of their diversities, bringing some unexpected obstacles to these methods.
Revised:  However, the complex real-world problems, especially when the global optimum is desired, could be very far from the a priori assumptions because of their diversities, bringing some unexpected obstacles to these methods.
Edit intent: fluency