wanyu/IteraTeR_v2 huggingface dataset:
Original:  By exploiting non-linear prior information, compressed sensing techniques in combination with sophisticate reconstruction algorithms allow a reduction of number of measurements while maintaining a high spatial resolution.
Revised:  By exploiting non-linear prior information, compressed sensing techniques in combination with sophisticated reconstruction algorithms allow a reduction of number of measurements while maintaining a high spatial resolution.
Edit intent: fluency
Original:  By exploiting non-linear prior information, compressed sensing techniques in combination with sophisticate reconstruction algorithms allow a reduction of number of measurements while maintaining a high spatial resolution.
Revised:  By exploiting non-linear prior information, compressed sensing techniques in combination with sophisticate reconstruction algorithms allow reducing the number of measurements while maintaining a high spatial resolution.
Edit intent: fluency
Original:  By exploiting non-linear prior information, compressed sensing techniques in combination with sophisticate reconstruction algorithms allow a reduction of number of measurements while maintaining a high spatial resolution.
Revised:  By exploiting non-linear prior information, compressed sensing techniques in combination with sophisticate reconstruction algorithms allow a reduction of number of measurements while maintaining high spatial resolution.
Edit intent: fluency
Original:  For this purpose , in this paper, we propose a multiscale factorization for the wave equation , which separates the data into a low frequency factor and sparse high frequency factors.
Revised:  To this end , in this paper, we propose a multiscale factorization for the wave equation , which separates the data into a low frequency factor and sparse high frequency factors.
Edit intent: coherence
Original:  For this purpose , in this paper, we propose a multiscale factorization for the wave equation , which separates the data into a low frequency factor and sparse high frequency factors.
Revised:  For this purpose , in this work we propose a multiscale factorization for the wave equation , which separates the data into a low frequency factor and sparse high frequency factors.
Edit intent: clarity
Original:  For this purpose , in this paper, we propose a multiscale factorization for the wave equation , which separates the data into a low frequency factor and sparse high frequency factors.
Revised:  For this purpose , in this paper, we propose a multiscale factorization for the wave equation that decomposes the measured data into a low frequency factor and sparse high frequency factors.
Edit intent: clarity
Original:  For this purpose , in this paper, we propose a multiscale factorization for the wave equation , which separates the data into a low frequency factor and sparse high frequency factors.
Revised:  For this purpose , in this paper, we propose a multiscale factorization for the wave equation , which separates the data into a low-frequency factor and sparse high frequency factors.
Edit intent: fluency
Original:  For this purpose , in this paper, we propose a multiscale factorization for the wave equation , which separates the data into a low frequency factor and sparse high frequency factors.
Revised:  For this purpose , in this paper, we propose a multiscale factorization for the wave equation , which separates the data into a low frequency factor and sparse high-frequency factors.
Edit intent: fluency
Original:  By extending the acoustic reciprocal principle, we transfer sparsity in measurements domain to spatial sparsity of the initial pressure, which allows the use of sparse reconstruction techniques.
Revised:  By extending the acoustic reciprocity principle, we transfer sparsity in measurements domain to spatial sparsity of the initial pressure, which allows the use of sparse reconstruction techniques.
Edit intent: fluency
Original:  By extending the acoustic reciprocal principle, we transfer sparsity in measurements domain to spatial sparsity of the initial pressure, which allows the use of sparse reconstruction techniques.
Revised:  By extending the acoustic reciprocal principle, we transfer sparsity in the measurement domain into spatial sparsity of the initial pressure, which allows the use of sparse reconstruction techniques.
Edit intent: fluency
Original:  Numerical results are presented which demonstrate the feasibility of the proposed framework.
Revised:  Numerical results are presented that demonstrate the feasibility of the proposed framework.
Edit intent: fluency
Original:  We instantiate our approach with two examples of topological algorithms (critical point extraction and persistence diagram computation), which generate exploitable outputs upon interruption requests and which progressively refine them otherwise.
Revised:  We demonstrate our approach with two examples of topological algorithms (critical point extraction and persistence diagram computation), which generate exploitable outputs upon interruption requests and which progressively refine them otherwise.
Edit intent: clarity
Original:  We instantiate our approach with two examples of topological algorithms (critical point extraction and persistence diagram computation), which generate exploitable outputs upon interruption requests and which progressively refine them otherwise.
Revised:  We instantiate our approach with two examples of topological algorithms (critical point extraction and persistence diagram computation), which generate interpretable outputs upon interruption requests and which progressively refine them otherwise.
Edit intent: clarity
Original:  As an example, the proposed strategy is applied  to Magnetic Field Integral Equation (MFIE) and the N-M\"uller formulation for scattering from metallic and dielectric objects  respectively.
Revised:  As an example, the proposed strategy is applied the to Magnetic Field Integral Equation (MFIE) and the N-M\"uller formulation for scattering from metallic and dielectric objects respectively.
Edit intent: fluency
Original:  As an example, the proposed strategy is applied  to Magnetic Field Integral Equation (MFIE) and the N-M\"uller formulation for scattering from metallic and dielectric objects  respectively.
Revised:  As an example, the proposed strategy is applied to Magnetic Field Integral Equation (MFIE) and the N-M\"uller formulation for scattering from metallic and dielectric objects , respectively.
Edit intent: fluency
Original:  The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint range-target location estimation (ANTARES) algorithm.
Revised:  The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint r (ANTARES) algorithm.
Edit intent: clarity
Original:  The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint range-target location estimation (ANTARES) algorithm.
Revised:  The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint range-target location estimationan (ANTARES) algorithm.
Edit intent: fluency
Original:  The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint range-target location estimation (ANTARES) algorithm.
Revised:  The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint range-target location estimationge- (ANTARES) algorithm.
Edit intent: fluency
Original:  The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint range-target location estimation (ANTARES) algorithm.
Revised:  The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint range-target location estimationtar (ANTARES) algorithm.
Edit intent: clarity
Original:  The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint range-target location estimation (ANTARES) algorithm.
Revised:  The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint range-target location estimationes (ANTARES) algorithm.
Edit intent: fluency
Original:  The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint range-target location estimation (ANTARES) algorithm.
Revised:  The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint range-target location estimationtimation (ANTARES) algorithm.
Edit intent: clarity
Original:  We express the control requirement in metric temporal logic (MTL) formulas and develop methods for synthesizing control inputs based on three specific COVID-19 epidemic models with MTL specifications.
Revised:  We express the control requirement in metric temporal logic (MTL) formulas and develop methods for synthesizing control strategies with MTL specifications.
Edit intent: clarity
Original:  To the best of our knowledge, this is the first paper to provide automatically-synthesized and fine-tuned control synthesis for the COVID-19 epidemic models with formal specifications.
Revised:  To the best of our knowledge, this is the first paper to systematically synthesize control strategies based on the COVID-19 epidemic models with formal specifications.
Edit intent: clarity
Original:  The results are also consistent with the claim that control in the early phases of COVID-19 is the most effective in the mitigation .
Revised:  The results also show that early intervention is essential in mitigating the spread of COVID-19 is the most effective in the mitigation .
Edit intent: clarity
Original:  More precisely, we consider random packing integer programs where the entries of the coefficient matrix and the objective function are randomly sampled.
Revised:  More precisely, we consider random integer programs where the entries of the coefficient matrix and the objective function are randomly sampled.
Edit intent: clarity
Original:  Informally, our definition captures the idea that an agent should not experience a large change in their utility as long as they make " small" or " innocuous" mistakes while reporting their preferences .
Revised:  Informally, our definition captures the idea that an agent should not experience a large change in their utility as long as they make " small" or `` innocuous" mistakes while reporting their preferences .
Edit intent: fluency
Original:  This leads us to the explore the possibility of designing new algorithms that are more stable.
Revised:  This leads us to explore the possibility of designing new algorithms that are more stable.
Edit intent: fluency
Original: Using evolution algorithms to unfold the neutron energy spectrum, fitness function design is an important fundamental work for evaluating the quality of  solution, but it has not attracted much attention.
Revised: When evolution algorithms are used to unfold the neutron energy spectrum, fitness function design is an important fundamental work for evaluating the quality of solution, but it has not attracted much attention.
Edit intent: fluency
Original: Using evolution algorithms to unfold the neutron energy spectrum, fitness function design is an important fundamental work for evaluating the quality of  solution, but it has not attracted much attention.
Revised: Using evolution algorithms to unfold the neutron energy spectrum, fitness function design is an important fundamental work for evaluating the quality of the solution, but it has not attracted much attention.
Edit intent: fluency
Original:  In this work, we investigated the performance of 8 fitness functions attached to  genetic algorithm (GA) and  differential evolution algorithm (DEA) used for unfolding four neutron spectra of IAEA 403 report.
Revised:  In this work, we investigated the performance of eight fitness functions attached to genetic algorithm (GA) and differential evolution algorithm (DEA) used for unfolding four neutron spectra of IAEA 403 report.
Edit intent: fluency
Original:  In this work, we investigated the performance of 8 fitness functions attached to  genetic algorithm (GA) and  differential evolution algorithm (DEA) used for unfolding four neutron spectra of IAEA 403 report.
Revised:  In this work, we investigated the performance of 8 fitness functions attached to the genetic algorithm (GA) and differential evolution algorithm (DEA) used for unfolding four neutron spectra of IAEA 403 report.
Edit intent: fluency
Original:  In this work, we investigated the performance of 8 fitness functions attached to  genetic algorithm (GA) and  differential evolution algorithm (DEA) used for unfolding four neutron spectra of IAEA 403 report.
Revised:  In this work, we investigated the performance of 8 fitness functions attached to genetic algorithm (GA) and the differential evolution algorithm (DEA) used for unfolding four neutron spectra of IAEA 403 report.
Edit intent: fluency
Original:  In this work, we investigated the performance of 8 fitness functions attached to  genetic algorithm (GA) and  differential evolution algorithm (DEA) used for unfolding four neutron spectra of IAEA 403 report.
Revised:  In this work, we investigated the performance of 8 fitness functions attached to genetic algorithm (GA) and differential evolution algorithm (DEA) used for unfolding four neutron spectra selected from the IAEA 403 report.
Edit intent: clarity
Original:  Experiments show that the fitness functions with a maximum in  GA can limit the ability of  population to percept the fitness change  but the ability can be made up in DEA, and the fitness function with a feature penalty item help to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-Squared shows the balance between algorithm and spectra.
Revised:  Experiments show that the fitness functions with a maximum in the GA can limit the ability of population to percept the fitness change but the ability can be made up in DEA, and the fitness function with a feature penalty item help to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-Squared shows the balance between algorithm and spectra.
Edit intent: fluency
Original:  Experiments show that the fitness functions with a maximum in  GA can limit the ability of  population to percept the fitness change  but the ability can be made up in DEA, and the fitness function with a feature penalty item help to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-Squared shows the balance between algorithm and spectra.
Revised:  Experiments show that the fitness functions with a maximum in GA can limit the ability of the population to percept the fitness change but the ability can be made up in DEA, and the fitness function with a feature penalty item help to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-Squared shows the balance between algorithm and spectra.
Edit intent: fluency
Original:  Experiments show that the fitness functions with a maximum in  GA can limit the ability of  population to percept the fitness change  but the ability can be made up in DEA, and the fitness function with a feature penalty item help to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-Squared shows the balance between algorithm and spectra.
Revised:  Experiments show that the fitness functions with a maximum in GA can limit the ability of population to percept the fitness change , but the ability can be made up in DEA, and the fitness function with a feature penalty item help to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-Squared shows the balance between algorithm and spectra.
Edit intent: fluency
Original:  Experiments show that the fitness functions with a maximum in  GA can limit the ability of  population to percept the fitness change  but the ability can be made up in DEA, and the fitness function with a feature penalty item help to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-Squared shows the balance between algorithm and spectra.
Revised:  Experiments show that the fitness functions with a maximum in GA can limit the ability of population to percept the fitness change but the ability can be made up in the DEA. The fitness function with a feature penalty item help to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-Squared shows the balance between algorithm and spectra.
Edit intent: fluency
Original:  Experiments show that the fitness functions with a maximum in  GA can limit the ability of  population to percept the fitness change  but the ability can be made up in DEA, and the fitness function with a feature penalty item help to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-Squared shows the balance between algorithm and spectra.
Revised:  Experiments show that the fitness functions with a maximum in GA can limit the ability of population to percept the fitness change but the ability can be made up in DEA, and the fitness function with a feature penalty term helps to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-Squared shows the balance between algorithm and spectra.
Edit intent: clarity
Original:  Experiments show that the fitness functions with a maximum in  GA can limit the ability of  population to percept the fitness change  but the ability can be made up in DEA, and the fitness function with a feature penalty item help to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-Squared shows the balance between algorithm and spectra.
Revised:  Experiments show that the fitness functions with a maximum in GA can limit the ability of population to percept the fitness change but the ability can be made up in DEA, and the fitness function with a feature penalty item help to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-squared result shows the balance between algorithm and spectra.
Edit intent: clarity
Original:  Experiments show that the fitness functions with a maximum in  GA can limit the ability of  population to percept the fitness change  but the ability can be made up in DEA, and the fitness function with a feature penalty item help to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-Squared shows the balance between algorithm and spectra.
Revised:  Experiments show that the fitness functions with a maximum in GA can limit the ability of population to percept the fitness change but the ability can be made up in DEA, and the fitness function with a feature penalty item help to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-Squared shows the balance between the algorithm and the spectra.
Edit intent: fluency
Original:  The purpose of this work is to provide evidence for structuring and modifying the fitness functions , and some genetic operations that should be paid attention were suggested for using the fitness function to unfold neutron spectra.
Revised:  The purposes of this work is to provide evidence for structuring and modifying the fitness functions , and some genetic operations that should be paid attention were suggested for using the fitness function to unfold neutron spectra.
Edit intent: fluency
Original:  The purpose of this work is to provide evidence for structuring and modifying the fitness functions , and some genetic operations that should be paid attention were suggested for using the fitness function to unfold neutron spectra.
Revised:  The purpose of this work are to provide evidence for structuring and modifying the fitness functions , and some genetic operations that should be paid attention were suggested for using the fitness function to unfold neutron spectra.
Edit intent: fluency
Original:  The purpose of this work is to provide evidence for structuring and modifying the fitness functions , and some genetic operations that should be paid attention were suggested for using the fitness function to unfold neutron spectra.
Revised:  The purpose of this work is to provide evidence for structuring and modifying the fitness functions and to suggest some genetic operations that should be paid attention were suggested for using the fitness function to unfold neutron spectra.
Edit intent: coherence
Original:  The purpose of this work is to provide evidence for structuring and modifying the fitness functions , and some genetic operations that should be paid attention were suggested for using the fitness function to unfold neutron spectra.
Revised:  The purpose of this work is to provide evidence for structuring and modifying the fitness functions , and some genetic operations that should receive attention when using the fitness function to unfold neutron spectra.
Edit intent: clarity
Original:  We prove that there exists a constant q_{p,k^\star such that if q < q\SUBSCRIPT}%DIFDELCMD < {%%% p , k %DIFDELCMD < \MBLOCKRIGHTBRACE%%% ^\star then a \sigma-almost-consensus is still reached in O(1) rounds, while, if q > q _{p,k^\star, the process spends n^{\omega(1)} rounds in a metastable phase where the fraction of volume in state \sigma is around a constant value depending only on p and k}  .
Revised:  We prove that there exists a constant q_{p,k^\star such that if q < q\SUBSCRIPT}%DIFDELCMD < {%%% p \SUBSCRIPT k %DIFDELCMD < \MBLOCKRIGHTBRACE%%% ^\star then a \sigma-almost-consensus is still reached in O(1) rounds, while, if q > q _{p,k^\star, the process spends n^{\omega(1)} rounds in a metastable phase where the fraction of volume in state \sigma is around a constant value depending only on p and k} .
Edit intent: fluency
Original:  We prove that there exists a constant q_{p,k^\star such that if q < q\SUBSCRIPT}%DIFDELCMD < {%%% p , k %DIFDELCMD < \MBLOCKRIGHTBRACE%%% ^\star then a \sigma-almost-consensus is still reached in O(1) rounds, while, if q > q _{p,k^\star, the process spends n^{\omega(1)} rounds in a metastable phase where the fraction of volume in state \sigma is around a constant value depending only on p and k}  .
Revised:  We prove that there exists a constant q_{p,k^\star such that if q < q\SUBSCRIPT}%DIFDELCMD < {%%% p , k %DIFDELCMD < \MBLOCKRIGHTBRACE%%% , q _{p,k^\star, the process spends n^{\omega(1)} rounds in a metastable phase where the fraction of volume in state \sigma is around a constant value depending only on p and k} .
Edit intent: coherence