wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this paper, we propose a new approach, medical Generative Adversarial Network (medGAN), to generate realistic synthetic EHRs . Based on an input EHR dataset , medGAN can generate high-dimensional discrete variables (e.g., binary and count features) via a combination of an autoencoder and generative adversarial networks.
Revised:  In this paper, we propose a new approach, medical Generative Adversarial Network (medGAN), to generate realistic synthetic EHRs . Based on input real patient records , medGAN can generate high-dimensional discrete variables (e.g., binary and count features) via a combination of an autoencoder and generative adversarial networks.
Edit intent: clarity
Original:  To demonstrate feasibility, we showed that medGAN generates synthetic EHR datasets that achieve comparable performance to real data on many experiments including distribution statistics, predictive modeling tasks and  medical expert review  .
Revised:  To demonstrate feasibility, we showed that medGAN generates synthetic patient records that achieve comparable performance to real data on many experiments including distribution statistics, predictive modeling tasks and medical expert review .
Edit intent: clarity
Original:  To demonstrate feasibility, we showed that medGAN generates synthetic EHR datasets that achieve comparable performance to real data on many experiments including distribution statistics, predictive modeling tasks and  medical expert review  .
Revised:  To demonstrate feasibility, we showed that medGAN generates synthetic EHR datasets that achieve comparable performance to real data on many experiments including distribution statistics, predictive modeling tasks and a medical expert review .
Edit intent: fluency
Original: In this paper, we deal with the acceleration of the  convergence of infinite series \sum^n=1a_n , when the terms a_n are in general complex and have asymptotic expansions that can be expressed in the form a_n \sim[\Gamma(n)]
Revised: In this paper, we discuss the application of the author's convergence of infinite series \sum^n=1a_n , when the terms a_n are in general complex and have asymptotic expansions that can be expressed in the form a_n \sim[\Gamma(n)]
Edit intent: clarity
Original: In this paper, we deal with the acceleration of the  convergence of infinite series \sum^n=1a_n , when the terms a_n are in general complex and have asymptotic expansions that can be expressed in the form a_n \sim[\Gamma(n)]
Revised: In this paper, we deal with the acceleration of the convergence of infinite series \sum^n=1a_n when the terms a_n are in general complex and have asymptotic expansions that can be expressed in the form a_n \sim[\Gamma(n)]
Edit intent: fluency
Original:  ^{s/m}\exp\left[ Q(n) \right]\sum^i=0w_i n^{\gamma-i/m}\quadas n\to\infty, where \Gamma(z) is the gamma function, m%DIFDELCMD < \geq1 %%% is an arbitrary integer, Q(n)=\sum^{m_{i=0}q_in^{i/m} is a polynomial of degree at most m in n^{1/m}, s is an arbitrary integer, and \gamma is an arbitrary complex number.
Revised:  ^{s/m}\exp\left[ \sum^{m \right]\sum^i=0w_i n^{\gamma-i/m}\quadas n\to\infty, where \Gamma(z) is the gamma function, m%DIFDELCMD < \geq1 %%% is an arbitrary integer, Q(n)=\sum^{m_{i=0}q_in^{i/m} is a polynomial of degree at most m in n^{1/m}, s is an arbitrary integer, and \gamma is an arbitrary complex number.
Edit intent: clarity
Original:  ^{s/m}\exp\left[ Q(n) \right]\sum^i=0w_i n^{\gamma-i/m}\quadas n\to\infty, where \Gamma(z) is the gamma function, m%DIFDELCMD < \geq1 %%% is an arbitrary integer, Q(n)=\sum^{m_{i=0}q_in^{i/m} is a polynomial of degree at most m in n^{1/m}, s is an arbitrary integer, and \gamma is an arbitrary complex number.
Revised:  ^{s/m}\exp\left[ Q(n) \right]\sum^i=0w_i n^{\gamma-i/m}\quadas n\to\infty, %DIFDELCMD < \geq1 %%% is an arbitrary integer, Q(n)=\sum^{m_{i=0}q_in^{i/m} is a polynomial of degree at most m in n^{1/m}, s is an arbitrary integer, and \gamma is an arbitrary complex number.
Edit intent: coherence
Original:  ^{s/m}\exp\left[ Q(n) \right]\sum^i=0w_i n^{\gamma-i/m}\quadas n\to\infty, where \Gamma(z) is the gamma function, m%DIFDELCMD < \geq1 %%% is an arbitrary integer, Q(n)=\sum^{m_{i=0}q_in^{i/m} is a polynomial of degree at most m in n^{1/m}, s is an arbitrary integer, and \gamma is an arbitrary complex number.
Revised:  ^{s/m}\exp\left[ Q(n) \right]\sum^i=0w_i n^{\gamma-i/m}\quadas n\to\infty, where \Gamma(z) is the gamma function, m%DIFDELCMD < \geq1 %%% _{i=0}q_in^{i/m} is a polynomial of degree at most m in n^{1/m}, s is an arbitrary integer, and \gamma is an arbitrary complex number.
Edit intent: coherence
Original:  This can be achieved effectively by applying } \quad  the d^{(m)} transformation of the author to the sequence \{A_n\} of the partial sums A_n=\sum^n_{k=1a_k, n=1,2,}%DIFDELCMD < \dots%%% \ . We give a detailed review of the properties of such series and of the%DIFDELCMD < \tilde{d}%%% ^{(m)
Revised:  This can be achieved effectively by applying } \quad the d^{(m)} transformation a_k, n=1,2,}%DIFDELCMD < \dots%%% \ . We give a detailed review of the properties of such series and of the%DIFDELCMD < \tilde{d}%%% ^{(m)
Edit intent: clarity
Original:  This can be achieved effectively by applying } \quad  the d^{(m)} transformation of the author to the sequence \{A_n\} of the partial sums A_n=\sum^n_{k=1a_k, n=1,2,}%DIFDELCMD < \dots%%% \ . We give a detailed review of the properties of such series and of the%DIFDELCMD < \tilde{d}%%% ^{(m)
Revised:  This can be achieved effectively by applying } \quad the d^{(m)} transformation of the author to the sequence \{A_n\} of the partial sums A_n=\sum^n_{k=1a_k, n=1,2,}%DIFDELCMD < \dots%%% %DIFDELCMD < \tilde{d}%%% ^{(m)
Edit intent: coherence
Original:  show that the \tilde{d}^{(m)} transformation can  be used efficiently to accelerate the convergence of some infinite products of the form \prod^\infty_{n=1}(1+v_n), where v_n\sim \sum^\infty_{i=0}e_in^{-t/m-i/m} %DIFDELCMD < \quad %%% \text{as n\to\infty,\ \ t\geq m+1 an integer,\to\tilde{d} .
Revised:  show that the \tilde{d}^{(m)} transformation can also be used efficiently to accelerate the convergence of some infinite products of the form \prod^\infty_{n=1}(1+v_n), where v_n\sim \sum^\infty_{i=0}e_in^{-t/m-i/m} %DIFDELCMD < \quad %%% \text{as n\to\infty,\ \ t\geq m+1 an integer,\to\tilde{d} .
Edit intent: clarity
Original:  show that the \tilde{d}^{(m)} transformation can  be used efficiently to accelerate the convergence of some infinite products of the form \prod^\infty_{n=1}(1+v_n), where v_n\sim \sum^\infty_{i=0}e_in^{-t/m-i/m} %DIFDELCMD < \quad %%% \text{as n\to\infty,\ \ t\geq m+1 an integer,\to\tilde{d} .
Revised:  show that the \tilde{d}^{(m)} transformation can be used efficiently to accelerate the convergence of infinite products \prod^\infty_{n=1}(1+v_n), where v_n\sim \sum^\infty_{i=0}e_in^{-t/m-i/m} %DIFDELCMD < \quad %%% \text{as n\to\infty,\ \ t\geq m+1 an integer,\to\tilde{d} .
Edit intent: clarity
Original:  show that the \tilde{d}^{(m)} transformation can  be used efficiently to accelerate the convergence of some infinite products of the form \prod^\infty_{n=1}(1+v_n), where v_n\sim \sum^\infty_{i=0}e_in^{-t/m-i/m} %DIFDELCMD < \quad %%% \text{as n\to\infty,\ \ t\geq m+1 an integer,\to\tilde{d} .
Revised:  show that the \tilde{d}^{(m)} transformation can be used efficiently to accelerate the convergence of some infinite products of the form \prod^\infty_{n=1}(1+v_n), where v_n\sim \sum^\infty_{i=0}e_in^{-t/m-i/m} %DIFDELCMD < \quad %%% \text{as n\to\infty,\ \ t\geq m+1 an integer,\to\tilde{d}^{(m) .
Edit intent: fluency
Original:  Here by solving the analytical steady-state distribution of the protein copy number in a general kinetic model of gene expression ,
Revised:  Here by solving the analytical steady-state protein distribution in a general kinetic model of gene expression ,
Edit intent: clarity
Original:  The theory is demonstrated by numerical simulations and  validated quantitatively by single-cell data analysis of a synthetic gene circuit integrated in human kidney cells.
Revised:  The theory is demonstrated by numerical simulations and , more importantly, validated quantitatively by single-cell data analysis of a synthetic gene circuit integrated in human kidney cells.
Edit intent: clarity
Original: Understanding the relationship between spontaneous stochastic fluctuations and the topology of the underlying gene regulatory network is fundamental for the study of single-cell stochastic gene expression.
Revised: Understanding the relationship between spontaneous stochastic fluctuations and the topology of the underlying gene regulatory network is of fundamental importance for the study of single-cell stochastic gene expression.
Edit intent: clarity
Original:  Based on such relation , we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data.
Revised:  Based on such relationship , we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data.
Edit intent: fluency
Original:  So how , then, is it possible for the network of American Physical Society journal collection citations to enjoy a log-normal citation distribution when it was found to have grown in accordance with preferential attachment?
Revised:  How , then, is it possible for the network of American Physical Society journal collection citations to enjoy a log-normal citation distribution when it was found to have grown in accordance with preferential attachment?
Edit intent: coherence
Original:  In this paper we propose a resolution to the paradox.
Revised:  Here we propose a resolution to the paradox.
Edit intent: clarity
Original:  There was a high-accuracy measurement procedure in general use at the time, but it could not have been used to shed light on the paradox, due to the presence of a systematic error inducing design flaw.
Revised:  There was a high-accuracy measurement procedure in use at the time, but it could not have been used to shed light on the paradox, due to the presence of a systematic error inducing design flaw.
Edit intent: clarity
Original:  There was a high-accuracy measurement procedure in general use at the time, but it could not have been used to shed light on the paradox, due to the presence of a systematic error inducing design flaw.
Revised:  There was a high-accuracy measurement procedure in general use at the time, but it would have have been difficult to use it to shed light on the paradox, due to the presence of a systematic error inducing design flaw.
Edit intent: clarity
Original:  But in recent years the design flaw had been recognised and corrected.
Revised:  In recent years the design flaw had been recognised and corrected.
Edit intent: coherence
Original:  Here we show that the bringing of the newly corrected measurement procedure to bare on the data leads to a resolution of the paradox with important ramifications for the working network scientist .
Revised:  We show that the bringing of the newly corrected measurement procedure to bare on the data leads to a resolution of the paradox with important ramifications for the working network scientist .
Edit intent: coherence
Original:  Here we show that the bringing of the newly corrected measurement procedure to bare on the data leads to a resolution of the paradox with important ramifications for the working network scientist .
Revised:  Here we show that the bringing of the newly corrected measurement procedure to bare on the data leads to a resolution of the paradox .
Edit intent: clarity
Original:  We show that the bringing of the newly corrected measurement procedure to bare on the data leads to a resolution of the paradox.
Revised:  We show that the bringing of the newly corrected measurement procedure to bear on the data leads to a resolution of the paradox.
Edit intent: fluency
Original:  This open up a whole new way to analyse temporal information of networks which is conducive to precisely answering research hypotheses and gathering novel insights based both on the connectivity and transient temporal dynamics of the data.
Revised:  This open up a whole new way to analyse temporal information of networks which is conducive to probing research hypotheses and gathering novel insights based both on the connectivity and transient temporal dynamics of the data.
Edit intent: clarity
Original:  We illustrate how the appropriate consideration of such functions allow for novel temporal probing of the connectivity information of multivariate signals , here referred to as temporal connectivity . Particularly, we present appropriate functions for three pertinent connectivity metrics- correlation, coherence and the phase-lag index.
Revised:  We illustrate how the appropriate consideration of such functions allow for a novel dynamic connectivity measure , here referred to as temporal connectivity . Particularly, we present appropriate functions for three pertinent connectivity metrics- correlation, coherence and the phase-lag index.
Edit intent: clarity
Original: Incorporating graph-based techniques in the analysis of multivariate signals is becoming a standard method to understand the interdependency of activity recorded at different sites.
Revised: Incorporating graphs in the analysis of multivariate signals is becoming a standard method to understand the interdependency of activity recorded at different sites.
Edit intent: clarity
Original: Incorporating graph-based techniques in the analysis of multivariate signals is becoming a standard method to understand the interdependency of activity recorded at different sites.
Revised: Incorporating graph-based techniques in the analysis of multivariate signals is becoming a standard way to understand the interdependency of activity recorded at different sites.
Edit intent: clarity
Original:  The new research frontier in this field includes the important problem of how to assess dynamic changes of signal activity.
Revised:  The new research frontier in this direction includes the important problem of how to assess dynamic changes of signal activity.
Edit intent: clarity
Original:  This opens up a powerful and robust approach to analyse joint signal and network dynamics at sample resolution.
Revised:  This opens up a new and robust approach to analyse joint signal and network dynamics at sample resolution.
Edit intent: clarity
Original:  Furthermore, this can be formulated as instantaneous networks for which standard network analysis can be implemented.
Revised:  Furthermore, our method can be formulated as instantaneous networks for which standard network analysis can be implemented.
Edit intent: clarity
Original:  Furthermore, this can be formulated as instantaneous networks for which standard network analysis can be implemented.
Revised:  Furthermore, this can be formulated as instantaneous networks on which standard network analysis can be implemented.
Edit intent: fluency
Original:  In the case for which graph connectivity is estimated from the multivariate signal itself, we illustrate how the appropriate consideration of instantaneous graph signal functions allow for a novel dynamic connectivity measure, here referred to as graph-variate dynamic (GVD) connectivity, robust to spurious short-term dependencies.
Revised:  When graph connectivity is estimated from the multivariate signal itself, we illustrate how the appropriate consideration of instantaneous graph signal functions allow for a novel dynamic connectivity measure, here referred to as graph-variate dynamic (GVD) connectivity, robust to spurious short-term dependencies.
Edit intent: clarity
Original:  In the case for which graph connectivity is estimated from the multivariate signal itself, we illustrate how the appropriate consideration of instantaneous graph signal functions allow for a novel dynamic connectivity measure, here referred to as graph-variate dynamic (GVD) connectivity, robust to spurious short-term dependencies.
Revised:  In the case for which graph connectivity is estimated from the multivariate signal itself, the appropriate consideration of instantaneous graph signal functions allow for a novel dynamic connectivity measure, here referred to as graph-variate dynamic (GVD) connectivity, robust to spurious short-term dependencies.
Edit intent: clarity
Original:  In the case for which graph connectivity is estimated from the multivariate signal itself, we illustrate how the appropriate consideration of instantaneous graph signal functions allow for a novel dynamic connectivity measure, here referred to as graph-variate dynamic (GVD) connectivity, robust to spurious short-term dependencies.
Revised:  In the case for which graph connectivity is estimated from the multivariate signal itself, we illustrate how the appropriate consideration of instantaneous graph signal functions allows for a novel dynamic connectivity measure, here referred to as graph-variate dynamic (GVD) connectivity, robust to spurious short-term dependencies.
Edit intent: fluency
Original:  In the case for which graph connectivity is estimated from the multivariate signal itself, we illustrate how the appropriate consideration of instantaneous graph signal functions allow for a novel dynamic connectivity measure, here referred to as graph-variate dynamic (GVD) connectivity, robust to spurious short-term dependencies.
Revised:  In the case for which graph connectivity is estimated from the multivariate signal itself, we illustrate how the appropriate consideration of instantaneous graph signal functions allow for a novel dynamic connectivity measure-- graphvariate dynamic (GVD) connectivity, robust to spurious short-term dependencies.
Edit intent: coherence
Original:  In the case for which graph connectivity is estimated from the multivariate signal itself, we illustrate how the appropriate consideration of instantaneous graph signal functions allow for a novel dynamic connectivity measure, here referred to as graph-variate dynamic (GVD) connectivity, robust to spurious short-term dependencies.
Revised:  In the case for which graph connectivity is estimated from the multivariate signal itself, we illustrate how the appropriate consideration of instantaneous graph signal functions allow for a novel dynamic connectivity measure, here referred to as graph-variate dynamic (GVD) connectivity-- which is robust to spurious short-term dependencies.
Edit intent: coherence
Original:  Particularly, we present appropriate functions for three pertinent connectivity metrics- correlation, coherence and the phase-lag index.
Revised:  Particularly, we present appropriate functions for three pertinent connectivity metrics-- correlation, coherence and the phase-lag index.
Edit intent: fluency
Original:  Our approach can determine signals with a single correlated couple against wholly uncorrelated data  up to 128 nodes in signal size ;
Revised:  We show that our approach can determine signals with a single correlated couple against wholly uncorrelated data up to 128 nodes in signal size ;
Edit intent: clarity
Original:  Our approach can determine signals with a single correlated couple against wholly uncorrelated data  up to 128 nodes in signal size ;
Revised:  Our approach can determine signals with a single correlated couple against wholly uncorrelated data of up to 128 nodes in signal size ;
Edit intent: clarity
Original:  it is shown to be more robust than  other GSP approaches in detecting a randomly traveling spheroid on a 3D grid and than standard dynamic connectivity in determining differences in EEG resting-state and task related activity, and we demonstrate its use in revealing hidden depth correlations from geographical gamma ray data  .
Revised:  it is shown to be more robust than other GSP approaches at detecting a randomly traveling spheroid on a 3D grid and than standard dynamic connectivity in determining differences in EEG resting-state and task related activity, and we demonstrate its use in revealing hidden depth correlations from geographical gamma ray data .
Edit intent: clarity
Original:  it is shown to be more robust than  other GSP approaches in detecting a randomly traveling spheroid on a 3D grid and than standard dynamic connectivity in determining differences in EEG resting-state and task related activity, and we demonstrate its use in revealing hidden depth correlations from geographical gamma ray data  .
Revised:  it is shown to be more robust than other GSP approaches in detecting a randomly traveling spheroid on a 3D grid and than standard dynamic connectivity in determining differences in EEG restingstate and task-related activity. We also demonstrate its use in revealing hidden depth correlations from geographical gamma ray data .
Edit intent: clarity
Original:  it is shown to be more robust than  other GSP approaches in detecting a randomly traveling spheroid on a 3D grid and than standard dynamic connectivity in determining differences in EEG resting-state and task related activity, and we demonstrate its use in revealing hidden depth correlations from geographical gamma ray data  .
Revised:  it is shown to be more robust than other GSP approaches in detecting a randomly traveling spheroid on a 3D grid and than standard dynamic connectivity in determining differences in EEG resting-state and task related activity, and we demonstrate its use in revealing hidden depth correlations from geophysical gamma ray data .
Edit intent: clarity
Original: This paper introduces  QMDP-net, a neural network architecture for planning under partial observability.
Revised: This paper introduces the QMDP-net, a neural network architecture for planning under partial observability.
Edit intent: fluency
Original:  It is a recurrent policy network, but it represents a policy by connecting a model with a planning algorithm that solves the model, thus embedding the solution structure of planning in the network architecture.
Revised:  It is a recurrent policy network, but it represents a policy by connecting a model with a planning algorithm that solves the model, thus embedding the solution structure of planning in a network learning architecture.
Edit intent: clarity
Original:  We train a QMDP-net over a set of different environments so that it can generalize over new ones  .
Revised:  We train a QMDP-net in a set of different environments so that it can generalize over new ones .
Edit intent: style
Original:  Interestingly, it also sometimes outperformed the QMDP algorithm , which generated the data for learning , because of QMDP-net's robustness resulting from end-to-end learning.
Revised:  Interestingly, it also sometimes outperformed the QMDP algorithm in the experiments , because of QMDP-net's robustness resulting from end-to-end learning.
Edit intent: clarity
Original:  Interestingly, it also sometimes outperformed the QMDP algorithm , which generated the data for learning , because of QMDP-net's robustness resulting from end-to-end learning.
Revised:  Interestingly, it also sometimes outperformed the QMDP algorithm , which generated the data for learning , because of QMDP-net's increased robustness through end-to-end learning.
Edit intent: clarity
Original:  The QMDP-net is fully differentiable and allows  end-to-end training.
Revised:  The QMDP-net is fully differentiable and allows for end-to-end training.
Edit intent: fluency
Original:  We train a QMDP-net in a set of different environments so that it can generalize over new ones and "transfer" to larger environments as well .
Revised:  We train a QMDP-net on different tasks so that it can generalize over new ones and "transfer" to larger environments as well .
Edit intent: clarity
Original:  Interestingly, while QMDP-net encodes the QMDP algorithm, it sometimes outperforms the QMDP algorithm in the experiments, because of QMDP-net's increased robustness through end-to-end learning.
Revised:  Interestingly, while QMDP-net encodes the QMDP algorithm, it sometimes outperforms the QMDP algorithm in the experiments, as a result of end-to-end learning.
Edit intent: clarity