wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this study, we adapt a recent model of pacemaker cells (Severi et al., 2012), incorporating biophysical representations of ion channel and intracellular calcium dynamics, to capture physiological features of a heterogeneous population of pacemaker cells , in particular "center" and "peripheral" cells with distinct intrinsic frequencies and action potential morphology.
Revised:  In this study, we adapt a recent model of pacemaker cells (Severi et al., 2012), incorporating biophysical representations of ion channel and intracellular calcium dynamics, to capture physiological features of a heterogeneous population of pacemaker cells ; in particular "center" and "peripheral" cells with distinct intrinsic frequencies and action potential morphology.
Edit intent: fluency
Original:  Large-scale simulations of the SAN tissue, represented by heterogeneous networks of pacemaker cells, exhibit a rich repertoire of behaviors, including complete synchrony, traveling waves of activity originating from periphery to center, and transient traveling waves originating from the center.
Revised:  Large-scale simulations of the SAN tissue, represented by a heterogeneous tissue structure of pacemaker cells, exhibit a rich repertoire of behaviors, including complete synchrony, traveling waves of activity originating from periphery to center, and transient traveling waves originating from the center.
Edit intent: clarity
Original:  Moreover, the phase reduced models accurately predicts key properties of the tissue electrical dynamics, including wave frequencies when synchronization occurs, and wave propagation direction in a variety of network models. Further, with the reduced phase models, we analyze the relationship between cell distributions and coupling strengths and the resulting transient dynamics.
Revised:  Moreover, the phase reduced models accurately predicts key properties of the tissue electrical dynamics, including wave frequencies when synchronization occurs, and wave propagation direction in a variety of tissue models. With the reduced phase models, we analyze the relationship between cell distributions and coupling strengths and the resulting transient dynamics.
Edit intent: clarity
Original:  The procedure is composed of two steps: (1) determination of the time-bin size for transforming the point-process data to discrete time binary data and (2) screening of relevant couplings from the estimated networks.
Revised:  The procedure involves two steps: (1) determination of the time-bin size for transforming the point-process data to discrete time binary data and (2) screening of relevant couplings from the estimated networks.
Edit intent: clarity
Original:  The procedure is composed of two steps: (1) determination of the time-bin size for transforming the point-process data to discrete time binary data and (2) screening of relevant couplings from the estimated networks.
Revised:  The procedure is composed of two steps: (1) determining time-bin size for transforming the point-process data to discrete time binary data and (2) screening of relevant couplings from the estimated networks.
Edit intent: fluency
Original:  The procedure is composed of two steps: (1) determination of the time-bin size for transforming the point-process data to discrete time binary data and (2) screening of relevant couplings from the estimated networks.
Revised:  The procedure is composed of two steps: (1) determination of the time-bin size for transforming the point-process data into discrete-time binary data and (2) screening of relevant couplings from the estimated networks.
Edit intent: fluency
Original:  The procedure is composed of two steps: (1) determination of the time-bin size for transforming the point-process data to discrete time binary data and (2) screening of relevant couplings from the estimated networks.
Revised:  The procedure is composed of two steps: (1) determination of the time-bin size for transforming the point-process data to discrete time binary data and (2) screening relevant couplings from the estimated networks.
Edit intent: fluency
Original:  The procedure is composed of two steps: (1) determination of the time-bin size for transforming the point-process data to discrete time binary data and (2) screening of relevant couplings from the estimated networks. For these, we develop simple methods based on information theory and computational statistics. Applications to data from artificial andin vitro neuronal networks show that the proposed procedure performs fairly well when identifying relevant couplings, including the discrimination of their signs, with low computational cost.
Revised:  The procedure is composed of two steps: (1) determination of the time-bin size for transforming the point-process data to discrete time binary data and (2) screening of relevant couplings from the estimated in vitro neuronal networks show that the proposed procedure performs fairly well when identifying relevant couplings, including the discrimination of their signs, with low computational cost.
Edit intent: coherence
Original:  Applications to data from artificial andin vitro neuronal networks show that the proposed procedure performs fairly well when identifying relevant couplings, including the discrimination of their signs, with low computational cost.
Revised:  Applications to data from artificial andin vitro couplings. For the first step, we decide the optimal time-bin size by introducing the null hypothesis that all neurons would fire independently, then choosing a time-bin size so that the null hypothesis is rejected with the most strict criterion. The likelihood associated with the null hypothesis is analytically evaluated and used for the rejection process. For the second post-processing step, after a certain estimator of coupling is obtained based on the pre-processed dataset, the estimate is compared with many other estimates derived from datasets obtained by randomizing the original dataset in the time direction. We accept the original estimate as relevant only if its absolute value is sufficiently larger than them of randomized datasets. These manipulations suppress false positive couplings induced by statistical noise. We apply this inference procedure to spiking data from synthetic and in vitro neuronal networks. The results show that the proposed procedure performs fairly well when identifying relevant couplings, including the discrimination of their signs, with low computational cost.
Edit intent: meaning-changed
Original:  Applications to data from artificial andin vitro neuronal networks show that the proposed procedure performs fairly well when identifying relevant couplings, including the discrimination of their signs, with low computational cost. These results highlight the potential utility of the kinetic Ising modelto analyze real interacting systems with event occurrences .
Revised:  Applications to data from artificial andin vitro neuronal networks show that the proposed procedure performs fairly well when identifying relevant couplings, including the discrimination of their signs, for the synthetic and experimental data. In particular, the results support that we can infer the physical connections of underlying systems in favorable situations, even when using the simple statistical model .
Edit intent: coherence
Original:  Moreover, canalization plays an important role in the control, robustness, modularity and criticality of Boolean network dynamics, especially those used to model biochemical regulation ( Marques-Pita and Rocha, 2013;
Revised:  Moreover, canalization plays an important role in the control, robustness, modularity and criticality of Boolean network dynamics, especially those used to model biochemical regulation ( 
Edit intent: coherence
Original:  In this respect, important variables are the trading activity, defined here as the number of trades N, and traded volume V in the asset , its price P, the squared volatility \sigma^2, the corresponding bid-ask spread S and the cost of trading C. Different reasonings result in simple proportionality relations ("scaling laws") between these variables.
Revised:  In this respect, important variables are the trading activity, defined here as the number of trades N, the traded volume V in the asset , its price P, the squared volatility \sigma^2, the corresponding bid-ask spread S and the cost of trading C. Different reasonings result in simple proportionality relations ("scaling laws") between these variables.
Edit intent: fluency
Original:  In this respect, important variables are the trading activity, defined here as the number of trades N, and traded volume V in the asset , its price P, the squared volatility \sigma^2, the corresponding bid-ask spread S and the cost of trading C. Different reasonings result in simple proportionality relations ("scaling laws") between these variables.
Revised:  In this respect, important variables are the trading activity, defined here as the number of trades N, and traded volume V , the asset price P, the squared volatility \sigma^2, the corresponding bid-ask spread S and the cost of trading C. Different reasonings result in simple proportionality relations ("scaling laws") between these variables.
Edit intent: clarity
Original:  In this respect, important variables are the trading activity, defined here as the number of trades N, and traded volume V in the asset , its price P, the squared volatility \sigma^2, the corresponding bid-ask spread S and the cost of trading C. Different reasonings result in simple proportionality relations ("scaling laws") between these variables.
Revised:  In this respect, important variables are the trading activity, defined here as the number of trades N, and traded volume V in the asset , its price P, the squared volatility \sigma^2, the bid-ask spread S and the cost of trading C. Different reasonings result in simple proportionality relations ("scaling laws") between these variables.
Edit intent: clarity
Original: We propose a new multivariate conditional correlation model able to deal with data featuring both observational noise and asynchronicity. When modelling high-frequency multivariate financial time-series, the presence of both problems and the requirement for positive-definite estimates makes the estimation and forecast of the intraday dynamics of conditional covariance matrices particularly difficult.
Revised: The analysis of the intraday dynamics of correlations among high-frequency multivariate financial time-series, the presence of both problems and the requirement for positive-definite estimates makes the estimation and forecast of the intraday dynamics of conditional covariance matrices particularly difficult.
Edit intent: clarity
Original:  When modelling high-frequency multivariate financial time-series, the presence of both problems and the requirement for positive-definite estimates makes the estimation and forecast of the intraday dynamics of conditional covariance matrices particularly difficult.
Revised:  When modelling high-frequency returns is challenging due to the presence of both problems and the requirement for positive-definite estimates makes the estimation and forecast of the intraday dynamics of conditional covariance matrices particularly difficult.
Edit intent: clarity
Original:  When modelling high-frequency multivariate financial time-series, the presence of both problems and the requirement for positive-definite estimates makes the estimation and forecast of the intraday dynamics of conditional covariance matrices particularly difficult. Our approach tackles all these challenging tasks within a new Gaussian state-space model with score-driven time-varying parameters that can be estimated using standard maximum likelihood methods.
Revised:  When modelling high-frequency multivariate financial time-series, the presence of asynchronous trading and market microstructure noise. Both effects may lead to significant data reduction and may severely underestimate correlations if traditional methods for low-frequency data are employed. We propose to model intraday log-prices through a multivariate local-level model with score-driven time-varying parameters that can be estimated using standard maximum likelihood methods.
Edit intent: meaning-changed
Original:  Our approach tackles all these challenging tasks within a new Gaussian state-space model with score-driven time-varying parameters that can be estimated using standard maximum likelihood methods.
Revised:  Our approach tackles all these challenging tasks within a new Gaussian state-space model with score-driven covariance matrices and to treat asynchronicity as a missing value problem. The main advantages of this approach are: (i) all available data are used when filtering correlations, (ii) market microstructure noise is taken into account, (iii) estimation is performed through standard maximum likelihood methods.
Edit intent: meaning-changed
Original:  Similarly to DCC models, large dimensionality is handled by separating the estimation of correlations from individual volatilities. As an interesting outcome of this approach, intra-day patterns are recovered without the need of any cross-sectional averaging, allowing, for instance, to estimate the real-time response of the market covariances to macro-news announcements  .
Revised:  Our empirical analysis, performed on 1-second NYSE data, shows that opening hours are dominated by idiosyncratic risk and that a market factor progressively emerges in the second part of the day. The method can be used as a nowcasting tool for high-frequency data, allowing to study the real-time response of the market covariances to macro-news announcements .
Edit intent: meaning-changed
Original:  As an interesting outcome of this approach, intra-day patterns are recovered without the need of any cross-sectional averaging, allowing, for instance, to estimate the real-time response of the market covariances to macro-news announcements  .
Revised:  As an interesting outcome of this approach, intra-day patterns are recovered without the need of any cross-sectional averaging, allowing, for instance, to estimate the real-time response of covariances to macro-news announcements .
Edit intent: clarity
Original:  As an interesting outcome of this approach, intra-day patterns are recovered without the need of any cross-sectional averaging, allowing, for instance, to estimate the real-time response of the market covariances to macro-news announcements  .
Revised:  As an interesting outcome of this approach, intra-day patterns are recovered without the need of any cross-sectional averaging, allowing, for instance, to estimate the real-time response of the market covariances to macro-news announcements and to build intraday portfolios with very short optimization horizons .
Edit intent: meaning-changed
Original:  Analytical and jackknife corrections for the empirical distribution are derived that recenter the limit distribution and yield confidence intervals with correct coverage in large samples.
Revised:  The leading bias in the empirical quantile function is equally obtained. These calculations are new in the literature, where only results on smooth functionals such as the mean and variance have been derived. Given a closed-form expression for the bias, bias-corrected estimator of the distribution function and quantile function can be constructed. We provide both analytical and jackknife corrections that recenter the limit distribution and yield confidence intervals with correct coverage in large samples.
Edit intent: meaning-changed
Original:  We derive this model from microfoundations, provide its empirical verification including market return prediction (backtested and live-track) , demonstrate that it contains the efficient market as a particular regime and establish a link through which economic models can be attached for the study of economy-market interaction.
Revised:  We derive this model from microfoundations, provide its empirical verification , demonstrate that it contains the efficient market as a particular regime and establish a link through which economic models can be attached for the study of economy-market interaction.
Edit intent: clarity
Original:  We derive this model from microfoundations, provide its empirical verification including market return prediction (backtested and live-track) , demonstrate that it contains the efficient market as a particular regime and establish a link through which economic models can be attached for the study of economy-market interaction.
Revised:  We derive this model from microfoundations, provide its empirical verification including market return prediction (backtested and live-track) , demonstrate that it contains the efficient market as a particular regime and establish a link through which macroeconomic models can be attached for the study of economy-market interaction.
Edit intent: clarity
Original:  We derive this model from microfoundations, provide its empirical verification including market return prediction (backtested and live-track) , demonstrate that it contains the efficient market as a particular regime and establish a link through which economic models can be attached for the study of economy-market interaction.
Revised:  We derive this model from microfoundations, provide its empirical verification including market return prediction (backtested and live-track) , demonstrate that it contains the efficient market as a particular regime and establish a link through which economic models can be attached for the study of real economy and stock market interaction.
Edit intent: meaning-changed
Original:  The  economy and the market are shown to coevolve dynamically along the path governed by a stochastically-forced dynamical system with two stable equilibria, one where the economy expands and the other where it contracts, resulting in business cycles identified as the coherence resonance phenomenon.
Revised:  The real economy and the market are shown to coevolve dynamically along the path governed by a stochastically-forced dynamical system with two stable equilibria, one where the economy expands and the other where it contracts, resulting in business cycles identified as the coherence resonance phenomenon.
Edit intent: clarity
Original:  The  economy and the market are shown to coevolve dynamically along the path governed by a stochastically-forced dynamical system with two stable equilibria, one where the economy expands and the other where it contracts, resulting in business cycles identified as the coherence resonance phenomenon.
Revised:  The economy and the stock market coevolve dynamically along the path governed by a stochastically-forced dynamical system with two stable equilibria, one where the economy expands and the other where it contracts, resulting in business cycles identified as the coherence resonance phenomenon.
Edit intent: meaning-changed
Original:  Thus, the incorporation of  market dynamics into the macroeconomic framework, as presented here, allows the derivation of realistic behaviors in a tractable setting , and so could enhance models applied for policy analysis .
Revised:  Thus, the incorporation of stock market dynamics into the macroeconomic framework, as presented here, allows the derivation of realistic behaviors in a tractable setting , and so could enhance models applied for policy analysis .
Edit intent: meaning-changed
Original:  A comparison with the sliding window Pearson correlation of the raw time series in several stationary and non-stationary set-ups do not indicate any  mathematical advantages of the proposed metric over commonly used correlation methods .
Revised:  A comparison with the sliding window Pearson correlation of the raw time series in several stationary and non-stationary set-ups , including a realistic stationary network detection, reveals lower sensitivity of derivatives to low frequency drifts and to autocorrelations but also lower signal-to-noise ratio. It does not indicate any mathematical advantages of the proposed metric over commonly used correlation methods .
Edit intent: meaning-changed
Original:  A comparison with the sliding window Pearson correlation of the raw time series in several stationary and non-stationary set-ups do not indicate any  mathematical advantages of the proposed metric over commonly used correlation methods .
Revised:  A comparison with the sliding window Pearson correlation of the raw time series in several stationary and non-stationary set-ups do not indicate any evident mathematical advantages of the proposed metric over commonly used correlation methods .
Edit intent: clarity
Original:  A comparison with the sliding window Pearson correlation of the raw time series in several stationary and non-stationary set-ups do not indicate any  mathematical advantages of the proposed metric over commonly used correlation methods . Additionally, the results of our analysis show that a metric based on derivatives performs worse in a realistic scenario of stationary network detection .
Revised:  A comparison with the sliding window Pearson correlation of the raw time series in several stationary and non-stationary set-ups do not indicate any mathematical advantages of the proposed metric over commonly used correlation methods .
Edit intent: coherence
Original:   Methods. We used a network approach to identify and characterize biogeographical regions in southern France, based on a large database consisting in millions of geolocalized plant samples corresponding to more than 3,500 plant species.
Revised:  Taxon. Vascular plants. Methods. We used a network approach to identify and characterize biogeographical regions in southern France, based on a large database consisting in millions of geolocalized plant samples corresponding to more than 3,500 plant species.
Edit intent: meaning-changed
Original:   Methods. We used a network approach to identify and characterize biogeographical regions in southern France, based on a large database consisting in millions of geolocalized plant samples corresponding to more than 3,500 plant species.
Revised:  Methods. We used a network approach to identify and characterize bioregions in southern France, based on a large database consisting in millions of geolocalized plant samples corresponding to more than 3,500 plant species.
Edit intent: clarity
Original:  This methodology is performed following five steps, from the biogeographical bipartite network construction, to the identification of biogeographical regions under the form of spatial network communities , the analysis of their interactions and the identification of clusters of plant species based on the species contribution to the biogeographical regions.
Revised:  This methodology is performed following five steps, from the biogeographical bipartite network construction, to the identification of bioregions under the form of spatial network communities , the analysis of their interactions and the identification of clusters of plant species based on the species contribution to the biogeographical regions.
Edit intent: clarity
Original:  This methodology is performed following five steps, from the biogeographical bipartite network construction, to the identification of biogeographical regions under the form of spatial network communities , the analysis of their interactions and the identification of clusters of plant species based on the species contribution to the biogeographical regions.
Revised:  This methodology is performed following five steps, from the biogeographical bipartite network construction, to the identification of biogeographical regions under the form of spatial network clusters , the analysis of their interactions and the identification of clusters of plant species based on the species contribution to the biogeographical regions.
Edit intent: clarity