wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We then extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part, allowing one to establish formulas similar to the Madan-Roynette-Yor option pricing formulas for larger class of processes.
Revised:  We then extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part, allowing one to establish formulas similar to the Madan-Roynette-Yor option pricing formula for larger class of processes.
Edit intent: fluency
Original: Given a finite honest time, we first derive a representation of the additive (the multiplicative ) decomposition of its Az\'ema optional supermartingale in terms of the draw-down (the relative draw-down) of some optional supermartingales with continuous running supremum.
Revised: Given a finite honest time, we first derive representations of the additive (the multiplicative ) decomposition of its Az\'ema optional supermartingale in terms of the draw-down (the relative draw-down) of some optional supermartingales with continuous running supremum.
Edit intent: fluency
Original: Given a finite honest time, we first derive a representation of the additive (the multiplicative ) decomposition of its Az\'ema optional supermartingale in terms of the draw-down (the relative draw-down) of some optional supermartingales with continuous running supremum.
Revised: Given a finite honest time, we first derive a representation of the additive and the multiplicative decomposition of its Az\'ema optional supermartingale in terms of the draw-down (the relative draw-down) of some optional supermartingales with continuous running supremum.
Edit intent: clarity
Original: Given a finite honest time, we first derive a representation of the additive (the multiplicative ) decomposition of its Az\'ema optional supermartingale in terms of the draw-down (the relative draw-down) of some optional supermartingales with continuous running supremum.
Revised: Given a finite honest time, we first derive a representation of the additive (the multiplicative ) decomposition of its Azema optional supermartingale in terms of the draw-down (the relative draw-down) of some optional supermartingales with continuous running supremum.
Edit intent: fluency
Original: Given a finite honest time, we first derive a representation of the additive (the multiplicative ) decomposition of its Az\'ema optional supermartingale in terms of the draw-down (the relative draw-down) of some optional supermartingales with continuous running supremum.
Revised: Given a finite honest time, we first derive a representation of the additive (the multiplicative ) decomposition of its Az\'ema optional supermartingale in terms of the drawdown and the relative drawdown of some optional supermartingales with continuous running supremum.
Edit intent: clarity
Original:  Finally, we extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part , allowing one to establish formulae similar to the Madan-Roynette-Yor option pricing formula for  larger class of processes  .
Revised:  Then we extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part , allowing one to establish formulae similar to the Madan-Roynette-Yor option pricing formula for larger class of processes .
Edit intent: fluency
Original:  Finally, we extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part , allowing one to establish formulae similar to the Madan-Roynette-Yor option pricing formula for  larger class of processes  .
Revised:  Finally, we extend the notion of semimartingales of class-(\Sigma) by allowing for jumps in its finite variation part , allowing one to establish formulae similar to the Madan-Roynette-Yor option pricing formula for larger class of processes .
Edit intent: clarity
Original:  Finally, we extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part , allowing one to establish formulae similar to the Madan-Roynette-Yor option pricing formula for  larger class of processes  .
Revised:  Finally, we extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part of the semimartingale decomposition. This allows one to establish formulae similar to the Madan-Roynette-Yor option pricing formula for larger class of processes .
Edit intent: meaning-changed
Original:  Finally, we extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part , allowing one to establish formulae similar to the Madan-Roynette-Yor option pricing formula for  larger class of processes  .
Revised:  Finally, we extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part , allowing one to establish the Madan-Roynette-Yor option pricing formula for larger class of processes .
Edit intent: clarity
Original:  Finally, we extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part , allowing one to establish formulae similar to the Madan-Roynette-Yor option pricing formula for  larger class of processes  .
Revised:  Finally, we extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part , allowing one to establish formulae similar to the Madan-Roynette-Yor option pricing formula for a larger class of processes .
Edit intent: fluency
Original:  Finally, we extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part , allowing one to establish formulae similar to the Madan-Roynette-Yor option pricing formula for  larger class of processes  .
Revised:  Finally, we extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part , allowing one to establish formulae similar to the Madan-Roynette-Yor option pricing formula for larger class of processes , and we apply the extended formula to the construction of finite honest times .
Edit intent: meaning-changed
Original: In this article, we introduce a regression based backtest for the risk measure Expected Shortfall (ES) which is based on a joint regression framework for the quantile and the ES . We also introduce a second variant of this ES backtest which allows for testing one-sided hypotheses by only testing an intercept parameter.
Revised: This paper introduces novel backtests for the risk measure Expected Shortfall (ES) which is based on a joint regression framework for the quantile and the ES . We also introduce a second variant of this ES backtest which allows for testing one-sided hypotheses by only testing an intercept parameter.
Edit intent: clarity
Original: In this article, we introduce a regression based backtest for the risk measure Expected Shortfall (ES) which is based on a joint regression framework for the quantile and the ES . We also introduce a second variant of this ES backtest which allows for testing one-sided hypotheses by only testing an intercept parameter.
Revised: In this article, we introduce a regression based backtest for the risk measure Expected Shortfall (ES) following the testing idea of Mincer and Zarnowitz (1969). Estimating a regression framework for the ES stand-alone is infeasible, and thus, our tests are based on a joint regression framework for the quantile and the ES . We also introduce a second variant of this ES backtest which allows for testing one-sided hypotheses by only testing an intercept parameter.
Edit intent: meaning-changed
Original: In this article, we introduce a regression based backtest for the risk measure Expected Shortfall (ES) which is based on a joint regression framework for the quantile and the ES . We also introduce a second variant of this ES backtest which allows for testing one-sided hypotheses by only testing an intercept parameter.
Revised: In this article, we introduce a regression based backtest for the risk measure Expected Shortfall (ES) which is based on a joint regression for the Value at Risk and the ES . We also introduce a second variant of this ES backtest which allows for testing one-sided hypotheses by only testing an intercept parameter.
Edit intent: meaning-changed
Original: In this article, we introduce a regression based backtest for the risk measure Expected Shortfall (ES) which is based on a joint regression framework for the quantile and the ES . We also introduce a second variant of this ES backtest which allows for testing one-sided hypotheses by only testing an intercept parameter.
Revised: In this article, we introduce a regression based backtest for the risk measure Expected Shortfall (ES) which is based on a joint regression framework for the quantile and the ES , which allows for testing one-sided hypotheses by only testing an intercept parameter.
Edit intent: coherence
Original: In this article, we introduce a regression based backtest for the risk measure Expected Shortfall (ES) which is based on a joint regression framework for the quantile and the ES . We also introduce a second variant of this ES backtest which allows for testing one-sided hypotheses by only testing an intercept parameter. These two backtests are the first backtests in the literature which solely backtest the risk measure ES as they only require ES forecasts as input parameters.
Revised: In this article, we introduce a regression based backtest for the risk measure Expected Shortfall (ES) which is based on a joint regression framework for the quantile and the ES . We also introduce a second variant of this ES backtest which allows for different test specifications. These ES backtests are the first backtests in the literature which solely backtest the risk measure ES as they only require ES forecasts as input parameters.
Edit intent: clarity
Original:  In contrast, the existing ES backtesting techniques require forecasts for further quantities such as the Value at Risk, the volatility or even the entire (tail) distribution. As the regulatory authorities only receive forecasts for the ES, backtests including further input parameters are not applicable in practice. We compare the empirical performanceof our new backtests to existing approaches in terms of their empirical size and power through several different simulation studies.
Revised:  As the tests are potentially subject to model misspecification, we provide asymptotic theory under misspecification for the underlying joint regression. We find that employing a misspecification robust covariance estimator substantially improves the tests' performance. We compare our backtests to existing approaches in terms of their empirical size and power through several different simulation studies.
Edit intent: meaning-changed
Original:  We compare the empirical performanceof our new backtests to existing approaches in terms of their empirical size and power through several different simulation studies. We find that our backtests clearly outperform the existing backtesting procedures in the literature in terms of their size and (size-adjusted) power properties throughout all considered simulation experiments.
Revised:  We compare the empirical performanceof our new backtests to existing approaches and find that our backtests clearly outperform the existing backtesting procedures in the literature in terms of their size and (size-adjusted) power properties throughout all considered simulation experiments.
Edit intent: clarity
Original:  We find that our backtests clearly outperform the existing backtesting procedures in the literature in terms of their size and (size-adjusted) power properties throughout all considered simulation experiments. We provide an R package for these ES backtests which is easily applicable for practitioners  .
Revised:  We find that our backtests clearly outperform the existing backtesting procedures in the literature in terms of their size and (size-adjusted) power properties throughout all considered simulations. In an empirical illustration, we apply our backtests to ES forecasts for 200 stocks of the S .
Edit intent: meaning-changed
Original:  Our model naturally predicts that successful integration events occur more often within more flexible or highly bent DNA, in agreement with long-standing observations. We further find that large-scale URLanisation biases integration events towards transcriptionally active genomic regions.
Revised:  Our model naturally predicts that successful integration events occur more often within more flexible or highly bent DNA, in agreement with long-standing unexplained observations: we show that HIV integration is favoured in nucleosomal rather than naked DNA, and in flexible over rigid DNA. We find that these biases arise due to the difference in the bending energy barrier associated with DNA insertions. By coupling our model to a well-established framework for large-scale URLanisation biases integration events towards transcriptionally active genomic regions.
Edit intent: meaning-changed
Original:  We further find that large-scale URLanisation biases integration events towards transcriptionally active genomic regions. We finally propose and solve a reaction-diffusion model which captures the observed distribution of HIV integration sites within the nuclear environment of T-cells.
Revised:  We further find that large-scale 3D URLanisation we discover that the non-random integration of HIV in human chromatin may be explained as due to large-scale accessibility of interphase chromosomes. Finally we propose and solve a reaction-diffusion model which captures the observed distribution of HIV integration sites within the nuclear environment of T-cells.
Edit intent: meaning-changed
Original: The Human Immunodeficiency Virus (HIV) integrates its genetic material in a small and non-random fraction of all possible integration sites along the host genome . The mechanisms driving this non-uniform selection are not known. To address this issue, we formulate a generic physical model where we treat the viral incorporation into DNA and chromosomes as a stochastic quasi-equilibrium  process.
Revised: The Human Immunodeficiency Virus (HIV) integrates its genetic material in a small and non-random fraction of all possible integration sites along the host genome via poorly understood selection mechanisms. Here, we develop a biophysical model for retroviral integrations as stochastic and quasi-equilibrium process.
Edit intent: clarity
Original:  To address this issue, we formulate a generic physical model where we treat the viral incorporation into DNA and chromosomes as a stochastic quasi-equilibrium  process.
Revised:  To address this issue, we formulate a generic physical model where we treat the viral incorporation into DNA and chromosomes as a stochastic quasi-equilibrium topological reconnections between polymers. We discover that physical effects, such as DNA accessibility and elasticity, play important and universal roles in this process.
Edit intent: meaning-changed
Original:  Our model rationalises a number of long-standing unexplained observations: we show that HIV integration is favoured in nucleosomal rather than naked DNA, and in flexible over rigid DNA. We find that these biases arise due to the difference in the bending energy barrier associated with DNA insertions.
Revised:  Our model rationalises a number of long-standing unexplained observations: we show that HIV integration is favoured within nucleosomal and flexible DNA, in line with experiments, and that these biases arise due to the difference in the bending energy barrier associated with DNA insertions.
Edit intent: meaning-changed
Original:  We find that these biases arise due to the difference in the bending energy barrier associated with DNA insertions. By coupling our model to a well-established framework for large-scale 3D URLanisation we discover that the non-random integration of HIV in human chromatin may be explained as due to large-scale accessibilityof interphase chromosomes.
Revised:  We find that these biases arise due to the difference in the bending energy barrier associated with DNA deformations. By considering a long chromosomal region in human T-cells during interphase, we discover that the non-random integration of HIV in human chromatin may be explained as due to large-scale accessibilityof interphase chromosomes.
Edit intent: meaning-changed
Original:  By coupling our model to a well-established framework for large-scale 3D URLanisation we discover that the non-random integration of HIV in human chromatin may be explained as due to large-scale accessibilityof interphase chromosomes. Finally we propose and solve a reaction-diffusion model that recapitulates the distribution of HIV hot-spots within the nucleus of human T-cells.
Revised:  By coupling our model to a well-established framework for large-scale 3D URLanisation we discover that at these larger scales integration sites are predominantly determined by chromatin accessibility. Finally, we propose and solve a reaction-diffusion model that recapitulates the distribution of HIV hot-spots within the nucleus of human T-cells.
Edit intent: clarity
Original:  We presented a deep reinforcement learning approach within an agent-based modeling system to characterize cell movement in the embryonic development of C. elegans .
Revised:  We presented a deep reinforcement learning approach within an agent-based modeling system to characterize cell movement in the embryonic development of C. elegans embryogenesis. Our modeling system captures the complexity of cell movement patterns in the embryo and overcomes the local optimization problem encountered by traditional rule-based, ABM that uses greedy algorithms.
Edit intent: meaning-changed
Original:  We tested our model through two scenarios within real developmental processes: the anterior movement of the Cpaaa cell via intercalation and the restoration of the superficial left-right symmetry. Our modeling system overcame the local optimization problems encountered by traditional rule-based, agent-based modeling by using greedy algorithms. It also overcame the computational challenges in the action selection which has been plagued by the traditional tabular-based reinforcement learning approach. Our system can automatically explore the cell movement path by using live microscopy images and it can provide a unique capability to model cell movement scenarios where regulatory mechanisms are not well studied. In addition, our system can be used to explore potential paths of a cell under different regulatory mechanisms or to facilitate new hypotheses for explaining certain cell movement behaviors .
Revised:  We tested our model through two scenarios within real developmental processes: the anterior movement of the Cpaaa cell via intercalation and the restoration of the superficial left-right asymmetry. In the first case, model results showed that Cpaaa's intercalation is an active directional cell movement caused by the continuous effects from a longer distance, as opposed to a passive movement caused by neighbor cell movements. This is because the learning-based simulation found that a passive movement model could not lead Cpaaa to the predefined destination. In the second case, a leader-follower mechanism well explained the collective cell movement pattern. These results showed that our approach to introduce deep reinforcement learning into ABM can test regulatory mechanisms by exploring cell migration paths in a reverse engineering perspective. This model opens new doors to explore large datasets generated by live imaging .
Edit intent: meaning-changed
Original:  We explore, quantitatively, how the parameters of our model that measure the shape of the coupling kernel, coupling strength, and aspects of the spatially-smoothed space-time noise, control the pattern in the resulting evolving random field.
Revised:  We explore, quantitatively, how the parameters that control the shape of the coupling kernel, coupling strength, and aspects of the spatially-smoothed space-time noise, control the pattern in the resulting evolving random field.
Edit intent: clarity
Original:  We explore, quantitatively, how the parameters of our model that measure the shape of the coupling kernel, coupling strength, and aspects of the spatially-smoothed space-time noise, control the pattern in the resulting evolving random field.
Revised:  We explore, quantitatively, how the parameters of our model that measure the shape of the coupling kernel, coupling strength, and aspects of spatially-smoothed space-time noise, control the pattern in the resulting evolving random field.
Edit intent: fluency
Original:  We explore, quantitatively, how the parameters of our model that measure the shape of the coupling kernel, coupling strength, and aspects of the spatially-smoothed space-time noise, control the pattern in the resulting evolving random field.
Revised:  We explore, quantitatively, how the parameters of our model that measure the shape of the coupling kernel, coupling strength, and aspects of the spatially-smoothed space-time noise, influence the pattern in the resulting evolving random field.
Edit intent: clarity
Original:  We find that a spatial pattern that is damped in time in a deterministic system may be sustained and amplified by stochasticity , most strikingly at an optimal spatio-temporal noise level. In addition, we find that spatially-smoothed noise alone causes pattern formation even without spatial coupling .
Revised:  We find that a spatial pattern that is damped in time in a deterministic system may be sustained and amplified by stochasticity . We find that spatially-smoothed noise alone causes pattern formation even without spatial coupling .
Edit intent: coherence
Original:  In contrast with classical methods, the surrogate data used here are realisations of a non-stationary linear stochastic process, preserving both the amplitude and time-frequency distributions of original data . These surrogate data are used in combination with an extension of the wavelet coherence to multivariate signals to detect short-lived coherent spatial patterns .
Revised:  In contrast with classical methods, the surrogate data used here are realisations of a non-stationary linear stochastic process, preserving both the amplitude and time-frequency distributions of original data .
Edit intent: coherence
Original:  We apply this approach to calculate the likelihood that cholesterol binds to widely-studied crystallographic sites on 3 GPCRs ( \beta_{2 , 5HT-2B, and \mu-opioid ) at a range of concentrations.
Revised:  We apply this approach to calculate the likelihood that cholesterol binds to widely-studied crystallographic sites on 3 GPCRs ( beta2-adrenergic , 5HT-2B, and \mu-opioid ) at a range of concentrations.
Edit intent: meaning-changed
Original:  We apply this approach to calculate the likelihood that cholesterol binds to widely-studied crystallographic sites on 3 GPCRs ( \beta_{2 , 5HT-2B, and \mu-opioid ) at a range of concentrations.
Revised:  We apply this approach to calculate the likelihood that cholesterol binds to widely-studied crystallographic sites on 3 GPCRs ( \beta_{2 , 5HT-2B, and mu-opioid ) at a range of concentrations.
Edit intent: fluency
Original: Persistent, direct interactions between proteins in complex phases and small molecules acting as ligands, including processes taking place in lamellar environments like the membrane or in crowded, non-dilute environments, are critical to action of numerous drugs. Few computational approaches have been developed for making quantitative predictions of binding probabilities in environments other than dilute isotropic solution.
Revised: The theory of receptor-ligand binding equilibria has long been well-established in biochemistry, and was primarily constructed to describe dilute aqueous solutions. Accordingly, few computational approaches have been developed for making quantitative predictions of binding probabilities in environments other than dilute isotropic solution.
Edit intent: meaning-changed