wanyu/IteraTeR_full_sent huggingface dataset:
Original:  While it is a long standing consensus that leverage effects exist, empirical evidence paradoxically show that most individual stocks  do not exhibit this correlation . We extend the standard linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method  . Examining 615 stocks that comprise the S&P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which most would have been lost under the standard linear assumption.
Revised:  While it is a long standing consensus that leverage effects exist, empirical evidence paradoxically show that most individual stocks do not exhibit this correlation or are very weak. This, at least partially, is due to an assumption that the correlation is linear, i.e., the effect of large shocks and small fluctuations changes linearly. We relax this standard assumption of linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method . Examining 615 stocks that comprise the S&P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which most would have been lost under the standard linear assumption.
Edit intent: meaning-changed
Original:  We extend the standard linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method  . Examining 615 stocks that comprise the S&P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which most would have been lost under the standard linear assumption.
Revised:  We extend the standard linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method that is fast, efficient, and on-line . Examining 615 stocks that comprise the S&P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which most would have been lost under the standard linear assumption.
Edit intent: meaning-changed
Original:  We extend the standard linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method  . Examining 615 stocks that comprise the S&P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which most would have been lost under the standard linear assumption.
Revised:  We extend the standard linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method . Examining 615 stocks that comprise the S&P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which many would have been lost under the standard linear assumption.
Edit intent: clarity
Original:  While it is a long standing consensus that leverage effects exist, empirical evidence paradoxically show that most individual stocks either do not exhibit this correlation or are very weak. This, at least partially, is due to an assumption that the correlationis linear, i. e., the effect of large shocks and small fluctuations changes linearly. We relax this standard assumption of linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method that is fast, efficient, and on-line  . Examining 615 stocks that comprise the S  P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which many would have been lost under the standard linear assumption.
Revised:  While it is a long standing consensus that leverage effects exist, empirical evidence paradoxically show that most individual stocks either do not exhibit this correlation . We examine this paradox by questioning the validity of the assumption of linearity in the correlation. Nonlinear generalizations of the leverage effect are proposed within the stochastic volatility framework in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method that is fast, efficient, and on-line . Examining 615 stocks that comprise the S P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which many would have been lost under the standard linear assumption.
Edit intent: meaning-changed
Original:  We relax this standard assumption of linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method that is fast, efficient, and on-line  . Examining 615 stocks that comprise the S  P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which many would have been lost under the standard linear assumption.
Revised:  We relax this standard assumption of linear correlation to a nonlinear generalization in order to capture flexible correlation structures. Efficient Bayesian sequential computation method that is fast, efficient, and on-line . Examining 615 stocks that comprise the S P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which many would have been lost under the standard linear assumption.
Edit intent: clarity
Original:  We relax this standard assumption of linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method that is fast, efficient, and on-line  . Examining 615 stocks that comprise the S  P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which many would have been lost under the standard linear assumption.
Revised:  We relax this standard assumption of linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation is implemented to estimate this effect in a practical, on-line . Examining 615 stocks that comprise the S P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which many would have been lost under the standard linear assumption.
Edit intent: clarity
Original:  We relax this standard assumption of linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method that is fast, efficient, and on-line  . Examining 615 stocks that comprise the S  P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which many would have been lost under the standard linear assumption.
Revised:  We relax this standard assumption of linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method that is fast, efficient, and on-line manner . Examining 615 stocks that comprise the S P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which many would have been lost under the standard linear assumption.
Edit intent: fluency
Original:  We relax this standard assumption of linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method that is fast, efficient, and on-line  . Examining 615 stocks that comprise the S  P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which many would have been lost under the standard linear assumption.
Revised:  We relax this standard assumption of linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method that is fast, efficient, and on-line . Examining 615 stocks that comprise the S \& P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which many would have been lost under the standard linear assumption.
Edit intent: fluency
Original:  We relax this standard assumption of linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method that is fast, efficient, and on-line  . Examining 615 stocks that comprise the S  P500 and Nikkei 225, we find nearly all of the stocks to exhibit leverage effects, of which many would have been lost under the standard linear assumption. We further the analysis by exploring whether there are clear traits that characterize the complexity of the leverage effect and find that different countries and certain sectors to have tendencies for more complex leverage effects.
Revised:  We relax this standard assumption of linear correlation to a nonlinear generalization in order to capture the complex nature of this effect using a newly developed Bayesian sequential computation method that is fast, efficient, and on-line . Examining 615 stocks that comprise the S P500 and Nikkei 225, we find that 89
Edit intent: coherence
Original:  While it is a long standing consensus that leverage effects exist  , empirical evidence paradoxically  show that most individual stocks do not exhibit this correlation .
Revised: The leverage effect-- the correlation between an asset's return and its volatility-- has played a key role in forecasting and understanding volatility and risk. While it is a long standing consensus that leverage effects exist , empirical evidence paradoxically show that most individual stocks do not exhibit this correlation .
Edit intent: meaning-changed
Original:  We examine this paradox by questioning the validity of the assumption of linearity in the correlation . Nonlinear generalizations of the leverage effect are proposed within the  stochastic volatility framework in order to capture flexible correlation structures . Efficient Bayesian sequential computation is  implemented to estimate this effect in a practical, on-line manner.
Revised:  We examine this paradox , with the goal to improve density forecasts, by relaxing the assumption of linearity in the correlation . Nonlinear generalizations of the leverage effect are proposed within the stochastic volatility framework in order to capture flexible correlation structures . Efficient Bayesian sequential computation is implemented to estimate this effect in a practical, on-line manner.
Edit intent: meaning-changed
Original:  We examine this paradox by questioning the validity of the assumption of linearity in the correlation . Nonlinear generalizations of the leverage effect are proposed within the  stochastic volatility framework in order to capture flexible correlation structures . Efficient Bayesian sequential computation is  implemented to estimate this effect in a practical, on-line manner.
Revised:  We examine this paradox by questioning the validity of the assumption of linearity in the leverage effect . Nonlinear generalizations of the leverage effect are proposed within the stochastic volatility framework in order to capture flexible correlation structures . Efficient Bayesian sequential computation is implemented to estimate this effect in a practical, on-line manner.
Edit intent: fluency
Original:  We examine this paradox by questioning the validity of the assumption of linearity in the correlation . Nonlinear generalizations of the leverage effect are proposed within the  stochastic volatility framework in order to capture flexible correlation structures . Efficient Bayesian sequential computation is  implemented to estimate this effect in a practical, on-line manner.
Revised:  We examine this paradox by questioning the validity of the assumption of linearity in the correlation . Nonlinear generalizations of the leverage effect are proposed within the Bayesian stochastic volatility framework in order to capture flexible correlation structures . Efficient Bayesian sequential computation is implemented to estimate this effect in a practical, on-line manner.
Edit intent: meaning-changed
Original:  We examine this paradox by questioning the validity of the assumption of linearity in the correlation . Nonlinear generalizations of the leverage effect are proposed within the  stochastic volatility framework in order to capture flexible correlation structures . Efficient Bayesian sequential computation is  implemented to estimate this effect in a practical, on-line manner.
Revised:  We examine this paradox by questioning the validity of the assumption of linearity in the correlation . Nonlinear generalizations of the leverage effect are proposed within the stochastic volatility framework in order to capture flexible leverage structures, where small fluctuations in prices have a different effect from large shocks . Efficient Bayesian sequential computation is implemented to estimate this effect in a practical, on-line manner.
Edit intent: meaning-changed
Original:  We examine this paradox by questioning the validity of the assumption of linearity in the correlation . Nonlinear generalizations of the leverage effect are proposed within the  stochastic volatility framework in order to capture flexible correlation structures . Efficient Bayesian sequential computation is  implemented to estimate this effect in a practical, on-line manner.
Revised:  We examine this paradox by questioning the validity of the assumption of linearity in the correlation . Nonlinear generalizations of the leverage effect are proposed within the stochastic volatility framework in order to capture flexible correlation structures . Efficient Bayesian sequential computation is developed and implemented to estimate this effect in a practical, on-line manner.
Edit intent: meaning-changed
Original:  Examining 615 stocks that comprise the S\&P500 and Nikkei 225, we find that  89\\%DIF < of all stocks exhibit general leverage effects.
Revised:  Examining 615 stocks that comprise the S\&P500 and Nikkei 225, we find that relaxing the linear assumption to our proposed nonlinear leverage effect function improves predictive performances for 89\\%DIF < of all stocks exhibit general leverage effects.
Edit intent: meaning-changed
Original:  In some circumstances, two species of motors that are directed oppositely (e.g. kinesin, dynein) can be attached to the same cargo . The resulting net motion is known to be bidirectional , but the mechanism of switching remains unclear.
Revised:  In some circumstances, two species of motors that are directed oppositely (e.g. kinesin, dynein) can be attached to the same cargo , which is known to be bidirectional , but the mechanism of switching remains unclear.
Edit intent: clarity
Original:  The resulting net motion is known to be bidirectional , but the mechanism of switching remains unclear. In this work , we propose a mean-field mathematical model of the mechanical interactions of two populations of molecular motors with diffusion of the cargo (thermal fluctuations ) as the fundamental noise source.
Revised:  The resulting net motion is known to be bidirectional , but the mechanism of switching remains subtle. Although previous work focuses on the motor number as the driving noise source for switching, this work proposes an alternative possibility: cargo diffusion. A mean-field mathematical model of the mechanical interactions of two populations of molecular motors with diffusion of the cargo (thermal fluctuations ) as the fundamental noise source.
Edit intent: meaning-changed
Original:  In this work , we propose a mean-field mathematical model of the mechanical interactions of two populations of molecular motors with diffusion of the cargo (thermal fluctuations ) as the fundamental noise source. By studying a simplified model, the delayed response of motors to rapid fluctuations in the cargo  is quantified, allowing for the reduction of the full model to two "characteristic positions " of each of the motor populations .
Revised:  In this work , we propose a mean-field mathematical model of the mechanical interactions of two populations of molecular motors with cargo thermal fluctuations (diffusion) is presented to study this phenomenon. The delayed response of motors to rapid fluctuations in the cargo is quantified, allowing for the reduction of the full model to two "characteristic positions " of each of the motor populations .
Edit intent: coherence
Original:  By studying a simplified model, the delayed response of motors to rapid fluctuations in the cargo  is quantified, allowing for the reduction of the full model to two "characteristic positions " of each of the motor populations .
Revised:  By studying a simplified model, the delayed response of a motor to fluctuations in the cargo is quantified, allowing for the reduction of the full model to two "characteristic positions " of each of the motor populations .
Edit intent: clarity
Original:  By studying a simplified model, the delayed response of motors to rapid fluctuations in the cargo  is quantified, allowing for the reduction of the full model to two "characteristic positions " of each of the motor populations .
Revised:  By studying a simplified model, the delayed response of motors to rapid fluctuations in the cargo velocity is quantified, allowing for the reduction of the full model to two "characteristic positions " of each of the motor populations .
Edit intent: clarity
Original:  By studying a simplified model, the delayed response of motors to rapid fluctuations in the cargo  is quantified, allowing for the reduction of the full model to two "characteristic positions " of each of the motor populations .
Revised:  By studying a simplified model, the delayed response of motors to rapid fluctuations in the cargo is quantified, allowing for the reduction of the full model a single "characteristic positions " of each of the motor populations .
Edit intent: meaning-changed
Original:  By studying a simplified model, the delayed response of motors to rapid fluctuations in the cargo  is quantified, allowing for the reduction of the full model to two "characteristic positions " of each of the motor populations .
Revised:  By studying a simplified model, the delayed response of motors to rapid fluctuations in the cargo is quantified, allowing for the reduction of the full model to two "characteristic position " of each of the motor populations .
Edit intent: fluency
Original:  By studying a simplified model, the delayed response of motors to rapid fluctuations in the cargo  is quantified, allowing for the reduction of the full model to two "characteristic positions " of each of the motor populations .
Revised:  By studying a simplified model, the delayed response of motors to rapid fluctuations in the cargo is quantified, allowing for the reduction of the full model to two "characteristic positions " , a proxy for the net force on the cargo .
Edit intent: meaning-changed
Original:  We show that the the joint distribution of any finite sequence of log returns admits a Gram-Charlier A expansion in closed-form . We use this to derive closed-form series representations for option prices whose payoff is a function of the underlying asset price trajectory at finitely many time points.
Revised:  We show that the the joint distribution of any finite sequence of log returns admits a Gram-Charlier A expansion in closed-form coefficients. We derive closed-form series representations for option prices whose payoff is a function of the underlying asset price trajectory at finitely many time points.
Edit intent: clarity
Original:  This includes European call, put, and digital options, forward start options, and forward start optionson the underlying return. We derive sharp analytical and numerical bounds on the series truncation errors. We illustrate the performance by numerical examples, which show that our approach offers a viable alternative to Fourier transform techniques .
Revised:  This includes European call, put, and digital options, forward start options, and can be applied to discretely monitored Asian options. In a numerical analysis we find that the price approximations become accurate within short CPU time .
Edit intent: meaning-changed
Original:  We show that the full solution of the master equation contains two components: a model-specific, upstream effective drive, which encapsulates the effect of the cellular drives (e.g., entrainment, periodicity or promoter randomness), and a downstream transcriptional Poissonian part, which is common to all models.
Revised:  We show that the full solution of the master equation contains two components: a model-specific, upstream effective drive, which encapsulates the effect of cellular drives (e.g., entrainment, periodicity or promoter randomness), and a downstream transcriptional Poissonian part, which is common to all models.
Edit intent: fluency
Original:  We apply the obtained solution to characterize several gene transcription models of experimental relevance, and to explain the influence on gene transcription of synchrony, stationarity, ergodicity, as well as the effect of time-scales and other dynamic characteristics of drives.
Revised:  We apply the obtained solution to characterise different models of experimental relevance, and to explain the influence on gene transcription of synchrony, stationarity, ergodicity, as well as the effect of time-scales and other dynamic characteristics of drives.
Edit intent: clarity
Original:  Results: We address these limitations by: 1) exploiting well-established graphlet measures via a new network approach for protein classification , 2) introducing novel normalized graphlet measures to remove the bias of PSN size, 3) allowing for integrating multiple PSN measures, and 4) using ordered graphlets to combine the complementary ideas of PSN data and sequence data.
Revised:  We address these limitations by: 1) exploiting well-established graphlet measures via a new network approach for protein classification , 2) introducing novel normalized graphlet measures to remove the bias of PSN size, 3) allowing for integrating multiple PSN measures, and 4) using ordered graphlets to combine the complementary ideas of PSN data and sequence data.
Edit intent: coherence
Original:  Results: We address these limitations by: 1) exploiting well-established graphlet measures via a new network approach for protein classification , 2) introducing novel normalized graphlet measures to remove the bias of PSN size, 3) allowing for integrating multiple PSN measures, and 4) using ordered graphlets to combine the complementary ideas of PSN data and sequence data.
Revised:  Results: We address these limitations by: 1) exploiting well-established graphlet measures via a new network approach , 2) introducing novel normalized graphlet measures to remove the bias of PSN size, 3) allowing for integrating multiple PSN measures, and 4) using ordered graphlets to combine the complementary ideas of PSN data and sequence data.
Edit intent: clarity
Original:  Results: We address these limitations by: 1) exploiting well-established graphlet measures via a new network approach for protein classification , 2) introducing novel normalized graphlet measures to remove the bias of PSN size, 3) allowing for integrating multiple PSN measures, and 4) using ordered graphlets to combine the complementary ideas of PSN data and sequence data.
Revised:  Results: We address these limitations by: 1) exploiting well-established graphlet measures via a new network approach for protein classification , 2) introducing normalized graphlet measures to remove the bias of PSN size, 3) allowing for integrating multiple PSN measures, and 4) using ordered graphlets to combine the complementary ideas of PSN data and sequence data.
Edit intent: clarity