wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The leave-one-out LSM (LOOLSM) method is illustrated with examples, including  multi-asset options whose LSM price is biased high. The asymptotic behavior of look-ahead bias is also discussed with the LOOLSM approach .
Revised:  The leave-one-out LSM (LOOLSM) method is illustrated with examples, including multi-asset cases that the LSM algorithm significantly overvalues. We also obtain the convergence rates of look-ahead bias is also discussed with the LOOLSM approach .
Edit intent: meaning-changed
Original:  For the single-stock Black-Scholes market, Cover and Ordentlich (1998) only priced this derivative at time-0, giving C (S,0) =1+\sigmaT/(2\pi).
Revised:  For the single-stock Black-Scholes market, Ordentlich and Cover (1998) only priced this derivative at time-0, giving C (S,0) =1+\sigmaT/(2\pi).
Edit intent: clarity
Original:  For the single-stock Black-Scholes market, Cover and Ordentlich (1998) only priced this derivative at time-0, giving C (S,0) =1+\sigmaT/(2\pi).
Revised:  For the single-stock Black-Scholes market, Cover and Ordentlich (1998) only priced this derivative at time-0, giving C _0 =1+\sigmaT/(2\pi).
Edit intent: meaning-changed
Original:  This yields C(S,t)=T/t\cdot\,\exp ( rt+\sigma^2b(S,t)^2  t/2 )\exp(rt+ t\cdot b'\Sigma b  /2), where \Sigma is the covariance of instantaneous returns per unit time.
Revised:  This yields C(S,t)=T/t\cdot\,\exp \{ rt+\sigma^2b(S,t)^2 t/2 )\exp(rt+ t\cdot b'\Sigma b /2), where \Sigma is the covariance of instantaneous returns per unit time.
Edit intent: meaning-changed
Original:  This yields C(S,t)=T/t\cdot\,\exp ( rt+\sigma^2b(S,t)^2  t/2 )\exp(rt+ t\cdot b'\Sigma b  /2), where \Sigma is the covariance of instantaneous returns per unit time.
Revised:  This yields C(S,t)=T/t\cdot\,\exp ( rt+\sigma^2b(S,t)^2 \cdot t/2 )\exp(rt+ t\cdot b'\Sigma b /2), where \Sigma is the covariance of instantaneous returns per unit time.
Edit intent: meaning-changed
Original:  This yields C(S,t)=T/t\cdot\,\exp ( rt+\sigma^2b(S,t)^2  t/2 )\exp(rt+ t\cdot b'\Sigma b  /2), where \Sigma is the covariance of instantaneous returns per unit time.
Revised:  This yields C(S,t)=T/t\cdot\,\exp ( rt+\sigma^2b(S,t)^2 t/2 \} , where b(S,t) is the best rebalancing rule in hindsight over the observed history [0,t]. I show that the replicating strategy amounts to betting the fraction b(S,t) of wealth on the stock over the interval [t,t+dt]. This fact holds for the general market with n correlated stocks in geometric Brownian motion: we get C(S,t)=(T/t)^{n/2\exp(rt+ t\cdot b'\Sigma b /2), where \Sigma is the covariance of instantaneous returns per unit time.
Edit intent: meaning-changed
Original:  This yields C(S,t)=T/t\cdot\,\exp ( rt+\sigma^2b(S,t)^2  t/2 )\exp(rt+ t\cdot b'\Sigma b  /2), where \Sigma is the covariance of instantaneous returns per unit time.
Revised:  This yields C(S,t)=T/t\cdot\,\exp ( rt+\sigma^2b(S,t)^2 t/2 )\exp(rt+ b'\Sigma b /2), where \Sigma is the covariance of instantaneous returns per unit time.
Edit intent: fluency
Original:  This yields C(S,t)=T/t\cdot\,\exp ( rt+\sigma^2b(S,t)^2  t/2 )\exp(rt+ t\cdot b'\Sigma b  /2), where \Sigma is the covariance of instantaneous returns per unit time.
Revised:  This yields C(S,t)=T/t\cdot\,\exp ( rt+\sigma^2b(S,t)^2 t/2 )\exp(rt+ t\cdot b'\Sigma b \cdot t /2), where \Sigma is the covariance of instantaneous returns per unit time.
Edit intent: fluency
Original:  State-of-the-art training-based SSVEP decoding methods such as extended Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA) are the major players that elevate the efficiency of  SSVEP-based BCI through individualized calibration process.
Revised:  State-of-the-art training-based SSVEP decoding methods such as extended Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA) are the major players that elevate the efficiency of the SSVEP-based BCI through individualized calibration process.
Edit intent: fluency
Original:  State-of-the-art training-based SSVEP decoding methods such as extended Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA) are the major players that elevate the efficiency of  SSVEP-based BCI through individualized calibration process.
Revised:  State-of-the-art training-based SSVEP decoding methods such as extended Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA) are the major players that elevate the efficiency of SSVEP-based BCIs through an individualized calibration process.
Edit intent: fluency
Original:  The proposed least-squares transformation (LST) method was able to significantly reduce the training templates required for TRCA-based SSVEP detection on a 40-class SSVEP dataset from 8 subjects.
Revised:  Study results showed that a new least-squares transformation (LST) method was able to significantly reduce the training templates required for TRCA-based SSVEP detection on a 40-class SSVEP dataset from 8 subjects.
Edit intent: clarity
Original:  The proposed least-squares transformation (LST) method was able to significantly reduce the training templates required for TRCA-based SSVEP detection on a 40-class SSVEP dataset from 8 subjects. This study shed light on plug-and-play high speed SSVEP-based BCIfor further practical applications .
Revised:  The proposed least-squares transformation (LST) method was able to significantly reduce the training templates required for TRCA-based SSVEP detection on a 40-class TRCA-based SSVEP BCI. The LST method may lead to numerous practical applications of plug-and-play high speed SSVEP-based BCIfor further practical applications .
Edit intent: clarity
Original:  State-of-the-art training-based SSVEP decoding methods such as extended Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA) are the major players that elevate the efficiency of the SSVEP-based BCIs through an individualized calibration process.
Revised:  State-of-the-art training-based SSVEP decoding methods such as extended Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA) are the major players that elevate the efficiency of the SSVEP-based BCIs through a calibration process.
Edit intent: clarity
Original:  However, collecting sufficient calibration (e.g. trainingtemplates) data could be laborious and time-consuming, hindering the practicality in a real-world context.
Revised:  However, because of large human variability both across individuals and within individuals over time, we need to collect calibration (training) data from each individual before each session, which could be laborious and time-consuming, hindering the practicality in a real-world context.
Edit intent: meaning-changed
Original:  However, because of large human variability both across individuals and within individuals over time, we need to collect calibration (training) data from each individual before each session, which could be laborious and time-consuming, deteriorating its practicality in a real-world context.
Revised:  However, due to notable human variability across individuals and within individuals over time, we need to collect calibration (training) data from each individual before each session, which could be laborious and time-consuming, deteriorating its practicality in a real-world context.
Edit intent: clarity
Original:  However, because of large human variability both across individuals and within individuals over time, we need to collect calibration (training) data from each individual before each session, which could be laborious and time-consuming, deteriorating its practicality in a real-world context.
Revised:  However, because of large human variability both across individuals and within individuals over time, calibration (training) data from each individual before each session, which could be laborious and time-consuming, deteriorating its practicality in a real-world context.
Edit intent: coherence
Original:  However, because of large human variability both across individuals and within individuals over time, we need to collect calibration (training) data from each individual before each session, which could be laborious and time-consuming, deteriorating its practicality in a real-world context.
Revised:  However, because of large human variability both across individuals and within individuals over time, we need to collect calibration (training) data collection is non-negligible and often laborious and time-consuming, deteriorating its practicality in a real-world context.
Edit intent: meaning-changed
Original:  However, because of large human variability both across individuals and within individuals over time, we need to collect calibration (training) data from each individual before each session, which could be laborious and time-consuming, deteriorating its practicality in a real-world context.
Revised:  However, because of large human variability both across individuals and within individuals over time, we need to collect calibration (training) data from each individual before each session, which could be laborious and time-consuming, deteriorating the practicality of SSVEP BCIs in a real-world context.
Edit intent: meaning-changed
Original:  This study aims to develop a cross-subject transferring approach to reduce the need for  training data from a test user . Study results showed that a new least-squares transformation (LST) method was able to significantly reduce the training templates required for a 40-class SSVEP BCI.
Revised:  This study aims to develop a cross-subject transferring approach to reduce the need for collecting training data from a test user . Study results showed that a new least-squares transformation (LST) method was able to significantly reduce the training templates required for a 40-class SSVEP BCI.
Edit intent: clarity
Original:  This study aims to develop a cross-subject transferring approach to reduce the need for  training data from a test user . Study results showed that a new least-squares transformation (LST) method was able to significantly reduce the training templates required for a 40-class SSVEP BCI.
Revised:  This study aims to develop a cross-subject transferring approach to reduce the need for training data from a test user with a newly proposed least-squares transformation (LST) method was able to significantly reduce the training templates required for a 40-class SSVEP BCI.
Edit intent: meaning-changed
Original:  This study aims to develop a cross-subject transferring approach to reduce the need for  training data from a test user . Study results showed that a new least-squares transformation (LST) method was able to significantly reduce the training templates required for a 40-class SSVEP BCI.
Revised:  This study aims to develop a cross-subject transferring approach to reduce the need for training data from a test user . Study results showed that a new least-squares transformation (LST) method . Study results show the capability of the LST in reducing the number of training templates required for a 40-class SSVEP BCI.
Edit intent: clarity
Original:  In this paper, we explore the pitfalls and possibilities of the LASSO methods in this predictive regression A typical predictive regression employs a multitude of potential regressors with various degrees of persistence while their signal strength in explaining the dependent variable is often low. Variable selection in such context is of great importance. In this paper, we explore the pitfalls and possibilities of the LASSO methods in this predictive regression framework with mixed degrees of persistence.
Revised:  In this paper, we explore the pitfalls and possibilities of the LASSO methods in this predictive regression framework with mixed degrees of persistence.
Edit intent: coherence
Original:  With the presence of stationary, unit root and cointegrated predictors, we show that the adaptive LASSO maintains the consistent variable selection and the oracle property due to its penalty scheme that accommodates the system of regressors .
Revised:  In the presence of stationary, unit root and cointegrated predictors, we show that the adaptive LASSO maintains the consistent variable selection and the oracle property due to its penalty scheme that accommodates the system of regressors .
Edit intent: fluency
Original:  With the presence of stationary, unit root and cointegrated predictors, we show that the adaptive LASSO maintains the consistent variable selection and the oracle property due to its penalty scheme that accommodates the system of regressors .
Revised:  With the presence of stationary, unit root and cointegrated predictors, we show that the adaptive LASSO asymptotically breaks cointegrated groups although it cannot wipe out all inactive cointegrating variables. This new finding motivates a simple but novel post-selection adaptive LASSO, which we call the twin adaptive LASSO (TAlasso), to fix variable selection inconsistency. TAlasso's penalty scheme accommodates the system of regressors .
Edit intent: meaning-changed
Original:  With the presence of stationary, unit root and cointegrated predictors, we show that the adaptive LASSO maintains the consistent variable selection and the oracle property due to its penalty scheme that accommodates the system of regressors .
Revised:  With the presence of stationary, unit root and cointegrated predictors, we show that the adaptive LASSO maintains the consistent variable selection and the oracle property due to its penalty scheme that accommodates the system of heterogeneous regressors, and it recovers the well-known oracle property that implies variable selection consistency and optimal rate of convergence for all three types of regressors .
Edit intent: meaning-changed
Original: This paper considers uniformly valid (over a class of data generating processes) inference for convex functionals of partially identified parameters in cases where the identified set is defined by convex (in the parameter) moment inequalities.
Revised: This paper considers uniformly valid (over a class of data generating processes) inference for linear functionals of partially identified parameters in cases where the identified set is defined by convex (in the parameter) moment inequalities.
Edit intent: meaning-changed
Original: This paper considers uniformly valid (over a class of data generating processes) inference for convex functionals of partially identified parameters in cases where the identified set is defined by convex (in the parameter) moment inequalities.
Revised: This paper considers uniformly valid (over a class of data generating processes) inference for convex functionals of partially identified parameters in cases where the identified set is defined by linear (in the parameter) moment inequalities.
Edit intent: meaning-changed
Original:   Unlike other proposed subvector inference procedures, our procedure does not require the researcher to repeatedly invert a hypothesis test, and is extremely computationally efficient .
Revised:  In other words, this paper shows the conditions under which naively bootstrapping a linear program can be used to construct a confidence set with uniform correct coverage for a partially identified linear functional. Unlike other proposed subvector inference procedures, our procedure does not require the researcher to repeatedly invert a hypothesis test, and is extremely computationally efficient .
Edit intent: meaning-changed
Original:   Unlike other proposed subvector inference procedures, our procedure does not require the researcher to repeatedly invert a hypothesis test, and is extremely computationally efficient . Finally, we discuss sufficient conditions on the moment functions to ensure uniform validity, and show that our assumptions are in fact satisfied by a large class of data generating processes .
Revised:  Unlike other proposed subvector inference procedures, our procedure does not require the researcher to repeatedly invert a hypothesis test, and is extremely computationally efficient .
Edit intent: coherence
Original: This paper considers uniformly valid (over a class of data generating processes) inference for linear functionals  of partially identified parameters in cases where the identified set is defined by linear (in the parameter) moment inequalities.
Revised: This paper considers uniformly valid (over a class of data generating processes) inference for linear functionals and scalar subvectors of partially identified parameters in cases where the identified set is defined by linear (in the parameter) moment inequalities.
Edit intent: meaning-changed
Original:  The proposed method amounts to bootstrapping the value functions of a linear optimization problem, and subsumes subvector inference as a special case. In other words, this paper shows the conditions under which ``naively'' bootstrapping a linear program can be used to construct a confidence set with uniform correct coverage for a partially identified linear functional. Unlike other proposed subvector inference procedures, our procedure does not require the researcher to repeatedly invert a hypothesis test, and is extremely computationally efficient.
Revised:  The proposed method amounts to bootstrapping the value functions of four carefully constructed "perturbed" linear programming problems, and does not require the researcher to repeatedly invert a hypothesis test, and is extremely computationally efficient.
Edit intent: clarity
Original:  Unlike other proposed subvector inference procedures, our procedure does not require the researcher to repeatedly invert a hypothesis test, and is extremely computationally efficient. In addition to the new procedure , the paper also discusses connections between the literature on optimization and the literature on subvector inference in partially identified models .
Revised:  Unlike other proposed subvector inference procedures, our procedure does not require the researcher to grid over the parameter space. Our low-level conditions for uniform validity rely on a novel application of Sard's Theorem from differential topology, and may be of substantial separate interest. Our procedure is asymptotically conservative for the true partially identified parameter, but draws its appeal from the fact that it is valid under weak assumptions, it performs well in finite samples, and is computationally simple to implement .
Edit intent: meaning-changed
Original:  In this work, we present a minimal stochastic model that qualitatively reproduces {\it  observations of polymerization, of formation of dynamic contractile ring that is stable for a long time and of depolymerization shown by FtsZ polymer .
Revised:  In this work, we present a minimal stochastic model that qualitatively reproduces {\it in vitro observations of polymerization, of formation of dynamic contractile ring that is stable for a long time and of depolymerization shown by FtsZ polymer .
Edit intent: meaning-changed
Original:  In this work, we present a minimal stochastic model that qualitatively reproduces {\it  observations of polymerization, of formation of dynamic contractile ring that is stable for a long time and of depolymerization shown by FtsZ polymer .
Revised:  In this work, we present a minimal stochastic model that qualitatively reproduces {\it observations of polymerization, formation of dynamic contractile ring that is stable for a long time and of depolymerization shown by FtsZ polymer .
Edit intent: fluency
Original:  In this work, we present a minimal stochastic model that qualitatively reproduces {\it  observations of polymerization, of formation of dynamic contractile ring that is stable for a long time and of depolymerization shown by FtsZ polymer .
Revised:  In this work, we present a minimal stochastic model that qualitatively reproduces {\it observations of polymerization, of formation of dynamic contractile ring that is stable for a long time and depolymerization shown by FtsZ polymer .
Edit intent: fluency
Original:  In this work, we present a minimal stochastic model that qualitatively reproduces {\it  observations of polymerization, of formation of dynamic contractile ring that is stable for a long time and of depolymerization shown by FtsZ polymer . We explore different mechanisms for ring breaking and hydrolysis.
Revised:  In this work, we present a minimal stochastic model that qualitatively reproduces {\it observations of polymerization, of formation of dynamic contractile ring that is stable for a long time and of depolymerization shown by FtsZ polymer filaments. In this stochastic model, we explore different mechanisms for ring breaking and hydrolysis.
Edit intent: meaning-changed
Original:  Hydrolysis is known to regulate the dynamics of other tubulin polymers like microtubules . We find that the presence of the ring allows for an additional mechanism for regulating the dynamics of FtsZ polymers.
Revised:  Hydrolysis is known to regulate the dynamics of other tubulin polymers like microtubules , we find that the presence of the ring allows for an additional mechanism for regulating the dynamics of FtsZ polymers.
Edit intent: fluency
Original:  Ring breaking dynamics in the presence of hydrolysis naturally induce rescue and catastrophe events , irrespective of the mechanism of hydrolysis . Based on our model, we conclude that the Z-ring undergoes random breaking and closing during the process of cell division .
Revised:  Ring breaking dynamics in the presence of hydrolysis naturally induce rescue and catastrophe events in this model irrespective of the mechanism of hydrolysis . Based on our model, we conclude that the Z-ring undergoes random breaking and closing during the process of cell division .
Edit intent: meaning-changed
Original:  However, it is challenging to train an automatic method for predicting  ATR rehabilitation outcome from treatment data, due to a massive amount of missing data in the data recorded from ATR patients, as well as complex nonlinear relations between measurements and outcomes.
Revised:  However, it is challenging to train an automatic method for predicting the ATR rehabilitation outcome from treatment data, due to a massive amount of missing data in the data recorded from ATR patients, as well as complex nonlinear relations between measurements and outcomes.
Edit intent: fluency
Original:  However, it is challenging to train an automatic method for predicting  ATR rehabilitation outcome from treatment data, due to a massive amount of missing data in the data recorded from ATR patients, as well as complex nonlinear relations between measurements and outcomes.
Revised:  However, it is challenging to train an automatic method for predicting ATR rehabilitation outcome from treatment data, due to a massive amount of missing entries in the data recorded from ATR patients, as well as complex nonlinear relations between measurements and outcomes.
Edit intent: clarity
Original: The job of market makers is to provide liquidity to other market participants. The main source of risk for market makers is holding inventory and the uncertainty of future price variation. In many cases, the market makers are in charge of a large range of assets . However, managing the risk in multiple asset cases is an important task. We propose in this paper closed-form approximations for the bid and ask quotes a market maker should set for optimally managing the risk of his/her multi-asset market making book.
Revised: A large proportion of market making models derive from the seminal model of Avellaneda and Stoikov. The numerical approximation of the value function and the optimal quotes in these models remains a challenge when the number of assets is large. In this article, we propose closed-form approximations for the bid and ask quotes a market maker should set for optimally managing the risk of his/her multi-asset market making book.
Edit intent: meaning-changed