wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To illustrate our approach we study panel data modelswhere the distribution of individual effects may be misspecifiedand the number of time periods is small , and we revisit the structural evaluation of a conditional cash transfer program in Mexico .
Revised:  To illustrate our approach we study panel data modelswhere the distribution of individual effects may be misspecifiedand the number of time periods is small and the distribution of individual effects is misspecified .
Edit intent: coherence
Original:  As illustrations, we study two binary choice models: a cross-sectional  model where the error distribution is misspecified, and a dynamic panel data  model where the number of time periods is small and the distribution of individual effects is misspecified.
Revised:  As illustrations, we study three applications: an empirical analysis of the impact of conditional cash transfers in Mexico where misspecification stems from the presence of stigma effects of the program, a cross-sectional model where the error distribution is misspecified, and a dynamic panel data model where the number of time periods is small and the distribution of individual effects is misspecified.
Edit intent: meaning-changed
Original:  As illustrations, we study two binary choice models: a cross-sectional  model where the error distribution is misspecified, and a dynamic panel data  model where the number of time periods is small and the distribution of individual effects is misspecified.
Revised:  As illustrations, we study two binary choice models: a cross-sectional binary choice model where the error distribution is misspecified, and a dynamic panel data model where the number of time periods is small and the distribution of individual effects is misspecified.
Edit intent: meaning-changed
Original:  As illustrations, we study two binary choice models: a cross-sectional  model where the error distribution is misspecified, and a dynamic panel data  model where the number of time periods is small and the distribution of individual effects is misspecified.
Revised:  As illustrations, we study two binary choice models: a cross-sectional model where the error distribution is misspecified, and a dynamic panel data binary choice model where the number of time periods is small and the distribution of individual effects is misspecified.
Edit intent: meaning-changed
Original:  Applying the estimator to U.S. Treasury security data, we find that the  systematic factor structure  differs in times of booms and recessions as well as in periods of high market volatility  .
Revised:  Applying the estimator to U.S. Treasury security data, we find that the P500 stock returns. The systematic factor structure differs in times of booms and recessions as well as in periods of high market volatility .
Edit intent: meaning-changed
Original:  Applying the estimator to U.S. Treasury security data, we find that the  systematic factor structure  differs in times of booms and recessions as well as in periods of high market volatility  .
Revised:  Applying the estimator to U.S. Treasury security data, we find that the systematic factor structure in treasury yields differs in times of booms and recessions as well as in periods of high market volatility .
Edit intent: meaning-changed
Original:  Applying the estimator to U.S. Treasury security data, we find that the  systematic factor structure  differs in times of booms and recessions as well as in periods of high market volatility  .
Revised:  Applying the estimator to U.S. Treasury security data, we find that the systematic factor structure differs in times of booms and recessions as well as in periods of high market volatility . State-varying factors based on the VIX capture significantly more variation and pricing information in individual stocks than constant factor models .
Edit intent: meaning-changed
Original:  However, a detailed understanding of such response is still lacking, as it involves processes occurring over a wide range of length- and time-scales. Experimentally, one of the preferred methods to investigate how crawling cells react to such stimulation is to apply cyclic stretching to the substrate to which they are attached. Studying the shape deformations and changes in motility, as well as the localization of characteristic proteins (e.g., actin or myosin) or the orientation of the actin filament network inside of the cell under stretching, has provided crucial information on their mechano-sensing abilities. In particular, cells have been shown to reorient under such perturbations, aligning at specific angles with respect to the stretching direction that depends strongly on the type of cell .
Revised:  In experiments, cells placed on cyclically stretched substrates have been shown to reorient under such perturbations, aligning at specific angles with respect to the stretching direction that depends strongly on the type of cell .
Edit intent: clarity
Original:  In particular, cells have been shown to reorient under such perturbations, aligning at specific angles with respect to the stretching direction that depends strongly on the type of cell . We introduce a computation model for crawling cells on such periodically stretching substrates that is able to account for the complex shape deformations, the URLanization of the actin-network, and the coupling to the substrate .
Revised:  In particular, cells have been shown to reorient under such perturbations, aligning at specific angles with respect to the stretching direction that depends strongly on the type of cell , but also on the mechanical properties of the substrate, and the amplitude and rate of stretching. However, the underlying biochemical and mechanical mechanisms responsible for this realignment are still not completely understood. In this study, we introduce a computational model for fast crawling on cyclically stretched substrates that accounts for the sub-cellular processes responsible for the cell shape and motility, as well as the coupling to the substrate .
Edit intent: clarity
Original:  We introduce a computation model for crawling cells on such periodically stretching substrates that is able to account for the complex shape deformations, the URLanization of the actin-network, and the coupling to the substrate . As in experiments, we are able to observe preferential alignment along perpendicular, parallel, or oblique angles to the stretching, depending on the frequency of stretching, as well as on the type of response exhibited by our model cells .
Revised:  We introduce a computation model for crawling cells on such periodically stretching substrates that is able to account for the complex shape deformations, the URLanization of the actin-network, and the coupling to the substrate through the focal adhesion sites. In particular, we focus on the role of the focal adhesion dynamics, and show that the reorientation under cyclic stretching is strongly dependent on the frequency of stretching, as well as on the type of response exhibited by our model cells .
Edit intent: clarity
Original:  As in experiments, we are able to observe preferential alignment along perpendicular, parallel, or oblique angles to the stretching, depending on the frequency of stretching, as well as on the type of response exhibited by our model cells .
Revised:  As in experiments, we are able to observe preferential alignment along perpendicular, parallel, or oblique angles to the stretching, depending on the frequency , as has been observed experimentally. Furthermore, we show that an asymmetry during the loading and unloading phases of the stretching, whether coming from the response of the cell itself, or from the stretching protocol, can be used to selectively align the cells in either the parallel or perpendicular directions .
Edit intent: meaning-changed
Original: A new realized joint Value-at-Risk (VaR) and expected shortfall (ES) regression framework is proposed, through incorporating a measurement equation into the original joint VaR and ES regression model .
Revised: A new realized conditional autoregressive Value-at-Risk (VaR) and expected shortfall (ES) regression framework is proposed, through incorporating a measurement equation into the original joint VaR and ES regression model .
Edit intent: meaning-changed
Original: A new realized joint Value-at-Risk (VaR) and expected shortfall (ES) regression framework is proposed, through incorporating a measurement equation into the original joint VaR and ES regression model .
Revised: A new realized joint Value-at-Risk (VaR) and expected shortfall (ES) regression framework is proposed, through incorporating a measurement equation into the original quantile regression model. The framework is further extended by employing various Expected Shortfall (ES) components, to jointly estimate and forecast VaR and ES regression model .
Edit intent: meaning-changed
Original:  The measurement equation models the contemporaneous dependence between the realized measures (e.g. Realized Variance and Realized Range) and the latent conditional quantile. Further, sub-sampling and scaling methods are applied to both the realized range and realized variance, to help deal with the inherent micro-structure noise and inefficiency.
Revised:  The measurement equation models the contemporaneous dependence between the realized measures (e.g. Realized Variance and Realized Range) and the latent conditional ES.
Edit intent: clarity
Original:  In a forecasting study , the proposed models are applied to 7 market indices and 2 individual assets, compared to a range of parametric, non-parametric and semi-parametric models, including GARCH, Realized-GARCH, CARE and Taylor (2017) joint VaR and ES quantile regression models, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed models, especially when incorporating the sub-sampled Realized Variance and the sub-sampled Realized Range in the model.
Revised:  In a comprehensive forecasting study on 1\% and 2.5 \% quantile levels , the proposed models are applied to 7 market indices and 2 individual assets, compared to a range of parametric, non-parametric and semi-parametric models, including GARCH, Realized-GARCH, CARE and Taylor (2017) joint VaR and ES quantile regression models, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed models, especially when incorporating the sub-sampled Realized Variance and the sub-sampled Realized Range in the model.
Edit intent: meaning-changed
Original:  In a forecasting study , the proposed models are applied to 7 market indices and 2 individual assets, compared to a range of parametric, non-parametric and semi-parametric models, including GARCH, Realized-GARCH, CARE and Taylor (2017) joint VaR and ES quantile regression models, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed models, especially when incorporating the sub-sampled Realized Variance and the sub-sampled Realized Range in the model.
Revised:  In a forecasting study , the proposed models are compared to a range of parametric, non-parametric and semi-parametric models, including GARCH, Realized-GARCH, CARE and Taylor (2017) joint VaR and ES quantile regression models, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed models, especially when incorporating the sub-sampled Realized Variance and the sub-sampled Realized Range in the model.
Edit intent: clarity
Original:  In a forecasting study , the proposed models are applied to 7 market indices and 2 individual assets, compared to a range of parametric, non-parametric and semi-parametric models, including GARCH, Realized-GARCH, CARE and Taylor (2017) joint VaR and ES quantile regression models, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed models, especially when incorporating the sub-sampled Realized Variance and the sub-sampled Realized Range in the model.
Revised:  In a forecasting study , the proposed models are applied to 7 market indices and 2 individual assets, compared to a range of parametric, non-parametric and semi-parametric models, based on 7 market indices and 7 individual assets. One-day-ahead VaR and ES quantile regression models, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed models, especially when incorporating the sub-sampled Realized Variance and the sub-sampled Realized Range in the model.
Edit intent: clarity
Original:  In a forecasting study , the proposed models are applied to 7 market indices and 2 individual assets, compared to a range of parametric, non-parametric and semi-parametric models, including GARCH, Realized-GARCH, CARE and Taylor (2017) joint VaR and ES quantile regression models, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed models, especially when incorporating the sub-sampled Realized Variance and the sub-sampled Realized Range in the model.
Revised:  In a forecasting study , the proposed models are applied to 7 market indices and 2 individual assets, compared to a range of parametric, non-parametric and semi-parametric models, including GARCH, Realized-GARCH, CARE and Taylor (2017) joint VaR and ES forecasting results favor the proposed models, especially when incorporating the sub-sampled Realized Variance and the sub-sampled Realized Range in the model.
Edit intent: coherence
Original:  Here, we study watchman routes outside a convex polygon, i.e., in R^2\O, where O^2\setminus \Omega, where \Omega } is a convex polygon  .
Revised:  Here, we study the online watchman path problem outside a convex polygon, i.e., in R^2\O, where O^2\setminus \Omega, where \Omega } is a convex polygon .
Edit intent: meaning-changed
Original:  Here, we study watchman routes outside a convex polygon, i.e., in R^2\O, where O^2\setminus \Omega, where \Omega } is a convex polygon  .
Revised:  Here, we study watchman routes outside a convex polygon, i.e., in \mathbb{R^2\setminus \Omega, where \Omega } is a convex polygon .
Edit intent: meaning-changed
Original:  Here, we study watchman routes outside a convex polygon, i.e., in R^2\O, where O^2\setminus \Omega, where \Omega } is a convex polygon  .
Revised:  Here, we study watchman routes outside a convex polygon, i.e., in R^2\O, where O^2\setminus \Omega, where \Omega } is a convex polygon that is not known in advance. We present an algorithm for the exploration of the region outside the polygon .
Edit intent: meaning-changed
Original:  We study the problem of a watchman route in an online setting, i.e., in a setting where the watchman is only aware of the vertices of the polygonto which it had a direct line of sight along its route. We present an algorithm guaranteeing a ~89.83 competitive ratio relative to the optimal offline path length .
Revised:  We prove that the presented algorithms guarantees a \approx 22.77 competitive ratio compared to the optimal offline path length .
Edit intent: meaning-changed
Original: We study chemical reaction networks with few chemical complexes.
Revised: Bistability and multistationarity are properties of reaction networks linked to switch-like responses and connected to cell memory and cell decision making. Determining whether and when a network exhibits bistability is a hard and open mathematical problem. One successful strategy consists of analyzing small networks and deducing that some of the properties are preserved upon passage to the full network. Motivated by this we study chemical reaction networks with few chemical complexes.
Edit intent: meaning-changed
Original:  We explore how the idea of Gale duality can be used to learn about the steady states of fewnomial networks. In particular, we give precise conditions in terms of the reaction rate constants for the number and stability of the steady states of families of reaction networks with one non-flow reaction.
Revised:  Using this Gale duality we give precise conditions in terms of the reaction rate constants for the number and stability of the steady states of families of reaction networks with one non-flow reaction.
Edit intent: clarity
Original:  For the Cech and Rips filtrations, we characterize the weight functions for which the corresponding feature maps converge as n approaches infinity, and by doing so, we prove laws of large numbers for the total persistences of such diagrams.
Revised:  For the \v{C and Rips filtrations, we characterize the weight functions for which the corresponding feature maps converge as n approaches infinity, and by doing so, we prove laws of large numbers for the total persistences of such diagrams.
Edit intent: meaning-changed
Original:  Both approaches lead to the same simple heuristic for tuning weight functions: if the data lies near a d-dimensional manifold, then a sensible choice of weight function is the persistence to the power \alpha with \alpha \geq d.
Revised:  Those two approaches (stability and convergence) lead to the same simple heuristic for tuning weight functions: if the data lies near a d-dimensional manifold, then a sensible choice of weight function is the persistence to the power \alpha with \alpha \geq d.
Edit intent: meaning-changed
Original:  In  cingulum, fornix, and superior longitudinal fasciculus, tract integrity is compromised, whereas in specific parts of tracts between thalamus and the precentral gyrus, tract integrity is enhanced in patients.
Revised:  In the cingulum, fornix, and superior longitudinal fasciculus, tract integrity is compromised, whereas in specific parts of tracts between thalamus and the precentral gyrus, tract integrity is enhanced in patients.
Edit intent: fluency
Original:  Our imaging evidence of structural alterations in IGEs may be applied as non-invasive diagnostic biomarker. We suggest that impaired connectivity from cortical regions to the thalamic reticular nucleus offers a therapeutic target for selectively modifying the brain circuit to reverse the mechanisms leading to epileptogenesis.
Revised:  Our imaging evidence of structural alterations suggest the loss of cortico-cortical and enhancement of cortico-thalamic fibre integrity in IGE. We further suggest that impaired connectivity from cortical regions to the thalamic reticular nucleus offers a therapeutic target for selectively modifying the brain circuit to reverse the mechanisms leading to epileptogenesis.
Edit intent: meaning-changed
Original:  We use data comprising 18-month longitudinal trajectories of 42 clinical variables from 1908 patients with Mild Cognitive Impairment (MCI) or Alzheimer's Disease (AD) to train a model for personalized forecasting of disease progression.
Revised:  We use data comprising 18-month trajectories of 44 clinical variables from 1908 patients with Mild Cognitive Impairment (MCI) or Alzheimer's Disease (AD) to train a model for personalized forecasting of disease progression.
Edit intent: meaning-changed
Original:  We use data comprising 18-month longitudinal trajectories of 42 clinical variables from 1908 patients with Mild Cognitive Impairment (MCI) or Alzheimer's Disease (AD) to train a model for personalized forecasting of disease progression.
Revised:  We use data comprising 18-month longitudinal trajectories of 42 clinical variables from 1908 patients with Mild Cognitive Impairment or Alzheimer's Disease (AD) to train a model for personalized forecasting of disease progression.
Edit intent: clarity
Original:  We use data comprising 18-month longitudinal trajectories of 42 clinical variables from 1908 patients with Mild Cognitive Impairment (MCI) or Alzheimer's Disease (AD) to train a model for personalized forecasting of disease progression.
Revised:  We use data comprising 18-month longitudinal trajectories of 42 clinical variables from 1908 patients with Mild Cognitive Impairment (MCI) or Alzheimer's Disease to train a model for personalized forecasting of disease progression.
Edit intent: clarity
Original:  Even though it is not trained to predict changes in disease severity, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models . We show how simulations can be used to interpret our model and demonstrate how to create synthetic control arm data for AD clinical trials. Our model's ability to simultaneously predict dozens of characteristics of a patient at any point in the future is a crucial step forward in computational precision medicine .
Revised:  Even though it is not trained to predict changes in disease severity, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models and identifies sub-components associated with word recall as predictive of progression. The ability to simultaneously predict dozens of characteristics of a patient at any point in the future is a crucial step forward in computational precision medicine .
Edit intent: meaning-changed
Original:  This is addressed by our improved estimators which use a larger set of martingale measures defined through a tradeoff between the radius of Wasserstein balls around the empirical measure and the allowed norm of martingale densities.
Revised:  To address this we propose novel estimators which use a larger set of martingale measures defined through a tradeoff between the radius of Wasserstein balls around the empirical measure and the allowed norm of martingale densities.
Edit intent: clarity
Original:  We also study convergence rates, convergence of superhedging strategies, and our study extends , in part, to the case of a market with traded options and to a multiperiod setting  .
Revised:  We establish consistency and robustness of these estimators and argue that they offer a superior performance relative to the plugin estimator. We generalise the results by replacing the superhedging criterion with acceptance relative to a risk measure. We further extend our study , in part, to the case of a market with traded options and to a multiperiod setting .
Edit intent: meaning-changed
Original:  We also study convergence rates, convergence of superhedging strategies, and our study extends , in part, to the case of a market with traded options and to a multiperiod setting  .
Revised:  We also study convergence rates, convergence of superhedging strategies, and our study extends , in part, to the case of a market with traded options and to a multiperiod setting and to settings with model uncertainty. We also study convergence rates of estimators and convergence of superhedging strategies .
Edit intent: meaning-changed
Original: Cellular metabolism is predicted accurately at the genome-scale using constraint based modeling. Such predictions typically rely on optimizing an assumed cellular objective function, which takes the form of a stoichiometrically-determined reaction such as biomass synthesis, ATP yield, or reactive oxygen species formation. While these objective functions are typically constructed by hand, several algorithms have been developed to estimate them from data . Generally, two approaches for data-driven objective estimation exist: estimating objective weights for existing reactions, and de novo generation of a new objective reaction .
Revised: Optimization models have been used to predict cellular behavior for over 25 years. The constraints in these models are reconstructed from genome annotations, measured macro-molecular composition of cells, and measuring the cell's growth rate and metabolism in different conditions. The cellular goal (the optimization problem the cell is trying to solve) can be challenging to define experimentally for URLanisms, including human or mammalian cells, which have complex metabolic capabilities and are not well understood. Existing approaches to learning goals from data include (a) estimating a linear objective function, or (b) estimating linear constraints that model complex biochemical reactions and constrain the cell's operation .
Edit intent: coherence