wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In particular, for this purpose we put forward the {\em  Jensen-Shannon divergence (JSD){\cal   as a metric , which is bounded and has an intuitive information-theoretic interpretation .
Revised:  In particular, for this purpose we put forward the {\em Survival Jensen-Shannon divergence (JSD){\cal as a metric , which is bounded and has an intuitive information-theoretic interpretation .
Edit intent: fluency
Original:  In particular, for this purpose we put forward the {\em  Jensen-Shannon divergence (JSD){\cal   as a metric , which is bounded and has an intuitive information-theoretic interpretation .
Revised:  In particular, for this purpose we put forward the {\em Jensen-Shannon divergence (SJS) and its empirical counterpart ({\cal as a metric , which is bounded and has an intuitive information-theoretic interpretation .
Edit intent: meaning-changed
Original:  In particular, for this purpose we put forward the {\em  Jensen-Shannon divergence (JSD){\cal   as a metric , which is bounded and has an intuitive information-theoretic interpretation .
Revised:  In particular, for this purpose we put forward the {\em Jensen-Shannon divergence (JSD){\cal E as a metric , which is bounded and has an intuitive information-theoretic interpretation .
Edit intent: meaning-changed
Original:  In particular, for this purpose we put forward the {\em  Jensen-Shannon divergence (JSD){\cal   as a metric , which is bounded and has an intuitive information-theoretic interpretation .
Revised:  In particular, for this purpose we put forward the {\em Jensen-Shannon divergence (JSD){\cal SJS) as a metric , which is bounded and has an intuitive information-theoretic interpretation .
Edit intent: meaning-changed
Original:  In particular, for this purpose we put forward the {\em  Jensen-Shannon divergence (JSD){\cal   as a metric , which is bounded and has an intuitive information-theoretic interpretation .
Revised:  In particular, for this purpose we put forward the {\em Jensen-Shannon divergence (JSD){\cal as a metric , which is bounded , and is a natural generalisation of the Jensen-Shannon divergence .
Edit intent: meaning-changed
Original:  We demonstrate, via a straightforward procedure making use of the JSD {\cal   , that it can be used as part of maximum likelihood estimation or curve fitting as a measure of goodness-of-fit, including the construction of a confidence interval for the fitted parametric distribution.
Revised:  We demonstrate, via a straightforward procedure making use of the {\cal , that it can be used as part of maximum likelihood estimation or curve fitting as a measure of goodness-of-fit, including the construction of a confidence interval for the fitted parametric distribution.
Edit intent: meaning-changed
Original:  We demonstrate, via a straightforward procedure making use of the JSD {\cal   , that it can be used as part of maximum likelihood estimation or curve fitting as a measure of goodness-of-fit, including the construction of a confidence interval for the fitted parametric distribution.
Revised:  We demonstrate, via a straightforward procedure making use of the JSD {\cal E , that it can be used as part of maximum likelihood estimation or curve fitting as a measure of goodness-of-fit, including the construction of a confidence interval for the fitted parametric distribution.
Edit intent: meaning-changed
Original:  We demonstrate, via a straightforward procedure making use of the JSD {\cal   , that it can be used as part of maximum likelihood estimation or curve fitting as a measure of goodness-of-fit, including the construction of a confidence interval for the fitted parametric distribution.
Revised:  We demonstrate, via a straightforward procedure making use of the JSD {\cal SJS , that it can be used as part of maximum likelihood estimation or curve fitting as a measure of goodness-of-fit, including the construction of a confidence interval for the fitted parametric distribution.
Edit intent: meaning-changed
Original:  A general approach to measure the goodness-of-fit of the fitted parameters, which we advocate herein, is to use a nonparametric measure for model comparison between the raw data and the fitted model.
Revised:  A general approach to measure the goodness-of-fit of the fitted parameters, which is advocated herein, is to use a nonparametric measure for model comparison between the raw data and the fitted model.
Edit intent: clarity
Original:  A general approach to measure the goodness-of-fit of the fitted parameters, which we advocate herein, is to use a nonparametric measure for model comparison between the raw data and the fitted model.
Revised:  A general approach to measure the goodness-of-fit of the fitted parameters, which we advocate herein, is to use a nonparametric measure for model comparison between the empirical distribution, comprising the raw data, and the fitted model.
Edit intent: meaning-changed
Original: We present non-parametric identification results for panel models in the presence of a vector of unobserved heterogeneity that is not additively separable in the structural function. We exploit the time-invariance and finite dimension of the heterogeneity to achieve identification of a number of objects of interest with the panel length fixed. Identification does not require that the researcher have access to an instrument that is uncorrelated with the unobserved heterogeneity. Instead the identification strategy relies on an assumption that some lags and leads of observables are independent conditional on the unobserved heterogeneity and some controls. The identification strategy motivates an estimation procedure based on penalized sieve minimum distance estimation in the non-parametric instrumental variables framework.
Revised: We present a novel approach to nonparametric identification and consistent estimation in economic models using `proxy controls'. Our approach is particularly well-suited to the context of panel data with a fixed time-dimension but also applies in cross-sectional settings. Proxy controls are proxies for unobserved `perfect controls', where perfect controls are variables that are sufficient for the association between potential outcomes and treatments. Our identification strategy requires that the set of available proxy controls be split into two subsets, one subset acting as an instrument for the other. In the panel case, observations from different periods can be used as proxy controls and our key identifying assumptions follow from restrictions on the serial dependence of the data. Our estimation problem is (under our assumptions) `well-posed' and our estimator is straight-forward to implement. The key step is a penalized sieve minimum distance estimation in the non-parametric instrumental variables framework.
Edit intent: meaning-changed
Original:  The identification strategy motivates an estimation procedure based on penalized sieve minimum distance estimation in the non-parametric instrumental variables framework. We give conditions under which the estimator is consistent and derive its rate of convergence . We present Monte Carlo evidence of its efficacy in finite samples .
Revised:  The identification strategy motivates an estimation procedure based on penalized sieve minimum distance estimator. We derive simple convergence rates under high-level assumptions .
Edit intent: clarity
Original: We present a novel approach to nonparametric identification and consistent estimation in economic models using `proxy controls'. Our approach is particularly well-suited to the context of panel data with a fixed time-dimension but also applies in cross-sectional settings. Proxy controls are proxies for unobserved `perfect controls', where perfect controls are variables that are sufficient for the association between potential outcomes and treatments .
Revised: We present a flexible approach to the identification and estimation of causal objects in nonparametric, non-separable models with confounding. Key to our analysis is the use of `proxy controls': covariates that do not satisfy a standard `unconfoundedness' assumption but which are informative proxies for variables that do. Our methods are presented in sufficient generality that they apply to both cross-sectional and panel models .
Edit intent: clarity
Original:  Our identification strategy requires that the set of available proxy controls be split into two subsets, one subset acting as an instrument for the other.
Revised:  Our identification results motivate an easy-to-implement nonparametric estimation method. Under our identifying assumptions the estimation problem is `well-posed'. We describe the estimator and derive convergence rates. When applied in panel settings our methods provide a novel approach to identification with non-separable general heterogeneity and a fixed time dimension.
Edit intent: meaning-changed
Original:  In the panel case, observations from different periods can be used as proxy controls and our key identifying assumptions follow from restrictions on the serial dependence  of the data . Our estimation problem is (under our assumptions) `well-posed' and our estimator is straight-forward to implement. The key step is a penalized sieve minimum distance estimator. We derive simple convergence rates under high-level assumptions .
Revised:  In the panel case, observations from different periods can be used as proxy controls and our key identifying assumptions follow from restrictions on the serial dependence of the data and latent variables. We apply our methodology to two separate empirical settings that respectively showcase the cross-sectional and the panel implementations of our approach. We estimate causal effects of grade retention on performance in a range of cognitive tests using cross-sectional variation in the data and we estimate a structural Engel curve for food using panel data .
Edit intent: meaning-changed
Original:  Under our identifying assumptions the estimation problem is `well-posed' . We describe the estimator and derive convergence rates. When applied in panel settings our methods provide a novel approach to  identification with non-separable general heterogeneity and a fixed time dimension.
Revised:  Under our identifying assumptions the estimation problem is `well-posed' nonparametric estimator and we analyze its asymptotic properties. In panel settings, our methods provide a novel approach to identification with non-separable general heterogeneity and a fixed time dimension.
Edit intent: meaning-changed
Original:  When applied in panel settings our methods provide a novel approach to  identification with non-separable general heterogeneity and a fixed time dimension. In the panel case , observations from different periods serve as proxies for unobserved individual heterogeneity and our key identifying assumptions follow from restrictions on the serial dependence structure of the data and latent variables .
Revised:  When applied in panel settings our methods provide a novel approach to identification with non-separable general heterogeneity and fixed T. In panels , observations from different periods serve as proxies for unobserved individual heterogeneity and our key identifying assumptions follow from restrictions on the serial dependence structure of the data and latent variables .
Edit intent: clarity
Original:  In the panel case , observations from different periods serve as proxies for unobserved individual heterogeneity and our key identifying assumptions follow from restrictions on the serial dependence structure of the data and latent variables .
Revised:  In the panel case , observations from different periods serve as proxies for unobserved heterogeneity and our key identifying assumptions follow from restrictions on the serial dependence structure of the data and latent variables .
Edit intent: clarity
Original: We present a flexible approach to the identification and estimation of causal objects in nonparametric, non-separable models with confounding. Key to our analysis is the use of `proxy controls': covariates that do not satisfy a standard `unconfoundedness' assumption but are informative proxies for variables that do.
Revised: We present a flexible approach to the identification and estimation of causal objects in nonparametric, non-separable models using `proxy controls': covariates that do not satisfy a standard `unconfoundedness' assumption but are informative proxies for variables that do.
Edit intent: clarity
Original:  In the empirical application, we estimate the as- sociation between income and health using linked data from the SHARE survey data and administrative pension information . The pension information which is a function of the full earnings history is used as an instrument.
Revised:  In the empirical application, we estimate the as- sociation between income and health using linked data from the SHARE survey data and administrative pension information and use pension entitlements as an instrument. In the second application we revisit the question how income affects the demand for housing based on data from the Socio-Economic Panel Study. In this application we use regional income information on the residential block level as an instrument.
Edit intent: meaning-changed
Original:  We prove upper and lower bounds on the ratio between the radii of an optimal but unstable solution and the radii of a topologically stable solution---the topological stability ratio---considering various metrics and various optimization criteria.
Revised:  We prove upper and lower bounds on the ratio between the radii of an optimal but unstable solution and the radii of a topologically stable solution -- the topological stability ratio -- considering various metrics and various optimization criteria.
Edit intent: fluency
Original:  Here, UP and DOWN state dynamics are analyzed in cortical recordings from human, monkey, and rat and found to be robustly different between deep sleep and anesthesia. We found that the temporal statistics of UP and DOWN states is robustly different in natural slow-wave sleep compared to the slow-waves of anesthesia.
Revised:  Here, we shed light onto this paradox, based on a combination of extracellular recordings in vivo, in vitro and computational models. We find two types of slow waves, based on analyzing the temporal patterns of successive slow-wave sleep compared to the slow-waves of anesthesia.
Edit intent: clarity
Original:  We found that the temporal statistics of UP and DOWN states is robustly different in natural slow-wave sleep compared to the slow-waves of anesthesia. Computational models predict that an interplay between noise and spike-frequency adaptation can yield a transition from sleep-like to anesthesia-like slow-wave dynamics.
Revised:  We found that the temporal statistics of UP and DOWN states is robustly different in natural slow-wave events. The first type of slow waves is seen during sleep, while the second type prevails in anesthetized states. Network models of spiking neurons predict that the two slow wave types emerge due to a different gain on inhibitory vs excitatory cells and that different levels of spike-frequency adaptation can yield a transition from sleep-like to anesthesia-like slow-wave dynamics.
Edit intent: meaning-changed
Original:  Computational models predict that an interplay between noise and spike-frequency adaptation can yield a transition from sleep-like to anesthesia-like slow-wave dynamics. These predictions were confirmed by pharmacological manipulations in vitro , inducing a switch between the two types of slow-waves.
Revised:  Computational models predict that an interplay between noise and spike-frequency adaptation in excitatory cells can account for dynamical distinctions between the two types. This prediction was tested in vitro by varying adaptation strength using an agonist of acetylcholine receptors, which demonstrated a neuromodulatory switch between the two types of slow-waves.
Edit intent: clarity
Original:  These predictions were confirmed by pharmacological manipulations in vitro , inducing a switch between the two types of slow-waves. The results show that the strong adaptation found in anesthesia, but not in sleep, can produce a different type of slow oscillations, less sensitive to external inputs, that may prevent memory formation .
Revised:  These predictions were confirmed by pharmacological manipulations in vitro , inducing a switch between the two types of slow waves. Finally, we show that the strong adaptation found in anesthesia, but not in sleep, can produce a different type of slow oscillations, less sensitive to external inputs, that may prevent memory formation .
Edit intent: coherence
Original:  The results show that the strong adaptation found in anesthesia, but not in sleep, can produce a different type of slow oscillations, less sensitive to external inputs, that may prevent memory formation .
Revised:  The results show that the strong adaptation found in anesthesia, but not in sleep, can produce a different type of slow oscillations, less sensitive to external stimuli, which can explain how slow waves in sleep and anesthesia differentially affect memory consolidation, as well as provide a link between slow-wave dynamics and memory diseases .
Edit intent: meaning-changed
Original:  Unlike Gaussian quadrature, the proposed quadrature exactly evaluates both positive and negative moments, thus improving evaluation accuracy. The generalized hyperbolic distribution is efficiently approximated as a finite normal variance-mean mixture with the quadrature .
Revised:  Unlike Gaussian quadrature, the proposed quadrature exactly evaluates both positive and negative orders. Using the quadrature, the generalized hyperbolic distribution is efficiently approximated as a finite normal variance-mean mixture with the quadrature .
Edit intent: clarity
Original:  Therefore, the expectations under the distribution, such as cumulative distribution function and  option price, are accurately computed as weighted sums of those under normal distributions  .
Revised:  Therefore, the expectations under the distribution, such as cumulative distribution function and option price, are accurately computed as weighted sums of those under normal distributions . The random variates from the generalized hyperbolic distribution are also sampled in a straightforward manner. The accuracy of the methods is illustrated with numerical examples .
Edit intent: meaning-changed
Original: This technical note presents an inference framework for hierarchically conditioned (embedded) Hermitian Gaussian Graphical Models (GGM). Our methodology, although extendible to several GGMs applications, is mainly centered on the specific neuroscientific context encompassing the estimation of brain sources activityand connectivity from Magnetoencephalography or Electroencephalography (MEEG) signals. This will be conceived in a series of papers with two parts. Part I: Analysis of a single frequency component from a single subject. Part II: Analysis of the Cross-Spectra for multiple subjects in a population. The strategy is based on Maximum Posterior Analysis, via the Expectation Maximization (EM) algorithm, of the Hermitian GGMs Parameters and Hyperparameters. The Hyperparameters set, which comprises MEEG signal noise covariance and source precision (connectivity) matrices of the MEEG generative model, is informed with Priors of special interest in the field and whose implementation in GGM estimation still poses a technical challenge. The theory presented here encapsulates two essential contributions . First: The formulation of a target function for the connectivity estimator that resembles an effective GGM of source activity, which is merged to the maximization step of the iterative EM strategy.
Revised: Statistical analysis of functional images (signals) allows the assessment of neural connectivity patterns at mesoscopic scale. Scalp fields, recorded with Magneto-Electro Encephalography MEEG, strongly correlate to the average effect of synaptic activity, i.e. Primary Current Density (PCD) at the Gray Matter space. Nevertheless, what is observed at the sensor space with either technique is a superposition of projected fields, from the whole Gray-Matter PCD. That is the reason for a major pitfall of MEEG analysis methods, distorted reconstruction of source activity and its connectivity or "Leakage". There has been a quest for the modelling to counterattack Leakage of both activation and connectivity. It has been proven that current methods produce incorrect connectomes, also related to incorrect connectivity modelling: they disregard either System Theory and Bayesian Formalism. We introduce a system theoretic formalism of MEEG connectivity alongside its Bayesian inference: Hidden Hermitian Gaussian Graphical Model (H-HGGM). This is a source (state) GGM hidden by the MEEG observation equation, i.e. equivalent to a frequency domain Linear State Space Model (LSSM). It is unhidden due to the Type II Likelihood approximated representation: Expected Log-Likelihood (ELL). The essential contributions here are a theory for HGGM solvers, and its implementation for the subsequent Maximization step of the iterative EM strategy.
Edit intent: meaning-changed
Original:  First: The formulation of a target function for the connectivity estimator that resembles an effective GGM of source activity, which is merged to the maximization step of the iterative EM strategy. Second: A new procedure for the estimation of the source activity effective GGM. These theoretical contributions provide for an explicit and highly optimal connectivity estimation procedure .
Revised:  First: The formulation of a target function for the connectivity estimator that resembles an effective GGM of source activity, which is merged to the maximization step of the H-HGGM ELL. Its efficacy is demonstrated in high resolution EEG simulations and a Steady State Visual Evoked potential. Open source packages, to reproduce the results presented in this paper and to analyze external MEEG databases, are freely available online .
Edit intent: meaning-changed
Original:  Once the propensity score is available, one can use it to estimate a variety of causal effects in a unified setting. Despite this appeal, a main practical difficulty arises because the propensity score is usually unknown, has to be estimated, and extreme propensity score estimates  can lead to distorted inference procedures .
Revised:  However, it is well documented that under slight model misspecifications, propensity score estimates can lead to distorted inference procedures .
Edit intent: meaning-changed
Original:  To address these limitations , this article proposes  to estimate the propensity score by fully exploiting its covariate balancing property.
Revised:  To address these limitations , this article proposes a new framework for estimating propensity scores that mimics randomize control trials (RCT) in settings where only observational data is available. More specifically, given that in RCTs the joint distritbution of covariates are balanced between treated and not-treated groups, we propose to estimate the propensity score by fully exploiting its covariate balancing property.
Edit intent: meaning-changed
Original:  To address these limitations , this article proposes  to estimate the propensity score by fully exploiting its covariate balancing property. We call the resulting estimator the integrated propensity score (IPS) as it is based on integrated moment conditions.
Revised:  To address these limitations , this article proposes to estimate the propensity score by maxizing the covariate distribution balance. The proposed propensity score estimators, which we call the integrated propensity score (IPS) as it is based on integrated moment conditions.
Edit intent: clarity