wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, we apply the recent massively parallelizable Bayesian uncertainty quantification framework %DIFDELCMD < \Pi4U %%% to a model of a disease spreading on a network of communities , showing that the method can accurately and tractably recover system parameters and select optimal models in this setting .
Revised:  In this paper, we apply the recent massively parallelizable Bayesian uncertainty quantification framework %DIFDELCMD < \Pi4U %%% to a model of a disease spreading on a network of communities from limited, noisy observations; the state-of-the-art computational framework compensates for the model complexity by exploiting massively parallel computing architectures. Using noisy, synthetic data, we show the potential of the approach to perform robust model fitting and additionally demonstrate that we can effectively identify the disease origin via Bayesian model selection. As disease-related data are increasingly available, the proposed framework has broad practical relevance for the prediction and management of epidemics .
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Original:  Focusing on the primary responses, we use an event time scale . We quantify the asymmetries of the distributions and of the market structures of cross-impacts, and find that the impacts across the market are asymmetric and non-random.
Revised:  Focusing on the primary responses, we use an event time scale , i.e., the immediate change of midpoint prices before and after a trade. We work out the price impacts across a correlated financial market. We quantify the asymmetries of the distributions and of the market structures of cross-impacts, and find that the impacts across the market are asymmetric and non-random.
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Original:  We present an approach to estimate periodic, time-varying parameters using nonlinear Bayesian filtering. Most parameter estimation methodology in the literature is aimed at estimating constant parameters or parameters whose values drift over time with no imposed structure. The proposed technique imposes periodic structure by treating the time-varying parameter as a piecewise function with unknown coefficients , estimated using the ensemble Kalman filter (EnKF) .
Revised:  We present an approach to estimate periodic, time-varying parameters that imposes periodic structure by treating the time-varying parameter as a piecewise function with unknown coefficients , estimated using the ensemble Kalman filter (EnKF) .
Edit intent: clarity
Original:  We compare the proposed method to an EnKF-based parameter drift algorithm, where periodicity is not guaranteed , using synthetic data generated from the  FitzHugh-Nagumo system which models the spiking dynamics of a neuron.
Revised:  We employ nonlinear filtering, more specifically, a version of the augmented ensemble Kalman filter (EnKF), to estimate the unknown coefficients comprising the piecewise approximation of the periodic, time-varying parameter. This allows for straightforward comparison of the proposed method with an EnKF-based parameter drift algorithm, where periodicity is not guaranteed , using synthetic data generated from the FitzHugh-Nagumo system which models the spiking dynamics of a neuron.
Edit intent: meaning-changed
Original:  We compare the proposed method to an EnKF-based parameter drift algorithm, where periodicity is not guaranteed , using synthetic data generated from the  FitzHugh-Nagumo system which models the spiking dynamics of a neuron.
Revised:  We compare the proposed method to an EnKF-based parameter tracking algorithm, where periodicity is not guaranteed , using synthetic data generated from the FitzHugh-Nagumo system which models the spiking dynamics of a neuron.
Edit intent: clarity
Original:  We compare the proposed method to an EnKF-based parameter drift algorithm, where periodicity is not guaranteed , using synthetic data generated from the  FitzHugh-Nagumo system which models the spiking dynamics of a neuron.
Revised:  We compare the proposed method to an EnKF-based parameter drift algorithm, where periodicity is not guaranteed . We demonstrate the effectiveness of the proposed approach on two biological examples: a synthetic example with data generated from the FitzHugh-Nagumo system which models the spiking dynamics of a neuron.
Edit intent: meaning-changed
Original:  We compare the proposed method to an EnKF-based parameter drift algorithm, where periodicity is not guaranteed , using synthetic data generated from the  FitzHugh-Nagumo system which models the spiking dynamics of a neuron.
Revised:  We compare the proposed method to an EnKF-based parameter drift algorithm, where periodicity is not guaranteed , using synthetic data generated from the nonlinear FitzHugh-Nagumo system which models the spiking dynamics of a neuron.
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Original:  We compare the proposed method to an EnKF-based parameter drift algorithm, where periodicity is not guaranteed , using synthetic data generated from the  FitzHugh-Nagumo system which models the spiking dynamics of a neuron. We further demonstrate the proposed method by estimating the seasonal transmission parameterin an epidemic model for the spread of measles. Results are obtained using time-series data of measles case reports from three locations during the pre-vaccine era , in particular the weekly reported cases in England and Wales (1948-1967) and monthly reported cases in New York City (1945-1964) and Baltimore (1928-1960).
Revised:  We compare the proposed method to an EnKF-based parameter drift algorithm, where periodicity is not guaranteed , using synthetic data generated from the FitzHugh-Nagumo system , modeling the excitability of a nerve cell, to estimate the external voltage parameter, and a case study using reported measles incidence data from three locations during the pre-vaccine era , in particular the weekly reported cases in England and Wales (1948-1967) and monthly reported cases in New York City (1945-1964) and Baltimore (1928-1960).
Edit intent: clarity
Original:  Results are obtained using time-series data of measles case reports from three locations during the pre-vaccine era , in particular the weekly reported cases in England and Wales (1948-1967) and monthly reported cases in New York City (1945-1964) and Baltimore (1928-1960). The augmented EnKF implementation also allows for simultaneous estimation of initial conditions and other static system parameters, such as the reporting probability of measles cases, which is vital for predicting under-reported incidence data.
Revised:  Results are obtained using time-series data of measles case reports from three locations during the pre-vaccine era to estimate the seasonal transmission parameter. The formulation of the proposed approach also allows for simultaneous estimation of initial conditions and other static system parameters, such as the reporting probability of measles cases, which is vital for predicting under-reported incidence data.
Edit intent: meaning-changed
Original:  To address this, we formalize the semantics of a core protocol language as a stochastic hybrid process , which provides a unified description for the models of biochemical systems being experimented on, together with the discrete events representing the liquid-handling steps of biological protocols.
Revised:  To address this, we formalize the syntax of a core protocol language as a stochastic hybrid process , which provides a unified description for the models of biochemical systems being experimented on, together with the discrete events representing the liquid-handling steps of biological protocols.
Edit intent: clarity
Original:  To address this, we formalize the semantics of a core protocol language as a stochastic hybrid process , which provides a unified description for the models of biochemical systems being experimented on, together with the discrete events representing the liquid-handling steps of biological protocols.
Revised:  To address this, we formalize the semantics of a core protocol language , which provides a unified description for the models of biochemical systems being experimented on, together with the discrete events representing the liquid-handling steps of biological protocols.
Edit intent: clarity
Original: In this paper we take advantage of a recently published algorithm for computing the number of realizations of minimally rigid graphs . Combining computational results with the theory of constructing new rigid graphs by gluing, we give a new lower bound on the maximal possible number of  realizations for graphs with a given number of vertices.
Revised: Computing the number of realizations of a minimally rigid graph is a notoriously difficult problem. Towards this goal, for graphs that are minimally rigid in the plane, we take advantage of a recently published algorithm for computing the number of realizations of minimally rigid graphs . Combining computational results with the theory of constructing new rigid graphs by gluing, we give a new lower bound on the maximal possible number of realizations for graphs with a given number of vertices.
Edit intent: meaning-changed
Original: In this paper we take advantage of a recently published algorithm for computing the number of realizations of minimally rigid graphs . Combining computational results with the theory of constructing new rigid graphs by gluing, we give a new lower bound on the maximal possible number of  realizations for graphs with a given number of vertices.
Revised: In this paper we take advantage of a recently published algorithm , which is the fastest available method, although its complexity is still exponential . Combining computational results with the theory of constructing new rigid graphs by gluing, we give a new lower bound on the maximal possible number of realizations for graphs with a given number of vertices.
Edit intent: meaning-changed
Original: In this paper we take advantage of a recently published algorithm for computing the number of realizations of minimally rigid graphs . Combining computational results with the theory of constructing new rigid graphs by gluing, we give a new lower bound on the maximal possible number of  realizations for graphs with a given number of vertices.
Revised: In this paper we take advantage of a recently published algorithm for computing the number of realizations of minimally rigid graphs . Combining computational results with the theory of constructing new rigid graphs by gluing, we give a new lower bound on the maximal possible number of (complex) realizations for graphs with a given number of vertices.
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Original: Optimal transportation is used to define a nonparametric density estimator as the solution of a convex optimization problem. The framework allows for density estimation subject to a variety of shape constraints, including \rho-concavity and Myerson's (1981) regularity condition. The mean integrated squared error for the density estimator of a random variable in \mathbb{R the  California Department of Transportation to explore whether their choice of awarding construction contracts using a first price auction is cost minimizing  .
Revised: Constraining the maximum likelihood density estimator to satisfy a sufficiently strong constraint, \log-concavity being a common example, has the effect of restoring consistency without requiring additional parameters. Since many results in economics require densities to satisfy a regularity condition, these estimators are also attractive for the structural estimation of economic models. In all of the examples of regularity conditions provided by Bagnoli and Bergstrom (2005) and Ewerhart (2013), \log-concavity is sufficient to ensure that the density satisfies the required conditions. However, in many cases \log-concavity is far from necessary, and it has the unfortunate side effect of ruling out sub-exponential tail behavior. In this paper, we use optimal transport to formulate a shape constrained density estimator. We initially describe the estimator using a \rho-concavity constraint. In this setting we provide results on consistency, asymptotic distribution, convexity of the optimization problem defining the estimator, and formulate a test for the California Department of Transportation to explore whether their choice of awarding construction contracts using a first price auction is cost minimizing .
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Original:  The mean integrated squared error for the density estimator of a random variable in \mathbb{R the  California Department of Transportation to explore whether their choice of awarding construction contracts using a first price auction is cost minimizing  .
Revised:  The mean integrated squared error for the density estimator of a random variable in \mathbb{R the null hypothesis that the population density satisfies a shape constraint. Afterward, we provide sufficient conditions for these results to hold using an arbitrary shape constraint. This generalization is used to explore whether the California Department of Transportation to explore whether their choice of awarding construction contracts using a first price auction is cost minimizing .
Edit intent: meaning-changed
Original:  The mean integrated squared error for the density estimator of a random variable in \mathbb{R the  California Department of Transportation to explore whether their choice of awarding construction contracts using a first price auction is cost minimizing  .
Revised:  The mean integrated squared error for the density estimator of a random variable in \mathbb{R the California Department of Transportation 's decision to award construction contracts with the use of a first price auction is cost minimizing .
Edit intent: clarity
Original:  The mean integrated squared error for the density estimator of a random variable in \mathbb{R the  California Department of Transportation to explore whether their choice of awarding construction contracts using a first price auction is cost minimizing  .
Revised:  The mean integrated squared error for the density estimator of a random variable in \mathbb{R the California Department of Transportation to explore whether their choice of awarding construction contracts using a first price auction is cost minimizing . We estimate the marginal costs of construction firms subject to Myerson's (1981) regularity condition, which is a requirement for the first price reverse auction to be cost minimizing. The proposed test fails to reject that the regularity condition is satisfied .
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Original:  We perform unsupervised clustering with Gaussian Mixture Model method on this dataset , and we find the cluster center found under a two-cluster restriction is close to the party centers, which are estimated using the mean position of the individuals self-identified with the parties .
Revised:  We perform unsupervised clustering with Gaussian Mixture Model method on this dataset . When clustered into two clusters, we find a large centrist cluster and a small right-wing cluster. The Democratic Party's position ( estimated using the mean position of the individuals self-identified with the parties .
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Original:  We perform unsupervised clustering with Gaussian Mixture Model method on this dataset , and we find the cluster center found under a two-cluster restriction is close to the party centers, which are estimated using the mean position of the individuals self-identified with the parties . We conclude that the major US parties are representative of the US population under the constraint that the political system is limited to two parties .
Revised:  We perform unsupervised clustering with Gaussian Mixture Model method on this dataset , and we find the cluster center found under a two-cluster restriction is close to the party centers, which are estimated using the mean position of the individuals self-identified with the parties ) is similar to that of the centrist cluster, and the Republican Party's position is between the two clusters .
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Original: In this paper, we study trading strategies based on exponential moving averages ( ExpMA) , an important indicator in technical analysis. We seek optimal ExpMA strategies when the drift of the underlying is modeled by either an Ornstein-Uhlenbeck process or a two-state continuous-time Markov chain.
Revised: In this paper, we study trading strategies based on exponential moving averages ( ExpMAs) of an underlying risky asset. We study both logarithmic utility maximization and long-term growth rate maximization problems and find closed-form solutions when the drift of the underlying is modeled by either an Ornstein-Uhlenbeck process or a two-state continuous-time Markov chain.
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Original: In this paper we construct two tests for the weights of the global minimum variance portfolio (GMVP) in a high-dimensional setting, namely  when the number of assets p depends on the sample size n such that p{n}\to c \in (0,1) as n tends to infinity.
Revised: In this study, we construct two tests for the weights of the global minimum variance portfolio (GMVP) in a high-dimensional setting, namely when the number of assets p depends on the sample size n such that p{n}\to c \in (0,1) as n tends to infinity.
Edit intent: clarity
Original: In this paper we construct two tests for the weights of the global minimum variance portfolio (GMVP) in a high-dimensional setting, namely  when the number of assets p depends on the sample size n such that p{n}\to c \in (0,1) as n tends to infinity.
Revised: In this paper we construct two tests for the weights of the global minimum variance portfolio (GMVP) in a high-dimensional setting, namely , when the number of assets p depends on the sample size n such that p{n}\to c \in (0,1) as n tends to infinity.
Edit intent: fluency
Original:  This experimental set up allows us to validate the overall quality of the generated nodules, which can then be used to (1) improve diagnostic decisions through highly discriminative imaging features, (2) train radiologists for educational purposes, and (3) generate realistic samples to train deep networks with big data.
Revised:  This experimental set up allows us to validate the overall quality of the generated nodules, which can then be used to (1) improve diagnostic decisions by mining highly discriminative imaging features, (2) train radiologists for educational purposes, and (3) generate realistic samples to train deep networks with big data.
Edit intent: clarity
Original:  Then, we present a CNN to obtain deep feature representation corresponding to each MRI modality ( T1 and T2). As the final step, we employ canonical correlation analysis (CCA) to perform a fusion operation at the feature level, leading to discriminative canonical correlation features.
Revised:  Then, we present a CNN to obtain deep feature representation corresponding to each MRI modality ( T1-weighted and T2-weighted). At the final step, we employ canonical correlation analysis (CCA) to perform a fusion operation at the feature level, leading to discriminative canonical correlation features.
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Original:  We greatly broaden the scope of ECIS by considering simultaneous tracking of multiple cell lines in a search for new methods that can help mitigate the current reproducibility crisis in the biological literature.
Revised:  We greatly broaden the scope of ECIS by considering simultaneous tracking of multiple cell lines , ECIS can be used to classify unknown or potentially mislabeled cells, which may help to mitigate the current reproducibility crisis in the biological literature.
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Original:  Our approach capitalizes on the multi-frequency data ECIS provides, which have been underutilized in previous studies .
Revised:  We assess a range of approaches to this new problem, testing different classification methods and deriving a dictionary of 29 features to characterize ECIS data. Our analysis also makes use of simultaneous multi-frequency data ECIS provides, which have been underutilized in previous studies .
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Original:  Our approach capitalizes on the multi-frequency data ECIS provides, which have been underutilized in previous studies . We consider classification of fifteen different mammalian cell lines, construct a dictionary of 29 different characteristic features, and obtain 95\% accuracy out-of-sample .
Revised:  Our approach capitalizes on the multi-frequency ECIS data, where previous studies have focused on only one frequency. In classification tests on fifteen mammalian cell lines, construct a dictionary of 29 different characteristic features, and obtain 95\% accuracy out-of-sample .
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Original:  We consider classification of fifteen different mammalian cell lines, construct a dictionary of 29 different characteristic features, and obtain 95\% accuracy out-of-sample . Our preliminary findings provide a baseline for future large-scale studies in this field.
Revised:  We consider classification of fifteen different mammalian cell lines, construct a dictionary of 29 different characteristic features, and obtain 95\% accuracy out-of-sample accuracy. These preliminary findings provide a baseline for future large-scale studies in this field.
Edit intent: clarity
Original: In this paper we develop new methods for estimating causal effects in settings with panel data, where a subset of units are exposed to a treatment during a subset of periods, and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations.
Revised: In this paper we study methods for estimating causal effects in settings with panel data, where a subset of units are exposed to a treatment during a subset of periods, and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations.
Edit intent: clarity
Original:  We develop a class of  estimators that uses the observed elements of the matrix of control outcomes corresponding to untreated unit/periods to predict the "missing" elements of the matrix, corresponding to treated units/periods.
Revised:  We develop a class of matrix completion estimators that uses the observed elements of the matrix of control outcomes corresponding to untreated unit/periods to predict the "missing" elements of the matrix, corresponding to treated units/periods.
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Original:  We also present new insights concerning the connections between the  interactive fixed effects models and the literatures on program evaluation under unconfoundedness as well as on synthetic control methods. If there are few time periods and many units, our method approximates a regression approach where counterfactual outcomes are estimated through a regression of current outcomes on lagged outcomes for the same unit. In contrast, if there are few units and many periods, our proposed method approximates a synthetic control estimator where counterfactual outcomes are estimated through a regression of the lagged outcomes for the treated unit on lagged outcomes for the control units. The advantage of our proposed method is that it moves seamlessly between these two different approaches, utilizing both cross-sectional and within-unit patterns in the data .
Revised:  We also present new insights concerning the connections between the interactive fixed effects models and the literatures on program evaluation under unconfoundedness and within-unit patterns in the data .
Edit intent: clarity
Original: In this paper we study methods for estimating causal effects in settings with panel data, where a subset of units are exposed to a treatment during a subset of periods , and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations.
Revised: In this paper we study methods for estimating causal effects in settings with panel data, where some units are exposed to a treatment during a subset of periods , and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations.
Edit intent: clarity