wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We use the dynamical renormalization group approach to study a stochastic model for chemotactic particles in which we introduce polarization effects in chemotaxis. We find exact dynamic scaling exponents that represent superdiffusive behavior of the particles. The number fluctuations within subregions of the system show a hyperuniform structure or exhibit giant number fluctuations, depending on whether or not the noise is conserved .
Revised:  We study the resulting large-scale dynamical properties of a system of such chemotactic particles using the exact stochastic formulation of Dean and Kawasaki along with dynamical renormalization group analysis of the critical state of the system show a hyperuniform structure or exhibit giant number fluctuations, depending on whether or not the noise is conserved .
Edit intent: meaning-changed
Original:  The number fluctuations within subregions of the system show a hyperuniform structure or exhibit giant number fluctuations, depending on whether or not the noise is conserved .
Revised:  The number fluctuations within subregions of the system . At this critical point, an emergent "Galilean" symmetry is identified, which allows us to obtain the dynamical scaling exponents exactly; these exponents reveal superdiffusive density fluctuations and non-Poissonian number fluctuations .
Edit intent: meaning-changed
Original:  We expect our results to shed light on how molecular regulation of chemotactic circuits can determine large-scale behavior of cell colonies and tissues through emergent properties that result from a subtle interplay between nonequilibrium fluctuations and long-range interactions that are constrained by exact symmetries .
Revised:  We expect our results to shed light on how molecular regulation of chemotactic circuits can determine large-scale behavior of cell colonies and tissues .
Edit intent: clarity
Original: When artificial intelligence is used in the medical sector, interpretability is a crucial factor to consider. Diagnosis based on machine decision produced by a black-box neural network, sometimes lacking clear rationale, is unlikely to be clinically adopted for fear of potentially dire consequences arising from unexplained misdiagnosis. In this work, we implement Layer-wise Relevance Propagation (LRP) , a visual interpretability method applied on 3D U-Net for lesion segmentation using the small dataset of multi-modal images provided by ISLES 2017 competition.
Revised: We implement a visual interpretability method Layer-wise Relevance Propagation (LRP) , a visual interpretability method applied on 3D U-Net for lesion segmentation using the small dataset of multi-modal images provided by ISLES 2017 competition.
Edit intent: clarity
Original:  In this work, we implement Layer-wise Relevance Propagation (LRP) , a visual interpretability method applied on 3D U-Net for lesion segmentation using the small dataset of multi-modal images provided by ISLES 2017 competition.
Revised:  In this work, we implement Layer-wise Relevance Propagation (LRP) on top of 3D U-Net for lesion segmentation using the small dataset of multi-modal images provided by ISLES 2017 competition.
Edit intent: coherence
Original:  In this work, we implement Layer-wise Relevance Propagation (LRP) , a visual interpretability method applied on 3D U-Net for lesion segmentation using the small dataset of multi-modal images provided by ISLES 2017 competition.
Revised:  In this work, we implement Layer-wise Relevance Propagation (LRP) , a visual interpretability method applied on 3D U-Net trained to perform lesion segmentation on the small dataset of multi-modal images provided by ISLES 2017 competition.
Edit intent: clarity
Original:  We begin with a  simple continuous model of news impact and then perform its discretization, adjusting it to dealing with discontinuous functions . Stock chartsare the main examples for us ;
Revised:  We begin with a simple continuous model of news impact on share-prices. In spite of its simplicity, it describes well the power growth of share-prices, periodicity, logarithmic periodicity, and the market phenomena like using price targets and profit-taking; Bessel and hypergeometric functions are used. Its discretization is performed, adjusted to discontinuous functions and aimed at stock charts, the main examples for us ;
Edit intent: meaning-changed
Original: We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management, and discuss its implementation: an automated momentum equity trading system  . Risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning ;
Revised: We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management, focused on short-term volatility of stock markets. Its implementation, an automated momentum equity trading system . Risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning ;
Edit intent: meaning-changed
Original: We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management, and discuss its implementation: an automated momentum equity trading system  . Risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning ;
Revised: We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management, and discuss its implementation: an automated momentum equity trading system , proved to be successful in extensive historical and real-time experiments, discussed in the paper . Risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning ;
Edit intent: meaning-changed
Original: We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management, and discuss its implementation: an automated momentum equity trading system  . Risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning ; stock markets are quite a test for any risk-management theories .
Revised: We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management, and discuss its implementation: an automated momentum equity trading system . Risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning .
Edit intent: coherence
Original:  We begin with a relatively simple continuous model of news impact on share-prices . In spite of its simplicity, it describes well the power growthof share-prices ,  periodicity, logarithmic periodicity, and the market phenomena like using price targets and profit-taking;
Revised:  We begin with a relatively simple continuous model of news impact on share-prices , periodicity, logarithmic periodicity, and the market phenomena like using price targets and profit-taking;
Edit intent: coherence
Original:  In spite of its simplicity, it describes well the power growthof share-prices ,  periodicity, logarithmic periodicity, and the market phenomena like using price targets and profit-taking;
Revised:  In spite of its simplicity, it describes well the power growthof share-prices , which describes well their power growth, periodicity, logarithmic periodicity, and the market phenomena like using price targets and profit-taking;
Edit intent: meaning-changed
Original:  Its discretization is performed, adjusted to discontinuous functions and aimed at stock charts, the main examples for us. An automated trading system based on our approachproved to be successful in extensive historical and real-time experiments. Its preimage is a new contract card game presented at the end, a combination of bridge and poker  .
Revised:  Its discretization results in some tables of bids, expected returns for different investment horizons. This is a new approach; its preimage is a new contract card game presented at the end, a combination of bridge and poker .
Edit intent: clarity
Original:  Its preimage is a new contract card game presented at the end, a combination of bridge and poker  .
Revised:  Its preimage is a new contract card game presented at the end, a combination of bridge and poker . The relations to random processes and fractional Brownian motion are outlined. We provide a reasonably complete description of our AI-type trading system, but our ML procedures are not discussed much (a special version of those in neural networking is used) .
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Original:  The network separates odors via a winnerless competition network, then classifies objects by projection into a high dimensional space where a support vector machine provides more precision in classification. We build this network using biophysical models of neurons with our results showing high discrimination among inputs and exceptional robustness to noise.
Revised:  The network separates odors via a winnerless competition network, then classifies objects by projection into a high dimensional space where a support vector machine (SVM) to provide precision to the space-time separation of the output. The combined network uses biophysical models of neurons with our results showing high discrimination among inputs and exceptional robustness to noise.
Edit intent: meaning-changed
Original:  We show that even when all variables have domains of size two and the underlying constraint graph (graph of variable interactions ) has bounded treewidth (in our construction, treewidth is 7), there are fitness landscapes for which an exponential number of steps may be required to reach a local optimum.
Revised:  We show that even when all variables have domains of size two and the underlying constraint graph of variable interactions ) has bounded treewidth (in our construction, treewidth is 7), there are fitness landscapes for which an exponential number of steps may be required to reach a local optimum.
Edit intent: fluency
Original:  We show that even when all variables have domains of size two and the underlying constraint graph (graph of variable interactions ) has bounded treewidth (in our construction, treewidth is 7), there are fitness landscapes for which an exponential number of steps may be required to reach a local optimum.
Revised:  We show that even when all variables have domains of size two and the underlying constraint graph (graph of variable interactions has bounded treewidth (in our construction, treewidth is 7), there are fitness landscapes for which an exponential number of steps may be required to reach a local optimum.
Edit intent: fluency
Original:  We show that even when all variables have domains of size two and the underlying constraint graph (graph of variable interactions ) has bounded treewidth (in our construction, treewidth is 7), there are fitness landscapes for which an exponential number of steps may be required to reach a local optimum.
Revised:  We show that even when all variables have domains of size two and the underlying constraint graph (graph of variable interactions ) has bounded treewidth (in our construction, treewidth 7), there are fitness landscapes for which an exponential number of steps may be required to reach a local optimum.
Edit intent: clarity
Original: We develop a class of tests for the structural stability of infinite-order models such as the infinite-order autoregressive model and the nonparametric sieve regression . When the number p of restrictions diverges, the traditional testsbased on the suprema of Wald, LM and LR statistics or their exponentially weighted averages diverge as well. We introduce a suitable transformation of these tests and obtain proper weak limits under the condition that p grows to infinity as the sample size n goes to infinity. In general, this limit distribution is different from the sequential limit, which can be obtained by increasing the order of the standardized tied-down Bessel process in Andrews (1993). In particular, our joint asymptotic analysis discovers a nonlinear high order serial correlation, for which we provide a consistent estimator. Our Monte Carlo simulation illustrates the importance of robustifying the structural break test against the nonlinear serial correlation even when p is moderate.
Revised: We develop a class of tests for the structural stability of infinite-order models such as the infinite-order autoregressive model and the nonparametric sieve regression and multiple regression with growing dimension. Examples include the Chow test, Andrews and Ploberger (1994) type exponential tests, and testing of general linear restrictions of growing rank p. Notably, our tests introduce a new scale correction to the conventional quadratic forms that are recentered and normalized to account for diverging p. This correction accounts for a high-order long-run variance that emerges as p grows with sample size in time series regression. Furthermore, we propose a bias correction via a null-imposed bootstrap to control finite sample bias without sacrificing power unduly. A simulation study stresses the importance of robustifying the structural break test against the nonlinear serial correlation even when p is moderate.
Edit intent: meaning-changed
Original:  We show that a subclass of synthesis functions under Bayesian predictive synthesis , which we categorize as non-linear synthesis,  entails an extra term  that "corrects" the bias from misspecification and dependence in the predictive error process , effectively improving forecasts.
Revised:  We identify a class of forecast combination methods , which we categorize as non-linear synthesis, entails an extra term that "corrects" the bias from misspecification and dependence in the predictive error process , effectively improving forecasts.
Edit intent: meaning-changed
Original:  We show that a subclass of synthesis functions under Bayesian predictive synthesis , which we categorize as non-linear synthesis,  entails an extra term  that "corrects" the bias from misspecification and dependence in the predictive error process , effectively improving forecasts.
Revised:  We show that a subclass of synthesis functions under Bayesian predictive synthesis , which we categorize as non-linear synthesis, and find that it entails an extra term that "corrects" the bias from misspecification and dependence in the predictive error process , effectively improving forecasts.
Edit intent: coherence
Original:  We show that a subclass of synthesis functions under Bayesian predictive synthesis , which we categorize as non-linear synthesis,  entails an extra term  that "corrects" the bias from misspecification and dependence in the predictive error process , effectively improving forecasts.
Revised:  We show that a subclass of synthesis functions under Bayesian predictive synthesis , which we categorize as non-linear synthesis, entails an extra term in the predictive error process that "corrects" the bias from misspecification and dependence in the predictive error process , effectively improving forecasts.
Edit intent: fluency
Original:  We show that a subclass of synthesis functions under Bayesian predictive synthesis , which we categorize as non-linear synthesis,  entails an extra term  that "corrects" the bias from misspecification and dependence in the predictive error process , effectively improving forecasts.
Revised:  We show that a subclass of synthesis functions under Bayesian predictive synthesis , which we categorize as non-linear synthesis, entails an extra term that "corrects" the bias from misspecification and dependence amongst forecasts , effectively improving forecasts.
Edit intent: clarity
Original:   Theoretical properties are examined and shown that this subclass improves the expected squared forecast error over any and all linear combination, averaging, and ensemble of forecasts, under mild conditions  .
Revised:  We show that a subclass of the recently developed framework of Bayesian predictive synthesis fits within this class. Theoretical properties are examined and shown that this subclass improves the expected squared forecast error over any and all linear combination, averaging, and ensemble of forecasts, under mild conditions .
Edit intent: meaning-changed
Original: This paper studies the theoretical predictive properties of classes of forecast combination methods. A novel strategy based on continuous time stochastic processesis proposed and developed , where the combined predictive error processes are expressed as stochastic differential equations, evaluated using Ito 's lemma.
Revised: This paper studies the theoretical predictive properties of classes of ensemble methods, including the recently developed Bayesian predictive synthesis (BPS). We develop a novel strategy based on continuous time stochastic processesis proposed and developed , where the combined predictive error processes are expressed as stochastic differential equations, evaluated using Ito 's lemma.
Edit intent: meaning-changed
Original:  We identify a class of forecast combination methods, which we categorize as non-linear synthesis, and find that it entails an extra term in the predictive error process that "corrects" the bias from misspecification and dependence amongst forecasts, effectively improving forecasts.
Revised:  Using this strategy, we identify two main classes of ensemble methods: linear combination and non-linear synthesis, and find that it entails an extra term in the predictive error process that "corrects" the bias from misspecification and dependence amongst forecasts, effectively improving forecasts.
Edit intent: meaning-changed
Original:  We identify a class of forecast combination methods, which we categorize as non-linear synthesis, and find that it entails an extra term in the predictive error process that "corrects" the bias from misspecification and dependence amongst forecasts, effectively improving forecasts. We show that a subclass of the recently developed framework of Bayesian predictive synthesis fits within this class.
Revised:  We identify a class of forecast combination methods, which we categorize as non-linear synthesis, and show that a subclass of the recently developed framework of Bayesian predictive synthesis fits within this class.
Edit intent: coherence
Original:  We show that a subclass of the recently developed framework of Bayesian predictive synthesis fits within this class. Theoretical properties are examined and we show that non-linear synthesis improves the expected squared forecast error over any and all linear combination, averaging, and ensemble of forecasts, under mild conditions that are met in most real applications.
Revised:  We show that a subclass of BPS is the latter. With regard to expected squared forecast error over any and all linear combination, averaging, and ensemble of forecasts, under mild conditions that are met in most real applications.
Edit intent: clarity
Original:  Theoretical properties are examined and we show that non-linear synthesis improves the expected squared forecast error over any and all linear combination, averaging, and ensemble of forecasts, under mild conditions that are met in most real applications.
Revised:  Theoretical properties are examined and we show that non-linear synthesis improves the expected squared forecast error , we identify the conditions and mechanism for which non-linear synthesis improves over linear combinations; conditions that are met in most real applications.
Edit intent: meaning-changed
Original:  Theoretical properties are examined and we show that non-linear synthesis improves the expected squared forecast error over any and all linear combination, averaging, and ensemble of forecasts, under mild conditions that are met in most real applications.
Revised:  Theoretical properties are examined and we show that non-linear synthesis improves the expected squared forecast error over any and all linear combination, averaging, and ensemble of forecasts, under mild conditions that are commonly met in real world applications.
Edit intent: clarity
Original: We examine and compare the predictive properties of classes of ensemble methods, including the recently developed Bayesian predictive synthesis (BPS). We develop a novel strategy based on stochastic processes, where the predictive processes are expressed as stochastic differential equations, evaluated using It\^{o show that a specific form of non-linear BPS (as in McAlinn and West, 2019) produces exact minimax predictive distributions for Kullback-Leibler risk and, under certain conditions, quadratic risk.
Revised: We analyze the combination of multiple predictive distributions for time series data when all forecasts are misspecified. We show that a specific form of non-linear BPS (as in McAlinn and West, 2019) produces exact minimax predictive distributions for Kullback-Leibler risk and, under certain conditions, quadratic risk.
Edit intent: meaning-changed
Original:  We develop a novel strategy based on stochastic processes, where the predictive processes are expressed as stochastic differential equations, evaluated using It\^{o show that a specific form of non-linear BPS (as in McAlinn and West, 2019) produces exact minimax predictive distributions for Kullback-Leibler risk and, under certain conditions, quadratic risk.
Revised:  We develop a novel strategy based on stochastic processes, where the predictive processes are expressed as stochastic differential equations, evaluated using It\^{o show that a specific dynamic form of Bayesian predictive synthesis -- a general and coherent Bayesian framework for ensemble methods -- produces exact minimax predictive distributions for Kullback-Leibler risk and, under certain conditions, quadratic risk.
Edit intent: meaning-changed
Original:  We develop a novel strategy based on stochastic processes, where the predictive processes are expressed as stochastic differential equations, evaluated using It\^{o show that a specific form of non-linear BPS (as in McAlinn and West, 2019) produces exact minimax predictive distributions for Kullback-Leibler risk and, under certain conditions, quadratic risk.
Revised:  We develop a novel strategy based on stochastic processes, where the predictive processes are expressed as stochastic differential equations, evaluated using It\^{o show that a specific form of non-linear BPS (as in McAlinn and West, 2019) produces exact minimax predictive densities with regard to Kullback-Leibler risk and, under certain conditions, quadratic risk.
Edit intent: clarity
Original:  We develop a novel strategy based on stochastic processes, where the predictive processes are expressed as stochastic differential equations, evaluated using It\^{o show that a specific form of non-linear BPS (as in McAlinn and West, 2019) produces exact minimax predictive distributions for Kullback-Leibler risk and, under certain conditions, quadratic risk. A finite sample simulation study is presented to illustrate our results .
Revised:  We develop a novel strategy based on stochastic processes, where the predictive processes are expressed as stochastic differential equations, evaluated using It\^{o show that a specific form of non-linear BPS (as in McAlinn and West, 2019) produces exact minimax predictive distributions for Kullback-Leibler loss, providing theoretical support for finite sample predictive performance over existing ensemble methods. A simulation study that highlights this theoretical result is presented, showing that dynamic Bayesian predictive synthesis is superior to other ensemble methods using multiple metrics .
Edit intent: meaning-changed