wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We introduce here a novel approach for learning a nonlinear differential equation model aimed at capturing brain dynamics . Specifically, we propose a variable-projection optimization approach to estimate the parameters of the multivariate (coupled ) van der Pol oscillator, and demonstrate that such a model can accurately represent nonlinear dynamics of the brain data. Furthermore, in order to improve the predictive accuracy when forecasting future brain-activity time series, we use this analytical model as an unlimited source of simulated data for pretraining LSTM; such model-specific dataaugmentation approach consistently improves LSTM performance on both calcium and fMRI imaging data .
Revised:  We introduce here a novel approach for learning a nonlinear differential equation model aimed at capturing brain dynamics . Specifically, we propose a variable-projection optimization approach to estimate the parameters of the multivariate (coupled ) van der Pol (VDP) oscillator, a nonlinear ordinary differential equation (ODE) model describing neural activity, with a new parameter estimation technique that combines variable projection optimization and stochastic search. We show that the approach successfully handles nonlinearities and hidden state variables in the coupled VDP. The approach is accurate, achieving 0.82 to 0.94 correlation between the actual and model-generated components, and interpretable, as VDP's coupling matrix reveals anatomically meaningful positive (excitatory) and negative (inhibitory) interactions across different brain subsystems corresponding to spatial SVD components. Moreover, VDP is comparable to (or sometimes better than) recurrent neural networks (LSTM) for (short-term) prediction of future brain activity; VDP needs less parameters to train, which was a plus on our small training data. Finally, the overall best predictive method, greatly outperforming both VDP and LSTM in short- and long-term predictive settings on both datasets, was the new hybrid VDP-LSTM approach that used VDP to simulate large domain-specific dataset for LSTM pretraining; note that simple LSTM data-augmentation via noisy versions of training data was much less effective .
Edit intent: meaning-changed
Original: The biological systems have been investigated by stochastic thermodynamic approach, especially for E. coli sensory adaptation model.
Revised: The biological systems have been investigated by stochastic thermodynamic approach, especially for E. coli is suitable for investigation because of its simplicity. To understand the adaptation processing quantitatively, stochastic thermodynamic approach has been attempted. Information processing can be assumed as state transition of a system that consists of signal transduction molecules using thermodynamic approach, and efficiency can be measured as a thermodynamic cost.
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Original:  For increasing external noise level in stimulation, the efficiency decreased, but for external stimulation strength, the efficiency was highly robust . Using this approach, we succeeded to discuss the relationship between speed of adaptation and rate of thermodynamic cost change in E. coli adaptation.
Revised:  For increasing external noise level in stimulation, the efficiency decreased, but the efficiency was highly robust . Using this approach, we succeeded to discuss the relationship between speed of adaptation and rate of thermodynamic cost change in E. coli adaptation.
Edit intent: clarity
Original:  For increasing external noise level in stimulation, the efficiency decreased, but for external stimulation strength, the efficiency was highly robust . Using this approach, we succeeded to discuss the relationship between speed of adaptation and rate of thermodynamic cost change in E. coli adaptation. Our quantification could be applied to various biological systems.
Revised:  For increasing external noise level in stimulation, the efficiency decreased, but for external stimulation strength, the efficiency was highly robust to external stimulation strength. Our quantification method provides a framework to understand the adaptation speed and the thermodynamic cost for various biological systems.
Edit intent: clarity
Original: Economists are often confronted with the choice between a completely specified, yet approximate model and an incomplete model that only imposes a set of behavioral restrictions that are believed to be true. We offer a reconciliation of these approaches and demonstrate its usefulness for estimation and economic inference. The approximate model , which can be structural or statistical, is distorted such that to satisfy the equilibrium conditionswhich are deemed as credible .
Revised: Economists are often confronted with the choice between a completely specified, yet approximate model and an incomplete model that only imposes a set of credible behavioral conditions. This is done by distorting the approximate model to satisfy these conditions .
Edit intent: clarity
Original:  We apply the methodology to the model of long run risks in aggregate consumption (Bansal and Yaron, 2004), where the auxiliary model is generated using the Campbell and Shiller (1988) approximation.
Revised:  We apply the methodology to the model of long run risks in aggregate consumption (Bansal and Yaron, 2004), where the complete model is generated using the Campbell and Shiller (1988) approximation.
Edit intent: clarity
Original:  More importantly, it completely overturns key qualitative predictions of the linear model , such as the absence of endogenous time variation in risk premia and level effects, which is crucial for understanding the link between asset prices and macroeconomic risk .
Revised:  Tilting also restores key qualitative predictions of the linear model , such as the absence of endogenous time variation in risk premia and level effects, which is crucial for understanding the link between asset prices and macroeconomic risk .
Edit intent: clarity
Original:  More importantly, it completely overturns key qualitative predictions of the linear model , such as the absence of endogenous time variation in risk premia and level effects, which is crucial for understanding the link between asset prices and macroeconomic risk .
Revised:  More importantly, it completely overturns key qualitative predictions of the non-linear model such as the absence of endogenous time variation in risk premia and level effects, which is crucial for understanding the link between asset prices and macroeconomic risk .
Edit intent: meaning-changed
Original:  More importantly, it completely overturns key qualitative predictions of the linear model , such as the absence of endogenous time variation in risk premia and level effects, which is crucial for understanding the link between asset prices and macroeconomic risk .
Revised:  More importantly, it completely overturns key qualitative predictions of the linear model , such as the endogenous time variation in risk premia and level effects, which is crucial for understanding the link between asset prices and macroeconomic risk .
Edit intent: clarity
Original:  More importantly, it completely overturns key qualitative predictions of the linear model , such as the absence of endogenous time variation in risk premia and level effects, which is crucial for understanding the link between asset prices and macroeconomic risk .
Revised:  More importantly, it completely overturns key qualitative predictions of the linear model , such as the absence of endogenous time variation in risk premia , a property which is lost with linearization .
Edit intent: meaning-changed
Original:  We find that distorting the model to satisfy the original equilibrium condition is strongly preferred by the data and substantially improves the identification of the structural parameters. Tilting also restores key qualitative predictions of the non-linear model such as the endogenous time variation in risk premia, a property which is lost with linearization .
Revised:  We find that distorting the model to satisfy the original equilibrium condition is strongly preferred by the data while the quality of the approximation is yet another reason for the downward bias to estimates of the intertemporal elasticity of substitution and the upward bias in risk aversion .
Edit intent: meaning-changed
Original:  Importantly, the model generalizes well across users: when trained on data pooled from previous users, it can successfully perform on new users. Thus, the time-consuming BCI calibration can be omitted. Moreover, the model can be incrementally updated, resulting in +8.9\% average accuracy improvement in offline experiments and +17.0\% in a real-time BCI. We argue that this model can be used in practical BCIs and basic neuroscience research .
Revised:  Importantly, the model is robust to inter-individual differences and can successfully generalize to new subjects in offline and online classification .
Edit intent: clarity
Original:  The stock price is determined by the inflow or outflow of stock investments. The model is able to replicate the most prominent features of financial markets, namely booms and crashes. In the case of random fundamental prices the model is even able to reproduce fat tails in logarithmic stock price return data. We want to point out that we derive the model from microscopic agent dynamics. On the microscopic level each financial agent is faced with an optimization problem, where each agent seeks to find a Nash equilibrium solution. We use model predictive control to approximate the control problem.
Revised:  The stock price evolution is driven by the aggregated investment decision of all agents. In fact, we are faced with a differential game since all agents aim to invest optimal. Mathematically such a problem is ill posed and we introduce the concept of Nash equilibrium solutions to ensure the existence of a solution. Especially, we denote an agent who solves this Nash equilibrium exactly a rational agent. As next step we use model predictive control to approximate the control problem.
Edit intent: coherence
Original:  This allows us to give a precise mathematical definition of rational and bounded rational financial agents. Moreover the approximation scheme of the microscopic optimal control problem gives a natural connections between rational and bounded rational agents. In fact the model can be regarded as  the result of the simplest approximation of the optimal control problem and thus considers financial agents of maximum boundedness.
Revised:  This allows us to give a precise mathematical characterization of the degree of rationality of a financial agent. This is a novel concept in portfolio optimization and can be regarded as the result of the simplest approximation of the optimal control problem and thus considers financial agents of maximum boundedness.
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Original:  In fact the model can be regarded as  the result of the simplest approximation of the optimal control problem and thus considers financial agents of maximum boundedness.
Revised:  In fact the model can be regarded as a general approach. In a second step we consider the case of a fully myopic agent, where we can solve the optimal investment decision of investors analytically. We select the running cost to be the result of the simplest approximation of the optimal control problem and thus considers financial agents of maximum boundedness.
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Original:  In fact the model can be regarded as  the result of the simplest approximation of the optimal control problem and thus considers financial agents of maximum boundedness. Mathematically, the model can be regarded as the moment model of the recently introduced mesoscopic kinetic portfolio model(Trimborn, Pareschi, Frank: Portfolio Optimization and Model Predictive Control: A Kinetic Approach, arXiv:1711.03291) .
Revised:  In fact the model can be regarded as the expected missed revenue of an agent and we assume quadratic transaction costs. More precisely the expected revenues are determined by a combination of a fundamentalist or chartist strategy. Then we derive the mean field limit of the microscopic model in order to obtain a macroscopic portfolio model. The novelty in comparison to existent macroeconomic models in literature is that our model is derived from microeconomic dynamics. The resulting portfolio model is a three dimensional ODE system which enables us to derive analytical results. Simulations reveal, that our model is able to replicate the most prominent features of financial markets, namely booms and crashes .
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Original:  Results: Here we present an efficient method for determining protein structures from experimental  NOESY distances, by combining the ASD/ ScaledASD algorithm with several afterwards procedures including chirality refinement, distance lower (upper)
Revised:  Here we present an efficient method for determining protein structures from experimental NOESY distances, by combining the ASD/ ScaledASD algorithm with several afterwards procedures including chirality refinement, distance lower (upper)
Edit intent: coherence
Original:  Results: Here we present an efficient method for determining protein structures from experimental  NOESY distances, by combining the ASD/ ScaledASD algorithm with several afterwards procedures including chirality refinement, distance lower (upper)
Revised:  Results: Here we present an efficient method for determining protein structures from experimental NMR NOESY distances, by combining the ASD/ ScaledASD algorithm with several afterwards procedures including chirality refinement, distance lower (upper)
Edit intent: meaning-changed
Original:  Results: Here we present an efficient method for determining protein structures from experimental  NOESY distances, by combining the ASD/ ScaledASD algorithm with several afterwards procedures including chirality refinement, distance lower (upper)
Revised:  Results: Here we present an efficient method for determining protein structures from experimental NOESY distances, by combining the ScaledASD algorithm with several afterwards procedures including chirality refinement, distance lower (upper)
Edit intent: clarity
Original:  bound refinement and forcefield-based energy minimization (EM)  . By comparing several metrics on conformation evaluation between our results and the PDB deposits, including RMSD, TMscore, MPscore, secondary structures and Ramachandran Plot, we conclude that our method is consistent with the popular used methods.
Revised:  bound refinement , force field-based energy minimization (EM) . By comparing several metrics on conformation evaluation between our results and the PDB deposits, including RMSD, TMscore, MPscore, secondary structures and Ramachandran Plot, we conclude that our method is consistent with the popular used methods.
Edit intent: fluency
Original:  bound refinement and forcefield-based energy minimization (EM)  . By comparing several metrics on conformation evaluation between our results and the PDB deposits, including RMSD, TMscore, MPscore, secondary structures and Ramachandran Plot, we conclude that our method is consistent with the popular used methods.
Revised:  bound refinement and forcefield-based energy minimization (EM) and water refinement . By comparing several metrics on conformation evaluation between our results and the PDB deposits, including RMSD, TMscore, MPscore, secondary structures and Ramachandran Plot, we conclude that our method is consistent with the popular used methods.
Edit intent: meaning-changed
Original:  bound refinement and forcefield-based energy minimization (EM)  . By comparing several metrics on conformation evaluation between our results and the PDB deposits, including RMSD, TMscore, MPscore, secondary structures and Ramachandran Plot, we conclude that our method is consistent with the popular used methods.
Revised:  bound refinement and forcefield-based energy minimization (EM) . By comparing several metrics on conformation evaluation between our results and the PDB deposits, we conclude that our method is consistent with the popular used methods.
Edit intent: coherence
Original:  In the end, we compared the EM results under different forcefields, showing that our reconstructed configurations are robust to the forcefields selection. Availability and implementation: The software and its MATLAB source codes are available at URLContact: qzhaoyuping@bit.edu.cn or xubiaopeng@bit.edu.cn.
Revised:  In the end, we compared our calculation results with PDB models by checking the structural similarity to X-ray crystallographic structure for a special dataset. The software and its MATLAB source codes are available at URLContact: qzhaoyuping@bit.edu.cn or xubiaopeng@bit.edu.cn.
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Original:  The policy-relevant treatment effects may not be estimated at the regular parametric \sqrt{n and Vytlacil, 2010). In this light, we propose a regularized estimator. The regularization improves the convergence rate when the rate is slower than \sqrt{n .
Revised:  The policy-relevant treatment effects may not be estimated at the regular parametric \sqrt{n and Vytlacil, 2001, 2005, 2007), we apply the methods and theory of average derivative estimation to the framework of the policy-relevant treatment effects .
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Original:  Based on the characterization of this treatment parameter as a weighted average derivative (Heckman and Vytlacil, 2001, 2005, 2007), we apply the methods and theory of average derivative estimation to the framework of the policy-relevant treatment effects. Our proposed method yields point estimates with valid standard errors. Simulation studies confirm our theoretical results .
Revised:  These preliminary estimators can affect the asymptotic distribution of the PRTE estimator in complicated and intractable manners. In this light, we propose an orthogonal score for double debiased estimation of the PRTE, whereby the asymptotic distribution of the PRTE estimator is obtained without any influence of preliminary parameter estimators as far as they satisfy mild requirements of convergence rates. To our knowledge, our work is the first to develop limit distribution theories for inference about the PRTE .
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Original: Estimation of the policy relevant treatment effects (PRTE)  involves estimation of multiple preliminary parameters, including propensity scores, conditional expectation functions of the outcome and covariates given the propensity score, and marginal treatment effects.
Revised: The policy relevant treatment effects (PRTE) involves estimation of multiple preliminary parameters, including propensity scores, conditional expectation functions of the outcome and covariates given the propensity score, and marginal treatment effects.
Edit intent: clarity
Original: Estimation of the policy relevant treatment effects (PRTE)  involves estimation of multiple preliminary parameters, including propensity scores, conditional expectation functions of the outcome and covariates given the propensity score, and marginal treatment effects.
Revised: Estimation of the policy relevant treatment effect (PRTE) involves estimation of multiple preliminary parameters, including propensity scores, conditional expectation functions of the outcome and covariates given the propensity score, and marginal treatment effects.
Edit intent: fluency
Original: Estimation of the policy relevant treatment effects (PRTE)  involves estimation of multiple preliminary parameters, including propensity scores, conditional expectation functions of the outcome and covariates given the propensity score, and marginal treatment effects.
Revised: Estimation of the policy relevant treatment effects (PRTE) measures the average effect of switching from a status-quo policy to a counterfactual policy. Estimation of the PRTE involves estimation of multiple preliminary parameters, including propensity scores, conditional expectation functions of the outcome and covariates given the propensity score, and marginal treatment effects.
Edit intent: meaning-changed
Original: The idea that the brain functions in a way that minimizes certain costs pervades theoretical neuro- science . Since a cost function by itself does not predict how the brain finds its minima, additional assumptions about the optimization method are usually made in order to obtain predictions about physiological quantities.
Revised: The idea that the brain functions in a way that minimizes certain costs pervades theoretical neuroscience . Since a cost function by itself does not predict how the brain finds its minima, additional assumptions about the optimization method are usually made in order to obtain predictions about physiological quantities.
Edit intent: clarity
Original:  Since this choice involves infinite degrees of freedom, the predictive power of models that are based on this principle can be called into question . Despite its great success in machine learning, especially in training deep neural networks, arguments about brain function that rely on gradient-based optimization have to be closely examined .
Revised:  Since this choice involves infinite degrees of freedom, the predictive power of models that are based on this principle can be called into question , unless there are strong constraints on the choice of the metric. Here we provide a didactic review of the mathematics of gradient descent, illustrate common pitfalls of using gradient descent as a principle of brain function with examples from the literature and propose ways forward to constrain the metric .
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Original:  This  document describes the format  of the SPHERE dataset , as well as the known and potential data quality problems  , their workarounds, and other information important to people working with the SPHERE data, software, and hardware  .
Revised:  This technical document describes the format of the SPHERE dataset , as well as the known and potential data quality problems , their workarounds, and other information important to people working with the SPHERE data, software, and hardware .
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Original:  This  document describes the format  of the SPHERE dataset , as well as the known and potential data quality problems  , their workarounds, and other information important to people working with the SPHERE data, software, and hardware  .
Revised:  This document describes the format and the expected content of the SPHERE dataset , as well as the known and potential data quality problems , their workarounds, and other information important to people working with the SPHERE data, software, and hardware .
Edit intent: meaning-changed
Original:  This  document describes the format  of the SPHERE dataset , as well as the known and potential data quality problems  , their workarounds, and other information important to people working with the SPHERE data, software, and hardware  .
Revised:  This document describes the format of the SPHERE dataset (s) under preparation. It includes a list of some data quality problems , their workarounds, and other information important to people working with the SPHERE data, software, and hardware .
Edit intent: meaning-changed
Original:  This  document describes the format  of the SPHERE dataset , as well as the known and potential data quality problems  , their workarounds, and other information important to people working with the SPHERE data, software, and hardware  .
Revised:  This document describes the format of the SPHERE dataset , as well as the known and potential data quality problems (both known to exist in the dataset(s) and potential ones) , their workarounds, and other information important to people working with the SPHERE data, software, and hardware .
Edit intent: meaning-changed