wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In addition, the concept of a maximum total intrinsic stress response, S_{max stress response capacity which provides a theoretical explanation of how therapies  can prevent cell death after lethal injuries.
Revised:  In addition, the concept of a maximum total intrinsic stress response, S_{max stress response capacity (LSRC) possessed by injured cells. The LSRC provides a theoretical explanation of how therapies can prevent cell death after lethal injuries.
Edit intent: meaning-changed
Original:  In addition, the concept of a maximum total intrinsic stress response, S_{max stress response capacity which provides a theoretical explanation of how therapies  can prevent cell death after lethal injuries.
Revised:  In addition, the concept of a maximum total intrinsic stress response, S_{max stress response capacity which provides a theoretical explanation of how therapies , such as hypothermia, can prevent cell death after lethal injuries.
Edit intent: meaning-changed
Original:  The method is neuromorphic in that the basic circuit elements mimic how ion channels shape the circuit conductance in specific voltage and temporal windows. As an input-output approach, I-V curve shaping proves effective in enabling robust and accurate control of the circuit behavior , suggesting a close analogy to the biophysical mechanisms of neuromodulation  .
Revised:  The methodology is general in that it only relies on a parallel interconnection of elementary voltage-controlled current sources. In contrast to controlling a nonlinear circuit through the parameter tuning of a state-space model, our approach is purely input-output approach, I-V curve shaping proves effective in enabling robust and accurate control of the circuit behavior , suggesting a close analogy to the biophysical mechanisms of neuromodulation .
Edit intent: meaning-changed
Original:  As an input-output approach, I-V curve shaping proves effective in enabling robust and accurate control of the circuit behavior , suggesting a close analogy to the biophysical mechanisms of neuromodulation  .
Revised:  As an input-output . The circuit elements are controlled and interconnected to shape the current-voltage characteristics ( I-V curve shaping proves effective in enabling robust and accurate control of the circuit behavior , suggesting a close analogy to the biophysical mechanisms of neuromodulation .
Edit intent: meaning-changed
Original:  As an input-output approach, I-V curve shaping proves effective in enabling robust and accurate control of the circuit behavior , suggesting a close analogy to the biophysical mechanisms of neuromodulation  .
Revised:  As an input-output approach, I-V curves) of the circuit in prescribed timescales. In turn, shaping those I-V curves determines the excitability properties of the circuit. We show that this methodology enables both robust and accurate control of the circuit behavior , suggesting a close analogy to the biophysical mechanisms of neuromodulation .
Edit intent: meaning-changed
Original:  As an input-output approach, I-V curve shaping proves effective in enabling robust and accurate control of the circuit behavior , suggesting a close analogy to the biophysical mechanisms of neuromodulation  .
Revised:  As an input-output approach, I-V curve shaping proves effective in enabling robust and accurate control of the circuit behavior , suggesting a close analogy to the biophysical mechanisms of neuromodulation . As a proof of concept, we simulate a SPICE model composed of MOSFET transconductance amplifiers operating in the weak inversion regime .
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Original:  This allows us to prove all together the existence of the optimal strategy,  the auxiliary dynamic programming principle as well as the convex duality theorem in our context with transaction costs.
Revised:  Using the one-period duality result in \mbox{%DIFAUXCMD bartl2016exponential to prove all together the existence of the optimal strategy, the auxiliary dynamic programming principle as well as the convex duality theorem in our context with transaction costs.
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Original:  Using the one-period duality result in bartl2016exponential, together with measurable selection arguments and minimax theorem, we are able to prove all together the existence of the optimal strategy , convex duality theorem as well as the auxiliary dynamic programming principle in our context with transaction costs.
Revised:  By suggesting a different dynamic programming argument than in bartl2016exponential, together with measurable selection arguments and minimax theorem, we are able to prove all together the existence of the optimal strategy , convex duality theorem as well as the auxiliary dynamic programming principle in our context with transaction costs.
Edit intent: meaning-changed
Original:  Using the one-period duality result in bartl2016exponential, together with measurable selection arguments and minimax theorem, we are able to prove all together the existence of the optimal strategy , convex duality theorem as well as the auxiliary dynamic programming principle in our context with transaction costs.
Revised:  Using the one-period duality result in bartl2016exponential, we are able to prove all together the existence of the optimal strategy , convex duality theorem as well as the auxiliary dynamic programming principle in our context with transaction costs.
Edit intent: coherence
Original:  Using the one-period duality result in bartl2016exponential, together with measurable selection arguments and minimax theorem, we are able to prove all together the existence of the optimal strategy , convex duality theorem as well as the auxiliary dynamic programming principle in our context with transaction costs.
Revised:  Using the one-period duality result in bartl2016exponential, together with measurable selection arguments and minimax theorem, we are able to prove the existence of the optimal strategy , convex duality theorem as well as the auxiliary dynamic programming principle in our context with transaction costs.
Edit intent: clarity
Original:  Using the one-period duality result in bartl2016exponential, together with measurable selection arguments and minimax theorem, we are able to prove all together the existence of the optimal strategy , convex duality theorem as well as the auxiliary dynamic programming principle in our context with transaction costs.
Revised:  Using the one-period duality result in bartl2016exponential, together with measurable selection arguments and minimax theorem, we are able to prove all together the existence of the optimal strategy and the convex duality theorem as well as the auxiliary dynamic programming principle in our context with transaction costs.
Edit intent: coherence
Original:  Using the one-period duality result in bartl2016exponential, together with measurable selection arguments and minimax theorem, we are able to prove all together the existence of the optimal strategy , convex duality theorem as well as the auxiliary dynamic programming principle in our context with transaction costs.
Revised:  Using the one-period duality result in bartl2016exponential, together with measurable selection arguments and minimax theorem, we are able to prove all together the existence of the optimal strategy , convex duality theorem in our context with transaction costs.
Edit intent: coherence
Original:  Also, we find that there exist in the respective networks, core-periphery structures (determined through centrality measures) that are composed of  similar set of countries -- a result that may be related through the `gravity model' of the country-level macroeconomic networks.
Revised:  Also, we find that there exist in the respective networks, core-periphery structures (determined through centrality measures) that are composed of the similar set of countries -- a result that may be related through the `gravity model' of the country-level macroeconomic networks.
Edit intent: fluency
Original:  In our simulations, the proposed method using MILP with a random starting point can reliably estimate regressions for a sample size of 1000 with 20 endogenous variables in 90 seconds;
Revised:  In our simulations, the proposed method using MILP with a random starting point can reliably estimate regressions for a sample size of 500 with 20 endogenous variables in 90 seconds;
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Original:  In our simulations, the proposed method using MILP with a random starting point can reliably estimate regressions for a sample size of 1000 with 20 endogenous variables in 90 seconds; for high-dimensional problems, our formulation can deliver decent estimates within minutes for problems with 550 endogenous regressors. We also establish asymptotic theory and provide an inference procedure. In our simulations, the asymptotic theory provides an excellent approximation even if we terminate MILP before a certified global solution is found. This suggests that MILP in our setting can quickly approach the global solution. In addition, we show that MILP can also be used for related problems, including censored regression, censored IV quantile regression and high-dimensional IV quantile regression .
Revised:  In our simulations, the proposed method using MILP with a random starting point can reliably estimate regressions for a sample size of 1000 with 20 endogenous variables in 5 seconds. Theoretical results for early termination of MILP are also provided. The second part of our proposal is a k-step correction framework, which is proved to be able to convert any point within a small but fixed neighborhood of the true parameter value into an estimate that is asymptotically equivalent to GMM. Our result does not require the initial estimate to be consistent and only 2\log n iterations are needed. Since the k-step correction does not require any optimization, applying the k-step correction to MILP estimate provides a computationally attractive way of obtaining efficient estimators. When dealing with very large data sets, we can run the MILP algorithm on only a small subsample and our theoretical results guarantee that the resulting estimator from the k-step correction is equivalent to computing GMM on the full sample. As a result, we can handle massive datasets of millions of observations within seconds .
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Original: This paper proposes a new framework for estimating instrumental variable (IV) quantile models. The first part of our proposal can be cast as a mixed integer linear program (MILP) , which allows us to capitalize on recent progress in mixed integer optimization. The computational advantage of the proposed method makes it an attractive alternative to existing estimators in the presence of multiple endogenous regressors. This is a situation that arises naturally when one endogenous variable is interacted with several other variables in a regression equation. In our simulations, the proposed method using MILP with a random starting point can reliably estimate regressions for a sample size of 500 with 20 endogenous variables in 5 seconds. Theoretical results for early termination of MILP are also provided. The second part of our proposal is a k-step correction framework,
Revised: This paper proposes computationally efficient methods that can be used for instrumental variable quantile regressions (IVQR) and related methods with statistical guarantees. This is much needed when we investigate heterogenous treatment effects since interactions between the endogenous treatment and control variables lead to an increased number of endogenous covariates. We prove that the GMM formulation of IVQR is NP-hard. Hence, solving the problem from a purely computational perspective seems unlikely. Instead, we aim to obtain an estimate that has good statistical properties and is not necessarily the global solution of any optimization problem. The proposal consists of employing k-step correction framework,
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Original:  The second part of our proposal is a k-step correction framework, which is proved to be able to convert any point within a small but fixed neighborhood of the true parameter value into an estimate that is asymptotically equivalent to GMM. Our result does not require the initial estimate to be consistent and only 2\log n iterations are needed . Since the k-step correction does not require any optimization, applying the k-step correction to MILP estimate provides a computationally attractive way of obtaining efficient estimators.
Revised:  The second part of our proposal is a k-step correction on an initial estimate. The initial estimate exploits the latest advances in mixed integer linear programming and can be computed within seconds. One theoretical contribution is that such initial estimators and Jacobian of the moment condition used in the k-step correction need not be even consistent and merely k=4\log n fast iterations are needed . Since the k-step correction does not require any optimization, applying the k-step correction to MILP estimate provides a computationally attractive way of obtaining efficient estimators.
Edit intent: meaning-changed
Original:  which is proved to be able to convert any point within a small but fixed neighborhood of the true parameter value into an estimate that is asymptotically equivalent to GMM. Our result does not require the initial estimate to be consistent and only 2\log n iterations are needed . Since the k-step correction does not require any optimization, applying the k-step correction to MILP estimate provides a computationally attractive way of obtaining efficient estimators.
Revised:  which is proved to be able to convert any point within a small but fixed neighborhood of the true parameter value into an estimate that is asymptotically equivalent to GMM. Our result does not require the initial estimate to be consistent and only 2\log n iterations are needed to obtain an efficient estimator. The overall proposal scales well to handle extremely large sample sizes because lack of consistency requirement allows one to use a very small subsample to obtain the initial estimate and the k-step correction does not require any optimization, applying the k-step correction to MILP estimate provides a computationally attractive way of obtaining efficient estimators.
Edit intent: meaning-changed
Original:  which is proved to be able to convert any point within a small but fixed neighborhood of the true parameter value into an estimate that is asymptotically equivalent to GMM. Our result does not require the initial estimate to be consistent and only 2\log n iterations are needed . Since the k-step correction does not require any optimization, applying the k-step correction to MILP estimate provides a computationally attractive way of obtaining efficient estimators. When dealing with very large data sets, we can run the MILP algorithm on only a small subsample and our theoretical results guarantee that the resulting estimator from the k-step correction is equivalent to computing GMM on the full sample .
Revised:  which is proved to be able to convert any point within a small but fixed neighborhood of the true parameter value into an estimate that is asymptotically equivalent to GMM. Our result does not require the initial estimate to be consistent and only 2\log n iterations are needed . Since the k-step iterations on the full sample .
Edit intent: coherence
Original:  When dealing with very large data sets, we can run the MILP algorithm on only a small subsample and our theoretical results guarantee that the resulting estimator from the k-step correction is equivalent to computing GMM on the full sample . As a result, we can handle massive datasets of millions of observations within seconds. As an empirical illustration, we examine the heterogeneous treatment effect of Job Training Partnership Act (JTPA) using a regression with 13 interaction terms of the treatment variable .
Revised:  When dealing with very large data sets, we can run the MILP algorithm on only a small subsample and our theoretical results guarantee that the resulting estimator from the k-step correction is equivalent to computing GMM on the full sample can be implemented efficiently. Another contribution that is of independent interest is to propose a tuning-free estimation for the Jacobian matrix, whose definition nvolves conditional densities. This Jacobian estimator generalizes bootstrap quantile standard errors and can be efficiently computed via closed-end solutions. We evaluate the performance of the proposal in simulations and an empirical example on the heterogeneous treatment effect of Job Training Partnership Act (JTPA) using a regression with 13 interaction terms of the treatment variable .
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Original:  To overcome this issue , this paper proposes a computational method for predicting rigorous upper and lower bounds of dynamic moments . We combine the differential equation model of the moment dynamics with a so-called moment condition to derive linear matrix inequalities that the moments of molecular copy numbers must satisfy . Using these conditions, we formulate a semidefinite program that efficiently and rigorously computes the bounds of transient moment dynamics.
Revised:  To overcome this issue , this paper proposes a computational method for predicting rigorous upper and lower bounds of transient moments for reactions governed by the law of mass action. We first derive an equation that transient moments must satisfy based on the moment equation. Although this equation is underdetermined, we introduce a set of semidefinite constraints known as moment condition to derive linear matrix inequalities that the moments of molecular copy numbers must satisfy . Using these conditions, we formulate a semidefinite program that efficiently and rigorously computes the bounds of transient moment dynamics.
Edit intent: meaning-changed
Original:  We combine the differential equation model of the moment dynamics with a so-called moment condition to derive linear matrix inequalities that the moments of molecular copy numbers must satisfy . Using these conditions, we formulate a semidefinite program that efficiently and rigorously computes the bounds of transient moment dynamics.
Revised:  We combine the differential equation model of the moment dynamics with a so-called moment condition to narrow the feasible set of the variables in the equation . Using these conditions, we formulate a semidefinite program that efficiently and rigorously computes the bounds of transient moment dynamics.
Edit intent: meaning-changed
Original: Motivation: Computational methods play a central role in annotating the functions of large amounts of proteins delivered by high-throughput technologies. Despite the encouraging results achieved by these methods, many functions still have a very low number of verified protein annotations, leading to a pronounced imbalance between annotated and unannotated proteins. Furthermore, functional taxonomies rarely report negative annotations. This leaves ill defined the set of negative examples, which is crucial for training the majority of machine learning methods. In practice, neglecting data imbalance and the problemof selecing negative examples can strongly limit the accuracy of protein function prediction. Results: We present a novel approach combining a suitable imbalance-aware classification strategy, addressing the scarcity of annotated proteins, with an active learning strategy for selecting the most reliable negative examples .
Revised: Motivated by applications in protein function prediction, we consider a challenging supervised classification setting in which positive labels are scarce and there are no explicit negative labels. The learning algorithm must thus select which unlabeled examples to use as negative training points, possibly ending up with an unbalanced learning problem .
Edit intent: coherence
Original:  When implemented in a Support Vector Machine, this combined approach shows improved accuracy on yeast and human proteomes over standard SVM and top-performing function prediction tools
Revised:  We address these issues by proposing an algorithm that combines active learning (for selecting negative examples) with imbalance-aware learning (for mitigating the label imbalance). In our experiments we observe that these two techniques operate synergistically, outperforming state-of-the-art methods on standard protein function prediction benchmarks.
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Original:  The associated value function is characterized as the unique classical solution to a nonlinear elliptic equation, among an appropriate class of functions.
Revised:  A non-standard stochastic differential equation, with neither Lipschitz continuity nor linear growth, specifies the dynamics of the controlled state process. A mixture of probabilistic arguments are used to construct the state process, and establish its non-explosiveness and strict positivity. This leads to the optimality of a feedback consumption process, defined in terms of the value function and the state process. Based on additional viscosity solutions techniques, we characterize the value function as the unique classical solution to a nonlinear elliptic equation, among an appropriate class of functions.
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Original:  An optimal consumption process, expressed in terms of the value function , is in a feedback form of the state process. The characterization of the value function relies on constructing a suitable sequence of approximating functions and employing viscosity solutions techniques. The derivation of the optimal consumption process involves a mixture of probabilistic arguments concerning the explosion and pathwise uniqueness of the controlled state process .
Revised:  An optimal consumption process, expressed in terms of the value function at the origin, which is the key to dealing with unbounded consumptions. Finally, relaxing the boundedness constraint is shown to increase, strictly, the expected utility at all wealth levels .
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Original:  However, in order to achieve excellent performance on these tasks they require increasingly complex, "very deep" model structure, which is costly in inference run-time, energy consumption and  training samples, only trainable on cloud-computing clusters.
Revised:  However, in order to perform well on these tasks they require increasingly complex, "very deep" model structure, which is costly in inference run-time, energy consumption and training samples, only trainable on cloud-computing clusters.
Edit intent: clarity
Original:  However, in order to achieve excellent performance on these tasks they require increasingly complex, "very deep" model structure, which is costly in inference run-time, energy consumption and  training samples, only trainable on cloud-computing clusters.
Revised:  However, in order to achieve excellent performance on these tasks they require increasingly complex, "very deep" model structure, which is costly in inference run-time, energy consumption and number of training samples, only trainable on cloud-computing clusters.
Edit intent: clarity
Original:  A single spiking neuron has been shown to be able to solve many of these required tasks for homogeneous Poisson input statistics, a commonly used model for spiking activity in the neocortex ;
Revised:  A single spiking neuron has been shown to be able to solve recognition tasks for homogeneous Poisson input statistics, a commonly used model for spiking activity in the neocortex ;
Edit intent: clarity