wanyu/IteraTeR_full_sent huggingface dataset:
Original:  PC enabled version of the same app demonstrates that the mobile has a stronger effect than PCs in helping patients make  behavioral modifications with respect to diet, exercise and life style, which ultimately leads to an improvement in their healthcare outcomes.
Revised:  PC enabled version of the same app demonstrates that the mobile has a stronger effect than PCs in helping patients make behavioral modifications with respect to diet, exercise and lifestyle, which leads to an improvement in their healthcare outcomes.
Edit intent: clarity
Original:  In this work, we aim to study systematic generalization in predicting future state trajectories of a dynamical system, conditioned on past states' trajectory (dependent variables), past and future actions (control variables).
Revised:  In this work, we aim to study systematic generalization of NNs in forecasting future time series of dependent variables in a dynamical system, conditioned on past states' trajectory (dependent variables), past and future actions (control variables).
Edit intent: meaning-changed
Original:  In this work, we aim to study systematic generalization in predicting future state trajectories of a dynamical system, conditioned on past states' trajectory (dependent variables), past and future actions (control variables). In our context, systematic generalization implies that a good model should perform well on all new combinations of future actions after being trained on all of them, but only on a limited set of their combinations.
Revised:  In this work, we aim to study systematic generalization in predicting future state trajectories of a dynamical system, conditioned on past states' trajectory (dependent variables), past and future control variables. We focus on systematic generalization wherein the NN-based forecasting model should perform well on all new combinations of future actions after being trained on all of them, but only on a limited set of their combinations.
Edit intent: clarity
Original:  In our context, systematic generalization implies that a good model should perform well on all new combinations of future actions after being trained on all of them, but only on a limited set of their combinations. For models to generalize out-of-distribution to unseen action combinations , they should reason about the states and their dependency relation with the applied actions.
Revised:  In our context, systematic generalization implies that a good model should perform well on all new combinations of future actions after being trained on all of them, but only on a limited set of the possible regimes. For NNs to depict such out-of-distribution to unseen action combinations , they should reason about the states and their dependency relation with the applied actions.
Edit intent: clarity
Original:  For models to generalize out-of-distribution to unseen action combinations , they should reason about the states and their dependency relation with the applied actions. We conduct a rigorous study of useful inductive biases that learn to predict the trajectories up to large horizons well, and capture true dependency relations between the states and the controls through our synthetic setup, and simulated data from electric motors .
Revised:  For models to generalize out-of-distribution to unseen action combinations , they should be able to disentangle the various dependencies between control variables and dependent variables. We hypothesize that a modular NN architecture guided by the readily-available knowledge of independence of control variables as a potentially useful inductive bias to this end. Through extensive empirical evaluation on a toy dataset and a simulated electric motor dataset, we show that our proposed modular NN architecture serves as a simple yet highly effective inductive bias that enabling better forecasting of the dependent variables up to large horizons well, and capture true dependency relations between the states and the controls through our synthetic setup, and simulated data from electric motors .
Edit intent: meaning-changed
Original:  We conduct a rigorous study of useful inductive biases that learn to predict the trajectories up to large horizons well, and capture true dependency relations between the states and the controls through our synthetic setup, and simulated data from electric motors .
Revised:  We conduct a rigorous study of useful inductive biases that learn to predict the trajectories up to large horizons in contrast to standard NNs, and indeed capture the true dependency relations between the states and the controls through our synthetic setup, and simulated data from electric motors .
Edit intent: meaning-changed
Original:  We conduct a rigorous study of useful inductive biases that learn to predict the trajectories up to large horizons well, and capture true dependency relations between the states and the controls through our synthetic setup, and simulated data from electric motors .
Revised:  We conduct a rigorous study of useful inductive biases that learn to predict the trajectories up to large horizons well, and capture true dependency relations between the dependent and the control variables .
Edit intent: clarity
Original:  In overall, we have employed six well-known instances belonging to TSPLIB to assess the performance of Quantum Computing - Tabu Search Algorithm in comparison to QBSolv -- a state-of-the-art decomposing solver.
Revised:  In overall, we have employed six well-known instances belonging to TSPLIB to assess the performance of Quantum Computing - Tabu Search Algorithm in comparison to QBSolv , a state-of-the-art decomposing solver.
Edit intent: fluency
Original: Cryo-electron microscopy (cryo-EM) has become a major experimental technology to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize.
Revised: Cryo-electron microscopy (cryo-EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize.
Edit intent: fluency
Original:  Our main ingredient is a novel incorporation of a Matrix Chernoff-type inequality to bound the spectrum of an empirical covariance matrix for well-behaved distributions, in conjunction with a careful exploration of the localization schemes of Awasthi et al.
Revised:  Our main ingredient is a novel incorporation of a matrix Chernoff-type inequality to bound the spectrum of an empirical covariance matrix for well-behaved distributions, in conjunction with a careful exploration of the localization schemes of Awasthi et al.
Edit intent: fluency
Original:  Our main ingredient is a novel incorporation of a Matrix Chernoff-type inequality to bound the spectrum of an empirical covariance matrix for well-behaved distributions, in conjunction with a careful exploration of the localization schemes of Awasthi et al.
Revised:  Our main ingredient is a novel incorporation of a Matrix Chernoff-type inequality to bound the spectrum of an empirical covariance matrix for well-behaved distributions, in conjunction with a careful exploration of the localization schemes of Awasthi ~et~ al.
Edit intent: fluency
Original:  This article introduces Monte Carlo Tree Search for the hedging of financial derivatives in realistic markets and shows that there are good reasons, both on the theoretical and practical side, to favor it over other Reinforcement Learning methods .
Revised:  This article introduces Monte Carlo Tree Search as a method to solve the stochastic optimal control problem underlying the pricing and hedging of financial derivatives in realistic markets and shows that there are good reasons, both on the theoretical and practical side, to favor it over other Reinforcement Learning methods .
Edit intent: meaning-changed
Original:  This article introduces Monte Carlo Tree Search for the hedging of financial derivatives in realistic markets and shows that there are good reasons, both on the theoretical and practical side, to favor it over other Reinforcement Learning methods .
Revised:  This article introduces Monte Carlo Tree Search for the hedging of financial derivatives . As compared to Q-learning it combines reinforcement learning with tree search techniques. As a consequence Monte Carlo Tree Search has higher sample efficiency, is less prone to over-fitting to specific market models and generally learns stronger policies faster. In our experiments we find that Monte Carlo Tree Search, being the world-champion in games like Chess and Go, is easily capable of directly maximizing the utility of investor's terminal wealth without an intermediate mathematical theory .
Edit intent: meaning-changed
Original:  While financial research mostly focused on variations of Q-learning, in Artificial Intelligence Monte Carlo Tree Search is the recognized state-of-the-art method for various planning problems, such as the games of Hex, Chess, Go,...
Revised:  While research in the derivatives area mostly focused on variations of Q-learning, in Artificial Intelligence Monte Carlo Tree Search is the recognized state-of-the-art method for various planning problems, such as the games of Hex, Chess, Go,...
Edit intent: meaning-changed
Original:  While financial research mostly focused on variations of Q-learning, in Artificial Intelligence Monte Carlo Tree Search is the recognized state-of-the-art method for various planning problems, such as the games of Hex, Chess, Go,...
Revised:  While financial research mostly focused on variations of Q-learning, in artificial intelligence Monte Carlo Tree Search is the recognized state-of-the-art method for various planning problems, such as the games of Hex, Chess, Go,...
Edit intent: fluency
Original:  In our experiments we find that Monte Carlo Tree Search, being the world-champion in games like Chess and Go, is easily capable of directly maximizing the utility of investor's terminal wealth without an intermediate mathematical theory .
Revised:  In our experiments we find that Monte Carlo Tree Search, being the world-champion in games like Chess and Go, is easily capable of directly maximizing the utility of investor's terminal wealth without setting up an auxiliary mathematical framework .
Edit intent: clarity
Original:  Within this framework, we introduce several  processes featuring negative jumps only and derive practical closed formulas for equity prices, which enable us to use a moment-based algorithm to calibrate the parameters from real market data and to estimate the associated default metrics.
Revised:  Within this framework, we introduce several pure jump processes featuring negative jumps only and derive practical closed formulas for equity prices, which enable us to use a moment-based algorithm to calibrate the parameters from real market data and to estimate the associated default metrics.
Edit intent: meaning-changed
Original:  Representation is a philosophical concept with a rich history going back at least to Aristotle. A careful reading of contemporary philosophy suggests four criteria of representation as they relate to neuroscience. First, a representation needs to correlate with the object in reality that it represents.
Revised:  Representation is a philosophical concept with a rich history going back to the ancient period. In order to clarify its usage in neuroscience, here we advance a link between the connotations of this term across these disciplines. We draw on a representation needs to correlate with the object in reality that it represents.
Edit intent: clarity
Original:  First, a representation needs to correlate with the object in reality that it represents. Second, a representation needs to have some causal impact on behavior. Third, a representation should have its causal roleexclusively -- the absence of the representation should block the behavior it enables. Fourth, a representation should have teleological justification that specifies the cases in which it is a misrepresentation. We argue that not taking all four criteria seriously leads to statements that are neither neuroscientifically nor philosophically satisfying. We discuss how neuroscience can move towards a deeper notion of representation .
Revised:  First, a broad range of discourse in philosophy to distinguish three key aspects of representation: correspondence, functional role, and teleology. We argue that each of these aspects are implied by the explanatory role the term plays in neuroscience. However, evidence related to all three aspects is rarely presented or discussed in the course of individual studies that aim to identify representations. Overlooking the significance of all three aspects hinders communication in neuroscience, as it obscures the limitations of experimental paradigms and conceals gaps in our understanding of the phenomena of primary interest. Working from this three-part view, we discuss how to move toward clearer communication about representations in the brain .
Edit intent: meaning-changed
Original:  In order to better understand the existing technological challenges and opportunities , in this paper we examine the status quo in terms of intelligent software web agents, guided by research with respect to requirements and architectural components, coming from that agents community.
Revised:  In order to better understand the existing technological opportunities and challenges , in this paper we examine the status quo in terms of intelligent software web agents, guided by research with respect to requirements and architectural components, coming from that agents community.
Edit intent: fluency
Original:  In order to better understand the existing technological challenges and opportunities , in this paper we examine the status quo in terms of intelligent software web agents, guided by research with respect to requirements and architectural components, coming from that agents community.
Revised:  In order to better understand the existing technological challenges and opportunities , in this paper we examine the status quo in terms of intelligent software web agents, guided by research with respect to requirements and architectural components, coming from the agents community.
Edit intent: fluency
Original:  We start by collating and summarising requirements and core architectural components relating to intelligent software agent. Following on from this, we use the identified requirements to both further elaborate on the semantic web agent motivating use case scenario, and to summarise different perspectives on the requirements when it comes to semantic web agent literature.
Revised:  We use the identified requirements to both further elaborate on the semantic web agent motivating use case scenario, and to summarise different perspectives on the requirements when it comes to semantic web agent literature.
Edit intent: coherence
Original:  Finally, we propose a hybrid semantic web agent architecture, discuss the role played by existing semantic web standards , and point to existing work in the broader semantic web community and beyond that could help us to make the semantic web agent vision a reality .
Revised:  We subsequently propose a hybrid semantic web agent architecture, discuss the role played by existing semantic web standards , and point to existing work in the broader semantic web community and beyond that could help us to make the semantic web agent vision a reality .
Edit intent: coherence
Original:  Finally, we propose a hybrid semantic web agent architecture, discuss the role played by existing semantic web standards , and point to existing work in the broader semantic web community and beyond that could help us to make the semantic web agent vision a reality .
Revised:  Finally, we propose a hybrid semantic web agent architecture, and use the various components and subcomponents in order to provide a focused discussion on the role played by existing semantic web standards , and point to existing work in the broader semantic web community and beyond that could help us to make the semantic web agent vision a reality .
Edit intent: meaning-changed
Original:  Finally, we propose a hybrid semantic web agent architecture, discuss the role played by existing semantic web standards , and point to existing work in the broader semantic web community and beyond that could help us to make the semantic web agent vision a reality .
Revised:  Finally, we propose a hybrid semantic web agent architecture, discuss the role played by existing semantic web standards and community activities. Finally, we highlight open research opportunities and challenges and take a broader perspective of the research by discussing the potential for intelligent software web agents as an enabling technology for emerging domains, such as digital assistants, cloud computing, and the internet of things .
Edit intent: meaning-changed
Original:  We present an elegant iterative rounding scheme for the MFN relaxation that yields an approximation guarantee of \left(10+67\right)/2 \approx 9.0927, a significant improvement upon the previous LP-based ratio  due to An et al  in~2014.
Revised:  We present an elegant iterative rounding scheme for the MFN relaxation that yields an approximation guarantee of \left(10+67\right)/2 \approx 9.0927, a significant improvement upon the previous LP-based ratio of 288 due to An et al in~2014.
Edit intent: meaning-changed
Original:  We present an elegant iterative rounding scheme for the MFN relaxation that yields an approximation guarantee of \left(10+67\right)/2 \approx 9.0927, a significant improvement upon the previous LP-based ratio  due to An et al  in~2014.
Revised:  We present an elegant iterative rounding scheme for the MFN relaxation that yields an approximation guarantee of \left(10+67\right)/2 \approx 9.0927, a significant improvement upon the previous LP-based ratio due to An et al . in~2014.
Edit intent: fluency
Original:  For CFL with cardinality facility cost (CFL-CFC), we present an LP-based 4-approximation algorithm, which  surpasses the long-standing ratio of~5 due to Levi et al  that ages up for decades since 2004.
Revised:  For CFL with cardinality facility cost (CFL-CFC), we present an LP-based 4-approximation algorithm, which not only surpasses the long-standing ratio of~5 due to Levi et al that ages up for decades since 2004.
Edit intent: clarity
Original:  For CFL with cardinality facility cost (CFL-CFC), we present an LP-based 4-approximation algorithm, which  surpasses the long-standing ratio of~5 due to Levi et al  that ages up for decades since 2004.
Revised:  For CFL with cardinality facility cost (CFL-CFC), we present an LP-based 4-approximation algorithm, which surpasses the long-standing ratio of~5 due to Levi et al . that ages up for decades since 2004.
Edit intent: fluency
Original:  For CFL with cardinality facility cost (CFL-CFC), we present an LP-based 4-approximation algorithm, which  surpasses the long-standing ratio of~5 due to Levi et al  that ages up for decades since 2004.
Revised:  For CFL with cardinality facility cost (CFL-CFC), we present an LP-based 4-approximation algorithm, which surpasses the long-standing ratio of~5 due to Levi et al that ages up for decades since 2004 but also unties the long-time match to the best approximation for CFL that is obtained via local search in 2012.
Edit intent: meaning-changed
Original: In this paper, we present strong numerical evidences that the 3D  axisymmetric Navier-Stokes equations with degenerate variable diffusion coefficients and smooth initial data of finite energy develop a potential finite time locally self-similar singularity at the origin.
Revised: In this paper, we present strong numerical evidences that the 3D incompressible axisymmetric Navier-Stokes equations with degenerate variable diffusion coefficients and smooth initial data of finite energy develop a potential finite time locally self-similar singularity at the origin.
Edit intent: meaning-changed
Original: In this paper, we present strong numerical evidences that the 3D  axisymmetric Navier-Stokes equations with degenerate variable diffusion coefficients and smooth initial data of finite energy develop a potential finite time locally self-similar singularity at the origin.
Revised: In this paper, we present strong numerical evidences that the 3D axisymmetric Navier-Stokes equations with degenerate diffusion coefficients and smooth initial data of finite energy develop a potential finite time locally self-similar singularity at the origin.
Edit intent: clarity
Original:   An important feature of this potential singularity is that the solution develops a two-scale traveling wave that travels towards the origin.
Revised:  The spatial part of the degenerate diffusion coefficient is a smooth function of r and z independent of the solution and vanishes like O(r^2)+O(z^2) near the origin. This potential singularity is induced by a potential singularity of the 3D Euler equations. An important feature of this potential singularity is that the solution develops a two-scale traveling wave that travels towards the origin.
Edit intent: meaning-changed
Original:  The driving mechanism for this potential singularity is due to two antisymmetric vortex dipoles that generate a strong shearing layer in both the radial and axial velocity fields , which transport the solution first towards z=0 and then towards the symmetry axis r=0.
Revised:  The driving mechanism for this potential singularity is due to two antisymmetric vortex dipoles that generate a strong shearing layer in both the radial and axial velocity fields . Without the viscous regularization, the 3D Euler equations develop an additional small scale characterizing the thickness of the sharp front. On the other hand, the Navier-Stokes equations with a constant diffusion coefficient regularize the two-scale solution structure and do not develop a finite time singularity for the same initial data.
Edit intent: meaning-changed
Original:  In this period the collaboration of great economist and the best generation of probabilists, most of them from the Strasbourg's School led by Paul Andr\'e Meyer, gave rise to the foundations of this discipline.
Revised:  In this period the collaboration of great economists and the best generation of probabilists, most of them from the Strasbourg's School led by Paul Andr\'e Meyer, gave rise to the foundations of this discipline.
Edit intent: fluency
Original:  We demonstrate this framework on a series of challenging 2D and 3D physical systems with different coefficients of restitution and friction  .
Revised:  We demonstrate this framework on a series of challenging 2D and 3D physical systems with different coefficients of restitution and friction . The learned dynamics can be used as a differentiable physics simulator for downstream gradient-based optimization tasks, such as planning and control .
Edit intent: meaning-changed
Original:  In this hierarchy, regular languages are sorted according to the new fundamental measure of NFA complexity p. Our main contributions are the following: (i) we show that the hierarchy is strict and does not collapse, (ii) we provide (exponential  ) upper and lower bounds relating the minimum widths of equivalent NFAs and DFAs , and (iii) we characterize DFAs of minimum p for a given \mathcal L via a co-lexicographic variant of the Myhill-Nerode theorem.
Revised:  We show that the hierarchy is strict and does not collapse, (ii) we provide (exponential ) upper and lower bounds relating the minimum widths of equivalent NFAs and DFAs , and (iii) we characterize DFAs of minimum p for a given \mathcal L via a co-lexicographic variant of the Myhill-Nerode theorem.
Edit intent: clarity