wanyu/IteraTeR_v2 huggingface dataset:
Original:  We conclude that  actually, in the limit of infinitely slow synaptic-dynamics, the two theories behave identically , but the separation of timescales required for the self-organization mechanism to be effective does not seem to be biologically plausible.
Revised:  We conclude that , actually, in the limit of infinitely slow synaptic-dynamics, the two theories behave identically , but the separation of timescales required for the self-organization mechanism to be effective does not seem to be biologically plausible.
Edit intent: fluency
Original:  We conclude that  actually, in the limit of infinitely slow synaptic-dynamics, the two theories behave identically , but the separation of timescales required for the self-organization mechanism to be effective does not seem to be biologically plausible.
Revised:  We conclude that actually, in the limit of infinitely slow synaptic-dynamics, the two theories become identical , but the separation of timescales required for the self-organization mechanism to be effective does not seem to be biologically plausible.
Edit intent: clarity
Original:  We conclude that  actually, in the limit of infinitely slow synaptic-dynamics, the two theories behave identically , but the separation of timescales required for the self-organization mechanism to be effective does not seem to be biologically plausible.
Revised:  We conclude that actually, in the limit of infinitely slow synaptic-dynamics, the two theories behave identically , but the timescales required for the self-organization mechanism to be effective does not seem to be biologically plausible.
Edit intent: clarity
Original:  We conclude that  actually, in the limit of infinitely slow synaptic-dynamics, the two theories behave identically , but the separation of timescales required for the self-organization mechanism to be effective does not seem to be biologically plausible.
Revised:  We conclude that actually, in the limit of infinitely slow synaptic-dynamics, the two theories behave identically , but the separation of timescales required for the self-organization mechanism to be effective do not seem to be biologically plausible.
Edit intent: fluency
Original:  We discuss the key differences between self-organization mechanisms with or without conservation and separated timescales, and in particular, we  scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problem of scale invariance  in cortical dynamics.
Revised:  We discuss the key differences between self-organization mechanisms with / without conservation and separated timescales, and in particular, we scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problem of scale invariance in cortical dynamics.
Edit intent: fluency
Original:  We discuss the key differences between self-organization mechanisms with or without conservation and separated timescales, and in particular, we  scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problem of scale invariance  in cortical dynamics.
Revised:  We discuss the key differences between self-organization mechanisms with or without conservation and separated timescales, and in particular, we scrutinize the implications of our findings in neuroscience, shedding new light into the problem of scale invariance in cortical dynamics.
Edit intent: clarity
Original:  We discuss the key differences between self-organization mechanisms with or without conservation and separated timescales, and in particular, we  scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problem of scale invariance  in cortical dynamics.
Revised:  We discuss the key differences between self-organization mechanisms with or without conservation and separated timescales, and in particular, we scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problems of scale invariance in cortical dynamics.
Edit intent: fluency
Original:  Interestingly, several works have recently shown that  graph neural networks do not perform much better than predefined low-pass filters followed by a linear classifier in these particular settings .
Revised:  Interestingly, several works have recently shown that in this particular setting, graph neural networks do not perform much better than predefined low-pass filters followed by a linear classifier in these particular settings .
Edit intent: fluency
Original:  Interestingly, several works have recently shown that  graph neural networks do not perform much better than predefined low-pass filters followed by a linear classifier in these particular settings .
Revised:  Interestingly, several works have recently shown that graph neural networks do not perform much better than predefined low-pass filters followed by a linear classifier .
Edit intent: clarity
Original:  However, when learning with little data in a high-dimensional space, it is not surprising that simple and heavily regularized learning methods are near-optimal.
Revised:  However, when learning from little data in a high-dimensional space, it is not surprising that simple and heavily regularized learning methods are near-optimal.
Edit intent: fluency
Original:  However, when learning with little data in a high-dimensional space, it is not surprising that simple and heavily regularized learning methods are near-optimal.
Revised:  However, when learning with little data in a high-dimensional space, it is not surprising that simple and heavily regularized methods are near-optimal.
Edit intent: fluency
Original:  In this paper, we show empirically that in settings with fewer features and more training data, more complex graph networks significantly outperform simpler architectures , and propose a few insights towards to the proper choice of graph neural networks architectures.
Revised:  In this paper, we show empirically that in settings with fewer features and more training data, more complex graph networks significantly outperform simple models , and propose a few insights towards to the proper choice of graph neural networks architectures.
Edit intent: clarity
Original:  In this paper, we show empirically that in settings with fewer features and more training data, more complex graph networks significantly outperform simpler architectures , and propose a few insights towards to the proper choice of graph neural networks architectures.
Revised:  In this paper, we show empirically that in settings with fewer features and more training data, more complex graph networks significantly outperform simpler architectures , and propose a few insights towards the proper choice of graph neural networks architectures.
Edit intent: fluency
Original:  In this paper, we show empirically that in settings with fewer features and more training data, more complex graph networks significantly outperform simpler architectures , and propose a few insights towards to the proper choice of graph neural networks architectures.
Revised:  In this paper, we show empirically that in settings with fewer features and more training data, more complex graph networks significantly outperform simpler architectures , and propose a few insights towards to the proper choice of graph network architectures.
Edit intent: clarity
Original:  In addition, we consider a discretized setting where indivisible items lie on a line and show a number of hardness results  strengthening those from prior work  .
Revised:  In addition, we consider a discretized setting where indivisible items lie on a line and show a number of hardness results extending and strengthening those from prior work .
Edit intent: clarity
Original:  Since in the presence of unbounded sessions any trace property becomes undecidable , we focus on (i) depth-bounded protocols, a  generalisation of a class of infinite-state protocols proposed by D'Osualdo, Ong and Tiu;
Revised:  Since the problem is undecidable in general, we introduce depth-bounded protocols, a generalisation of a class of infinite-state protocols proposed by D'Osualdo, Ong and Tiu;
Edit intent: clarity
Original:  To evaluate whether the approach is viable, we provide a prototype implementation and we report on its performance on some illustrative examples.
Revised:  We provide a prototype implementation and we report on its performance on some illustrative examples.
Edit intent: clarity
Original:  To evaluate whether the approach is viable, we provide a prototype implementation and we report on its performance on some illustrative examples.
Revised:  To evaluate whether the approach is viable, we provide a prototype implementation and we report on its performance on some textbook examples.
Edit intent: clarity
Original:  2019 ], we show that deep neural networks  with rectified power units (RePU) can give better approximation for sufficient smooth functions than those with rectified linear units by stably converting polynomial approximation given in power series into deep neural networks with optimal complexity and no approximation error.
Revised:  2019 ], it was shown that deep neural networks with rectified power units (RePU) can give better approximation for sufficient smooth functions than those with rectified linear units by stably converting polynomial approximation given in power series into deep neural networks with optimal complexity and no approximation error.
Edit intent: coherence
Original:  2019 ], we show that deep neural networks  with rectified power units (RePU) can give better approximation for sufficient smooth functions than those with rectified linear units by stably converting polynomial approximation given in power series into deep neural networks with optimal complexity and no approximation error.
Revised:  2019 ], we show that deep neural networks built with rectified power units (RePU) can give better approximation for sufficient smooth functions than those with rectified linear units by stably converting polynomial approximation given in power series into deep neural networks with optimal complexity and no approximation error.
Edit intent: clarity
Original:  2019 ], we show that deep neural networks  with rectified power units (RePU) can give better approximation for sufficient smooth functions than those with rectified linear units by stably converting polynomial approximation given in power series into deep neural networks with optimal complexity and no approximation error.
Revised:  2019 ], we show that deep neural networks with rectified power units (RePU) can give better approximation for sufficient smooth functions than those with rectified linear units , by converting polynomial approximation given in power series into deep neural networks with optimal complexity and no approximation error.
Edit intent: clarity
Original:  In this paper, we propose a new and more stable way to construct deep RePU neural networks using Chebyshev polynomial approximations.
Revised:  In this paper, we propose a new and more stable way to construct deep RePU neural networks based on Chebyshev polynomial approximations.
Edit intent: fluency
Original:  But , numerically, ChebNets are much more stable.
Revised:  But numerically, ChebNets are much more stable.
Edit intent: coherence
Original: We consider a coupled system composed of a  differential-algebraic equation and a large-scale ordinary differential equation where the latter stands for the dynamics of numerous identical particles.
Revised: We consider a coupled system composed of a differential-algebraic equation and a large-scale system of ordinary differential equations where the latter stands for the dynamics of numerous identical particles.
Edit intent: clarity
Original:  Replacing the discrete particles by a kinetic equation in terms of the particle density, we obtain a new class of models that we refer to as partially kinetic systems.
Revised:  Replacing the discrete particles by a kinetic equation for a particle density, we obtain a new class of models that we refer to as partially kinetic systems.
Edit intent: clarity
Original:  Replacing the discrete particles by a kinetic equation in terms of the particle density, we obtain a new class of models that we refer to as partially kinetic systems.
Revised:  Replacing the discrete particles by a kinetic equation in terms of the particle density, we obtain a new class of partially kinetic systems.
Edit intent: clarity
Original:  An essential tool from kinetic theory, the mean-field limit , also applies to partially kinetic systems as well, which yields a rigorous link between the kinetic equations and their underlying particle dynamics .
Revised:  We adapt the mean-field limit , also applies to partially kinetic systems as well, which yields a rigorous link between the kinetic equations and their underlying particle dynamics .
Edit intent: clarity
Original:  An essential tool from kinetic theory, the mean-field limit , also applies to partially kinetic systems as well, which yields a rigorous link between the kinetic equations and their underlying particle dynamics .
Revised:  An essential tool from kinetic theory, the mean-field limit , also applies to partially kinetic systems as well, which yields a rigorous link between the particle dynamics and their kinetic description .
Edit intent: clarity
Original:  Our research is inspired by the mathematical models for muscle tissue where the macroscopic behavior is governed by the equations of continuum mechanics, often discretized by the finite element method, and the microscopic muscle contraction process is described by Huxley's sliding filament theory.
Revised:  Our research is inspired by mathematical models for muscle tissue where the macroscopic behavior is governed by the equations of continuum mechanics, often discretized by the finite element method, and the microscopic muscle contraction process is described by Huxley's sliding filament theory.
Edit intent: fluency
Original:  Our research is inspired by the mathematical models for muscle tissue where the macroscopic behavior is governed by the equations of continuum mechanics, often discretized by the finite element method, and the microscopic muscle contraction process is described by Huxley's sliding filament theory.
Revised:  Our research is inspired by the mathematical models for muscle tissue where the macroscopic behaviour is governed by the equations of continuum mechanics, often discretized by the finite element method, and the microscopic muscle contraction process is described by Huxley's sliding filament theory.
Edit intent: fluency
Original:  Our research is inspired by the mathematical models for muscle tissue where the macroscopic behavior is governed by the equations of continuum mechanics, often discretized by the finite element method, and the microscopic muscle contraction process is described by Huxley's sliding filament theory.
Revised:  Our research is inspired by the mathematical models for muscle tissue where the macroscopic behavior is governed by the equations of continuum mechanics, often discretised by the finite element method, and the microscopic muscle contraction process is described by Huxley's sliding filament theory.
Edit intent: fluency
Original:  The latter represents a kinetic equation that characterizes the state of the actin-myosin bindings in the muscle filaments.
Revised:  The latter represents a kinetic equation that characterises the state of the actin-myosin bindings in the muscle filaments.
Edit intent: fluency
Original:  As a prime example, we analyse the influence of constraints on the kinetic theory of a simplified version of Huxley's sliding filament model.
Revised:  Linear partially kinetic systems are a simplified version of Huxley's sliding filament model.
Edit intent: clarity
Original: In this paper we conduct a study of both superiorization and optimization approaches for the reconstruction problem of superiorized/regularized solutions to underdetermined systems of linear equations with nonnegativity variable bounds.
Revised: We conduct a study of both superiorization and optimization approaches for the reconstruction problem of superiorized/regularized solutions to underdetermined systems of linear equations with nonnegativity variable bounds.
Edit intent: coherence
Original: In this paper we conduct a study of both superiorization and optimization approaches for the reconstruction problem of superiorized/regularized solutions to underdetermined systems of linear equations with nonnegativity variable bounds.
Revised: In this paper we conduct a study and comparison of superiorization and optimization approaches for the reconstruction problem of superiorized/regularized solutions to underdetermined systems of linear equations with nonnegativity variable bounds.
Edit intent: clarity
Original: In this paper we conduct a study of both superiorization and optimization approaches for the reconstruction problem of superiorized/regularized solutions to underdetermined systems of linear equations with nonnegativity variable bounds.
Revised: In this paper we conduct a study of both superiorization and optimization approaches for the reconstruction problem of superiorized/regularized least-squares solutions of underdetermined linear equations with nonnegativity variable bounds.
Edit intent: clarity
Original:  We consider two approaches: (a ) a superiorization approach that, in contrast to the classic gradient based superiorization methodology, employs proximal mappings and is structurally similar to a standard forward-backward optimization approach , and (b) an (inexact) accelerated optimization approach that mimics superiorization.
Revised:  We consider two approaches: (a ) a superiorization approach that, in contrast to the classic gradient based superiorization methodology, employs proximal mappings and is structurally similar to the established forward-backward optimization approach , and (b) an (inexact) accelerated optimization approach that mimics superiorization.
Edit intent: clarity
Original:  Reversing the roles of the terms treated by accelerated forward-backward optimization , on the other hand, slightly outperforms superiorization, which suggests that  optimization can approach superiorization too, using a suitable problem splitting .
Revised:  On the other hand, applying accelerated forward-backward optimization , on the other hand, slightly outperforms superiorization, which suggests that optimization can approach superiorization too, using a suitable problem splitting .
Edit intent: clarity
Original: We study ranked enumeration of the results to a join query in order of decreasing importance, as imposed by an appropriate ranking function .
Revised: We study ranked enumeration of join-query results according to very general orders defined by selective dioids .
Edit intent: clarity
Original:  Our main contribution is a framework for ranked enumeration over a class of Dynamic Programming problems that generalizes seemingly different problems that to date had been studied in isolation.
Revised:  Our main contribution is a framework for ranked enumeration over a class of dynamic programming problems that generalizes seemingly different problems that to date had been studied in isolation.
Edit intent: fluency
Original:  Our main contribution is a framework for ranked enumeration over a class of Dynamic Programming problems that generalizes seemingly different problems that to date had been studied in isolation.
Revised:  Our main contribution is a framework for ranked enumeration over a class of Dynamic Programming problems that generalizes seemingly different problems that had been studied in isolation.
Edit intent: clarity
Original:  To this end, we study and extend classic algorithms that find the k-shortest paths in a weighted graph.
Revised:  To this end, we extend classic algorithms that find the k-shortest paths in a weighted graph.
Edit intent: clarity
Original:  For full conjunctive queries, including cyclic ones, our approach is asymptotically optimal in terms of (1) time to return the top result , (2) delay between results , and (3) time until all results are returned in order .
Revised:  For full conjunctive queries, including cyclic ones, our approach is optimal in terms of (1) time to return the top result , (2) delay between results , and (3) time until all results are returned in order .
Edit intent: clarity
Original:  For full conjunctive queries, including cyclic ones, our approach is asymptotically optimal in terms of (1) time to return the top result , (2) delay between results , and (3) time until all results are returned in order .
Revised:  For full conjunctive queries, including cyclic ones, our approach is asymptotically optimal in terms of the time to return the top result , (2) delay between results , and (3) time until all results are returned in order .
Edit intent: fluency
Original:  For full conjunctive queries, including cyclic ones, our approach is asymptotically optimal in terms of (1) time to return the top result , (2) delay between results , and (3) time until all results are returned in order .
Revised:  For full conjunctive queries, including cyclic ones, our approach is asymptotically optimal in terms of (1) time to return the top result and the delay between results , and (3) time until all results are returned in order .
Edit intent: clarity
Original:  For full conjunctive queries, including cyclic ones, our approach is asymptotically optimal in terms of (1) time to return the top result , (2) delay between results , and (3) time until all results are returned in order .
Revised:  For full conjunctive queries, including cyclic ones, our approach is asymptotically optimal in terms of (1) time to return the top result , (2) delay between results .
Edit intent: clarity
Original:  These optimality properties were derived for a complexity measure that has been widely used in the context of worst-case optimal join algorithms, but which abstracts away factors that only depend on query size and a polylogarithmic cost factor in input size . By performing a more detailed cost analysis, we are able to uncover a previously unknown tradeoff between two incomparable enumeration approaches:
Revised:  These optimality properties are derived for the widely used notion of data complexity, which treats query size as a constant . By performing a more detailed cost analysis, we are able to uncover a previously unknown tradeoff between two incomparable enumeration approaches:
Edit intent: clarity
Original:  These optimality properties were derived for a complexity measure that has been widely used in the context of worst-case optimal join algorithms, but which abstracts away factors that only depend on query size and a polylogarithmic cost factor in input size . By performing a more detailed cost analysis, we are able to uncover a previously unknown tradeoff between two incomparable enumeration approaches:
Revised:  These optimality properties were derived for a complexity measure that has been widely used in the context of worst-case optimal join algorithms, but which abstracts away factors that only depend on query size and a polylogarithmic cost factor in input size . By performing a careful cost analysis, we are able to uncover a previously unknown tradeoff between two incomparable enumeration approaches:
Edit intent: clarity
Original:  We demonstrate theoretically and empirically  the superiority of our techniques to batch algorithmsthat produce the full result and then sort it.
Revised:  We theoretically and empirically the superiority of our techniques to batch algorithmsthat produce the full result and then sort it.
Edit intent: fluency
Original:  We demonstrate theoretically and empirically  the superiority of our techniques to batch algorithmsthat produce the full result and then sort it.
Revised:  We demonstrate theoretically and empirically demonstrate the superiority of our techniques to batch algorithmsthat produce the full result and then sort it.
Edit intent: fluency