wanyu/IteraTeR_v2 huggingface dataset:
Original:  With fixed feedback weights the performance degrades quickly as the depth of the network increases  , when they are updated the performance is comparable to regular back-propagation .
Revised:  With fixed feedback weights the performance degrades quickly as the depth of the network increases , performance is comparable to regular back-propagation .
Edit intent: clarity
Original:  With fixed feedback weights the performance degrades quickly as the depth of the network increases  , when they are updated the performance is comparable to regular back-propagation .
Revised:  With fixed feedback weights the performance degrades quickly as the depth of the network increases , when they are updated the performance is comparable on challenging datasets .
Edit intent: clarity
Original:  In particular, activities such as data selection, preprocessing, transformation, and mining, highly depend on iterative trial-and-error processes that could be sped up significantly by providing quick feedback on the impact of changes.
Revised:  In particular, activities such as data selection, preprocessing, transformation, and mining, highly depend on iterative trial-and-error processes that could be sped-up significantly by providing quick feedback on the impact of changes.
Edit intent: fluency
Original: The adoption of Software Defined Networking (SDN) within traditional networks has provided operators the ability to manage diverse resources and easily reconfigure or re-purpose networks as requirements change.
Revised: The adoption of Software Defined Networking (SDN) within traditional networks has provided operators the ability to manage diverse resources and easily reconfigure networks as requirements change.
Edit intent: clarity
Original:  This paper introduces Atomic-SDN : a low-latency, reliable, and energy-efficient cross-layer architecture for SDN control in low-power wireless networks .
Revised:  This paper introduces Atomic-SDN , a highly reliable and low-latency solution for SDN in low-power wireless networks .
Edit intent: clarity
Original:  This paper introduces Atomic-SDN : a low-latency, reliable, and energy-efficient cross-layer architecture for SDN control in low-power wireless networks .
Revised:  This paper introduces Atomic-SDN : a low-latency, reliable, and energy-efficient cross-layer architecture for SDN control in low-power wireless .
Edit intent: clarity
Original:  We evaluate the Atomic-SDN in comparison to other SDN architectural configurations for IEEE 802.15.4 , showing how Atomic-SDN improves SDN performance by orders-of-magnitude across latency, reliability, and energy-efficiency metrics.
Revised:  We evaluate the Atomic-SDN against another SDN implementation based on the IEEE 802.15.4 , showing how Atomic-SDN improves SDN performance by orders-of-magnitude across latency, reliability, and energy-efficiency metrics.
Edit intent: clarity
Original:  We evaluate the Atomic-SDN in comparison to other SDN architectural configurations for IEEE 802.15.4 , showing how Atomic-SDN improves SDN performance by orders-of-magnitude across latency, reliability, and energy-efficiency metrics.
Revised:  We evaluate the Atomic-SDN in comparison to other SDN architectural configurations for IEEE 802.15.4 network stack, and establish that Atomic-SDN improves SDN performance by orders-of-magnitude across latency, reliability, and energy-efficiency metrics.
Edit intent: clarity
Original:  We evaluate the Atomic-SDN in comparison to other SDN architectural configurations for IEEE 802.15.4 , showing how Atomic-SDN improves SDN performance by orders-of-magnitude across latency, reliability, and energy-efficiency metrics.
Revised:  We evaluate the Atomic-SDN in comparison to other SDN architectural configurations for IEEE 802.15.4 , showing how Atomic-SDN improves SDN control by orders-of-magnitude across latency, reliability, and energy-efficiency metrics.
Edit intent: clarity
Original:  Atomic-SDN introduces a novel Synchronous Flooding (SF) architecture capable of dynamically configuring SF protocols to satisfy complex SDN control requirements, and draws from the authors' previous experiences in the EWSN dependability competition : where SF solutions have consistently outperformed other entries.
Revised:  Atomic-SDN introduces a novel Synchronous Flooding (SF) architecture capable of dynamically configuring SF protocols to satisfy complex SDN control requirements, and draws from the authors' previous experiences in the IEEE EWSN Dependability Competition : where SF solutions have consistently outperformed other entries.
Edit intent: clarity
Original:  We compare Atomic-SDN against another SDN implementation based on the IEEE 802.15.4 network stack, and establish that Atomic-SDN improves SDN control by orders-of-magnitude across latency, reliability, and energy-efficiency metrics.
Revised:  We compare Atomic-SDN against other SDN implementations based on the IEEE 802.15.4 network stack, and establish that Atomic-SDN improves SDN control by orders-of-magnitude across latency, reliability, and energy-efficiency metrics.
Edit intent: fluency
Original:  Simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly-reliable task computation and lower delay performance, compared to  baselines.
Revised:  Simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly-reliable task computation and lower delay performance, compared to several baselines.
Edit intent: clarity
Original:  In this paper, we introduce a network epistemology model to explore this phenomenon of "epistemic factionization".
Revised:  In this paper, we introduce a model to explore this phenomenon of "epistemic factionization".
Edit intent: clarity
Original:  Agents attempt to discover the truth about multiple beliefs by testing the world and sharing evidence gathered.
Revised:  Agents attempt to discover the truth about multiple propositions by testing the world and sharing evidence gathered.
Edit intent: clarity
Original:  I present examples of simulated neural activity that follows different low dimensional configurations and compare the results of kdPCA to dPCA.
Revised:  I present simulated examples of high-dimensional neural activity generated from low-dimensional trajectories and compare the results of kdPCA to dPCA.
Edit intent: clarity
Original:  These simulations demonstrate that nonlinear interactions can impede the ability of dPCA to demix neural activity corresponding to experimental parameters , but kdPCA can still recover interpretable components  .
Revised:  These simulations demonstrate that nonlinear interactions can impede the ability of dPCA to demix neural activity corresponding to experimental parameters . However, kdPCA can still recover interpretable components .
Edit intent: coherence
Original:  These simulations demonstrate that nonlinear interactions can impede the ability of dPCA to demix neural activity corresponding to experimental parameters , but kdPCA can still recover interpretable components  .
Revised:  These simulations demonstrate that nonlinear interactions can impede the ability of dPCA to demix neural activity corresponding to experimental parameters , but kdPCA can still recover interpretable components from such data .
Edit intent: clarity
Original:  Additionally, I compare kdPCA and dPCA to a neural population  from rat orbitofrontal cortex during an odor classification task in recovering decision-related activity  .
Revised:  Additionally, I apply kdPCA to a neural population from rat orbitofrontal cortex during an odor classification task in recovering decision-related activity .
Edit intent: clarity
Original:  I present simulated examples of high-dimensional neural activity generated from low-dimensional trajectories and compare the results of kdPCA to dPCA .
Revised:  I present simulated examples of neural populations with low-dimensional trajectories and compare the results of kdPCA to dPCA .
Edit intent: clarity
Original:  I present simulated examples of high-dimensional neural activity generated from low-dimensional trajectories and compare the results of kdPCA to dPCA .
Revised:  I present simulated examples of high-dimensional neural activity generated from low-dimensional activity to compare the components recovered from dPCA and kdPCA .
Edit intent: clarity
Original:  These simulations demonstrate that neurally relevant nonlinearities - such as stimulus-dependent gain and rotations - impede the ability of dPCA to demix neural activity corresponding to experimental parameters.
Revised:  These simulations demonstrate that neurally relevant nonlinearities , such as stimulus-dependent gain and rotations - impede the ability of dPCA to demix neural activity corresponding to experimental parameters.
Edit intent: fluency
Original:  These simulations demonstrate that neurally relevant nonlinearities - such as stimulus-dependent gain and rotations - impede the ability of dPCA to demix neural activity corresponding to experimental parameters.
Revised:  These simulations demonstrate that neurally relevant nonlinearities - such as stimulus-dependent gain and rotation, interfere with linear demixing of neural activity into components that represent to individual experimental parameters.
Edit intent: clarity
Original:  We then present two empirical case studies, one tracing historical developments at the interface between the community detection  and the scientometric literature and one examining the development of the literature on peer review.
Revised:  We then present two empirical case studies, one tracing historical developments at the interface between the community detection literature and the scientometric literature and one examining the development of the literature on peer review.
Edit intent: fluency
Original: The success of many Internet of Things (IoT) applications relies on the ability of the network to deliver sensing measurements from the IoT devices to the destination nodes while they are still fresh.
Edit intent: clarity
Original:  In this article, we provide an accessible introduction to the emerging idea of Age-of-Information (AoI) that quantifies freshness of information and explore its possible role in the efficient design of freshness-aware IoT .
Revised:  In this article, we provide an accessible introduction to the emerging idea of Age-of-Information (AoI) that quantifies freshness of information and explore its possible role in the efficient design of freshness-aware Internet of Things (IoT) .
Edit intent: fluency
Original:  This policy jointly optimizes wireless energy transfer and scheduling of update packet transmissions from IoT devices  . Using this, we also characterize the achievable AoI region and demonstrate a fundamental trade-off between achieving fairness among different processes and achieving the minimum sum-AoI.
Revised:  This policy jointly optimizes wireless energy transfer and scheduling of update packet transmissions from IoT devices . Using this, we characterize the achievable AoI region and demonstrate a fundamental trade-off between achieving fairness among different processes and achieving the minimum sum-AoI.
Edit intent: clarity
Original:  Multiple promising directions for future research and extensions for our proposed system setup are presented.
Revised:  Multiple promising directions for future research in this direction are presented.
Edit intent: clarity
Original: In this article, we provide an accessible introduction to the emerging idea of Age-of-Information (AoI) that quantifies freshness of information and explore its possible role in the efficient design of freshness-aware Internet of Things (IoT).
Revised: In this article, we provide an accessible introduction to the emerging idea of Age of Information (AoI) that quantifies freshness of information and explore its possible role in the efficient design of freshness-aware Internet of Things (IoT).
Edit intent: fluency
Original:  Several promising directions for future research in this direction are presented.
Revised:  Several promising directions for future research are also presented.
Edit intent: clarity
Original: A numerical method for the fractional Poisson problem in a 2D bounded domain is proposed, based on iso-geometric analysis and the singularity subtraction.
Revised: We consider the iso-geometric analysis and the singularity subtraction.
Edit intent: clarity
Original: We consider the iso-geometric analysis for fractional PDEs involving the fractional Laplacian in two-dimension.
Revised: We consider the isogeometric analysis for fractional PDEs involving the fractional Laplacian in two-dimension.
Edit intent: fluency
Original:  Numerical studies exhibit monotonous convergence with a rate  O(N^{-1}), where N is the degrees of freedom.
Revised:  Numerical studies exhibit monotonous convergence with a rate of O(N^{-1}), where N is the degrees of freedom.
Edit intent: fluency
Original:  Comparison with finite element analysis shows that the method enjoys higher accuracy per degree of freedom and  better convergence rate.
Revised:  A comparison with finite element analysis shows that the method enjoys higher accuracy per degree of freedom and better convergence rate.
Edit intent: coherence
Original:  Comparison with finite element analysis shows that the method enjoys higher accuracy per degree of freedom and  better convergence rate.
Revised:  Comparison with finite element analysis shows that the method enjoys higher accuracy per degree of freedom and has a better convergence rate.
Edit intent: coherence
Original:  We demonstrate that iso-geometric analysis is a promising tool and opens a new venue for nonlocal problems.
Revised:  We demonstrate that isogeometric analysis offers a novel and promising computational tool for nonlocal problems.
Edit intent: clarity
Original:  the former leads to \textit{manifold Galerkin projection, while the latter leads to \textit{manifold least-squares Petrov--Galerkin (LSPG) projection.
Revised:  the former leads to \textit{manifold Galerkin projection, while the latter leads to \textit{manifold least-squares Petrov--Galerkin (LSPG) projection.
Edit intent: fluency
Original: The main goal in many fields in  empirical sciences is to discover causal relationships among a set of variables from observational data.
Revised: The main goal in many fields in the empirical sciences is to discover causal relationships among a set of variables from observational data.
Edit intent: fluency
Original:  PC algorithm is one of the promising solutions to learn the underlying causal structure by performing a number of conditional independence tests.
Revised:  PC algorithm is one of the promising solutions to learn underlying causal structure by performing a number of conditional independence tests.
Edit intent: fluency
Original:  In this paper, we propose a novel GPU-based parallel algorithm, called cuPC, to accelerate an order-independent version of PC.
Revised:  In this paper, we propose a novel GPU-based parallel algorithm, called cuPC, to execute an order-independent version of PC.
Edit intent: clarity
Original:  The cuPC algorithm has two variants, cuPC-E and cuPC-S, which parallelize conditional independence tests over the pairs of variables under the tests, and over the conditional sets, respectively.
Revised:  The proposed solution has two variants, cuPC-E and cuPC-S, which parallelize conditional independence tests over the pairs of variables under the tests, and over the conditional sets, respectively.
Edit intent: clarity
Original:  For a given value of the deterministic parameter , a stochastic Galerkin finite element (SGFE) method can estimate the corresponding expectation and variance of a linear output at the cost of a single solution of a large block-structured linear algebraic system of equations.
Revised:  For a given value of the deterministic parameter , a stochastic Galerkin finite element (SGFE) method can estimate the corresponding expectation and variance of a linear output at the cost of a single solution of a large block-structured linear system of equations.
Edit intent: clarity
Original:   To derive an SGRB model, we project the spatial-stochastic weak solution of a parameter-dependent SGFE model onto a POD reduced basis generated from snapshots of the SGFE solution at representative values of the parameter.
Revised:  To derive an SGRB model, we project the spatial-stochastic weak solution of a parameter-dependent SGFE model onto a POD reduced basis generated from snapshots of SGFE solutions at representative values of the parameter.
Edit intent: fluency
Original:   We test the SGRB method numerically for a convection-diffusion-reaction problem, choosing the convective velocity as a deterministic parameter and the parametrized reactivity field as a random input.
Revised:  ~ We test the SGRB method numerically for a convection-diffusion-reaction problem, choosing the convective velocity as a deterministic parameter and the parametrized reactivity field as a random input.
Edit intent: fluency
Original:  Unexpectedly, the problem of computing a temporal matching of maximum size turns out to be NP-difficult.
Revised:  We show that the problem of computing a temporal matching of maximum size turns out to be NP-difficult.
Edit intent: coherence
Original:  As a byproduct we also depict a 2-approximation algorithm.
Revised:  As a byproduct we also give a 2-approximation algorithm.
Edit intent: clarity
Original:  We observe that finding temporal matchings is non trivial when sampling our data from such a perspective as: managing peer-working when any pair of peers X and Y are to collaborate over a period of one month, at an average rate of at least two  exchanges every week.
Revised:  We observe that finding temporal matchings is a sensitive question when mining our data from such a perspective as: managing peer-working when any pair of peers X and Y are to collaborate over a period of one month, at an average rate of at least two exchanges every week.
Edit intent: clarity
Original:  ToC is used to transform PDEs with boundary conditions into unconstrained optimization problems by embedding the boundary conditions into a "constrained expression  " that contains a neural network .
Revised:  ToC is used to transform PDEs with boundary conditions into unconstrained optimization problems by embedding the boundary conditions into a "constrained expression . " that contains a neural network .
Edit intent: fluency
Original:  ToC is used to transform PDEs with boundary conditions into unconstrained optimization problems by embedding the boundary conditions into a "constrained expression  " that contains a neural network .
Revised:  ToC is used to transform PDEs with boundary conditions into unconstrained optimization problems by embedding the boundary conditions into a "constrained expression " In this work, a neural network .
Edit intent: clarity
Original:  The loss functionfor the unconstrained optimization problem is taken to be the square of the residual of the PDE.
Revised:  The loss functionfor the unconstrained optimization problem . The loss function is taken as the square of the residual of the PDE.
Edit intent: fluency
Original:  This methodology has two major advantages over other popular methods used to estimate the solutions of PDEs.
Revised:  This methodology has two major differences when compared with popular methods used to estimate the solutions of PDEs.
Edit intent: clarity
Original:  We provide general conditions for the existence of such a map .
Revised:  We provide general conditions for the existence of such an assignment .
Edit intent: clarity
Original:  We conclude that such a map exists for all simple and simplicial polytopes, as well as all polytopes of dimension d\le 6.
Revised:  We conclude that such exist for all simple and simplicial polytopes, as well as all polytopes of dimension d\le 6.
Edit intent: clarity
Original:  We provide reasonable sharp constants that are independent from the number of nodes as long as non-colliding nodes are well-separated.
Revised:  We provide reasonable sharp constants that are independent of the number of nodes as long as non-colliding nodes are well-separated.
Edit intent: fluency
Original: The complex and computationally expensive features of the forward landscape and sedimentary basin evolution models pose a major challenge in the development of efficient inference and optimization methods . Bayesian inference provides a methodology for estimation and uncertainty quantification of free model parameters.
Revised: The complex and computationally expensive nature of landscape evolution models pose a major challenge in the development of efficient inference and optimization methods . Bayesian inference provides a methodology for estimation and uncertainty quantification of free model parameters.
Edit intent: clarity
Original: The complex and computationally expensive features of the forward landscape and sedimentary basin evolution models pose a major challenge in the development of efficient inference and optimization methods . Bayesian inference provides a methodology for estimation and uncertainty quantification of free model parameters.
Revised: The complex and computationally expensive features of the forward landscape and sedimentary basin evolution models pose significant challenges in the inference and optimisation of unknown parameters . Bayesian inference provides a methodology for estimation and uncertainty quantification of free model parameters.
Edit intent: clarity
Original: The complex and computationally expensive features of the forward landscape and sedimentary basin evolution models pose a major challenge in the development of efficient inference and optimization methods . Bayesian inference provides a methodology for estimation and uncertainty quantification of free model parameters.
Revised: The complex and computationally expensive features of the forward landscape and sedimentary basin evolution models pose a major challenge in the development of efficient inference and optimization methods . Bayesian inference provides a methodology for estimation and uncertainty quantification of unknown model parameters.
Edit intent: clarity
Original:  In our previous work,  parallel tempering Bayeslands was developed as a framework for parameter estimation and uncertainty quantification for the landscape and basin evolution modelling software Badlands . Parallel tempering Bayeslands features high-performance computing with dozens of processing cores running in parallel to enhance computational efficiency.
Revised:  In our previous work, we developed parallel tempering Bayeslands was developed as a framework for parameter estimation and uncertainty quantification for the landscape and basin evolution modelling software Badlands . Parallel tempering Bayeslands features high-performance computing with dozens of processing cores running in parallel to enhance computational efficiency.
Edit intent: coherence
Original:  In our previous work,  parallel tempering Bayeslands was developed as a framework for parameter estimation and uncertainty quantification for the landscape and basin evolution modelling software Badlands . Parallel tempering Bayeslands features high-performance computing with dozens of processing cores running in parallel to enhance computational efficiency.
Revised:  In our previous work, parallel tempering Bayeslands as a framework for parameter estimation and uncertainty quantification for the landscape and basin evolution modelling software Badlands . Parallel tempering Bayeslands features high-performance computing with dozens of processing cores running in parallel to enhance computational efficiency.
Edit intent: clarity