wanyu/IteraTeR_v2 huggingface dataset:
Original:  We test our approach on pattern recognition using the standard MNIST data set of hand-written numbers . DMMs sample very effectively the vast phase space defined by the probability distribution of RBMs over the test sample inputs , and provide a very good approximation close to the optimum.
Revised:  We test our approach on pattern recognition using the standard MNIST data set . DMMs sample very effectively the vast phase space defined by the probability distribution of RBMs over the test sample inputs , and provide a very good approximation close to the optimum.
Edit intent: clarity
Original:  We test our approach on pattern recognition using the standard MNIST data set of hand-written numbers . DMMs sample very effectively the vast phase space defined by the probability distribution of RBMs over the test sample inputs , and provide a very good approximation close to the optimum.
Revised:  We test our approach on pattern recognition using the standard MNIST data set of hand-written numbers . DMMs sample effectively the vast phase space defined by the probability distribution of RBMs over the test sample inputs , and provide a very good approximation close to the optimum.
Edit intent: fluency
Original:  We test our approach on pattern recognition using the standard MNIST data set of hand-written numbers . DMMs sample very effectively the vast phase space defined by the probability distribution of RBMs over the test sample inputs , and provide a very good approximation close to the optimum.
Revised:  We test our approach on pattern recognition using the standard MNIST data set of hand-written numbers . DMMs sample very effectively the vast phase space given by the model distribution of the RBM , and provide a very good approximation close to the optimum.
Edit intent: clarity
Original:  This efficient search significantly reduces the number of generative pre-training iterations necessary to achieve a given level of accuracy in the MNIST data set , as well as a total performance gain over the traditional approaches .
Revised:  This efficient search significantly reduces the number of pretraining iterations necessary to achieve a given level of accuracy in the MNIST data set , as well as a total performance gain over the traditional approaches .
Edit intent: clarity
Original:  This efficient search significantly reduces the number of generative pre-training iterations necessary to achieve a given level of accuracy in the MNIST data set , as well as a total performance gain over the traditional approaches .
Revised:  This efficient search significantly reduces the number of generative pre-training iterations necessary to achieve a given level of accuracy , as well as a total performance gain over the traditional approaches .
Edit intent: clarity
Original:  This efficient search significantly reduces the number of generative pre-training iterations necessary to achieve a given level of accuracy in the MNIST data set , as well as a total performance gain over the traditional approaches .
Revised:  This efficient search significantly reduces the number of generative pre-training iterations necessary to achieve a given level of accuracy in the MNIST data set , as well as a total performance gain over CD .
Edit intent: clarity
Original:  In fact, the acceleration of the pre-training achieved by simulating DMMs is comparable to, in number of iterations, the recently reported hardware application of the quantum annealing method on the same network and data set.
Revised:  In fact, the acceleration of pretraining achieved by simulating DMMs is comparable to, in number of iterations, the recently reported hardware application of the quantum annealing method on the same network and data set.
Edit intent: fluency
Original:  Our approach\\%DIF > in accuracy, and a 20\\% reduction in error rate)  is agnostic about the connectivity of the network.
Revised:  Our approach\\%DIF > in accuracy, and a 20\\% reduction in error rate) over these approaches. Furthermore, our method is agnostic about the connectivity of the network.
Edit intent: clarity
Original: Restricted Boltzmann machines (RBMs) and their extensions, called 'deep-belief networks', are powerful neural networks that have found applications in the fields of machine learning and big data .
Revised: Restricted Boltzmann machines (RBMs) and their extensions, called 'deep-belief networks', are powerful neural networks that have found applications in the fields of machine learning and artificial intelligence .
Edit intent: clarity
Original:  We find that the memcomputing method still maintains a quality advantage (>1\\%DIF < in accuracy, and a 20\\% reduction in error rate) %DIF > in accuracy, and a 20\\%  over these approaches.
Revised:  We find that the memcomputing method still maintains a quality advantage (>1\\%DIF < in accuracy, and a 20\\% reduction in error rate) %DIF > in accuracy, and a 20\\% reduction in error rate) over these approaches.
Edit intent: fluency
Original:  We define the sketching/clustering gap to be the supremum over all mm-spaces of the ratio of the sketching and clustering objectives.
Edit intent: coherence
Original:  1. For metric spaces, we consider the case where f is the maximum cluster diameter , and \rho is the Gromov-Hausdorff distance .
Revised:  1. For metric spaces, we consider the case where the clustering objective is minimizing the maximum cluster diameter , and \rho is the Gromov-Hausdorff distance .
Edit intent: clarity
Original: We propose a new splitting algorithm to solve a class of semilinear parabolic PDEs with convex and quadratic growth gradients.
Revised: We propose an approximation scheme for a class of semilinear parabolic PDEs with convex and quadratic growth gradients.
Edit intent: clarity
Original: In this work, methods to detect one or several change points in multivariate time series are reviewed.
Revised: This article presents a selective survey of algorithms for the offline detection of multiple change points in multivariate time series are reviewed.
Edit intent: clarity
Original:  Precisely, all methods are described as a collection of three elements: a cost function, a search method and a constraint on the number of changes to detect.
Revised:  More precisely, detection algorithms considered in this review are characterized by three elements: a cost function, a search method and a constraint on the number of changes to detect.
Edit intent: clarity
Original:  We explore a rich semantic modeling approach to describe them , how they were produced , and the reasons for their differing aesthetic.
Revised:  We explore a rich semantic modeling approach to describe them , how they were produced and the reasons for their differing aesthetic.
Edit intent: fluency
Original: Different from training common neural networks (NNs) for inference on general-purpose processors, the development of NNs for neuromorphic chips is usually faced with a number of hardware-specific restrictions, including the limited precision of network signals and parameters, constrained computation scale  and limited types of non-linear functions.
Revised: Different from developing neural networks (NNs) for inference on general-purpose processors, the development of NNs for neuromorphic chips is usually faced with a number of hardware-specific restrictions, including the limited precision of network signals and parameters, constrained computation scale and limited types of non-linear functions.
Edit intent: clarity
Original: Different from training common neural networks (NNs) for inference on general-purpose processors, the development of NNs for neuromorphic chips is usually faced with a number of hardware-specific restrictions, including the limited precision of network signals and parameters, constrained computation scale  and limited types of non-linear functions.
Revised: Different from training common neural networks (NNs) for general-purpose processors, the development of NNs for neuromorphic chips is usually faced with a number of hardware-specific restrictions, including the limited precision of network signals and parameters, constrained computation scale and limited types of non-linear functions.
Edit intent: clarity
Original: Different from training common neural networks (NNs) for inference on general-purpose processors, the development of NNs for neuromorphic chips is usually faced with a number of hardware-specific restrictions, including the limited precision of network signals and parameters, constrained computation scale  and limited types of non-linear functions.
Revised: Different from training common neural networks (NNs) for inference on general-purpose processors, the development for NN chips usually faces with some hardware-specific restrictions, including the limited precision of network signals and parameters, constrained computation scale and limited types of non-linear functions.
Edit intent: clarity
Original: Different from training common neural networks (NNs) for inference on general-purpose processors, the development of NNs for neuromorphic chips is usually faced with a number of hardware-specific restrictions, including the limited precision of network signals and parameters, constrained computation scale  and limited types of non-linear functions.
Revised: Different from training common neural networks (NNs) for inference on general-purpose processors, the development of NNs for neuromorphic chips is usually faced with a number of hardware-specific restrictions, such as limited precision of network signals and parameters, constrained computation scale and limited types of non-linear functions.
Edit intent: fluency
Original: Different from training common neural networks (NNs) for inference on general-purpose processors, the development of NNs for neuromorphic chips is usually faced with a number of hardware-specific restrictions, including the limited precision of network signals and parameters, constrained computation scale  and limited types of non-linear functions.
Revised: Different from training common neural networks (NNs) for inference on general-purpose processors, the development of NNs for neuromorphic chips is usually faced with a number of hardware-specific restrictions, including the limited precision of network signals and parameters, constrained computation scale , and limited types of non-linear functions.
Edit intent: fluency
Original:  It can transform an existing trained, unrestricted NN (usually for software execution substrate) into an equivalent network that meets the given hardware constraints, which decouples NN applications from target hardware.
Revised:  It can transform an existing trained, unrestricted NN into an equivalent network that meets the given hardware constraints, which decouples NN applications from target hardware.
Edit intent: coherence
Original:  Its effectiveness has been demonstrated with a real neuromorphic chip and a processing-in-memory (PIM) design.
Revised:  We have demonstrated its effectiveness with a fabricated neuromorphic chip and a processing-in-memory (PIM) design.
Edit intent: clarity
Original:  Tests show that the extra inference error caused by this solution is very limited and the transformation time is much less than the retraining time.
Revised:  Tests show that the inference error caused by this solution is very limited and the transformation time is much less than the retraining time.
Edit intent: clarity
Original:  Tests show that the extra inference error caused by this solution is very limited and the transformation time is much less than the retraining time.
Revised:  Tests show that the extra inference error caused by this solution is insignificant and the transformation time is much less than the retraining time.
Edit intent: clarity
Original:  Tests show that the extra inference error caused by this solution is very limited and the transformation time is much less than the retraining time.
Revised:  Tests show that the extra inference error caused by this solution is very limited and the transformation time is much shorter than the retraining time.
Edit intent: clarity
Original:  In addition, quite a few parameter-sensitivity evaluations have been completed to explore the tradeoff between network error , resource consumption and different transformation strategies, which could provide insights for co-design optimization of neuromorphic hardware and software.
Revised:  Also, we have studied the parameter-sensitivity evaluations have been completed to explore the tradeoff between network error , resource consumption and different transformation strategies, which could provide insights for co-design optimization of neuromorphic hardware and software.
Edit intent: clarity
Original:  In addition, quite a few parameter-sensitivity evaluations have been completed to explore the tradeoff between network error , resource consumption and different transformation strategies, which could provide insights for co-design optimization of neuromorphic hardware and software.
Revised:  In addition, quite a few parameter-sensitivity evaluations to explore the tradeoff between network error , resource consumption and different transformation strategies, which could provide insights for co-design optimization of neuromorphic hardware and software.
Edit intent: clarity
Original:  In addition, quite a few parameter-sensitivity evaluations have been completed to explore the tradeoff between network error , resource consumption and different transformation strategies, which could provide insights for co-design optimization of neuromorphic hardware and software.
Revised:  In addition, quite a few parameter-sensitivity evaluations have been completed to explore the tradeoffs between network error , resource consumption and different transformation strategies, which could provide insights for co-design optimization of neuromorphic hardware and software.
Edit intent: fluency
Original:  In addition, quite a few parameter-sensitivity evaluations have been completed to explore the tradeoff between network error , resource consumption and different transformation strategies, which could provide insights for co-design optimization of neuromorphic hardware and software.
Revised:  In addition, quite a few parameter-sensitivity evaluations have been completed to explore the tradeoff between network error and resource utilization for different transformation strategies, which could provide insights for co-design optimization of neuromorphic hardware and software.
Edit intent: fluency
Original: Wireless Internet access has brought legions of heterogeneous apps all sharing the same resources .
Revised: The predominant use of wireless access networks is for media streaming applications, which are only gaining popularity as ever more devices become available for this purpose .
Edit intent: clarity
Original:  However, current wireless edge networks with tightly coupled PHY/MAC that cater to worst or average case performance lack the agility to best serve these diverse sessions.
Revised:  However, current access networks treat all packets identically, and lack the agility to best serve these diverse sessions.
Edit intent: clarity
Original:  The goal of this work is to design, develop and demonstrate FlowBazaar, an open market-based approach to create a value chain from the end-user of an application on one side, to algorithms operating over reconfigurable infrastructure on the other, so as to enable an ecosystem wherein disparate applications are able to obtain necessary resources for optimal performance  .
Revised:  The goal of this work is to design, develop and demonstrate FlowBazaar, an market-based approach to create a value chain from the end-user of an application on one side, to algorithms operating over reconfigurable infrastructure on the other, so as to enable an ecosystem wherein disparate applications are able to obtain necessary resources for optimal performance .
Edit intent: clarity
Original:  The goal of this work is to design, develop and demonstrate FlowBazaar, an open market-based approach to create a value chain from the end-user of an application on one side, to algorithms operating over reconfigurable infrastructure on the other, so as to enable an ecosystem wherein disparate applications are able to obtain necessary resources for optimal performance  .
Revised:  The goal of this work is to design, develop and demonstrate FlowBazaar, an open market-based approach to create a value chain from the application on one side, to algorithms operating over reconfigurable infrastructure on the other, so as to enable an ecosystem wherein disparate applications are able to obtain necessary resources for optimal performance .
Edit intent: clarity
Original:  The goal of this work is to design, develop and demonstrate FlowBazaar, an open market-based approach to create a value chain from the end-user of an application on one side, to algorithms operating over reconfigurable infrastructure on the other, so as to enable an ecosystem wherein disparate applications are able to obtain necessary resources for optimal performance  .
Revised:  The goal of this work is to design, develop and demonstrate FlowBazaar, an open market-based approach to create a value chain from the end-user of an application on one side, to algorithms operating over reconfigurable infrastructure on the other, so that applications are able to obtain necessary resources for optimal performance .
Edit intent: clarity
Original:  Taking advantage of the sparse character of brain-like computation, eventdriven technique allows us to carry out efficient simulation of large-scale Spiking Neural Networks (SNN).
Revised:  Taking advantage of the sparse character of brain-like computation, the event-driven technique could represent a way to carry out efficient simulation of large-scale Spiking Neural Networks (SNN).
Edit intent: clarity
Original:  In this paper we present FNS, a LIFL-based exact event-driven spiking neural network framework implemented in Java and oriented to wholebrain simulations.
Revised:  In this paper we present FNS, a LIFL-based spiking neural network framework implemented in Java and oriented to wholebrain simulations.
Edit intent: clarity
Original:  A novel parallelization strategy is also implemented in order to further speed up simulations .
Revised:  A novel parallelization strategy is also introduced .
Edit intent: clarity
Original:  The conducted test shows a good matching between the activity of  model and that of the emulated subject, in outstanding simulation times (about 20s for simulating 4s of activity with a normal PC).
Revised:  The conducted test shows a good matching between the activity of the model and that of the emulated subject, in outstanding simulation times (about 20s for simulating 4s of activity with a normal PC).
Edit intent: fluency
Original: Minimizing the sum of weighted completion times on m identical parallel machines is one of the most important  classical scheduling problems.
Revised: Minimizing the sum of weighted completion times on m identical parallel machines is one of the most important and classical scheduling problems.
Edit intent: clarity
Original:  For the stochastic variant where processing times of jobs are random variables, in 1999, M\"ohring, Schulz, and Uetz  presented the first and still best known approximation result , achieving, for arbitrarily many machines, performance guarantee 1+ %DIFDELCMD < \tfrac %%% 1 2 \cdot (1+\Delta), where \Delta is an upper bound on the squared coefficient of variation of the processing times.
Revised:  For the stochastic variant where processing times of jobs are random variables, M\"ohring, Schulz, and Uetz presented the first and still best known approximation result , achieving, for arbitrarily many machines, performance guarantee 1+ %DIFDELCMD < \tfrac %%% 1 2 \cdot (1+\Delta), where \Delta is an upper bound on the squared coefficient of variation of the processing times.
Edit intent: coherence
Original:  For the stochastic variant where processing times of jobs are random variables, in 1999, M\"ohring, Schulz, and Uetz  presented the first and still best known approximation result , achieving, for arbitrarily many machines, performance guarantee 1+ %DIFDELCMD < \tfrac %%% 1 2 \cdot (1+\Delta), where \Delta is an upper bound on the squared coefficient of variation of the processing times.
Revised:  For the stochastic variant where processing times of jobs are random variables, in 1999, M\"ohring, Schulz, and Uetz presented the first and still best known approximation result achieving, for arbitrarily many machines, performance guarantee 1+ %DIFDELCMD < \tfrac %%% 1 2 \cdot (1+\Delta), where \Delta is an upper bound on the squared coefficient of variation of the processing times.
Edit intent: fluency
Original:  For the stochastic variant where processing times of jobs are random variables, in 1999, M\"ohring, Schulz, and Uetz  presented the first and still best known approximation result , achieving, for arbitrarily many machines, performance guarantee 1+ %DIFDELCMD < \tfrac %%% 1 2 \cdot (1+\Delta), where \Delta is an upper bound on the squared coefficient of variation of the processing times.
Revised:  For the stochastic variant where processing times of jobs are random variables, in 1999, M\"ohring, Schulz, and Uetz presented the first and still best known approximation result , achieving, for arbitrarily many machines, performance ratio 1+ %DIFDELCMD < \tfrac %%% 1 2 \cdot (1+\Delta), where \Delta is an upper bound on the squared coefficient of variation of the processing times.
Edit intent: clarity
Original:  We prove performance guarantees 1+ %DIFDELCMD < \tfrac %%% 1 2 (\sqrt 2 - 1)\cdot (1+\Delta) for arbitrary \Delta and 1 +%DIFDELCMD < \tfrac %%% 1 6 \cdot (1+\Delta) for \Delta%DIFDELCMD < \ge %%% 1 for the same underlying algorithm--the Weighted Shortest Expected Processing Time (WSEPT) rule.
Revised:  We prove performance ratio 1+ %DIFDELCMD < \tfrac %%% 1 2 (\sqrt 2 - 1)\cdot (1+\Delta) for arbitrary \Delta and 1 +%DIFDELCMD < \tfrac %%% 1 6 \cdot (1+\Delta) for \Delta%DIFDELCMD < \ge %%% 1 for the same underlying algorithm--the Weighted Shortest Expected Processing Time (WSEPT) rule.
Edit intent: clarity
Original:  We prove performance guarantees 1+ %DIFDELCMD < \tfrac %%% 1 2 (\sqrt 2 - 1)\cdot (1+\Delta) for arbitrary \Delta and 1 +%DIFDELCMD < \tfrac %%% 1 6 \cdot (1+\Delta) for \Delta%DIFDELCMD < \ge %%% 1 for the same underlying algorithm--the Weighted Shortest Expected Processing Time (WSEPT) rule.
Revised:  We prove performance guarantees 1+ %DIFDELCMD < \tfrac %%% 1 2 (\sqrt 2 - 1)\cdot (1+\Delta) for %DIFDELCMD < \tfrac %%% 1 6 \cdot (1+\Delta) for \Delta%DIFDELCMD < \ge %%% 1 for the same underlying algorithm--the Weighted Shortest Expected Processing Time (WSEPT) rule.
Edit intent: coherence
Original:  We prove performance guarantees 1+ %DIFDELCMD < \tfrac %%% 1 2 (\sqrt 2 - 1)\cdot (1+\Delta) for arbitrary \Delta and 1 +%DIFDELCMD < \tfrac %%% 1 6 \cdot (1+\Delta) for \Delta%DIFDELCMD < \ge %%% 1 for the same underlying algorithm--the Weighted Shortest Expected Processing Time (WSEPT) rule.
Revised:  We prove performance guarantees 1+ %DIFDELCMD < \tfrac %%% 1 2 (\sqrt 2 - 1)\cdot (1+\Delta) for arbitrary \Delta and 1 +%DIFDELCMD < \tfrac %%% 1 6 \cdot (1+\Delta) for \Delta%DIFDELCMD < \ge %%% the same underlying algorithm--the Weighted Shortest Expected Processing Time (WSEPT) rule.
Edit intent: coherence
Original:  We prove performance guarantees 1+ %DIFDELCMD < \tfrac %%% 1 2 (\sqrt 2 - 1)\cdot (1+\Delta) for arbitrary \Delta and 1 +%DIFDELCMD < \tfrac %%% 1 6 \cdot (1+\Delta) for \Delta%DIFDELCMD < \ge %%% 1 for the same underlying algorithm--the Weighted Shortest Expected Processing Time (WSEPT) rule.
Revised:  We prove performance guarantees 1+ %DIFDELCMD < \tfrac %%% 1 2 (\sqrt 2 - 1)\cdot (1+\Delta) for arbitrary \Delta and 1 +%DIFDELCMD < \tfrac %%% 1 6 \cdot (1+\Delta) for \Delta%DIFDELCMD < \ge %%% 1 for the same underlying algorithm---the Weighted Shortest Expected Processing Time (WSEPT) rule.
Edit intent: fluency