wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our experiments show that with the right combination of these techniques  it is possible to perform run-on speech recognition with a low word-based latency without sacrificing performance in terms of word error rate  .
Revised:  Our experiments show that with the right combination of these techniques it is possible to perform run-on speech recognition with low word-based latency without sacrificing performance in terms of word error rate .
Edit intent: fluency
Original:  Our experiments show that with the right combination of these techniques  it is possible to perform run-on speech recognition with a low word-based latency without sacrificing performance in terms of word error rate  .
Revised:  Our experiments show that with the right combination of these techniques it is possible to perform run-on speech recognition with a low word-based latency without sacrificing in word error rate .
Edit intent: clarity
Original:  Our experiments show that with the right combination of these techniques  it is possible to perform run-on speech recognition with a low word-based latency without sacrificing performance in terms of word error rate  .
Revised:  Our experiments show that with the right combination of these techniques it is possible to perform run-on speech recognition with a low word-based latency without sacrificing performance in terms of word error rate performance .
Edit intent: clarity
Original:  given a set X of n point sets in R^{d} and an error parameter \epsilon, this is a linear transformation \Psi: R^{d} \to R^{O(\epsilon^{-2}n)} such that \|\Psi x\|_{2} \in (1- \epsilon, 1+\epsilon)\cdot \|x\|_{2} for all x\in X, and such that for each x\in X, \Psi x can be computed in time O(dd + \min\{dn + \epsilon^{-2}\log^{3}{n}\log^{3}(\epsilon^{-1}n)\}) with only a constant amount of memory overhead.
Revised:  given a set X of n points in R^{d} and an error parameter \epsilon, this is a linear transformation \Psi: R^{d} \to R^{O(\epsilon^{-2}n)} such that \|\Psi x\|_{2} \in (1- \epsilon, 1+\epsilon)\cdot \|x\|_{2} for all x\in X, and such that for each x\in X, \Psi x can be computed in time O(dd + \min\{dn + \epsilon^{-2}\log^{3}{n}\log^{3}(\epsilon^{-1}n)\}) with only a constant amount of memory overhead.
Edit intent: clarity
Original:  given a set X of n point sets in R^{d} and an error parameter \epsilon, this is a linear transformation \Psi: R^{d} \to R^{O(\epsilon^{-2}n)} such that \|\Psi x\|_{2} \in (1- \epsilon, 1+\epsilon)\cdot \|x\|_{2} for all x\in X, and such that for each x\in X, \Psi x can be computed in time O(dd + \min\{dn + \epsilon^{-2}\log^{3}{n}\log^{3}(\epsilon^{-1}n)\}) with only a constant amount of memory overhead.
Revised:  given a set X of n point sets in R^{d} and an error parameter \epsilon, this is a linear transformation \Psi: R^{d} \to R^{O(\epsilon^{-2}n)} such that \|\Psi x\|_{2} \in (1- \epsilon, 1+\epsilon)\cdot \|x\|_{2} for all x\in X, and such that for each x\in X, \Psi x can be computed in time O(dd + \min\{dn , \epsilon^{-2}\log^{3}{n}\log^{3}(\epsilon^{-1}n)\}) with only a constant amount of memory overhead.
Edit intent: fluency
Original:  given a set X of n points in R^{d and an error parameter \epsilon, this is a linear transformation \Psi: R^{d} }%DIFDELCMD < \to %%% \mathbb{R^{O(\epsilon^{-2}n)} such that \|\Psi x\|_{2} \in (1- \epsilon, }  1 +\epsilon)\cdot \|x\|_{2 for all x\in X, and such that for each x\in X, \Psi x can be computed }  ] ] ]  in time O(dd + \min\{d\log{n, \epsilon^{-2}\log^{3}{n}\log^{3}(\epsilon^{-1}n) \}) with only a constant amount of memory overhead.
Revised:  given a set X of n points in R^{d and an error parameter \epsilon, this is a linear transformation \Psi: R^{d} }%DIFDELCMD < \to %%% \mathbb{R^{O(\epsilon^{-2}n)} such that \|\Psi x\|_{2} \in (1- \epsilon, } 1 +\epsilon)\cdot \|x\|_{2 for all x\in X, and such that for each x\in X, \Psi x can be computed } ) For n points in \mathbb{R ] ] ] in time O(dd + \min\{d\log{n, \epsilon^{-2}\log^{3}{n}\log^{3}(\epsilon^{-1}n) \}) with only a constant amount of memory overhead.
Edit intent: clarity
Original: They are built on the numerical approximation of the modified symmetrizable SWMHD equations with a particularly adding source term.
Revised: They are built on the numerical approximation of the modified SWMHD equations with a particularly adding source term.
Edit intent: clarity
Original:  The key is to match both discretizations for the fluxes and the source terms (related to the non-flat river bed bottom and symmetrization) , and to find the affordable EC fluxes of the second-order EC schemes.
Revised:  The key is to match both discretizations for the fluxes and the non-flat river bed bottom and symmetrization) , and to find the affordable EC fluxes of the second-order EC schemes.
Edit intent: clarity
Original:  After that, the semi-discrete schemes are integrated in time by using the high-order strong stability-preserving explicit Runge-Kutta schemes to obtain the fully-discrete high-order well-balanced schemes.
Revised:  After that, the semi-discrete schemes are integrated in time by using the high-order strong stability preserving explicit Runge-Kutta schemes to obtain the fully-discrete high-order well-balanced schemes.
Edit intent: fluency
Original:  The ES property of the Lax-Friedrichs flux is also proved and then the positivity preserving ES schemes are studied by using the positivity-preserving flux limiter.
Revised:  The ES property of the Lax-Friedrichs flux is also proved and then the positivity-preserving ES schemes are studied by using the positivity-preserving flux limiter.
Edit intent: fluency
Original:  Finally, extensive numerical tests are conducted to validate the accuracy, the well-balance , ES and positivity properties, and the ability to capture discontinuities of our schemes.
Revised:  Finally, extensive numerical tests are conducted to validate the accuracy, the well-balanced , ES and positivity properties, and the ability to capture discontinuities of our schemes.
Edit intent: fluency
Original:  Finally, extensive numerical tests are conducted to validate the accuracy, the well-balance , ES and positivity properties, and the ability to capture discontinuities of our schemes.
Revised:  Finally, extensive numerical tests are conducted to validate the accuracy, the well-balance , ES and positivity-preserving properties, and the ability to capture discontinuities of our schemes.
Edit intent: clarity
Original: We analyze the spread of COVID-19 by considering the transmission of the disease among individuals both within and between communities .
Revised: We analyze the spread of COVID-19 by considering the transmission of the disease among individuals both within and between regions .
Edit intent: clarity
Original:  A set of communities can be defined as any partition of a population such that travel/social contact within each community far exceeds that between them (e. g. the U.S. could be partitioned by state or commuting zone boundaries).
Revised:  A set of communities can be defined as any partition of a population such that travel/social contact within each community far exceeds that between them .
Edit intent: clarity
Original:  We find that this community-to-community reproductive number is proportional to the travel rate between communities and exponential in the length of the time-delay before community-level action is taken .
Revised:  We find that this community-to-community reproductive number is proportional to the travel rate between regions and exponential in the length of the time-delay before community-level action is taken .
Edit intent: clarity
Original:  We find that this community-to-community reproductive number is proportional to the travel rate between communities and exponential in the length of the time-delay before community-level action is taken .
Revised:  We find that this community-to-community reproductive number is proportional to the travel rate between communities and exponential in the length of the time-delay before region-level control measures are imposed .
Edit intent: clarity
Original:  Thus, reductions in travel and the speed at which communities take action can play decisive roles in stopping the outbreak.
Revised:  Thus, reductions in travel and the speed with which regions take action play decisive roles in stopping the outbreak.
Edit intent: clarity
Original: This paper proposes a radio channel selection algorithm based on a contextual multi-armed bandit (CMAB) for a wireless local area network (WLAN)environment, in which the access probability of each access point (AP) and the throughput model are not given in advance.
Revised: This paper proposes a radio channel selection for wireless local area network (WLAN)environment, in which the access probability of each access point (AP) and the throughput model are not given in advance.
Edit intent: clarity
Original:  To reduce the computational complexity of the CMAB algorithms, feature extraction is designed by focusing on interference with neighboring and same-channel APs after channel selection of a target AP , which also contributes to the learning efficiency.
Revised:  To reduce the computational complexity of the CMAB algorithms, feature extraction is designed by focusing on contention with neighboring and same-channel APs after channel selection of a target AP , which also contributes to the learning efficiency.
Edit intent: clarity
Original:  The simulation results reveal that the algorithm based on JointLinUCB learns most efficiently under the environment where the access probabilities of the APs are extremely different .
Revised:  The simulation results confirm that contention-driven FE enables a target AP to learn the optimal channel in a scalable manner for the number of APs and available channels and to have low regret using the CMAB algorithm .
Edit intent: clarity
Original: To achieve low-regret learning in a radio channel selection for wireless local area networks (WLANs), we propose a contention-driven feature extraction ( FE)scheme for a contextual multi-armed bandit (CMAB) algorithm.
Revised: To achieve low-regret learning in a densely deployed wireless local area networks (WLANs), we propose a contention-driven feature extraction ( FE)scheme for a contextual multi-armed bandit (CMAB) algorithm.
Edit intent: clarity
Original: This paper proposes an online decentralized channel allocation scheme based on contextual multi-armed bandit ( CMAB)learning in a densely deployed wireless local area network (WLAN) environment.
Revised: A multi-armed bandit ( CMAB)learning in a densely deployed wireless local area network (WLAN) environment.
Edit intent: clarity
Original:  Therefore, we aim to validate the effectiveness of prior information ( i.e., the channels of neighboring APs ) in improving the system throughput.
Revised:  Therefore, we aim to validate the effectiveness of prior information ( i.e., being aware of channels of neighboring APs ) in improving the system throughput.
Edit intent: clarity
Original:  To this end, we propose contention-driven feature extraction (CDFE), which leverages the channels of neighboring APs to extract features corresponding to the adjacency relation of the contention graph.
Revised:  To this end, we propose contention-driven feature extraction (CDFE), which extracts the adjacency relation of the contention graph.
Edit intent: clarity
Original:  To this end, we propose contention-driven feature extraction (CDFE), which leverages the channels of neighboring APs to extract features corresponding to the adjacency relation of the contention graph.
Revised:  To this end, we propose contention-driven feature extraction (CDFE), which leverages the channels of neighboring APs to extract features corresponding to the adjacency relation of a contention graph.
Edit intent: fluency
Original:  Furthermore , we address the problem of non-convergence -- the channel allocation cycle -- which is an inherent difficulty in selfish decentralized learning.
Revised:  Furthermore , we address the problem of non-convergence -- the channel exploration cycle -- which is an inherent difficulty in selfish decentralized learning.
Edit intent: clarity
Original:  To prevent such a cycle, we propose a penalized JointLinUCB (P-JLinUCB) based on the key idea of introducing a discount parameter to the reward of exploiting a different channel before and after the learning round .
Revised:  To prevent such a cycle, we propose a penalized JLinUCB (P-JLinUCB) based on the key idea of introducing a discount parameter to the reward of exploiting a different channel before and after the learning round .
Edit intent: fluency
Original:  To prevent such a cycle, we propose a penalized JointLinUCB (P-JLinUCB) based on the key idea of introducing a discount parameter to the reward of exploiting a different channel before and after the learning round .
Revised:  To prevent such a cycle, we propose a penalized JointLinUCB (P-JLinUCB) based on the key idea of introducing a discount parameter to the reward for exploiting a different channel before and after the learning round .
Edit intent: fluency
Original: A multi-armed bandit ( MAB )-based decentralized channel exploration framework both adapting unknown traffics of neighboring access points (APs) and ensuring convergence is proposed.
Revised: A multi-armed bandit ( MAB )-based decentralized channel exploration framework both adapting unknown traffics of neighboring access points (APs) is proposed.
Edit intent: coherence
Original:  This allows to formulate the traffic-adaptive channel exploration as contextual MAB (CMAB ) problem with joint linear upper confidence bound (JLinUCB) exploration where the graph edge of the feature is leveraged as the weights of a linear throughput estimator .
Revised:  This allows the CMAB to be leveraged with joint a linear upper confidence bound (JLinUCB) exploration where the graph edge of the feature is leveraged as the weights of a linear throughput estimator .
Edit intent: clarity
Original: We study the ex-ante maximization of market efficiency , defined in terms of minimum deviation of market prices from fundamental values, from a centralized planner's perspective.
Revised: We study the ex-ante minimization of market inefficiency , defined in terms of minimum deviation of market prices from fundamental values, from a centralized planner's perspective.
Edit intent: clarity
Original:  We develop an explicit expression for the matrix of asset holdings which minimizes market inefficiency , and characterize it in terms of two key sufficient statistics: the banks' systemic significances and the statistical moments of asset shocks  .
Revised:  We characterize market inefficiency in terms of two key sufficient statistics: the banks' systemic significances and the statistical moments of asset shocks .
Edit intent: clarity
Original:  Our analysis shows that higher homogeneity in banks' systemic significances requires banks' portfolio holdings to be further away from a full diversification strategy to reduce inefficiencies .
Revised:  Our analysis shows that to reduce inefficiencies, portfolio holdings should deviate more from a full diversification strategy to reduce inefficiencies .
Edit intent: clarity
Original: We introduce the inhomogeneous geometric Brownian motion (IGBM) as a test equation for analysing qualitative features of numerical methods applied to multiplicative noise stochastic ordinary differential equations of Ito type with an inhomogeneous drift.
Revised: We provide a comparative analysis of qualitative features of numerical methods applied to multiplicative noise stochastic ordinary differential equations of Ito type with an inhomogeneous drift.
Edit intent: clarity
Original:  However, the conditional and asymptotic mean and variance of the IGBM are explicitly known and the process can be characterised according to Feller's boundary classification.
Revised:  However, the conditional and asymptotic mean and variance of the IGBM are known and the process can be characterised according to Feller's boundary classification.
Edit intent: clarity
Original:  We compare four splitting schemes, two based on the Lie-Trotter composition and two based on the Strang approach, with the frequently used Euler-Maruyama and Milstein methods.
Revised:  We compare the frequently used Euler-Maruyama and Milstein methods, two Lie-Trotter composition and two based on the Strang approach, with the frequently used Euler-Maruyama and Milstein methods.
Edit intent: clarity
Original:  First , we derive closed-form expressions for the conditional and asymptotic means and variances of all considered numerical schemes and analyse the resulting errors with respect to the true quantities.
Revised:  First , we derive closed-form expressions for the conditional and asymptotic means and variances of all considered schemes and analyse the resulting errors with respect to the true quantities.
Edit intent: clarity
Original: This study presents both the theoretical framework and the finite element solution to the fractional-order nonlocal thermoelastic response of beams.
Revised: This study presents a comprehensive framework for constitutive modeling of a frame-invariant fractional-order nonlocal thermoelastic response of beams.
Edit intent: clarity
Original:  The fractional finite element method (f-FEM) is used to numerically solve the linear and nonlinear fractional-order system of equations .
Revised:  These fractional-order system of equations .
Edit intent: clarity
Original:  We propose a distributed stochastic preconditioned projected reflected gradient algorithm and show its almost sure convergence when the pseudogradient mapping is cocoercive .
Revised:  We propose a distributed stochastic projected reflected gradient algorithm and show its almost sure convergence when the pseudogradient mapping is cocoercive .
Edit intent: clarity
Original:  Finally, we show that a non-preconditioned variant of our proposed algorithm has less restrictive convergence guarantees than state-of-the-art (preconditioned) forward-backward algorithms .
Revised:  Finally, we show that a preconditioned variant of our proposed algorithm has less restrictive convergence guarantees than state-of-the-art (preconditioned) forward-backward algorithms .
Edit intent: clarity
Original:  The algorithm is based on monotone operator splitting methods  for SGNEPs when the expected-value pseudogradient mapping is approximated at each iteration via an increasing number of samples of the random variable , an approach known as stochastic approximation with variance reduction .
Revised:  The algorithm is based on monotone operator splitting methods tailored for SGNEPs when the expected-value pseudogradient mapping is approximated at each iteration via an increasing number of samples of the random variable , an approach known as stochastic approximation with variance reduction .
Edit intent: clarity
Original:  The algorithm is based on monotone operator splitting methods  for SGNEPs when the expected-value pseudogradient mapping is approximated at each iteration via an increasing number of samples of the random variable , an approach known as stochastic approximation with variance reduction .
Revised:  The algorithm is based on monotone operator splitting methods for SGNEPs when the expected-value pseudogradient mapping is approximated at each iteration via an increasing number of samples of the random variable .
Edit intent: clarity
Original: One of the main and largely unexplored challenges in evolvingthe weights of neural networks using genetic algorithms is to finda sensible crossover operation between parent networks.
Revised: One of the main and largely unexplored challenges in evolving the weights of neural networks using genetic algorithms is to finda sensible crossover operation between parent networks.
Edit intent: fluency
Original: One of the main and largely unexplored challenges in evolvingthe weights of neural networks using genetic algorithms is to finda sensible crossover operation between parent networks.
Revised: One of the main and largely unexplored challenges in evolvingthe weights of neural networks using genetic algorithms is to find a sensible crossover operation between parent networks.
Edit intent: fluency
Original:  Indeed, naive crossover leads to functionally damaged offspring that donot retain information from the parents.
Revised:  Indeed, naive crossover leads to functionally damaged offspring that do not retain information from the parents.
Edit intent: fluency
Original:  This is because neuralnetworks are invariant to permutations of neurons, giving riseto multiple ways of representing the same solution.
Revised:  This is because neural networks are invariant to permutations of neurons, giving riseto multiple ways of representing the same solution.
Edit intent: fluency
Original:  This is because neuralnetworks are invariant to permutations of neurons, giving riseto multiple ways of representing the same solution.
Revised:  This is because neuralnetworks are invariant to permutations of neurons, giving rise to multiple ways of representing the same solution.
Edit intent: fluency
Original:  This is oftenreferred to as the competing conventions problem.
Revised:  This is often referred to as the competing conventions problem.
Edit intent: fluency
Original:  In this paper, wepropose a two-stepsafe crossover(SC) operator.
Revised:  In this paper, we propose a two-step safe crossover(SC) operator.
Edit intent: fluency
Original:  First, the neuronsof the parents are functionally aligned by computing how well theycorrelate , and only then are the parents recombined.
Revised:  First, the neurons of the parents are functionally aligned by computing how well theycorrelate , and only then are the parents recombined.
Edit intent: fluency
Original:  First, the neuronsof the parents are functionally aligned by computing how well theycorrelate , and only then are the parents recombined.
Revised:  First, the neuronsof the parents are functionally aligned by computing how well they correlate , and only then are the parents recombined.
Edit intent: fluency
Original:  We comparetwo ways of measuring relationships between neurons: PairwiseCorrelation (PwC) and Canonical Correlation Analysis (CCA).
Revised:  We compare two ways of measuring relationships between neurons: PairwiseCorrelation (PwC) and Canonical Correlation Analysis (CCA).
Edit intent: fluency
Original:  We comparetwo ways of measuring relationships between neurons: PairwiseCorrelation (PwC) and Canonical Correlation Analysis (CCA).
Revised:  We comparetwo ways of measuring relationships between neurons: Pairwise Correlation (PwC) and Canonical Correlation Analysis (CCA).
Edit intent: fluency
Original:  Wetest our safe crossover operators (SC-PwC and SC-CCA) on MNISTand CIFAR-10 by performing arithmetic crossover on the weightsof feed-forward neural network pairs.
Revised:  We test our safe crossover operators (SC-PwC and SC-CCA) on MNISTand CIFAR-10 by performing arithmetic crossover on the weightsof feed-forward neural network pairs.
Edit intent: fluency
Original:  Wetest our safe crossover operators (SC-PwC and SC-CCA) on MNISTand CIFAR-10 by performing arithmetic crossover on the weightsof feed-forward neural network pairs.
Revised:  Wetest our safe crossover operators (SC-PwC and SC-CCA) on MNIST and CIFAR-10 by performing arithmetic crossover on the weightsof feed-forward neural network pairs.
Edit intent: fluency
Original:  Wetest our safe crossover operators (SC-PwC and SC-CCA) on MNISTand CIFAR-10 by performing arithmetic crossover on the weightsof feed-forward neural network pairs.
Revised:  Wetest our safe crossover operators (SC-PwC and SC-CCA) on MNISTand CIFAR-10 by performing arithmetic crossover on the weights of feed-forward neural network pairs.
Edit intent: fluency
Original:  We show that it effectivelytransmits information from parents to offspring and significantlyimproves upon naive crossover.
Revised:  We show that it effectively transmits information from parents to offspring and significantlyimproves upon naive crossover.
Edit intent: fluency