wanyu/IteraTeR_full_sent huggingface dataset:
Original: We propose a novel time discretization for the log-normal SABR model dS_t = \sigma_t S_t dW_t, d\sigma_t = \omega \sigma_t dZ_t, with \mbox{corr(W_t,Z_t)=\varrho, } which is a  variant of the Euler-Maruyama scheme , and study its asymptotic properties in the limit of a large number of time steps n%DIFDELCMD < \to %%% \infty at fixed \beta =%DIFDELCMD < \frac12%%% \omega^2 n^2\tau, \rho = \sigma_0\sqrt{\tau .
Revised: We propose a novel time discretization for the log-normal SABR model dS_t = \sigma_t S_t dW_t, d\sigma_t = \omega \sigma_t dZ_t, with \mbox{corr(W_t,Z_t)=\varrho, } which is a variant of the Euler-Maruyama scheme , and study its asymptotic properties in the limit of a large number of time steps %DIFDELCMD < \to %%% \infty at fixed \beta =%DIFDELCMD < \frac12%%% \omega^2 n^2\tau, \rho = \sigma_0\sqrt{\tau .
Edit intent: meaning-changed
Original: We propose a novel time discretization for the log-normal SABR model dS_t = \sigma_t S_t dW_t, d\sigma_t = \omega \sigma_t dZ_t, with \mbox{corr(W_t,Z_t)=\varrho, } which is a  variant of the Euler-Maruyama scheme , and study its asymptotic properties in the limit of a large number of time steps n%DIFDELCMD < \to %%% \infty at fixed \beta =%DIFDELCMD < \frac12%%% \omega^2 n^2\tau, \rho = \sigma_0\sqrt{\tau .
Revised: We propose a novel time discretization for the log-normal SABR model dS_t = \sigma_t S_t dW_t, d\sigma_t = \omega \sigma_t dZ_t, with \mbox{corr(W_t,Z_t)=\varrho, } which is a variant of the Euler-Maruyama scheme , and study its asymptotic properties in the limit of a large number of time steps n%DIFDELCMD < \to %%% %DIFDELCMD < \frac12%%% \omega^2 n^2\tau, \rho = \sigma_0\sqrt{\tau .
Edit intent: meaning-changed
Original: We propose a novel time discretization for the log-normal SABR model dS_t = \sigma_t S_t dW_t, d\sigma_t = \omega \sigma_t dZ_t, with \mbox{corr(W_t,Z_t)=\varrho, } which is a  variant of the Euler-Maruyama scheme , and study its asymptotic properties in the limit of a large number of time steps n%DIFDELCMD < \to %%% \infty at fixed \beta =%DIFDELCMD < \frac12%%% \omega^2 n^2\tau, \rho = \sigma_0\sqrt{\tau .
Revised: We propose a novel time discretization for the log-normal SABR model dS_t = \sigma_t S_t dW_t, d\sigma_t = \omega \sigma_t dZ_t, with \mbox{corr(W_t,Z_t)=\varrho, } which is a variant of the Euler-Maruyama scheme , and study its asymptotic properties in the limit of a large number of time steps n%DIFDELCMD < \to %%% \infty at fixed \beta =%DIFDELCMD < \frac12%%% under a certain asymptotic regime which includes the case of finite maturity, small vol-of-vol and large initial volatility with fixed product of vol-of-vol and initial volatility .
Edit intent: meaning-changed
Original:  We derive an almost sure limit and a large deviations result for the log-asset price in the n%DIFDELCMD < \to %%% \infty limit . The rate function of the large deviations result does not depend on the discretization time step \tau. The implied volatility surface \sigma_{\rm BS for arbitrary maturity and strike in the limit \omega^2 T%DIFDELCMD < \to %%% 0 , \sigma_0^2 T%DIFDELCMD < \to %%% \infty at fixed (\omega^2 T)(\sigma_0^2 T) is represented as an extremal problem .
Revised:  We derive an almost sure limit and a large deviations result for the log-asset price in the n%DIFDELCMD < \to %%% limit of large number of time steps. We derive an exact representation of the implied volatility surface \sigma_{\rm BS for arbitrary maturity and strike in the limit \omega^2 T%DIFDELCMD < \to %%% 0 , \sigma_0^2 T%DIFDELCMD < \to %%% \infty at fixed (\omega^2 T)(\sigma_0^2 T) is represented as an extremal problem .
Edit intent: clarity
Original:  The implied volatility surface \sigma_{\rm BS for arbitrary maturity and strike in the limit \omega^2 T%DIFDELCMD < \to %%% 0 , \sigma_0^2 T%DIFDELCMD < \to %%% \infty at fixed (\omega^2 T)(\sigma_0^2 T) is represented as an extremal problem .
Revised:  The implied volatility surface for arbitrary maturity and strike in the limit \omega^2 T%DIFDELCMD < \to %%% 0 , \sigma_0^2 T%DIFDELCMD < \to %%% \infty at fixed (\omega^2 T)(\sigma_0^2 T) is represented as an extremal problem .
Edit intent: meaning-changed
Original:  The implied volatility surface \sigma_{\rm BS for arbitrary maturity and strike in the limit \omega^2 T%DIFDELCMD < \to %%% 0 , \sigma_0^2 T%DIFDELCMD < \to %%% \infty at fixed (\omega^2 T)(\sigma_0^2 T) is represented as an extremal problem .
Revised:  The implied volatility surface \sigma_{\rm BS for arbitrary maturity and strike in %DIFDELCMD < \to %%% 0 , \sigma_0^2 T%DIFDELCMD < \to %%% \infty at fixed (\omega^2 T)(\sigma_0^2 T) is represented as an extremal problem .
Edit intent: clarity
Original:  The implied volatility surface \sigma_{\rm BS for arbitrary maturity and strike in the limit \omega^2 T%DIFDELCMD < \to %%% 0 , \sigma_0^2 T%DIFDELCMD < \to %%% \infty at fixed (\omega^2 T)(\sigma_0^2 T) is represented as an extremal problem .
Revised:  The implied volatility surface \sigma_{\rm BS for arbitrary maturity and strike in the limit \omega^2 T%DIFDELCMD < \to %%% %DIFDELCMD < \to %%% \infty at fixed (\omega^2 T)(\sigma_0^2 T) is represented as an extremal problem .
Edit intent: coherence
Original:  The implied volatility surface \sigma_{\rm BS for arbitrary maturity and strike in the limit \omega^2 T%DIFDELCMD < \to %%% 0 , \sigma_0^2 T%DIFDELCMD < \to %%% \infty at fixed (\omega^2 T)(\sigma_0^2 T) is represented as an extremal problem .
Revised:  The implied volatility surface \sigma_{\rm BS for arbitrary maturity and strike in the limit \omega^2 T%DIFDELCMD < \to %%% 0 , \sigma_0^2 T%DIFDELCMD < \to %%% this regime .
Edit intent: meaning-changed
Original:  Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train  unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to shallow networks with only one learnable layer and can't achieve satisfactory results when compared with multi-layer SNNs. In this paper, we ease this limitation by: 1)We propose Spiking Inception (Sp-Inception) module, inspired by the Inception module in  Artificial Neural Network (ANN) literature.
Revised:  Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train an unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to shallow networks with only one learnable layer and can't achieve satisfactory results when compared with multi-layer SNNs. In this paper, we ease this limitation by: 1)We propose Spiking Inception (Sp-Inception) module, inspired by the Inception module in Artificial Neural Network (ANN) literature.
Edit intent: clarity
Original:  Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train  unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to shallow networks with only one learnable layer and can't achieve satisfactory results when compared with multi-layer SNNs. In this paper, we ease this limitation by: 1)We propose Spiking Inception (Sp-Inception) module, inspired by the Inception module in  Artificial Neural Network (ANN) literature.
Revised:  Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to a shallow network with only one learnable layer and can't achieve satisfactory results when compared with multi-layer SNNs. In this paper, we ease this limitation by: 1)We propose Spiking Inception (Sp-Inception) module, inspired by the Inception module in Artificial Neural Network (ANN) literature.
Edit intent: coherence
Original:  Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train  unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to shallow networks with only one learnable layer and can't achieve satisfactory results when compared with multi-layer SNNs. In this paper, we ease this limitation by: 1)We propose Spiking Inception (Sp-Inception) module, inspired by the Inception module in  Artificial Neural Network (ANN) literature.
Revised:  Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to shallow networks with only one learnable layer and cannot achieve satisfactory results when compared with multi-layer SNNs. In this paper, we ease this limitation by: 1)We propose Spiking Inception (Sp-Inception) module, inspired by the Inception module in Artificial Neural Network (ANN) literature.
Edit intent: fluency
Original:  Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train  unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to shallow networks with only one learnable layer and can't achieve satisfactory results when compared with multi-layer SNNs. In this paper, we ease this limitation by: 1)We propose Spiking Inception (Sp-Inception) module, inspired by the Inception module in  Artificial Neural Network (ANN) literature.
Revised:  Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to shallow networks with only one learnable layer and can't achieve satisfactory results when compared with multi-layer SNNs. In this paper, we eased this limitation by: 1)We propose Spiking Inception (Sp-Inception) module, inspired by the Inception module in Artificial Neural Network (ANN) literature.
Edit intent: fluency
Original:  Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train  unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to shallow networks with only one learnable layer and can't achieve satisfactory results when compared with multi-layer SNNs. In this paper, we ease this limitation by: 1)We propose Spiking Inception (Sp-Inception) module, inspired by the Inception module in  Artificial Neural Network (ANN) literature.
Revised:  Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to shallow networks with only one learnable layer and can't achieve satisfactory results when compared with multi-layer SNNs. In this paper, we ease this limitation by: 1)We proposed a Spiking Inception (Sp-Inception) module, inspired by the Inception module in Artificial Neural Network (ANN) literature.
Edit intent: clarity
Original:  Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train  unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to shallow networks with only one learnable layer and can't achieve satisfactory results when compared with multi-layer SNNs. In this paper, we ease this limitation by: 1)We propose Spiking Inception (Sp-Inception) module, inspired by the Inception module in  Artificial Neural Network (ANN) literature.
Revised:  Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to shallow networks with only one learnable layer and can't achieve satisfactory results when compared with multi-layer SNNs. In this paper, we ease this limitation by: 1)We propose Spiking Inception (Sp-Inception) module, inspired by the Inception module in the Artificial Neural Network (ANN) literature.
Edit intent: fluency
Original:  3)We stack multiple Sp-Inception modules to construct multi-layer SNNs. Our method greatly exceeds baseline methods on image classification tasks and reaches state-of-the-art results on  MNIST dataset among  existing unsupervised SNNs.
Revised:  3)We stacked multiple Sp-Inception modules to construct multi-layer SNNs. Our method greatly exceeds baseline methods on image classification tasks and reaches state-of-the-art results on MNIST dataset among existing unsupervised SNNs.
Edit intent: fluency
Original:  3)We stack multiple Sp-Inception modules to construct multi-layer SNNs. Our method greatly exceeds baseline methods on image classification tasks and reaches state-of-the-art results on  MNIST dataset among  existing unsupervised SNNs.
Revised:  3)We stack multiple Sp-Inception modules to construct multi-layer SNNs. Our algorithm outperforms the baseline algorithms on the hand-written digit classification task, and reaches state-of-the-art results on MNIST dataset among existing unsupervised SNNs.
Edit intent: meaning-changed
Original:  3)We stack multiple Sp-Inception modules to construct multi-layer SNNs. Our method greatly exceeds baseline methods on image classification tasks and reaches state-of-the-art results on  MNIST dataset among  existing unsupervised SNNs.
Revised:  3)We stack multiple Sp-Inception modules to construct multi-layer SNNs. Our method greatly exceeds baseline methods on image classification tasks and reaches state-of-the-art results on the MNIST dataset among existing unsupervised SNNs.
Edit intent: fluency
Original:  3)We stack multiple Sp-Inception modules to construct multi-layer SNNs. Our method greatly exceeds baseline methods on image classification tasks and reaches state-of-the-art results on  MNIST dataset among  existing unsupervised SNNs.
Revised:  3)We stack multiple Sp-Inception modules to construct multi-layer SNNs. Our method greatly exceeds baseline methods on image classification tasks and reaches state-of-the-art results on MNIST dataset among the existing unsupervised SNNs.
Edit intent: fluency
Original: The activity of neuronal networks can exhibit periods of bursting, the properties of which remain unclear. To study the bursting dynamics of a network , we develop a new stochastic model based on synaptic properties that also accounts for afterhyperpolarization , which shapes the end of a burst.
Revised: The activity of neuronal networks can alternate between bursts and interburst intervals (IBI), with heterogeneous length distributions. As this dynamic remains unclear, especially the durations of each epoch , we develop a new stochastic model based on synaptic properties that also accounts for afterhyperpolarization , which shapes the end of a burst.
Edit intent: meaning-changed
Original:  To study the bursting dynamics of a network , we develop a new stochastic model based on synaptic properties that also accounts for afterhyperpolarization , which shapes the end of a burst.
Revised:  To study the bursting dynamics of a network , we develop a new stochastic model based on synaptic depression and facilitation that also accounts for afterhyperpolarization , which shapes the end of a burst.
Edit intent: meaning-changed
Original:  To study the bursting dynamics of a network , we develop a new stochastic model based on synaptic properties that also accounts for afterhyperpolarization , which shapes the end of a burst. A stochastic perturbation of this system leads to a succession of bursts and interbursts and we characterize their durations using a three dimensional phase-space analysis and numerical simulations.
Revised:  To study the bursting dynamics of a network , we develop a new stochastic model based on synaptic properties that also accounts for afterhyperpolarization (AHP), which is a key component of IBI. The framework is a novel stochastic three dimensional dynamical system perturbed by noise: numerical simulations can reproduce a succession of bursts and interbursts and we characterize their durations using a three dimensional phase-space analysis and numerical simulations.
Edit intent: meaning-changed
Original:  A stochastic perturbation of this system leads to a succession of bursts and interbursts and we characterize their durations using a three dimensional phase-space analysis and numerical simulations. The phase-space contains three critical points (one attractor and two saddles) separated by a two-dimensional stable manifold \Sigma.
Revised:  A stochastic perturbation of this system leads to a succession of bursts and interbursts and we characterize their durations using a three dimensional phase-space , which contains three critical points (one attractor and two saddles) separated by a two-dimensional stable manifold \Sigma.
Edit intent: clarity
Original:  To characterize the distribution of the burst durations, we determine the distribution of exit points located on the two-dimensional separatrix \Sigma . In addition, we compute analytically the mean burst and AHP durations using a linearization approximation of the dynamical system .
Revised:  To characterize the distribution of the burst durations, we determine the distribution of exit points located on \Sigma that we compute using WKB and the method of characteristics .
Edit intent: meaning-changed
Original:  However, limited by reasoning capability and knowledge level , it is challenging for a robot to self diagnose and correct their abnormal behaviors. To solve this problem, a novel method is proposed, human-to-robot attention transfer ( H2R-AT) to seek help from a human . H2R-AT is developed based on \textbf{ \textit{\textbf{  a novel stacked neural networks model, transferring human attention embedded in verbal reminders to robot attention embedded in robot visual perceiving .
Revised:  However, limited by reasoning capability and knowledge level , it is challenging for robots to self-diagnose and -correct their own abnormality in both planning and executing. To improve robot self diagnosis capability, in this research a novel human-to-robot attention transfer ( H2R-AT) to seek help from a human . H2R-AT is developed based on \textbf{ \textit{\textbf{ a novel stacked neural networks model, transferring human attention embedded in verbal reminders to robot attention embedded in robot visual perceiving .
Edit intent: coherence
Original:  To solve this problem, a novel method is proposed, human-to-robot attention transfer ( H2R-AT) to seek help from a human . H2R-AT is developed based on \textbf{ \textit{\textbf{  a novel stacked neural networks model, transferring human attention embedded in verbal reminders to robot attention embedded in robot visual perceiving .
Revised:  To solve this problem, a novel method is proposed, human-to-robot attention transfer ( \textbf{ \textit{\textbf{ a novel stacked neural networks model, transferring human attention embedded in verbal reminders to robot attention embedded in robot visual perceiving .
Edit intent: coherence
Original:  To solve this problem, a novel method is proposed, human-to-robot attention transfer ( H2R-AT) to seek help from a human . H2R-AT is developed based on \textbf{ \textit{\textbf{  a novel stacked neural networks model, transferring human attention embedded in verbal reminders to robot attention embedded in robot visual perceiving .
Revised:  To solve this problem, a novel method is proposed, human-to-robot attention transfer ( H2R-AT) to seek help from a human . H2R-AT is developed based on \textbf{H2R-AT \textit{\textbf{ a novel stacked neural networks model, transferring human attention embedded in verbal reminders to robot attention embedded in robot visual perceiving .
Edit intent: meaning-changed
Original:  To solve this problem, a novel method is proposed, human-to-robot attention transfer ( H2R-AT) to seek help from a human . H2R-AT is developed based on \textbf{ \textit{\textbf{  a novel stacked neural networks model, transferring human attention embedded in verbal reminders to robot attention embedded in robot visual perceiving .
Revised:  To solve this problem, a novel method is proposed, human-to-robot attention transfer ( H2R-AT) to seek help from a human . H2R-AT is developed based on \textbf{ ) method was developed to identify robot manipulation errors by leveraging human instructions.\textit{\textbf{ a novel stacked neural networks model, transferring human attention embedded in verbal reminders to robot attention embedded in robot visual perceiving .
Edit intent: meaning-changed
Original:  To solve this problem, a novel method is proposed, human-to-robot attention transfer ( H2R-AT) to seek help from a human . H2R-AT is developed based on \textbf{ \textit{\textbf{  a novel stacked neural networks model, transferring human attention embedded in verbal reminders to robot attention embedded in robot visual perceiving .
Revised:  To solve this problem, a novel method is proposed, human-to-robot attention transfer ( H2R-AT) to seek help from a human . H2R-AT is developed based on \textbf{ \textit{\textbf{H2R-AT a novel stacked neural networks model, transferring human attention embedded in verbal reminders to robot attention embedded in robot visual perceiving .
Edit intent: meaning-changed
Original:  To solve this problem, a novel method is proposed, human-to-robot attention transfer ( H2R-AT) to seek help from a human . H2R-AT is developed based on \textbf{ \textit{\textbf{  a novel stacked neural networks model, transferring human attention embedded in verbal reminders to robot attention embedded in robot visual perceiving .
Revised:  To solve this problem, a novel method is proposed, human-to-robot attention transfer ( H2R-AT) to seek help from a human . H2R-AT is developed based on \textbf{ \textit{\textbf{ was developed by fusing attention mapping mechanism into a novel stacked neural networks model, transferring human attention embedded in verbal reminders to robot attention embedded in robot visual perceiving .
Edit intent: meaning-changed
Original:  To solve this problem, a novel method is proposed, human-to-robot attention transfer ( H2R-AT) to seek help from a human . H2R-AT is developed based on \textbf{ \textit{\textbf{  a novel stacked neural networks model, transferring human attention embedded in verbal reminders to robot attention embedded in robot visual perceiving .
Revised:  To solve this problem, a novel method is proposed, human-to-robot attention transfer ( H2R-AT) to seek help from a human . H2R-AT is developed based on \textbf{ \textit{\textbf{ a novel stacked neural networks model, transferring human verbal attention into robot visual attention .
Edit intent: clarity
Original:  With the attention transfer from a human , a robot understands what and where \textit{ \textit{ human concerns are to identify and correct its abnormal executions. To validate the effectiveness of H2R-AT, two representative task scenarios , " serve water for a human in a kitchen" and " pick up a defective gear in a factory" with abnormal robot executions, were designed in an open-access simulation platform V-REP;
Revised:  With the attention transfer from a human , a robot understands what and where \textit{ \textit{ human concerns are to identify and correct abnormal manipulations. Two representative task scenarios , " serve water for a human in a kitchen" and " pick up a defective gear in a factory" with abnormal robot executions, were designed in an open-access simulation platform V-REP;
Edit intent: clarity
Original:  To validate the effectiveness of H2R-AT, two representative task scenarios , " serve water for a human in a kitchen" and " pick up a defective gear in a factory" with abnormal robot executions, were designed in an open-access simulation platform V-REP;
Revised:  To validate the effectiveness of H2R-AT, two representative task scenarios : `` serve water for a human in a kitchen" and " pick up a defective gear in a factory" with abnormal robot executions, were designed in an open-access simulation platform V-REP;
Edit intent: fluency