wanyu/IteraTeR_v2 huggingface dataset:
Original:  However, existing SNNs error backpropagation (BP) track methods lack proper handling of spiking discontinuities and suffer from low performance compared to BP methods for traditional artificial neural networks.
Revised:  However, existing SNNs error backpropagation (BP) track methods lack proper handling of spiking discontinuities and suffer from low performance compared with the BP methods for traditional artificial neural networks.
Edit intent: fluency
Original:  In addition, a large number of time steps are typically required for SNNs to achieve decent performance, leading to high latency and rendering spike-based computation unscalable to deep architectures.
Revised:  In addition, a large number of time steps are typically required to achieve decent performance, leading to high latency and rendering spike-based computation unscalable to deep architectures.
Edit intent: clarity
Original:  It considers the all-or-none characteristics of firing activities, capturing inter-neuron dependencies through presynaptic firing times , and internal evolution of each neuronal state through timecapturing intra-neuron dependencies.
Revised:  It considers the all-or-none characteristics of firing activities, capturing inter-neuron dependencies through presynaptic firing times by considering the all-or-none characteristics of firing activities and captures intra-neuron dependencies by handling the internal evolution of each neuronal state through timecapturing intra-neuron dependencies.
Edit intent: coherence
Original:  For various image classification datasets, TSSL-BP efficiently trains deep SNNs within a short temporal time window of a few steps with improved accuracy and runtime efficiency including achieving more than 2\% accuracy improvement over the previously reported SNN work on CIFAR10.
Revised:  For various image classification datasets, TSSL-BP efficiently trains deep SNNs within a much shortened temporal window of a few steps with improved accuracy and runtime efficiency including achieving more than 2\% accuracy improvement over the previously reported SNN work on CIFAR10.
Edit intent: clarity
Original:  For various image classification datasets, TSSL-BP efficiently trains deep SNNs within a short temporal time window of a few steps with improved accuracy and runtime efficiency including achieving more than 2\% accuracy improvement over the previously reported SNN work on CIFAR10.
Revised:  For various image classification datasets, TSSL-BP efficiently trains deep SNNs within a short temporal time window of a few steps while improving the accuracy for various image classification datasets including CIFAR10.
Edit intent: clarity
Original: Representing shapes as level sets of neural networks has been recently proved to be useful for different shape analysis and reconstruction tasks  So far, such representations were computed using either: (i) pre-computed implicit shape representations;
Revised: Representing shapes as level sets of neural networks has been recently proved to be useful for different shape analysis and reconstruction tasks . So far, such representations were computed using either: (i) pre-computed implicit shape representations;
Edit intent: fluency
Original: EXTRA is a popular method for the dencentralized distributed optimization and has broad applications.
Revised: EXTRA is a popular method for dencentralized distributed optimization and has broad applications.
Edit intent: fluency
Original:  Our algorithm is geared toward high-dimensional instances  MFGs that are beyond reach with existing solution methods.
Revised:  Our algorithm is geared toward high-dimensional instances of MFGs that are beyond reach with existing solution methods.
Edit intent: clarity
Original:  By phrasing the problem in this manner, solving the MFG can be interpreted as a special case of training a generative adversarial generative network (GAN).
Revised:  By phrasing the problem in this manner, solving the MFG can be interpreted as a special case of training a generative adversarial network (GAN).
Edit intent: clarity
Original:  Through extensive simulations on Twitter data, we study how aggressive behavior could propagate in the network , and validate our models with ground truth from crawled data and crowdsourced annotations .
Revised:  Through extensive simulations on Twitter data, we study how aggressive behavior could propagate in the network . We validate the models with ground truth from crawled data and crowdsourced annotations .
Edit intent: coherence
Original:  In this paper, we take a first step towards this direction , by studying propagation of aggression on social media .
Revised:  In this paper, we take a first step towards this direction by studying propagation of aggression on social media .
Edit intent: fluency
Original:  We propose ways to enhance these classical models to accommodate how aggression may propagate from one user to another, depending on how each user is connected to other aggressive or regular users.
Revised:  We propose ways to model how aggression may propagate from one user to another, depending on how each user is connected to other aggressive or regular users.
Edit intent: clarity
Original:  We validate the models with ground truth crawled and annotated  data, reaching up to 80\% AUC . We discuss the results and implications of our work.
Revised:  We validate our models with crawled and annotated data, reaching up to 80\% AUC . We discuss the results and implications of our work.
Edit intent: clarity
Original:  We validate the models with ground truth crawled and annotated  data, reaching up to 80\% AUC . We discuss the results and implications of our work.
Revised:  We validate the models with ground truth crawled and annotated data, reaching up to 80\% AUC , and discuss the results and implications of our work.
Edit intent: coherence
Original:  In this paper, we address this problem by proposing a deep neural network model that takes an audio signal A of a source person and a very short video V of a target person as input, and outputs a synthesized high-quality talking face video with natural head pose (making use of the visual information in V), expression and lip synchronization (by considering both A and V).
Revised:  In this paper, we address this problem by proposing a deep neural network model that takes an audio signal A of a source person and a very short video V of a target person as input, and outputs a synthesized high-quality talking face video with personalized head pose (making use of the visual information in V), expression and lip synchronization (by considering both A and V).
Edit intent: clarity
Original:   Extensive experiments and three user studies show that our method can generate high-quality (i.e., natural head movements, expressions and good lip synchronization) personalized talking face videos, outperforming the state-of-the-art methods.
Revised:  Extensive experiments and three user studies show that our method can generate high-quality (i.e., personalized head movements, expressions and good lip synchronization) personalized talking face videos, outperforming the state-of-the-art methods.
Edit intent: clarity
Original:   Extensive experiments and three user studies show that our method can generate high-quality (i.e., natural head movements, expressions and good lip synchronization) personalized talking face videos, outperforming the state-of-the-art methods.
Revised:  Extensive experiments and three user studies show that our method can generate high-quality (i.e., natural head movements, expressions and good lip synchronization) talking face videos, outperforming the state-of-the-art methods.
Edit intent: clarity
Original:  Since both tasks aim to model the association between natural language and program-ming language, recent studies have combined these two tasks to improve their performance.
Revised:  Since both tasks aim to model the association between natural language and programming language, recent studies have combined these two tasks to improve their performance.
Edit intent: fluency
Original: In this paper, we introduce a novel architecture to connecting adaptive learning and neural networks into and arbitrary machine's control system paradigm.
Revised: In this paper, we introduce a novel architecture to connecting adaptive learning and neural networks into an arbitrary machine's control system paradigm.
Edit intent: fluency
Original:  Data mining on CNN architectures can discover useful patterns and fundamental sub-comments from existing CNN architectures, providing common researchers with strong prior knowledge to design CNN architectures when they have no expertise in CNNs.
Revised:  Data mining on CNN architectures can discover useful patterns and fundamental sub-comments from existing CNN architectures, providing researchers with strong prior knowledge to design CNN architectures when they have no expertise in CNNs.
Edit intent: clarity
Original:  Specifically, the current CNN architecture descriptions cannot be exactly vectorized to the input to data mining algorithms.
Revised:  Specifically, the current CNN architecture descriptions cannot be exactly vectorized as input to data mining algorithms.
Edit intent: clarity
Original:  Particularly, three different units of ArcText and an order method have been elaborately designed, to uniquely describe the same architecture including the sufficient information.
Revised:  Particularly, three different units of ArcText and an ordering method have been elaborately designed, to uniquely describe the same architecture including the sufficient information.
Edit intent: fluency
Original:  Particularly, three different units of ArcText and an order method have been elaborately designed, to uniquely describe the same architecture including the sufficient information.
Revised:  Particularly, three different units of ArcText and an order method have been elaborately designed, to uniquely describe the same architecture with sufficient information.
Edit intent: clarity
Original: Numerous Convolutional Neural Network (CNN) models have demonstrated their promising performance mostly in computer vision.
Revised: Convolutional Neural Networks (CNNs) have demonstrated their promising performance mostly in computer vision.
Edit intent: clarity
Original: Numerous Convolutional Neural Network (CNN) models have demonstrated their promising performance mostly in computer vision.
Revised: Numerous Convolutional Neural Network (CNN) models have demonstrated their promising performance in the field of computer vision.
Edit intent: clarity
Original:  The superiority of CNNs mainly relies on their complex architectures that are often manually designed with extensive human expertise.
Revised:  The superiority of CNNs mainly relies on their architectures that are often manually designed with extensive human expertise.
Edit intent: clarity
Original:  The superiority of CNNs mainly relies on their complex architectures that are often manually designed with extensive human expertise.
Revised:  The superiority of CNNs mainly relies on their complex architectures that are often manually crafted with extensive human expertise.
Edit intent: clarity
Original:  Data mining on CNN architectures can discover useful patterns and fundamental sub-comments from existing CNN architectures, providing researchers with strong prior knowledge to design  CNN architectures when they have no expertise in CNNs.
Revised:  Data mining on existing CNN can discover useful patterns and fundamental sub-comments from existing CNN architectures, providing researchers with strong prior knowledge to design CNN architectures when they have no expertise in CNNs.
Edit intent: fluency
Original:  Data mining on CNN architectures can discover useful patterns and fundamental sub-comments from existing CNN architectures, providing researchers with strong prior knowledge to design  CNN architectures when they have no expertise in CNNs.
Revised:  Data mining on CNN architectures can discover useful patterns and fundamental sub-comments from their architectures, providing researchers with strong prior knowledge to design CNN architectures when they have no expertise in CNNs.
Edit intent: clarity
Original:  There have been various state-of-the-art data mining algorithms at hand, while there is  rare work that has been used for this aspect.
Revised:  There have been various state-of-the-art data mining algorithms at hand, while there is only rare work that has been used for this aspect.
Edit intent: clarity
Original:  In this paper, we propose a unified approach, named ArcTxt , to describing CNN architectures based on text.
Revised:  In this paper, we propose a unified approach, named ArcText , to describing CNN architectures based on text.
Edit intent: fluency
Original:  Particularly, three different units of ArcText and an ordering method have been elaborately designed  , to uniquely describe the same architecture with sufficient information.
Revised:  Particularly, four different units and an ordering method have been elaborately designed , to uniquely describe the same architecture with sufficient information.
Edit intent: clarity
Original:  Also, the resulted description can also be exactly converted back to the corresponding CNN architecture.
Revised:  Also, the resulted description can be exactly converted back to the corresponding CNN architecture.
Edit intent: fluency
Original:  ArcText bridge the gap between CNN  and data mining researchers, and has the potentiality to be utilized to wider scenarios.
Revised:  ArcText bridges the gap between CNN and data mining researchers, and has the potentiality to be utilized to wider scenarios.
Edit intent: fluency
Original:  Convolutional Neural Networks (CNNs) have demonstrated their promising performance in the field of computer vision.
Revised: The superiority of Convolutional Neural Networks (CNNs) have demonstrated their promising performance in the field of computer vision.
Edit intent: clarity
Original:  One of the main reasons is the barrier between CNN architectures and data mining algorithms.
Revised:  One of the main reasons is the gap between CNN architectures and data mining algorithms.
Edit intent: clarity
Original:  Specifically, the current CNN architecture descriptions cannot be exactly vectorized as input to data mining algorithms.
Revised:  Specifically, the current CNN architecture descriptions cannot be exactly vectorized to the input of data mining algorithms.
Edit intent: clarity
Original: Extracting the governing stochastic differential equation model from elusive data is crucial to understand and forecast dynamics for various systems.
Revised: Extracting governing stochastic differential equation model from elusive data is crucial to understand and forecast dynamics for various systems.
Edit intent: fluency
Original: Extracting the governing stochastic differential equation model from elusive data is crucial to understand and forecast dynamics for various systems.
Revised: Extracting the governing stochastic differential equation models from elusive data is crucial to understand and forecast dynamics for various systems.
Edit intent: fluency
Original: Extracting the governing stochastic differential equation model from elusive data is crucial to understand and forecast dynamics for various systems.
Revised: Extracting the governing stochastic differential equation model from elusive data is crucial to understand and forecast dynamics for complex systems.
Edit intent: clarity
Original:  We devise a method to extract the drift term and estimate the diffusion coefficient of a governing stochastic dynamical system, from its time-series data for the most probable transition trajectory.
Revised:  We devise a method to extract the drift term and estimate the diffusion coefficient of a governing stochastic dynamical system, from its time-series data of the most probable transition trajectory.
Edit intent: clarity
Original:  We first estimate the coefficients of the Euler-Lagrange equation based on the data of the most probable trajectory, and then we calculate the drift and diffusion coefficient of the governing stochastic dynamical system.
Revised:  We first estimate the coefficients of the Euler-Lagrange equation based on the data of the most probable trajectory, and then we calculate the drift and diffusion coefficients of the governing stochastic dynamical system.
Edit intent: fluency
Original:  We finally illustrate our method with an example  .
Revised:  Finally, we illustrate our method with an example .
Edit intent: fluency
Original:  The fixation probability under intermediate/fast random and periodic switching can hence vary significantly, with markedly different asymptotic behaviors .
Revised:  The fixation probability under intermediate/fast random and periodic switching .
Edit intent: clarity
Original:  We also determine the  conditions under which the fixation probability of the slow strain is optimal when the dynamics is driven by asymmetric switching .
Revised:  We also determine the conditions under which the fixation probability of the slow strain is maximal .
Edit intent: clarity
Original: Eukaryotic cells are able to sense chemical gradients over a wide range of different environments.
Revised: Eukaryotic cells are able to sense chemical gradients in a wide range of different environments.
Edit intent: clarity
Original: Eukaryotic cells are able to sense chemical gradients over a wide range of different environments.
Revised: Eukaryotic cells are able to sense chemical gradients over a wide range of environments.
Edit intent: clarity
Original:  We show that, if a cell is exposed to a widely variable environment, cells may gain additional chemotactic accuracy by expressing multiple types of receptors with varying affinities for the same signal .
Revised:  We show that, if a cell is exposed to a highly variable environment, cells may gain additional chemotactic accuracy by expressing multiple types of receptors with varying affinities for the same signal .
Edit intent: clarity
Original:  We show that, if a cell is exposed to a widely variable environment, cells may gain additional chemotactic accuracy by expressing multiple types of receptors with varying affinities for the same signal .
Revised:  We show that, if a cell is exposed to a widely variable environment, it may gain chemotactic accuracy by expressing multiple types of receptors with varying affinities for the same signal .
Edit intent: clarity
Original:  We show that, if a cell is exposed to a widely variable environment, cells may gain additional chemotactic accuracy by expressing multiple types of receptors with varying affinities for the same signal .
Revised:  We show that, if a cell is exposed to a widely variable environment, cells may gain additional chemotactic accuracy by expressing multiple receptor types with varying affinities for the same signal .
Edit intent: fluency
Original:  As the uncertainty of the environment is increased, there is a transition between cells preferring to express a single receptor type and a mixture of types - hedging their bets against the possibility of an unfavorable environment.
Revised:  As the uncertainty of the environment is increased, there is a transition between cells preferring a single receptor type and a mixture of types - hedging their bets against the possibility of an unfavorable environment.
Edit intent: clarity
Original:  We use this to predict the optimal receptor dissociation constants given a particular environment.
Revised:  We predict the optimal receptor dissociation constants given a particular environment.
Edit intent: coherence
Original:  We use this to predict the optimal receptor dissociation constants given a particular environment.
Revised:  We use this to predict the optimal receptor affinities given a particular environment.
Edit intent: clarity
Original:  In sensing this signal , cells may also integrate their measurement over time.
Revised:  In chemotaxing , cells may also integrate their measurement over time.
Edit intent: clarity
Original: This paper presents Neural Lyapunov MPC, an algorithm to alternately train a Lyapunov neural network and a stabilising constrained Model Predictive Controller (MPC) , given a neural network model of the system dynamics.
Revised: With a growing interest in data-driven control techniques, Model Predictive Control (MPC) , given a neural network model of the system dynamics.
Edit intent: coherence
Original: We develop exact representations of two layer neural networks with rectified linear units  in terms of a single convex program with number of variables polynomial in the number of training samples and  number of hidden neurons.
Revised: We develop exact representations of training two-layer neural networks with rectified linear units in terms of a single convex program with number of variables polynomial in the number of training samples and number of hidden neurons.
Edit intent: clarity
Original: We develop exact representations of two layer neural networks with rectified linear units  in terms of a single convex program with number of variables polynomial in the number of training samples and  number of hidden neurons.
Revised: We develop exact representations of two layer neural networks with rectified linear units in terms of a single convex program with number of variables polynomial in the number of training samples and the number of hidden neurons.
Edit intent: fluency
Original:   Moreover, we show that certain standard multi-layer convolutional neural networks are equivalent to L1 regularized linear models in a polynomial sized discrete Fourier feature space .
Revised:  Moreover, we show that certain standard convolutional linear networks are equivalent to L1 regularized linear models in a polynomial sized discrete Fourier feature space .
Edit intent: clarity
Original: We combine stochastic thermodynamics, large deviation theory, and information theory to derive fundamental limits on the accuracy with which single cells can detect external chemical concentrationsthrough arbitrarily complex, energy consuming nonequilibrium cell-surface receptors .
Revised: We combine stochastic thermodynamics, large deviation theory, and information theory to derive fundamental limits on the accuracy with which single cells can detect external concentrations .
Edit intent: clarity
Original: In this paper , we address highly mobile vehicular networks from users' perspectives in highway transportation.
Revised: This paper investigates highly mobile vehicular networks from users' perspectives in highway transportation.
Edit intent: clarity
Original:  Particularly, we consider a centralized software-defined environment in which centralized resources can be assigned, programmed, and controlled using the anchor nodes (ANs) of the edge servers.
Revised:  Particularly, a centralized software-defined environment in which centralized resources can be assigned, programmed, and controlled using the anchor nodes (ANs) of the edge servers.
Edit intent: coherence
Original:  Particularly, we consider a centralized software-defined environment in which centralized resources can be assigned, programmed, and controlled using the anchor nodes (ANs) of the edge servers.
Revised:  Particularly, we consider a centralized software-defined architecture is introduced in which centralized resources can be assigned, programmed, and controlled using the anchor nodes (ANs) of the edge servers.
Edit intent: clarity
Original:  Unlike the legacy networks, where a typical user is served from only one access point (AP), in our proposed system model, a vehicle user (VU) is served from multiple APs simultaneously.
Revised:  Unlike the legacy networks, where a typical user is served from only one access point (AP), in the proposed system model, a vehicle user (VU) is served from multiple APs simultaneously.
Edit intent: fluency
Original:  Unlike the legacy networks, where a typical user is served from only one access point (AP), in our proposed system model, a vehicle user (VU) is served from multiple APs simultaneously.
Revised:  Unlike the legacy networks, where a typical user is served from only one access point (AP), in our proposed system model, a vehicle user is served from multiple APs simultaneously.
Edit intent: clarity