wanyu/IteraTeR_v2 huggingface dataset:
Original:  From our analysis it appears that the spreading is reaching saturation in China, due to the strong containment policy of the national governments .
Revised:  From our analysis it appears that the spreading is reaching saturation in China, due to the strong containment policy of the national government .
Edit intent: fluency
Original:  Since  financial market is inherently noisy and stochastic, a majority of financial time-series of interests are non-stationary, and often obtained from different modalities.
Revised:  Since the financial market is inherently noisy and stochastic, a majority of financial time-series of interests are non-stationary, and often obtained from different modalities.
Edit intent: fluency
Original:  The class Tally(d(n), g(n)) defines the class of all those tally sets T\subseteq \{1\}^* such that d(n) gives an upper bound for the number of elements of in T^{\le n, and g(n) gives the gap between a string 1^n\in T and the next string 1^m in T with n<m and g(n)<m (there is no string 1^k\in T with n<k<m).
Revised:  , and g(n) gives the gap between a string 1^n\in T and the next string 1^m in T with n<m and g(n)<m (there is no string 1^k\in T with n<k<m).
Edit intent: clarity
Original:  We identify a class of function pairs d(n) and g(n) (for example, d(n)=\log \log n and g(n)=2^{2^{2n}}) such }  that if MCSP ~is ZPP\cap Tally(d(n), g(n))-hard under polynomial time truth-table reductions, then EXP\not= ZPP.
Revised:  We identify a class of function pairs d(n) and g(n) (for example, d(n)=\log \log n and g(n)=2^{2^{2n}}) such } We also show that if MCSP ~is ZPP\cap Tally(d(n), g(n))-hard under polynomial time truth-table reductions, then EXP\not= ZPP.
Edit intent: clarity
Original:  Although efficient and accurate, the latest video analytics systems have not supported analytics beyond selection and aggregation yet .
Revised:  Although efficient and accurate, the latest video analytic systems have not supported analytics beyond selection and aggregation yet .
Edit intent: fluency
Original:  Although efficient and accurate, the latest video analytics systems have not supported analytics beyond selection and aggregation yet .
Revised:  Although efficient and accurate, the latest video analytics systems have not supported analytics beyond simple queries like selection .
Edit intent: clarity
Original:  Everest can help rank and identify the most interesting frames/moments from videos with theoretical accuracy guarantees.
Revised:  Everest ranks and identifies the most interesting frames/moments from videos with theoretical accuracy guarantees.
Edit intent: clarity
Original:  Everest is built on a careful synthesis of  machine learning, computer vision , and Top-K query processing.
Revised:  Everest is built with a careful synthesis of machine learning, computer vision , and Top-K query processing.
Edit intent: clarity
Original:  Evaluations on six real-world video streams and the latest Visual Road benchmark show that Everest can achieve between 10.8x to 17.9 x higher efficiency than baseline approaches with high  accuracy.
Revised:  Evaluations on six real-world videos and the latest Visual Road benchmark show that Everest can achieve between 10.8x to 17.9 x higher efficiency than baseline approaches with high accuracy.
Edit intent: clarity
Original: Recently, the impressive accuracy of deep neural networks (DNNs) has created great demands on practical analytics over video data.
Revised: The impressive accuracy of deep neural networks (DNNs) has created great demands on practical analytics over video data.
Edit intent: coherence
Original:  Although efficient and accurate, the latest video analytic systems have not supported analytics beyond simple queries like selection .
Revised:  Although efficient and accurate, the latest video analytic systems have not supported analytics beyond selection and aggregation queries .
Edit intent: clarity
Original:  Everest is  built with a careful synthesis of deep computer vision , machine learning, uncertain data management, and Top-K query processing.
Revised:  Everest is a system built with a careful synthesis of deep computer vision , machine learning, uncertain data management, and Top-K query processing.
Edit intent: clarity
Original:  Everest is  built with a careful synthesis of deep computer vision , machine learning, uncertain data management, and Top-K query processing.
Revised:  Everest is built with a careful synthesis of deep computer vision models, uncertain data management, and Top-K query processing.
Edit intent: clarity
Original:  Evaluations on five real-world videos and the latest Visual Road benchmark show that Everest achieves between 16.3 x to 20.6x higher efficiency than baseline approaches with high result accuracy .
Revised:  Evaluations on real-world videos and the latest Visual Road benchmark show that Everest achieves between 16.3 x to 20.6x higher efficiency than baseline approaches with high result accuracy .
Edit intent: clarity
Original:  Evaluations on five real-world videos and the latest Visual Road benchmark show that Everest achieves between 16.3 x to 20.6x higher efficiency than baseline approaches with high result accuracy .
Revised:  Evaluations on five real-world videos and the latest Visual Road benchmark show that Everest achieves between 16.3 x to 20.6x higher efficiency than baseline approaches with high result accuracy 
Edit intent: fluency
Original:  Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of  IoT and create a new world featured by data-driven AI.
Revised:  Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world featured by data-driven AI.
Edit intent: fluency
Original:  In this paper, we propose a method for predicting TUG score from gait characteristics extracted from video based on computer vision and machine learning technologies.
Revised:  In this paper, we propose a method for predicting TUG score from gait characteristics extracted from video with computer vision and machine learning technologies.
Edit intent: coherence
Original: YouTube recently announced a decision to exclude potentially harmful content from video recommendations while allowing such videos to remain on the platform , but it is unclear whether this compromise is sufficient in mitigating YouTube's role in propagating this content .
Revised: YouTube recently announced a decision to exclude potentially harmful content from video recommendations while allowing such videos to remain on the platform . While this step intends to reduce YouTube's role in propagating this content .
Edit intent: clarity
Original:  Results suggest YouTube's de-recommending AIN-produced content has a significant suppressive effect on sharing of AIN videos in both Twitter and Reddit , with our models showing a significant and downward trend in sharing.
Revised:  Results suggest YouTube's de-recommendation experience a significant decreasing trend in sharing on both Twitter and Reddit , with our models showing a significant and downward trend in sharing.
Edit intent: clarity
Original:  For conspiracy video sharing , however, we see no significant change in Reddit and find an increase in the level of conspiracy sharing in Twitter.
Revised:  For conspiracy video sharing , however, we see no significant effect in Twitter but find a significant increase in the level of conspiracy sharing in Twitter.
Edit intent: clarity
Original:  We see no significant effect on makeup sharingin either platform , suggesting YouTube's targeting particular content types has a targeted effect.
Revised:  We see no significant effect on makeup sharingin either platform , suggesting YouTube's suppressing particular content types has a targeted effect.
Edit intent: clarity
Original:  Reducing exposure to anti-social videos without deleting them therefore has the potential to improve quality across the information ecosystem, but additional transparency is needed to evaluate these effects further.
Revised:  This work finds evidence that reducing exposure to anti-social videos without deleting them therefore has the potential to improve quality across the information ecosystem, but additional transparency is needed to evaluate these effects further.
Edit intent: clarity
Original:  In the previous work 13%DIFDELCMD < ] %%% upper bounds for constants which appear in the standard finite element error estimates for elliptic regularized geometric evolution equations in dependence on the regularization parameter have been addressed and an exponential relation in the inverse regularization parameter has been observed.
Revised:  In the previous work %DIFDELCMD < ] %%% upper bounds for constants which appear in the standard finite element error estimates for elliptic regularized geometric evolution equations in dependence on the regularization parameter have been addressed and an exponential relation in the inverse regularization parameter has been observed.
Edit intent: coherence
Original:  In this paper the aim is twofold: First, we extend the results from 13%DIFDELCMD < ] %%% to H^2(\Omega) conforming approaches which are of interest in the special case of higher regularity of the solution in order to detect level sets , and second, we present a strong indication that the previously mentioned exponential bound which carries over to the higher conforming case is optimal (independent from the degree of conformity).
Revised:  In this paper the aim is twofold: First, we extend the results from %DIFDELCMD < ] %%% to H^2(\Omega) conforming approaches which are of interest in the special case of higher regularity of the solution in order to detect level sets , and second, we present a strong indication that the previously mentioned exponential bound which carries over to the higher conforming case is optimal (independent from the degree of conformity).
Edit intent: coherence
Original:   ]  The scheme is based on a known regularization procedure and produces different kinds of errors, like regularization error, FE approximation error for the regularized problems and  full error.
Revised:  17] The scheme is based on a known regularization procedure and produces different kinds of errors, like regularization error, FE approximation error for the regularized problems and full error.
Edit intent: fluency
Original:   ]  The scheme is based on a known regularization procedure and produces different kinds of errors, like regularization error, FE approximation error for the regularized problems and  full error.
Revised:  ] The scheme is based on a known regularization procedure and produces different kinds of errors, a regularization error, FE approximation error for the regularized problems and full error.
Edit intent: coherence
Original:   ]  The scheme is based on a known regularization procedure and produces different kinds of errors, like regularization error, FE approximation error for the regularized problems and  full error.
Revised:  ] The scheme is based on a known regularization procedure and produces different kinds of errors, like regularization error, FE approximation error for the regularized problems and a full error.
Edit intent: fluency
Original:  While in the literature and own previous work different aspects of the aforementioned error types are treated, here, we solely and for the first time focus on the FE approximation error in the H ^{2,\mu} norm for the regularized equation inclusively the aspect of working out all dependencies from the regularization parameter explicitly .
Revised:  While in the literature and own previous work different aspects of the aforementioned error types are treated, here, we solely and for the first time focus on the FE approximation error in the H ^{2,\mu} norm for the regularized equation analyzing also the dependencies from the regularization parameter explicitly .
Edit intent: clarity
Original:  While in the literature and own previous work different aspects of the aforementioned error types are treated, here, we solely and for the first time focus on the FE approximation error in the H ^{2,\mu} norm for the regularized equation inclusively the aspect of working out all dependencies from the regularization parameter explicitly .
Revised:  While in the literature and own previous work different aspects of the aforementioned error types are treated, here, we solely and for the first time focus on the FE approximation error in the H ^{2,\mu} norm for the regularized equation inclusively the aspect of working out all dependencies from the regularization parameter .
Edit intent: clarity
Original:  The proposed method , called culling , can be used for rapid analysis or combined with a normative approach by compiling a survey on the subset of measures that satisfy certain normative conditions (axioms).
Revised:  The proposed method called culling can be used for rapid analysis or combined with a normative approach by compiling a survey on the subset of measures that satisfy certain normative conditions (axioms).
Edit intent: fluency
Original:  Although the recent deep reinforcement learning (DRL) approaches have been successfully applied to navigation, their high energy consumption limits their use in many robotic applications.
Revised:  Although the recent deep reinforcement learning (DRL) approaches have been successfully applied to navigation, their high energy consumption limits their use in several robotic applications.
Edit intent: clarity
Original:  Our hybrid framework, Spiking deep deterministic policy gradient (SDDPG), consists of a spiking actor network (SAN) and a deep critic network, where the two networks were trained jointly using gradient descent.
Revised:  Our hybrid framework, spiking deep deterministic policy gradient (SDDPG), consists of a spiking actor network (SAN) and a deep critic network, where the two networks were trained jointly using gradient descent.
Edit intent: fluency
Original:   When validated on both simulated and real-world complex environments, our method on Loihi not only consumed 75 times less energy per inference as compared to DDPG on Jetson TX2, but also had a higher rate of successfully navigating to the goal which ranged by 1 %DIFDELCMD < \\%%% %DIF < to 4.2\\%, depending on the forward-propagation timestep size.
Revised:  When validated on simulated and real-world complex environments, our method on Loihi not only consumed 75 times less energy per inference as compared to DDPG on Jetson TX2, but also had a higher rate of successfully navigating to the goal which ranged by 1 %DIFDELCMD < \\%%% %DIF < to 4.2\\%, depending on the forward-propagation timestep size.
Edit intent: coherence
Original:   When validated on both simulated and real-world complex environments, our method on Loihi not only consumed 75 times less energy per inference as compared to DDPG on Jetson TX2, but also had a higher rate of successfully navigating to the goal which ranged by 1 %DIFDELCMD < \\%%% %DIF < to 4.2\\%, depending on the forward-propagation timestep size.
Revised:  When validated on both simulated and real-world complex environments, our method on Loihi consumed 75 times less energy per inference as compared to DDPG on Jetson TX2, but also had a higher rate of successfully navigating to the goal which ranged by 1 %DIFDELCMD < \\%%% %DIF < to 4.2\\%, depending on the forward-propagation timestep size.
Edit intent: clarity
Original:   When validated on both simulated and real-world complex environments, our method on Loihi not only consumed 75 times less energy per inference as compared to DDPG on Jetson TX2, but also had a higher rate of successfully navigating to the goal which ranged by 1 %DIFDELCMD < \\%%% %DIF < to 4.2\\%, depending on the forward-propagation timestep size.
Revised:  When validated on both simulated and real-world complex environments, our method on Loihi not only consumed 75 times less energy per inference as compared to DDPG on Jetson TX2, and also exhibited a higher rate of successfully navigating to the goal which ranged by 1 %DIFDELCMD < \\%%% %DIF < to 4.2\\%, depending on the forward-propagation timestep size.
Edit intent: clarity
Original:   When validated on both simulated and real-world complex environments, our method on Loihi not only consumed 75 times less energy per inference as compared to DDPG on Jetson TX2, but also had a higher rate of successfully navigating to the goal which ranged by 1 %DIFDELCMD < \\%%% %DIF < to 4.2\\%, depending on the forward-propagation timestep size.
Revised:  When validated on both simulated and real-world complex environments, our method on Loihi not only consumed 75 times less energy per inference as compared to DDPG on Jetson TX2, but also had a higher rate of successful navigation to the goal which ranged by 1 %DIFDELCMD < \\%%% %DIF < to 4.2\\%, depending on the forward-propagation timestep size.
Edit intent: fluency
Original:   When validated on both simulated and real-world complex environments, our method on Loihi not only consumed 75 times less energy per inference as compared to DDPG on Jetson TX2, but also had a higher rate of successfully navigating to the goal which ranged by 1 %DIFDELCMD < \\%%% %DIF < to 4.2\\%, depending on the forward-propagation timestep size.
Revised:  When validated on both simulated and real-world complex environments, our method on Loihi not only consumed 75 times less energy per inference as compared to DDPG on Jetson TX2, but also had a higher rate of successfully navigating to the goal , which ranged from 1 %DIFDELCMD < \\%%% %DIF < to 4.2\\%, depending on the forward-propagation timestep size.
Edit intent: coherence
Original:  These  results reinforce our ongoing effort to design brain-inspired algorithms for controlling autonomous robots with neuromorphic hardware.
Revised:  These results reinforce our ongoing efforts to design brain-inspired algorithms for controlling autonomous robots with neuromorphic hardware.
Edit intent: fluency
Original:  A  variational inference method is employed to train a recurrent neural network jointly with a feedforward neural network for an approximately posterior distribution .
Revised:  A variational inference method is employed to train a feedforward neural network for an approximately posterior distribution .
Edit intent: clarity
Original: We propose LETO, a new hybrid Lagrangian-Eulerian method for topology optimization .
Revised: In this paper, a hybrid Lagrangian-Eulerian method for topology optimization .
Edit intent: clarity
Original:  The  quadrature points act as MPM particles embedded in a lower-resolution grid and enable  sub-cell  resolution of intricate structures with a reduced computational cost.
Revised:  The quadrature points act as MPM particles embedded in a lower-resolution grid and enable a sub-cell resolution of intricate structures with a reduced computational cost.
Edit intent: fluency
Original:  The transfer is based on a smooth radial kernel involved in the compliance objective to avoid the artificial checkerboard pattern.
Revised:  This transfer is based on a smooth radial kernel involved in the compliance objective to avoid the artificial checkerboard pattern.
Edit intent: fluency
Original:  A quadrature-level connectivity graph-based method is adopted to avoid the artificial QR patterns commonly existing in multi-resolution topology optimization methods.
Revised:  A quadrature-level connectivity graph-based method is adopted to avoid the artificial checkerboard issues commonly existing in multi-resolution topology optimization methods.
Edit intent: clarity
Original: Class selectivity, typically defined as how different a neuron's responses are across different classes of stimuli or data samples, is a common metric used to interpret the function of individual neurons in biological and artificial neural networks .
Revised: Class selectivity, typically defined as how different a neuron's responses are across different classes of stimuli or data samples-is commonly used for this purpose .
Edit intent: clarity
Original:  In order to investigate the causal impact of class selectivity on network function , we directly regularize for or against class selectivity.
Revised:  We investigated the causal impact of class selectivity on network function , we directly regularize for or against class selectivity.
Edit intent: coherence
Original:  In order to investigate the causal impact of class selectivity on network function , we directly regularize for or against class selectivity.
Revised:  In order to investigate the causal impact of class selectivity on network function by directly regularizing for or against class selectivity.
Edit intent: coherence
Original:  In contrast, increasing class selectivity beyond the levels naturally learned during training had rapid and disastrous effects on test accuracy  .
Revised:  In contrast, regularizing to increase class selectivity had rapid and disastrous effects on test accuracy .
Edit intent: clarity
Original:  Using this regularizer to reduce class selectivity across units in convolutional neural networks increased test accuracy by over 2\% in ResNet18 trained on Tiny ImageNet.
Revised:  Using this regularizer to reduce class selectivity across units in convolutional neural networks increased test accuracy by over 2\% for ResNet18 trained on Tiny ImageNet.
Edit intent: clarity
Original:  In contrast, regularizing to increase class selectivity had rapid and disastrous effects on test accuracy across all models and datasets.
Revised:  In contrast, regularizing to increase class selectivity significantly decreased test accuracy across all models and datasets.
Edit intent: clarity
Original:  As the silence feature is extracted per utterance, we grouped the silence feature with high statistical functions from a set of acoustic features.
Revised:  Since the silence feature is extracted per utterance, we grouped the silence feature with high statistical functions from a set of acoustic features.
Edit intent: fluency
Original:  The result reveals that the silence feature affects the arousal dimension more than other emotion dimensions.
Revised:  The result reveals that the silence features affect the arousal dimension more than other emotion dimensions.
Edit intent: fluency
Original:  The proper choice of a  factor in the calculation of silence feature improves the performance of dimensional speech emotion recognition performance  in terms of a concordance correlation coefficient.
Revised:  The proper choice of a factor in the calculation of silence feature improved the performance of dimensional speech emotion recognition performance in terms of a concordance correlation coefficient.
Edit intent: fluency
Original:  The proper choice of a  factor in the calculation of silence feature improves the performance of dimensional speech emotion recognition performance  in terms of a concordance correlation coefficient.
Revised:  The proper choice of a factor in the calculation of silence feature improves the performance of dimensional speech emotion recognition performance , in terms of a concordance correlation coefficient.
Edit intent: fluency
Original: We confirm current taxonomic classification of the 2019-nCoVs through alignment-free comparative genomics with IC-PIC method.
Revised: The origin and early spread of 2019-nCoV is studied by phylogenetic analysis using IC-PIC method.
Edit intent: clarity
Original:  The obtained IC-PIC tree of Betacoronavirus supports the assumption that the novel viruses are of bat origin with pangolin as one of the possible intermediate hosts and RaTG13 having played important role in the virus mutation.
Revised:  The obtained IC-PIC tree of Betacoronavirus supports the assumption that these novel viruses are of bat origin with pangolin as one of the possible intermediate hosts and RaTG13 having played important role in the virus mutation.
Edit intent: fluency