wanyu/IteraTeR_v2 huggingface dataset:
Original:  Using correlation-based community detection, the method yields an optimally contrasted mesoscopic functional structure , i.e. groups of neurons (or small brain regions) that are positively correlated internally , while at the same time negatively correlated with each other .
Revised:  Using correlation-based community detection, the method yields an optimally contrasted functional `signature' , i.e. groups of neurons (or small brain regions) that are positively correlated internally , while at the same time negatively correlated with each other .
Edit intent: clarity
Original:  Using correlation-based community detection, the method yields an optimally contrasted mesoscopic functional structure , i.e. groups of neurons (or small brain regions) that are positively correlated internally , while at the same time negatively correlated with each other .
Revised:  Using correlation-based community detection, the method yields an optimally contrasted mesoscopic functional structure , i.e. a partition into modules that are positively correlated internally , while at the same time negatively correlated with each other .
Edit intent: clarity
Original:  Using correlation-based community detection, the method yields an optimally contrasted mesoscopic functional structure , i.e. groups of neurons (or small brain regions) that are positively correlated internally , while at the same time negatively correlated with each other .
Revised:  Using correlation-based community detection, the method yields an optimally contrasted mesoscopic functional structure , i.e. groups of neurons (or small brain regions) that are positively correlated internally and negatively correlated across .
Edit intent: clarity
Original:  The method is purely data driven and does not use any presumptions or network representation.
Revised:  The method is purely data-driven, does not use any presumptions or network representation.
Edit intent: fluency
Original:  To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, analyzes the effect of the features on the algorithm performance.
Revised:  To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates the effect of the features on the algorithm performance.
Edit intent: clarity
Original:  In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves even further the forecasting accuracy .
Revised:  In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy .
Edit intent: clarity
Original:  In detail, we show that the three proposed models lead to improvements that are statistically significant.
Revised:  We show that the three proposed models lead to improvements that are statistically significant.
Edit intent: clarity
Original:  Particularly, due to market integration,  predictive accuracy is improved from 15.7\% to 12.5\% sMAPE (symmetric mean absolute percentage error).
Revised:  Particularly, due to market integration, the predictive accuracy is improved from 15.7\% to 12.5\% sMAPE (symmetric mean absolute percentage error).
Edit intent: coherence
Original:  In addition, we also show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features.
Revised:  In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features.
Edit intent: coherence
Original: In this paper, we show a phenomenon  where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods , which we named " super-convergence " . One of the key elements of super-convergence is training with cyclical learning rates and a large maximum learning rate.
Revised: In this paper, we show a phenomenon , which we named "super-convergence", where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods , which we named " super-convergence " . One of the key elements of super-convergence is training with cyclical learning rates and a large maximum learning rate.
Edit intent: fluency
Original: In this paper, we show a phenomenon  where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods , which we named " super-convergence " . One of the key elements of super-convergence is training with cyclical learning rates and a large maximum learning rate.
Revised: In this paper, we show a phenomenon where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods . The existence of super-convergence " . One of the key elements of super-convergence is training with cyclical learning rates and a large maximum learning rate.
Edit intent: coherence
Original:  We also provide an explanation for the benefits of a large learning rate using a simplification of the Hessian Free optimization method to compute an estimate of the optimal learning rate.
Revised:  We also derive a simplification of the Hessian Free optimization method to compute an estimate of the optimal learning rate.
Edit intent: clarity
Original: In this paper, we show a phenomenon, which we named "super-convergence", where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods.
Revised: In this paper, we describe a phenomenon, which we named "super-convergence", where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods.
Edit intent: clarity
Original: In this paper, we show a phenomenon, which we named "super-convergence", where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods.
Revised: In this paper, we show a phenomenon, which we named "super-convergence", where residual networks can be trained an order of magnitude fewer iterations than is used with standard training methods.
Edit intent: fluency
Original: In this paper, we show a phenomenon, which we named "super-convergence", where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods.
Revised: In this paper, we show a phenomenon, which we named "super-convergence", where residual networks can be trained using an order of magnitude faster than with standard training methods.
Edit intent: clarity
Original:  One of the key elements of super-convergence is training with cyclical learning rates and a large maximum learning rate.
Revised:  One of the key elements of super-convergence is training with one learning rate cycle and a large maximum learning rate.
Edit intent: clarity
Original:  We also derive a simplification of the Hessian Free optimization method to compute an estimate of the optimal learning rate.
Edit intent: coherence
Original:  This paper presents a general overview of IDSs, the way they are classified  and the different algorithms used to detect anomalous activities  .
Revised:  This paper presents a general overview of IDSs, the way they are classified , and the different algorithms used to detect anomalous activities .
Edit intent: fluency
Original: In this paper we derive a second order approximation for an infinite dimensional limit order book model, in which the dynamics of the incoming order flow is allowed to depend on the current market price as well as on a volume indicator (e.g.  the volume standing at the top of the book).
Revised: In this paper we derive a second order approximation for an infinite dimensional limit order book model, in which the dynamics of the incoming order flow is allowed to depend on the current market price as well as on a volume indicator (e.g. ~ the volume standing at the top of the book).
Edit intent: fluency
Original:  This leads to a measure valued SDE driven by an infinite dimensional Brownian motion in the second order approximation of the volume process.
Revised:  This leads to a measure-valued SDE driven by an infinite dimensional Brownian motion in the second order approximation of the volume process.
Edit intent: fluency
Original:  The PDE is approximated with a deep neural network , which is trained on random batches of spatial points to satisfy the differential operator  and boundary conditions.
Revised:  The PDE is approximated with a deep neural network which is trained on random batches of spatial points to satisfy the differential operator and boundary conditions.
Edit intent: fluency
Original:  The PDE is approximated with a deep neural network , which is trained on random batches of spatial points to satisfy the differential operator  and boundary conditions.
Revised:  The PDE is approximated with a deep neural network , which is trained to satisfy the differential operator and boundary conditions.
Edit intent: clarity
Original:  Instead of forming a mesh, sequences of spatial points are randomly sampled .
Revised:  Instead of forming a mesh, the neural network is trained on batches of randomly sampled time and space points .
Edit intent: clarity
Original:  We prove that the neural network converges to the solution of the partial differential equation as the number of hidden units increases.
Edit intent: coherence
Original:  The first is a brute-force algorithm which in most cases produces optimal decompositions  but is generally impractical for high-rank tensors, as the number of possible choices grows exponentially in rank.
Revised:  The first is a brute-force algorithm which produces optimal decompositions but is generally impractical for high-rank tensors, as the number of possible choices grows exponentially in rank.
Edit intent: clarity
Original:  It is commonly assumed in neuronal coding that repeated presentations of a stimulus to a coding neuron elicit similar responses.
Revised: Background: It is commonly assumed in neuronal coding that repeated presentations of a stimulus to a coding neuron elicit similar responses.
Edit intent: coherence
Original:   We use independent steady-rate Poisson processes as surrogates for spike trains with fixed rate and no timing information to address two basic questions: How does the sensitivity of the different spike train distances to temporal coding depend on the rates of the two processes and how do the distances deal with very low rates?
Revised:  New Method: We use independent steady-rate Poisson processes as surrogates for spike trains with fixed rate and no timing information to address two basic questions: How does the sensitivity of the different spike train distances to temporal coding depend on the rates of the two processes and how do the distances deal with very low rates?
Edit intent: clarity
Original:   Spike-resolved distances always contain rate information even for parameters indicating time coding.
Revised:  Results: Spike-resolved distances always contain rate information even for parameters indicating time coding.
Edit intent: fluency
Original:   While our results on rate-dependent expectation values for the spike-resolved distances agree with Chicharro et al. (2011) , we here go one step further and specifically investigate applicability for very low rates.
Revised:  While our results on rate-dependent expectation values for the spike-resolved distances agree with \mbox{ Chicharro11 , we here go one step further and specifically investigate applicability for very low rates.
Edit intent: clarity
Original:   The most appropriate measure depends on the rates of the data being analysed.
Revised:  Conclusions: The most appropriate measure depends on the rates of the data being analysed.
Edit intent: coherence
Original:  Our model not only allows mixed membership for each entity , but also provides reliable statistical inference on network structure.
Revised:  Our model not only allows each entity to possess more than one membership , but also provides reliable statistical inference on network structure.
Edit intent: clarity
Original:  Our model not only allows mixed membership for each entity , but also provides reliable statistical inference on network structure.
Revised:  Our model not only allows mixed membership for each entity , but also provides reliable statistical inference about network structure.
Edit intent: clarity
Original:  We design a Bayesian-based algorithm, the Gibbs sampling, to estimate membership parameters.
Revised:  We utilize a Bayesian-based algorithm, the Gibbs sampling, to estimate membership parameters.
Edit intent: clarity
Original:  Some concluding remarks and future work are discussed briefly at last.
Revised:  Some concluding remarks and future work are addressed briefly at last.
Edit intent: fluency
Original:  A flexible distance function is adopted to measure affinities among a set of entities in a social network.
Revised:  A flexible function is adopted to measure affinities among a set of entities in a social network.
Edit intent: clarity
Original:  The model not only allows each entity  to possess more than one membership, but also provides reliable statistical inference about network structure.
Revised:  The model not only allows each entity in the network to possess more than one membership, but also provides reliable statistical inference about network structure.
Edit intent: clarity
Original:  The model not only allows each entity  to possess more than one membership, but also provides reliable statistical inference about network structure.
Revised:  The model not only allows each entity to possess more than one membership, but also provides accurate statistical inference about network structure.
Edit intent: clarity
Original:  We evaluate the performance of our algorithm by applying our model to two empirical social network data, the Zachary club data and the bottlenose dolphin network data.
Revised:  We evaluate the performance of the proposed algorithm by applying our model to two empirical social network data, the Zachary club data and the bottlenose dolphin network data.
Edit intent: clarity
Original:  Some concluding remarks and future work are addressed briefly at last .
Revised:  We also conduct some numerical studies for different types of simulated networks for assessing the effectiveness of our algorithm. In the end, some concluding remarks and future work are addressed briefly at last .
Edit intent: clarity
Original:  Some concluding remarks and future work are addressed briefly at last .
Revised:  Some concluding remarks and future work are addressed briefly .
Edit intent: clarity
Original:  Due to the complexity of the system, most e-commerce platforms employ heuristic allocation algorithms that mainly depend on the sellers' transaction records and without taking the rationality of the sellers into account, which makes them susceptible to  price manipulations.
Revised:  Due to the complexity of the system, most e-commerce platforms employ heuristic allocation algorithms that mainly depend on the sellers' transaction records and without taking the rationality of the sellers into account, which makes them susceptible to several price manipulations.
Edit intent: clarity
Original:  To tackle the problem of continuity and high-dimensionality of states and actions, we adopt the ideas of the DDPG algorithm to design an actor-critic gradient policy algorithm which takes advantage of the problem domain in order to achieve convergence and stability.
Revised:  To tackle the problem of continuity and high-dimensionality of states and actions, we adopt the ideas of the DDPG algorithm to design an actor-critic gradient policy algorithm which takes advantage of the problem domain in order to achieve covergence and stability.
Edit intent: fluency
Original:  The impression allocation problem is modeled as a Markov decision process, where the states encode the history of impressions, prices, transactions and generated revenue and the actions are the possible impression allocations at each round.
Revised:  The impression allocation problem as a Markov decision process, where the states encode the history of impressions, prices, transactions and generated revenue and the actions are the possible impression allocations at each round.
Edit intent: clarity
Original:  The impression allocation problem is modeled as a Markov decision process, where the states encode the history of impressions, prices, transactions and generated revenue and the actions are the possible impression allocations at each round.
Revised:  The impression allocation problem is modeled as a Markov decision process, where the states encode the history of impressions, prices, transactions and generated revenue and the actions are the possible impression allocations in each round.
Edit intent: clarity
Original:  To tackle the problem of continuity and high-dimensionality of states and actions, we adopt the ideas of the DDPG algorithm to design an actor-critic gradient policy algorithm which takes advantage of the problem domain in order to achieve covergence and stability.
Revised:  To tackle the problem of continuity and high-dimensionality of states and actions, we adopt the ideas of the DDPG algorithm to design an actor-critic policy gradient algorithm which takes advantage of the problem domain in order to achieve covergence and stability.
Edit intent: fluency
Original:  To tackle the problem of continuity and high-dimensionality of states and actions, we adopt the ideas of the DDPG algorithm to design an actor-critic gradient policy algorithm which takes advantage of the problem domain in order to achieve covergence and stability.
Revised:  To tackle the problem of continuity and high-dimensionality of states and actions, we adopt the ideas of the DDPG algorithm to design an actor-critic gradient policy algorithm which takes advantage of the problem domain in order to achieve convergence and stability.
Edit intent: fluency
Original: In this paper , we develop some clique-based methods for social network clustering.
Revised: In this article , we develop some clique-based methods for social network clustering.
Edit intent: fluency
Original: In this paper , we develop some clique-based methods for social network clustering.
Revised: In this paper , we develop a clique-based methods for social network clustering.
Edit intent: clarity
Original: In this paper , we develop some clique-based methods for social network clustering.
Revised: In this paper , we develop some clique-based method for social network clustering.
Edit intent: fluency
Original:  The quality of clustering result is measured by a novel clique-based index, which is innovated from the modularity index proposed in Newman 2006%DIFDELCMD < ]%%% . We design an effective algorithm based on recursive bipartition in order to maximize the objective function of the proposed index.
Revised:  The quality of clustering Newman 2006%DIFDELCMD < ]%%% . We design an effective algorithm based on recursive bipartition in order to maximize the objective function of the proposed index.
Edit intent: coherence
Original:  The quality of clustering result is measured by a novel clique-based index, which is innovated from the modularity index proposed in Newman 2006%DIFDELCMD < ]%%% . We design an effective algorithm based on recursive bipartition in order to maximize the objective function of the proposed index.
Revised:  The quality of clustering result is measured by a novel clique-based index, which is innovated from the modularity index proposed in %DIFDELCMD < ]%%% . We design an effective algorithm based on recursive bipartition in order to maximize the objective function of the proposed index.
Edit intent: coherence
Original:  The quality of clustering result is measured by a novel clique-based index, which is innovated from the modularity index proposed in Newman 2006%DIFDELCMD < ]%%% . We design an effective algorithm based on recursive bipartition in order to maximize the objective function of the proposed index.
Revised:  The quality of clustering result is measured by a novel clique-based index, which is innovated from the modularity index proposed in Newman 2006%DIFDELCMD < ]%%% results, and propose an efficient algorithm based on recursive bipartition in order to maximize the objective function of the proposed index.
Edit intent: clarity
Original:  The quality of clustering result is measured by a novel clique-based index, which is innovated from the modularity index proposed in Newman 2006%DIFDELCMD < ]%%% . We design an effective algorithm based on recursive bipartition in order to maximize the objective function of the proposed index.
Revised:  The quality of clustering result is measured by a novel clique-based index, which is innovated from the modularity index proposed in Newman 2006%DIFDELCMD < ]%%% . We design an effective algorithm based on recursive bipartition to maximize an objective function of the proposed index.
Edit intent: coherence
Original:  Noting the optimization of the objective function is NP-hard when the network size or the parameter space is large, we relax the problem via an implicitly restarted Lanczos method from numerical algebra .
Revised:  The optimization problem is NP-hard when the network size or the parameter space is large, we relax the problem via an implicitly restarted Lanczos method from numerical algebra .
Edit intent: clarity
Original:  Noting the optimization of the objective function is NP-hard when the network size or the parameter space is large, we relax the problem via an implicitly restarted Lanczos method from numerical algebra .
Revised:  Noting the optimization of the objective function is NP-hard , so we approximate the semi-optimal solution via an implicitly restarted Lanczos method from numerical algebra .
Edit intent: clarity
Original:  Noting the optimization of the objective function is NP-hard when the network size or the parameter space is large, we relax the problem via an implicitly restarted Lanczos method from numerical algebra .
Revised:  Noting the optimization of the objective function is NP-hard when the network size or the parameter space is large, we relax the problem via an implicitly restarted Lanczos method .
Edit intent: clarity
Original:  One of the contributions of our method is that the proposed index of each community in the clustering result is higher than a predefined threshold, p, which is completely controlled by users.
Revised:  One of the advantages of our algorithm is that the proposed index of each community in the clustering result is higher than a predefined threshold, p, which is completely controlled by users.
Edit intent: clarity
Original:  One of the contributions of our method is that the proposed index of each community in the clustering result is higher than a predefined threshold, p, which is completely controlled by users.
Revised:  One of the contributions of our method is that the proposed index of each community in the clustering result is guaranteed to be higher than some predetermined threshold, p, which is completely controlled by users.
Edit intent: clarity
Original:  Finally, we exploit simulation experiments based on the stochastic block model to demonstrate the accuracy and efficiency of our algorithms,  numerically and graphically.
Revised:  Finally, we exploit the stochastic blockmodels to conduct simulation studies and demonstrate the accuracy and efficiency of our algorithms, numerically and graphically.
Edit intent: clarity
Original:  Finally, we exploit simulation experiments based on the stochastic block model to demonstrate the accuracy and efficiency of our algorithms,  numerically and graphically.
Revised:  Finally, we exploit simulation experiments based on the stochastic block model to demonstrate the accuracy and efficiency of our algorithms, both numerically and graphically.
Edit intent: clarity