wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our results thereby suggest a new framework for unifying the mechanisms for generating OEE with features distinctive to life and its artifacts, with wide applicability to both biological and artificial systems.
Revised:  Our results thereby suggest a new framework for unifying the mechanisms for generating OEE with features distinctive to life and its artifacts, with broad applicability to biological and artificial systems.
Edit intent: clarity
Original:   Interestingly, the results show that the steady-state performance of the learning strategy is not affected , while the convergence rate suffers some degradation.
Revised:  Interestingly, the results show that the steady-state performance of the learning strategy is not always degraded , while the convergence rate suffers some degradation.
Edit intent: clarity
Original: We consider, for complete bipartite graphs, the convex hulls of characteristic vectors of  matchings, extended by a binary number indicating whether the matching contains two specific edges.
Revised: We consider, for complete bipartite graphs, the convex hulls of characteristic vectors of all matchings, extended by a binary number indicating whether the matching contains two specific edges.
Edit intent: clarity
Original: We consider, for complete bipartite graphs, the convex hulls of characteristic vectors of  matchings, extended by a binary number indicating whether the matching contains two specific edges.
Revised: We consider, for complete bipartite graphs, the convex hulls of characteristic vectors of matchings, extended by a binary entry indicating whether the matching contains two specific edges.
Edit intent: clarity
Original:  This polytope is associated to the quadratic matching problem with a single linearized quadratic term.
Revised:  These polytopes are associated to the quadratic matching problem with a single linearized quadratic term.
Edit intent: fluency
Original:  This polytope is associated to the quadratic matching problem with a single linearized quadratic term.
Revised:  This polytope is associated to the quadratic matching problems with a single linearized quadratic term.
Edit intent: fluency
Original:  Previously, we investigated a structured HDNN architecture for speech recognition, in which the two gate functions are tied across all the hidden layers, and we were able to train a much smaller model without sacrificing the recognition accuracy.
Revised:  Previously, we investigated a structured HDNN architecture for speech recognition, in which the two gate functions were tied across all the hidden layers, and we were able to train a much smaller model without sacrificing the recognition accuracy.
Edit intent: fluency
Original:  Furthermore, we demonstrate that the two gate functions that are tied across all the hidden layers are able to control the information flow over the whole network, and we achieved considerable improvements by only updating these gate functions in both sequence training and adaptation experiments.
Revised:  Furthermore, we demonstrate that the two gate functions that are tied across all the hidden layers are able to control the information flow over the whole network, and we can achieve considerable improvements by only updating these gate functions in both sequence training and adaptation experiments.
Edit intent: clarity
Original:  We demonstrate with several examples that binomials in polynomial ideals can be well hidden.
Revised:  Binomials in polynomial ideals can be well hidden.
Edit intent: clarity
Original:  This realization has motivated the development of new tools such as persistent homology for exploring topological invariants, or features, in data large sets.
Revised:  This realization has motivated the development of new tools such as persistent homology for exploring topological invariants, or features, in large data sets.
Edit intent: fluency
Original: Cellular automata (CAs) are dynamical systems that exhibit complex global behavior from simple local interaction and computation.
Revised: Cellular automata (CAs) are dynamic frameworks which exhibit complex global behavior from simple local interaction and computation.
Edit intent: clarity
Original: Cellular automata (CAs) are dynamic frameworks which exhibit complex global behavior from simple local interaction and computation.
Revised: Cellular automata (CAs) are dynamical systems which exhibit complex global behavior from simple local interaction and computation.
Edit intent: clarity
Original:  Since the inception of CA by von Neumann in 1950s, it has attracted the attention of several researchers over various backgrounds and fields for modeling different physical, natural as well as real-life phenomena.
Revised:  Since the inception of CA by von Neumann in 1950s, it has attracted the attention of several researchers over various backgrounds and fields for modelling different physical, natural as well as real-life phenomena.
Edit intent: fluency
Original:  Special attention is given to non-uniformity in CAs and specially, the non-uniform elementary CAs, which have been very useful in solving several real-life problems.
Revised:  Special attention is given to non-uniformity in CAs and especially to non-uniform elementary CAs, which have been very useful in solving several real-life problems.
Edit intent: coherence
Original:  Our system reaches an overall recognition accuracy of 81.2\% and  outperforms the DCASE 2016 baseline with an absolute improvement of 8.7\%  on the development data .
Revised:  Our system reaches an overall recognition accuracy of 81.2\% and outperforms the DCASE 2016 baseline with absolute improvements of 8.7\% on the development data .
Edit intent: fluency
Original:  Experimental results on the development data show that our systems outperform the DCASE 2016 challenge baselines with absolute gains of 64.4\% and 8.0\% on Task2 and Task3 , respectively .
Revised:  Experimental results on the development data show that our systems significantly outperform the baseline on the DCASE 2016 challenge baselines with absolute gains of 64.4\% and 8.0\% on Task2 and Task3 , respectively .
Edit intent: clarity
Original:  Experimental results on the development data show that our systems outperform the DCASE 2016 challenge baselines with absolute gains of 64.4\% and 8.0\% on Task2 and Task3 , respectively .
Revised:  Experimental results on the development data show that our systems outperform the Task2 and Task3 , respectively .
Edit intent: clarity
Original:  Experimental results on the development data show that our systems outperform the DCASE 2016 challenge baselines with absolute gains of 64.4\% and 8.0\% on Task2 and Task3 , respectively .
Revised:  Experimental results on the development data show that our systems outperform the DCASE 2016 challenge baselines with absolute gains of 64.4\% and 8.0\% on Task2 and Task3 evaluation .
Edit intent: clarity
Original: We consider the problem of estimating the underlying graph associated with a Markov random field, with the added twist that the decoding algorithm can iteratively choose which subsets of nodes to sample based on the previous samples, i.e., active learning . Considering both Ising and Gaussian models, we provide algorithm-independent lower bounds for high-probability recovery within the class of degree-bounded graphs.
Revised: We consider the problem of estimating the underlying graph associated with a Markov random field, with the added twist that the decoding algorithm can iteratively choose which subsets of nodes to sample based on the previous samples, resulting in an active learning setting . Considering both Ising and Gaussian models, we provide algorithm-independent lower bounds for high-probability recovery within the class of degree-bounded graphs.
Edit intent: clarity
Original:  Our main results are minimax lower bounds for the active setting that match the best known lower bounds for the passive setting, which  are known to be tight in several cases  .
Revised:  Our main results are minimax lower bounds for the active setting that match the best known lower bounds for the passive setting, which in turn are known to be tight in several cases .
Edit intent: clarity
Original:  Our analysis is based on a novel variation of Fano's inequality  for the active learning setting  .
Revised:  Our analysis is based on Fano's inequality for the active learning setting .
Edit intent: clarity
Original:  While we consider graph ensembles that are similar or identical to those considered for the passive setting, we require different techniques to analyze them, with the key challenge being bounding a mutual information quantity associated with observed subsets of the nodes, as opposed to full observations.
Revised:  While we consider ensembles that are similar or identical to those considered for the passive setting, we require different techniques to analyze them, with the key challenge being bounding a mutual information quantity associated with observed subsets of the nodes, as opposed to full observations.
Edit intent: clarity
Original:  While we consider graph ensembles that are similar or identical to those considered for the passive setting, we require different techniques to analyze them, with the key challenge being bounding a mutual information quantity associated with observed subsets of the nodes, as opposed to full observations.
Revised:  While we consider graph ensembles that are similar or identical to those used in the passive setting, we require different techniques to analyze them, with the key challenge being bounding a mutual information quantity associated with observed subsets of the nodes, as opposed to full observations.
Edit intent: clarity
Original:  While we consider graph ensembles that are similar or identical to those considered for the passive setting, we require different techniques to analyze them, with the key challenge being bounding a mutual information quantity associated with observed subsets of the nodes, as opposed to full observations.
Revised:  While we consider graph ensembles that are similar or identical to those considered for the passive setting, we require different analysis techniques, with a key challenge being bounding a mutual information quantity associated with observed subsets of the nodes, as opposed to full observations.
Edit intent: clarity
Original:  While we consider graph ensembles that are similar or identical to those considered for the passive setting, we require different techniques to analyze them, with the key challenge being bounding a mutual information quantity associated with observed subsets of the nodes, as opposed to full observations.
Revised:  While we consider graph ensembles that are similar or identical to those considered for the passive setting, we require different techniques to analyze them, with the key challenge being bounding a mutual information quantity associated with observed subsets of nodes, as opposed to full observations.
Edit intent: fluency
Original: Statistical ensembles define probability spaces of all networks  consistent with given aggregate statistics and have become instrumental in the analysis of relational data on networked systems .
Revised: Statistical ensembles of networks, i.e., probability spaces of all networks consistent with given aggregate statistics and have become instrumental in the analysis of relational data on networked systems .
Edit intent: clarity
Original: Statistical ensembles define probability spaces of all networks  consistent with given aggregate statistics and have become instrumental in the analysis of relational data on networked systems .
Revised: Statistical ensembles define probability spaces of all networks that are consistent with given aggregate statistics and have become instrumental in the analysis of relational data on networked systems .
Edit intent: clarity
Original: Statistical ensembles define probability spaces of all networks  consistent with given aggregate statistics and have become instrumental in the analysis of relational data on networked systems .
Revised: Statistical ensembles define probability spaces of all networks consistent with given aggregate statistics , have become instrumental in the analysis of relational data on networked systems .
Edit intent: fluency
Original: Statistical ensembles define probability spaces of all networks  consistent with given aggregate statistics and have become instrumental in the analysis of relational data on networked systems .
Revised: Statistical ensembles define probability spaces of all networks consistent with given aggregate statistics and have become instrumental in the analysis of complex networks .
Edit intent: clarity
Original:  Contributing to the foundation of these important data science techniques, in this article we introduce generalized hypergeometric ensembles, a framework of analytically tractable statistical ensembles of finite, directed and weighted networks.
Revised:  Contributing to the foundation of these data analysis techniques, in this article we introduce generalized hypergeometric ensembles, a framework of analytically tractable statistical ensembles of finite, directed and weighted networks.
Edit intent: clarity
Original:  Contributing to the foundation of these important data science techniques, in this article we introduce generalized hypergeometric ensembles, a framework of analytically tractable statistical ensembles of finite, directed and weighted networks.
Revised:  Contributing to the foundation of these important data science techniques, in this Letter we introduce generalized hypergeometric ensembles, a framework of analytically tractable statistical ensembles of finite, directed and weighted networks.
Edit intent: fluency
Original:  Contributing to the foundation of these important data science techniques, in this article we introduce generalized hypergeometric ensembles, a framework of analytically tractable statistical ensembles of finite, directed and weighted networks.
Revised:  Contributing to the foundation of these important data science techniques, in this article we introduce generalized hypergeometric ensembles, a broad class of analytically tractable statistical ensembles of finite, directed and weighted networks.
Edit intent: clarity
Original:  Studying empirical and synthetic data, we show that our approach provides broad perspectives for community detection, model selection and statistical hypothesis testing  .
Revised:  Studying empirical and synthetic data, we show that our approach provides broad perspectives for model selection and statistical hypothesis testing .
Edit intent: clarity
Original: We introduce LL-RNNs (Log-Linear RNNs), an extension of Recurrent Neural Networks that replaces the softmax output layer by a log-linear output layer, of which the softmax is a special case.
Revised: We introduce LL-RNNs (Log-Linear RNNs), an extension of Recurrent Neural Networks that replaces the softmax output layer by a log-linear output layer, of which the softmax is a special case.
Edit intent: fluency
Original:  Second, it permits the inclusion of flexible prior knowledge in the form of \emph{a priori specified modular features, where the neural network component learns to dynamically control the weights of a log-linear distribution exploiting these features.
Revised:  Second, it permits the inclusion of flexible prior knowledge in the form of \emph{a priori specified modular features, where the neural network component learns to dynamically control the weights of a log-linear distribution exploiting these features.
Edit intent: fluency
Original:  We present here a general approach to estimate the probability and the conditional mean first passage time for such a viral particle to attain safely the nucleus, covered with many (around two thousands ) small absorbing pores.
Revised:  We present here a general approach to estimate the probability and the conditional mean first passage time for a viral particle to attain safely the nucleus, covered with many (around two thousands ) small absorbing pores.
Edit intent: clarity
Original:  In the present paper we propose an algorithm to obtain statistically-validated  projections of bipartite networks .
Revised:  In the present paper we propose an algorithm to obtain statistically-validated monopartite projections of bipartite networks .
Edit intent: fluency
Original:  The output of our algorithm thus consists of a matrix of  p-values, from which a validated projection can be straightforwardly obtained, upon running a multiple hypothesis testing and linking only the nodes which pass it.
Revised:  Our algorithm outputs a matrix of p-values, from which a validated projection can be straightforwardly obtained, upon running a multiple hypothesis testing and linking only the nodes which pass it.
Edit intent: clarity
Original: Bipartite networks are currently regarded as providing a major insight into the organization of  real-world systems, unveiling the mechanisms shaping the interactions occurring between distinct groups of nodes.
Revised: Bipartite networks are currently regarded as providing a major insight into the organization of many real-world systems, unveiling the mechanisms shaping the interactions occurring between distinct groups of nodes.
Edit intent: clarity
Original: Bipartite networks are currently regarded as providing a major insight into the organization of  real-world systems, unveiling the mechanisms shaping the interactions occurring between distinct groups of nodes.
Revised: Bipartite networks are currently regarded as providing a major insight into the organization of real-world systems, unveiling the mechanisms driving the interactions which occur between distinct groups of nodes.
Edit intent: clarity
Original:  In the present paper we propose an algorithm to obtain statistically-validated monopartite projections of bipartite networks, which implements a simple rule:
Revised:  In the present paper we propose an algorithm to obtain statistically-validated projections of bipartite networks, which implements a simple rule:
Edit intent: fluency
Original:  Our algorithm outputs a matrix of link-specific p-values, from which a validated projection can be straightforwardly obtained, upon running a multiple hypothesis test and retaining only the statistically significant links.
Revised:  Our algorithm outputs a matrix of link-specific p-values, from which a validated projection can be straightforwardly obtained, upon running a multiple hypothesis test and retaining only the statistically-significant links.
Edit intent: fluency
Original: Bipartite networks are currently regarded as providing a major insight into the organization of many real-world systems, unveiling the mechanisms driving the interactions which occur between distinct groups of nodes.
Revised: Bipartite networks are currently regarded as providing a major insight into the organization of many real-world systems, unveiling the mechanisms driving the interactions occurring between distinct groups of nodes.
Edit intent: clarity
Original:  Naturally, assessing the statistical significance of nodes similarity requires the definition of a proper statistical benchmark : here we consider a set of four null models, defined within the Exponential Random Graph framework.
Revised:  Naturally, assessing the statistical significance of nodes similarity requires a proper statistical benchmark : here we consider a set of four null models, defined within the Exponential Random Graph framework.
Edit intent: clarity
Original:  Naturally, assessing the statistical significance of nodes similarity requires the definition of a proper statistical benchmark : here we consider a set of four null models, defined within the Exponential Random Graph framework.
Revised:  Naturally, assessing the statistical significance of nodes similarity requires the definition of a proper statistical benchmark , here we consider a set of four null models, defined within the Exponential Random Graph framework.
Edit intent: fluency
Original:  Naturally, assessing the statistical significance of nodes similarity requires the definition of a proper statistical benchmark : here we consider a set of four null models, defined within the Exponential Random Graph framework.
Revised:  Naturally, assessing the statistical significance of nodes similarity requires the definition of a proper statistical benchmark : here we consider a set of four null models, defined within the exponential random graph framework.
Edit intent: fluency
Original:  Our algorithm outputs a matrix of link-specific p-values, from which a validated projection can be straightforwardly obtained , upon running a multiple hypothesis test and retaining only the statistically-significant links .
Revised:  Our algorithm outputs a matrix of link-specific p-values, from which a validated projection is straightforwardly obtainable , upon running a multiple hypothesis test and retaining only the statistically-significant links .
Edit intent: clarity
Original:  Our algorithm outputs a matrix of link-specific p-values, from which a validated projection can be straightforwardly obtained , upon running a multiple hypothesis test and retaining only the statistically-significant links .
Revised:  Our algorithm outputs a matrix of link-specific p-values, from which a validated projection can be straightforwardly obtained , upon running a multiple hypothesis testing procedure .
Edit intent: clarity
Original: It is necessary to thoroughly evaluate the safety of Automated Vehicles (AVs)  before their release and deployment.
Revised: The safety of Automated Vehicles (AVs) before their release and deployment.
Edit intent: clarity
Original: It is necessary to thoroughly evaluate the safety of Automated Vehicles (AVs)  before their release and deployment.
Revised: It is necessary to thoroughly evaluate the safety of Automated Vehicles (AVs) must be assured before their release and deployment.
Edit intent: coherence
Original:  Current evaluation approach mainly relies on i) testing AVs on public roads or  ii) track testing with scenarios defined in a test matrix.
Revised:  The current approach to evaluation relies primarily on ( i) testing AVs on public roads or ii) track testing with scenarios defined in a test matrix.
Edit intent: fluency
Original:  Current evaluation approach mainly relies on i) testing AVs on public roads or  ii) track testing with scenarios defined in a test matrix.
Revised:  Current evaluation approach mainly relies on i) testing AVs on public roads or ( ii) track testing with scenarios defined in a test matrix.
Edit intent: fluency
Original:  These two methods have completely opposite drawbacks: the former  takes too much time to execute but is realistic ;
Revised:  These two methods have completely opposing drawbacks: the former takes too much time to execute but is realistic ;
Edit intent: fluency
Original:  These two methods have completely opposite drawbacks: the former  takes too much time to execute but is realistic ;
Revised:  These two methods have completely opposite drawbacks: the former takes too much time to execute ;
Edit intent: coherence
Original:  the latter can be finished in a short time but has no clear correlation to the safety benefits in the real world.
Revised:  the latter , though it can be completed in a short time but has no clear correlation to the safety benefits in the real world.
Edit intent: coherence
Original:  the latter can be finished in a short time but has no clear correlation to the safety benefits in the real world.
Revised:  the latter can be finished in a short amount of time, has no clear correlation to the safety benefits in the real world.
Edit intent: clarity
Original:  the latter can be finished in a short time but has no clear correlation to the safety benefits in the real world.
Revised:  the latter can be finished in a short time but has no clear correlation to safety benefits in the real world.
Edit intent: fluency
Original:  To avoid the aforementioned problems, we propose the Accelerated Evaluationapproach focusing on the car-following scenario.
Revised:  To avoid the aforementioned problems, we propose Accelerated Evaluation, focusing on the car-following scenario.
Edit intent: fluency