wanyu/IteraTeR_v2 huggingface dataset:
Original:  To the best of our knowledge, our work is the first that considers a full-stack  end-to-end approach for the design of  V2V networks, discussing open issues that span from the physical to the higher layers, and reporting the results of an end-to-end performance evaluation that highlight the potentials of mmWaves for V2V communications.
Revised:  To the best of our knowledge, our work is the first that considers a full-stack end-to-end approach for the design of V2V networks, discussing open issues that span from the physical to the higher layers, and reporting the results of an end-to-end performance evaluation that highlight the potential of mmWaves for V2V communications.
Edit intent: fluency
Original:  We show that the expected  number of inclusion-wise minimal edges of B_{n,m,p}  undergoes a phase transition with respect to m.
Revised:  We show that the expected number of inclusion-wise minimal edges of B_{n,m,p} , undergoes a phase transition with respect to m.
Edit intent: fluency
Original:  If m %DIFDELCMD < \le %%%  1/(1-p)^{(1-p)n}, the expectation[|\min(B_{n,m,p})|] } is of order  (m), while for m \ge 1/(1-p)^{(1-p  )n}  , it is  ( 2^{(  (\alpha) + (1-\alpha) %DIFDELCMD < \operatorname{ld} %DIFDELCMD < %%%  p) n}  ).
Revised:  If m %DIFDELCMD < \le %%% is at most 1/(1-p)^{(1-p)n}, the expectation[|\min(B_{n,m,p})|] } is of order (m), while for m \ge 1/(1-p)^{(1-p )n} , it is ( 2^{( (\alpha) + (1-\alpha) %DIFDELCMD < \operatorname{ld} %DIFDELCMD < %%% p) n} ).
Edit intent: clarity
Original:  This implies that the maximum expected number of minimal edges over all m is %DIFDELCMD < \operatorname{O}%%%  ((1+p)^n ).
Revised:  The result implies that the maximum expected number of minimal edges over all m is %DIFDELCMD < \operatorname{O}%%% ((1+p)^n ).
Edit intent: clarity
Original:  We show that the expected size of the minimization \min(\mathcal{B , i.e., the number of  inclusion-wise minimal edges of \mathcal{B , undergoes a phase transition with respect to m.
Revised:  We show that the expected size of the minimization \min(\mathcal{B , i.e., the number of inclusion-wise minimal edges , undergoes a phase transition with respect to m.
Edit intent: clarity
Original:  If m is at most 1/(1-p)^{(1-p)n}, then \mathrm{E[|\min(B_{n,m,p})|] is of order }  \Theta(m) , while for m%DIFDELCMD < \ge %%%  1/(1-p)^{ (1-p+\varepsilon) n} for any \varepsilon > 0 , it is \Theta(\left({(\alpha\,{-}\,(1-p)) (1\,{-\,\alpha) n}}}\right)  2^{(\mathrm{H}(\alpha) + (1-\alpha) \log_2 p) n} / \sqrt{n).
Revised:  If m is at most 1/(1-p)^{(1-p)n}, then \mathrm{E[|\min(B_{n,m,p})|] is of order } \Theta(m) %DIFDELCMD < \ge %%% 1/(1-p)^{ (1-p+\varepsilon) n} for any \varepsilon > 0 , it is \Theta(\left({(\alpha\,{-}\,(1-p)) (1\,{-\,\alpha) n}}}\right) 2^{(\mathrm{H}(\alpha) + (1-\alpha) \log_2 p) n} / \sqrt{n).
Edit intent: coherence
Original:  The result }  implies that the maximum expected number of minimal edges over all m is \Theta((1+p)^n/\sqrt{n}).
Revised:  The result } . This implies that the maximum expected number of minimal edges over all m is \Theta((1+p)^n/\sqrt{n}).
Edit intent: coherence
Original:  Our structural findings have algorithmic implications for minimizing an input hypergraph, which  has applications in the profiling of relational databases as well as for the Orthogonal Vectors problem studied in fine-grained complexity.
Revised:  Our structural findings have algorithmic implications for minimizing an input hypergraph, which in turn has applications in the profiling of relational databases as well as for the Orthogonal Vectors problem studied in fine-grained complexity.
Edit intent: clarity
Original:  In detail, we show that for a binomial variable Y \sim%DIFDELCMD < \operatorname{Bin}%%%  (n,p) and any 0 < x < \le p, it holds that \mathrm{P[Y \le xn] } [X \le xn] } = \Theta(\left({(p-x) \sqrt{xn}}}\right)  2^{-\!\mathrm{D}(x \,{\|}\, p) n} /\sqrt{n , where \mathrm{D} is the binary Kullback--Leibler divergence between Bernoulli distributions.
Revised:  In detail, we show that for a binomial variable %DIFDELCMD < \operatorname{Bin}%%% (n,p) and any 0 < x < \le p, it holds that \mathrm{P[Y \le xn] } [X \le xn] } = \Theta(\left({(p-x) \sqrt{xn}}}\right) 2^{-\!\mathrm{D}(x \,{\|}\, p) n} /\sqrt{n , where \mathrm{D} is the binary Kullback--Leibler divergence between Bernoulli distributions.
Edit intent: clarity
Original:  In detail, we show that for a binomial variable Y \sim%DIFDELCMD < \operatorname{Bin}%%%  (n,p) and any 0 < x < \le p, it holds that \mathrm{P[Y \le xn] } [X \le xn] } = \Theta(\left({(p-x) \sqrt{xn}}}\right)  2^{-\!\mathrm{D}(x \,{\|}\, p) n} /\sqrt{n , where \mathrm{D} is the binary Kullback--Leibler divergence between Bernoulli distributions.
Revised:  In detail, we show that for a binomial variable Y \sim%DIFDELCMD < \operatorname{Bin}%%% (n,p) and real number 0 < x < \le p, it holds that \mathrm{P[Y \le xn] } [X \le xn] } = \Theta(\left({(p-x) \sqrt{xn}}}\right) 2^{-\!\mathrm{D}(x \,{\|}\, p) n} /\sqrt{n , where \mathrm{D} is the binary Kullback--Leibler divergence between Bernoulli distributions.
Edit intent: clarity
Original:  In detail, we show that for a binomial variable Y \sim%DIFDELCMD < \operatorname{Bin}%%%  (n,p) and any 0 < x < \le p, it holds that \mathrm{P[Y \le xn] } [X \le xn] } = \Theta(\left({(p-x) \sqrt{xn}}}\right)  2^{-\!\mathrm{D}(x \,{\|}\, p) n} /\sqrt{n , where \mathrm{D} is the binary Kullback--Leibler divergence between Bernoulli distributions.
Revised:  In detail, we show that for a binomial variable Y \sim%DIFDELCMD < \operatorname{Bin}%%% (n,p) and any 0 < x \le p, it holds that \mathrm{P[Y \le xn] } [X \le xn] } = \Theta(\left({(p-x) \sqrt{xn}}}\right) 2^{-\!\mathrm{D}(x \,{\|}\, p) n} /\sqrt{n , where \mathrm{D} is the binary Kullback--Leibler divergence between Bernoulli distributions.
Edit intent: fluency
Original:  In detail, we show that for a binomial variable Y \sim%DIFDELCMD < \operatorname{Bin}%%%  (n,p) and any 0 < x < \le p, it holds that \mathrm{P[Y \le xn] } [X \le xn] } = \Theta(\left({(p-x) \sqrt{xn}}}\right)  2^{-\!\mathrm{D}(x \,{\|}\, p) n} /\sqrt{n , where \mathrm{D} is the binary Kullback--Leibler divergence between Bernoulli distributions.
Revised:  In detail, we show that for a binomial variable Y \sim%DIFDELCMD < \operatorname{Bin}%%% (n,p) and any 0 < x < \le p, it holds that [Y \le xn] } [X \le xn] } = \Theta(\left({(p-x) \sqrt{xn}}}\right) 2^{-\!\mathrm{D}(x \,{\|}\, p) n} /\sqrt{n , where \mathrm{D} is the binary Kullback--Leibler divergence between Bernoulli distributions.
Edit intent: coherence
Original:  In detail, we show that for a binomial variable Y \sim%DIFDELCMD < \operatorname{Bin}%%%  (n,p) and any 0 < x < \le p, it holds that \mathrm{P[Y \le xn] } [X \le xn] } = \Theta(\left({(p-x) \sqrt{xn}}}\right)  2^{-\!\mathrm{D}(x \,{\|}\, p) n} /\sqrt{n , where \mathrm{D} is the binary Kullback--Leibler divergence between Bernoulli distributions.
Revised:  In detail, we show that for a binomial variable Y \sim%DIFDELCMD < \operatorname{Bin}%%% (n,p) and any 0 < x < \le p, it holds that \mathrm{P[Y \le xn] } \mathrm{P[X \le xn] } = \Theta(\left({(p-x) \sqrt{xn}}}\right) 2^{-\!\mathrm{D}(x \,{\|}\, p) n} /\sqrt{n , where \mathrm{D} is the binary Kullback--Leibler divergence between Bernoulli distributions.
Edit intent: fluency
Original:  In detail, we show that for a binomial variable Y \sim%DIFDELCMD < \operatorname{Bin}%%%  (n,p) and any 0 < x < \le p, it holds that \mathrm{P[Y \le xn] } [X \le xn] } = \Theta(\left({(p-x) \sqrt{xn}}}\right)  2^{-\!\mathrm{D}(x \,{\|}\, p) n} /\sqrt{n , where \mathrm{D} is the binary Kullback--Leibler divergence between Bernoulli distributions.
Revised:  In detail, we show that for a binomial variable Y \sim%DIFDELCMD < \operatorname{Bin}%%% (n,p) and any 0 < x < \le p, it holds that \mathrm{P[Y \le xn] } [X \le xn] } = \Theta(\left({(p-x) \sqrt{xn}}}\right) 2^{-\!\mathrm{D}(x \,{\|}\, p) n} , where \mathrm{D} is the binary Kullback--Leibler divergence between Bernoulli distributions.
Edit intent: coherence
Original:  In detail, we show that for a binomial variable Y \sim%DIFDELCMD < \operatorname{Bin}%%%  (n,p) and any 0 < x < \le p, it holds that \mathrm{P[Y \le xn] } [X \le xn] } = \Theta(\left({(p-x) \sqrt{xn}}}\right)  2^{-\!\mathrm{D}(x \,{\|}\, p) n} /\sqrt{n , where \mathrm{D} is the binary Kullback--Leibler divergence between Bernoulli distributions.
Revised:  In detail, we show that for a binomial variable Y \sim%DIFDELCMD < \operatorname{Bin}%%% (n,p) and any 0 < x < \le p, it holds that \mathrm{P[Y \le xn] } [X \le xn] } = \Theta(\left({(p-x) \sqrt{xn}}}\right) 2^{-\!\mathrm{D}(x \,{\|}\, p) n} /\sqrt{n , where \mathrm{D} denotes the Kullback--Leibler divergence between Bernoulli distributions.
Edit intent: clarity
Original:  Research Domain Criteria (RDoC) construct is a multi-dimensional and broad framework to describe mental health disorders by combining knowledge from genomics tobehaviour . Non-availability of RDoC labelled dataset and tedious labelling process hinders the use of RDoC framework to reach its full potential in Biomedical research community and Healthcare industry.
Revised:  Research Domain Criteria (RDoC) construct is a multi-dimensional and broad framework to describe mental health disorders by combining knowledge from tobehaviour . Non-availability of RDoC labelled dataset and tedious labelling process hinders the use of RDoC framework to reach its full potential in Biomedical research community and Healthcare industry.
Edit intent: clarity
Original:  Research Domain Criteria (RDoC) construct is a multi-dimensional and broad framework to describe mental health disorders by combining knowledge from genomics tobehaviour . Non-availability of RDoC labelled dataset and tedious labelling process hinders the use of RDoC framework to reach its full potential in Biomedical research community and Healthcare industry.
Revised:  Research Domain Criteria (RDoC) construct is a multi-dimensional and broad framework to describe mental health disorders by combining knowledge from genomics behaviour . Non-availability of RDoC labelled dataset and tedious labelling process hinders the use of RDoC framework to reach its full potential in Biomedical research community and Healthcare industry.
Edit intent: fluency
Original:  Research Domain Criteria (RDoC) construct is a multi-dimensional and broad framework to describe mental health disorders by combining knowledge from genomics tobehaviour . Non-availability of RDoC labelled dataset and tedious labelling process hinders the use of RDoC framework to reach its full potential in Biomedical research community and Healthcare industry.
Revised:  Research Domain Criteria (RDoC) construct is a multi-dimensional and broad framework to describe mental health disorders by combining knowledge from genomics togenomics to behaviour . Non-availability of RDoC labelled dataset and tedious labelling process hinders the use of RDoC framework to reach its full potential in Biomedical research community and Healthcare industry.
Edit intent: fluency
Original:  Therefore, Task-1 aims at retrieval and ranking of PubMed abstracts relevant to a given RDoC construct and Task-2 aims at extraction of the most relevant sentence from a given PubMed abstract.
Revised:  Therefore, Task-1 aims at retrieval and ranking of PubMed abstracts relevant to a given RDoC construct and Task-2 aims at extraction of the most relevant sentence from a given PubMed abstract.
Edit intent: fluency
Original:  We investigate (1) attention based supervised neural topic model and SVM for retrieval and ranking of PubMed abstracts and, further utilize BM25 and other relevance measures for re-ranking, (2) supervised and unsupervised sentence ranking models utilizing multi-view representations comprising of \textit{query-aware attention-based sentence representation (QAR) , bag-of-words (BoW) and TF-IDF.
Revised:  We investigate (1) attention based supervised neural topic model and SVM for retrieval and ranking of PubMed abstracts and, further utilize BM25 and other relevance measures for re-ranking, (2) supervised and unsupervised sentence ranking models utilizing multi-view representations comprising of \textit{query-aware attention-based sentence representation (QAR) , bag-of-words (BoW) and TF-IDF.
Edit intent: fluency
Original: Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and the remarkable capability of reducing the burden of neural network design.
Revised: Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and remarkable capability to reduce the burden of neural network design.
Edit intent: fluency
Original:  To achieve better performance, however, the searching process usually costs massive computation, which might not be affordable to researchers and practitioners.
Revised:  To achieve better performance, however, the searching process usually costs massive computations that might not be affordable to researchers and practitioners.
Edit intent: fluency
Original:  To achieve better performance, however, the searching process usually costs massive computation, which might not be affordable to researchers and practitioners.
Revised:  To achieve better performance, however, the searching process usually costs massive computation, which might not be affordable for researchers and practitioners.
Edit intent: fluency
Original:  While recent attempts have employed ensemble learning methods to mitigate the enormous computation, an essential characteristic of diversity in ensemble methodsis missed out, causing more similar sub-architectures to be gathered and potential redundancy in the final ensemble architecture.
Revised:  While recent attempts have employed ensemble learning methods to mitigate the enormous computational cost, however, they neglect a key property of ensemble methods, namely diversity, which leads to collecting more similar sub-architectures to be gathered and potential redundancy in the final ensemble architecture.
Edit intent: clarity
Original:  While recent attempts have employed ensemble learning methods to mitigate the enormous computation, an essential characteristic of diversity in ensemble methodsis missed out, causing more similar sub-architectures to be gathered and potential redundancy in the final ensemble architecture.
Revised:  While recent attempts have employed ensemble learning methods to mitigate the enormous computation, an essential characteristic of diversity in ensemble methodsis missed out, causing more similar sub-architectures with potential redundancy in the final ensemble architecture.
Edit intent: clarity
Original:  To bridge this gap , we propose a pruning method for NAS ensembles , named as '' Sub-Architecture Ensemble Pruning in Neural Architecture Search (SAEP).
Revised:  To bridge this gap , we propose a pruning method for NAS ensembles called " Sub-Architecture Ensemble Pruning in Neural Architecture Search (SAEP).
Edit intent: fluency
Original:  '' It targets to utilize diversity and achieve sub-ensemble architectures in a smaller size with comparable performance to the unpruned ensemble architectures .
Revised:  " It targets to utilize diversity and achieve sub-ensemble architectures in a smaller size with comparable performance to the unpruned ensemble architectures .
Edit intent: fluency
Original:  '' It targets to utilize diversity and achieve sub-ensemble architectures in a smaller size with comparable performance to the unpruned ensemble architectures .
Revised:  '' It targets to leverage diversity and to achieve sub-ensemble architectures in a smaller size with comparable performance to the unpruned ensemble architectures .
Edit intent: fluency
Original:  '' It targets to utilize diversity and achieve sub-ensemble architectures in a smaller size with comparable performance to the unpruned ensemble architectures .
Revised:  '' It targets to utilize diversity and achieve sub-ensemble architectures at a smaller size with comparable performance to the unpruned ensemble architectures .
Edit intent: fluency
Original:  '' It targets to utilize diversity and achieve sub-ensemble architectures in a smaller size with comparable performance to the unpruned ensemble architectures .
Revised:  '' It targets to utilize diversity and achieve sub-ensemble architectures in a smaller size with comparable performance to ensemble architectures that are not pruned .
Edit intent: clarity
Original:  Three possible solutions are proposed to decide which subarchitectures should be pruned during the searching process.
Revised:  Three possible solutions are proposed to decide which sub-architectures to prune during the searching process.
Edit intent: fluency
Original:  Experimental results demonstrate the effectiveness of the proposed method in largely reducing the size of ensemble architectures while maintaining the final performance.
Revised:  Experimental results exhibit the effectiveness of the proposed method in largely reducing the size of ensemble architectures while maintaining the final performance.
Edit intent: fluency
Original:  Experimental results demonstrate the effectiveness of the proposed method in largely reducing the size of ensemble architectures while maintaining the final performance.
Revised:  Experimental results demonstrate the effectiveness of the proposed method by largely reducing the size of ensemble architectures while maintaining the final performance.
Edit intent: fluency
Original: Automatic analyses and comparisons of different stages of embryonic development largely depend on a highly accurate spatio-temporal alignment of the investigated data sets.
Revised: Automatic analyses and comparisons of different stages of embryonic development largely depend on a highly accurate spatiotemporal alignment of the investigated data sets.
Edit intent: fluency
Original:  In this contribution, we compare multiple approaches to perform automatic staging of developing embryos that were imaged with time-resolved 3D light-sheet microscopy.
Revised:  In this contribution, we assess multiple approaches to perform automatic staging of developing embryos that were imaged with time-resolved 3D light-sheet microscopy.
Edit intent: clarity
Original:  The proof-of-concept experiments with four wild-type zebrafish embryos render both approaches suitable for automatic staging with average deviations of 0.45 - 0.57 hours .
Revised:  The experiments with four wild-type zebrafish embryos render both approaches suitable for automatic staging with average deviations of 0.45 - 0.57 hours .
Edit intent: clarity
Original:  In this contribution, we assess multiple approaches to perform automatic staging of developing embryos that were imaged with time-resolved 3D light-sheet microscopy.
Revised:  In this contribution, we assess multiple approaches for automatic staging of developing embryos that were imaged with time-resolved 3D light-sheet microscopy.
Edit intent: clarity
Original: Welcome to the documentation of Conjure ! Conjure is an automated modelling tool for Constraint Programming.
Revised: Conjure is an automated modelling tool for Constraint Programming.
Edit intent: coherence
Original:  For both betweenness and percolation centrality the computation of the%DIFDELCMD < {\it %%% exact value for a given vertex v is not known to be easier than the computation the same value, all at once, for all n vertices of G. In any one of these cases it is an open problem whether these measures can be computed in O(n^{3-c}) time, for any constant c>0.
Revised:  %DIFDELCMD < {\it %%% exact value for a given vertex v is not known to be easier than the computation the same value, all at once, for all n vertices of G. In any one of these cases it is an open problem whether these measures can be computed in O(n^{3-c}) time, for any constant c>0.
Edit intent: clarity
Original:  For both betweenness and percolation centrality the computation of the%DIFDELCMD < {\it %%% exact value for a given vertex v is not known to be easier than the computation the same value, all at once, for all n vertices of G. In any one of these cases it is an open problem whether these measures can be computed in O(n^{3-c}) time, for any constant c>0.
Revised:  For both betweenness and percolation centrality the computation of the%DIFDELCMD < {\it %%% value for a given vertex v is not known to be easier than the computation the same value, all at once, for all n vertices of G. In any one of these cases it is an open problem whether these measures can be computed in O(n^{3-c}) time, for any constant c>0.
Edit intent: clarity
Original:  For both betweenness and percolation centrality the computation of the%DIFDELCMD < {\it %%% exact value for a given vertex v is not known to be easier than the computation the same value, all at once, for all n vertices of G. In any one of these cases it is an open problem whether these measures can be computed in O(n^{3-c}) time, for any constant c>0.
Revised:  For both betweenness and percolation centrality the computation of the%DIFDELCMD < {\it %%% exact It is an open problem whether these measures can be computed in O(n^{3-c}) time, for any constant c>0.
Edit intent: coherence
Original:  For both betweenness and percolation centrality the computation of the%DIFDELCMD < {\it %%% exact value for a given vertex v is not known to be easier than the computation the same value, all at once, for all n vertices of G. In any one of these cases it is an open problem whether these measures can be computed in O(n^{3-c}) time, for any constant c>0.
Revised:  For both betweenness and percolation centrality the computation of the%DIFDELCMD < {\it %%% exact value for a given vertex v is not known to be easier than the computation the same value, all at once, for all n vertices of G. In any one of these cases it is an open problem whether these centrality measure can be computed in O(n^{3-c}) time, for any constant c>0.
Edit intent: fluency
Original:  In this paper we first present a O(m \log^2 n) randomized approximation algorithm for the percolation centrality for every vertex of G, generalizing techniques developed by RiondatoUpfal (this complexity is reduced to O((m+n) \log n) for unweighted graphs).
Revised:  In this paper we present a O(m \log^2 n) randomized approximation algorithm for the percolation centrality for every vertex of G, generalizing techniques developed by RiondatoUpfal (this complexity is reduced to O((m+n) \log n) for unweighted graphs).
Edit intent: coherence
Original:  The estimative obtained by the algorithm is within \epsilon of the exact value with probability 1- \delta, for {\it fixed} constants 0 < \epsilon,\delta \leq 1. Additionally, we show that sample complexity theory can be used to distinguish the problem of estimating the percolation centrality of a single vertex, refered as computing (v), from the problem of estimating the percolation centrality of every vertex of G, refered as computing%DIFDELCMD < p%%% (G).
Revised:  The estimative obtained by the algorithm is within \epsilon of the exact value with probability 1- \delta, for {\it fixed} constants 0 < \epsilon,\delta \leq 1. (v), from the problem of estimating the percolation centrality of every vertex of G, refered as computing%DIFDELCMD < p%%% (G).
Edit intent: coherence
Original:  The estimative obtained by the algorithm is within \epsilon of the exact value with probability 1- \delta, for {\it fixed} constants 0 < \epsilon,\delta \leq 1. Additionally, we show that sample complexity theory can be used to distinguish the problem of estimating the percolation centrality of a single vertex, refered as computing (v), from the problem of estimating the percolation centrality of every vertex of G, refered as computing%DIFDELCMD < p%%% (G).
Revised:  The estimative obtained by the algorithm is within \epsilon of the exact value with probability 1- \delta, for {\it fixed} constants 0 < \epsilon,\delta \leq 1. Additionally, we show that sample complexity theory can be used to distinguish the problem of estimating the percolation centrality of a single vertex, refered as computing %DIFDELCMD < p%%% (G).
Edit intent: clarity