wanyu/IteraTeR_v2 huggingface dataset:
Original:  Several sufficient and necessary connectivity conditions are obtained for the system to reach robust consensus or integral robust consensus, based on which we reach a clear answer to the following basic questionon multi-agent studies : how much communication capacity is required for a multi-agent network to deal with certain amount of disturbance.
Revised:  Several sufficient and necessary connectivity conditions are obtained for the system to reach robust consensus or integral robust consensus, based on which we reach a clear answer to the following basic questionon multi-agent studies : how much communication capacity is required for a multi-agent network to converge despite certain amount of disturbance.
Edit intent: clarity
Original:   In addition, as an illustration of the application of the results, distributed event-triggered coordination is studied.
Revised:  In addition, as an illustration of the applicability of the results, distributed event-triggered coordination is studied.
Edit intent: fluency
Original:  It is based on identifying local optima of moment polynomials over the unit sphere and can be seen as a higher-order extension of Principal Component Analysis (PCA).
Revised:  Our algorithm can be viewed as a higher-order extension of Principal Component Analysis (PCA).
Edit intent: clarity
Original:  Using these local optima that are typically not global optima, we give algorithms for factoring distributions and for learning a function of an unknown k-dimensional subspace in Rn.
Revised:  Our motivating problem is learning a labeling function in R^n, which is determined by an unknown k-dimensional subspace in Rn.
Edit intent: clarity
Original:  This latter problem of learning a k-subspace junta , generalizes the well-known problem of learning a k-junta (a function of k coordinates in n-dimensional space) and encompasses concept classes such as intersections of k halfspaces and intersections of bounded-degree polynomials.
Revised:  This latter problem of learning a k-subspace junta is a common generalization of learning a k-junta (a function of k coordinates in n-dimensional space) and encompasses concept classes such as intersections of k halfspaces and intersections of bounded-degree polynomials.
Edit intent: clarity
Original:  This latter problem of learning a k-subspace junta , generalizes the well-known problem of learning a k-junta (a function of k coordinates in n-dimensional space) and encompasses concept classes such as intersections of k halfspaces and intersections of bounded-degree polynomials.
Revised:  This latter problem of learning a k-subspace junta , generalizes the well-known problem of learning a k-junta (a function of k coordinates in R^n) and learning intersections of k halfspaces and intersections of bounded-degree polynomials.
Edit intent: clarity
Original:  We introduce an irrelevant noisy attributes model where the distribution in the relevant subspace is independent of the distribution in the subspace orthogonal to it , and we give an algorithm to identify the relevant subspace and thereby learn k-subspace juntas in this model .
Revised:  We introduce an irrelevant noisy attributes model where the distribution over the "relevant" k-dimensional subspace is independent of the distribution in the subspace orthogonal to it , and we give an algorithm to identify the relevant subspace and thereby learn k-subspace juntas in this model .
Edit intent: clarity
Original:  We introduce an irrelevant noisy attributes model where the distribution in the relevant subspace is independent of the distribution in the subspace orthogonal to it , and we give an algorithm to identify the relevant subspace and thereby learn k-subspace juntas in this model .
Revised:  We introduce an irrelevant noisy attributes model where the distribution in the relevant subspace is independent of the distribution in the subspace orthogonal to it , and we give an algorithm to identify the relevant subspace , and thereby learns k-subspace juntas in this model .
Edit intent: fluency
Original:  Our main result is that when the distribution in the irrelevant  subspace is any Gaussian, the complexity of our algorithm is T(k, %DIFDELCMD < \eps%%% ) poly\poly (n), where T is  a function of only the k-dimensional concept class being learned.
Revised:  Our main result is that when the distribution in the irrelevant subspace is any Gaussian, the complexity of our algorithm is T(k, %DIFDELCMD < \eps%%% ) poly\poly (n), where T is a function of the k-dimensional concept class being learned.
Edit intent: clarity
Original:  This substantially generalizes existing results on learning low-dimensional concepts (based on polynomial regression or standard PCA) Klivans et al 2008, Vempala 2010
Revised:  This substantially generalizes existing results on learning low-dimensional concepts Klivans et al 2008, Vempala 2010
Edit intent: clarity
Original:  This substantially generalizes existing results on learning low-dimensional concepts (based on polynomial regression or standard PCA) Klivans et al 2008, Vempala 2010
Revised:  This substantially generalizes existing results on learning low-dimensional concepts (based on polynomial regression or standard PCA) .
Edit intent: clarity
Original:  Results: Based on the sparse k-mers graph model, we develop SparseAssembler2.
Revised:  Results: 
Edit intent: coherence
Original:  Since the new sparse k-mers graph shares almost all advantages of de Bruijn graph, we are able to adapt a Dijkstra-like breadth-first search algorithm , for the new sparse k-mer graph in order to circumvent sequencing errors and resolve polymorphisms.
Revised:  Since the new sparse k-mers graph shares almost all advantages of de Bruijn graph, we are able to adapt a Dijkstra-like breadth-first search algorithm to circumvent sequencing errors and resolve polymorphisms.
Edit intent: coherence
Original:  The experimental relevance of the model results is further pointed out.
Revised:  The experimental relevance of the model results is also pointed out.
Edit intent: clarity
Original: A graph is called pseudo-outerplanar if each block has an embedding in the plane such that the vertices lie on a fixed circle and the edges lie inside the disk of this circle with each of them crossing at most one another.
Revised: A graph is called pseudo-outerplanar if each block has an embedding on the plane in such a way that the vertices lie on a fixed circle and the edges lie inside the disk of this circle with each of them crossing at most one another.
Edit intent: clarity
Original:  In this paper, we show that each pseudo-outerplanar graph admits edge decompositions into a linear forest and an outerplanar graph, or a star forest and an outerplanar graph, or two forests and a matching .
Revised:  In this paper, we prove that each pseudo-outerplanar graph admits edge decompositions into a linear forest and an outerplanar graph, or a star forest and an outerplanar graph, or two forests and a matching .
Edit intent: coherence
Original:  Yet the manual curation of research data can be di?cult and expensive, particularly in very large digital repositories, hence the importance of models and tools for automating digital curation tasks.
Revised:  Yet the manual curation of research data can be difficult and expensive, particularly in very large digital repositories, hence the importance of models and tools for automating digital curation tasks.
Edit intent: fluency
Original:  Speci?cally , we propose a scalable strategy for addressing the research-data problem, ranging from the extraction of legacy data to its long-term storage.
Revised:  Specifically , we propose a scalable strategy for addressing the research-data problem, ranging from the extraction of legacy data to its long-term storage.
Edit intent: fluency
Original: We consider an infinite dimensional system of stochastic differential equations which describes the evolution of type frequencies in a large population.
Revised: We consider an infinite dimensional system of stochastic differential equations describing the evolution of type frequencies in a large population.
Edit intent: clarity
Original:  Random reproduction is modeled by a Wright-Fisher noise whose inverse diffusion coefficient N corresponds to the total population size.
Edit intent: coherence
Original:  The type of an individual is the number k of deleterious mutations it carries .
Revised:  The type of an individual is the number of deleterious mutations it carries .
Edit intent: clarity
Original:  Along the individual lines of descent, (new ) mutations accumulate at rate \lambda per generation, and each of these mutations has a small probability \gamma per generation to disappear.
Revised:  Along the individual lines of descent, new mutations accumulate at rate \lambda per generation, and each of these mutations has a small probability \gamma per generation to disappear.
Edit intent: fluency
Original:  Along the individual lines of descent, (new ) mutations accumulate at rate \lambda per generation, and each of these mutations has a small probability \gamma per generation to disappear.
Revised:  Along the individual lines of descent, (new ) mutations accumulate at rate \lambda per generation, and each of these mutations has a probability \gamma per generation to disappear.
Edit intent: clarity
Original:  While the case \gamma =0 is known as (the Fleming-Viot version of) {\em Muller's ratchet}, the case \gamma > 0 is referred to as that of {\em compensatory mutations} in the biological literature.
Revised:  While the case \gamma =0 is known as (the Fleming--Viot version of) {\em Muller's ratchet}, the case \gamma > 0 is referred to as that of {\em compensatory mutations} in the biological literature.
Edit intent: fluency
Original:  While the case \gamma =0 is known as (the Fleming-Viot version of) {\em Muller's ratchet}, the case \gamma > 0 is referred to as that of {\em compensatory mutations} in the biological literature.
Revised:  While the case \gamma =0 is known as (the Fleming-Viot version of) {\em Muller's ratchet}, the case \gamma > 0 is associated with {\em compensatory mutations} in the biological literature.
Edit intent: clarity
Original:  In the former case (\gamma=0), an ever increasing number of mutations is accumulated over time, while in the latter (\gamma > 0) this is prevented by the compensatory mutations which in this sensehalt the ratchet .
Revised:  halt the ratchet .
Edit intent: clarity
Original:  In the former case (\gamma=0), an ever increasing number of mutations is accumulated over time, while in the latter (\gamma > 0) this is prevented by the compensatory mutations which in this sensehalt the ratchet .
Revised:  In the former case (\gamma=0), an ever increasing number of mutations is accumulated over time, while in the latter (\gamma > 0) this is prevented by the compensatory mutations which in this sense .
Edit intent: clarity
Original:  In the former case (\gamma=0), an ever increasing number of mutations is accumulated over time, while in the latter (\gamma > 0) this is prevented by the compensatory mutations which in this sensehalt the ratchet .
Revised:  In the former case (\gamma=0), an ever increasing number of mutations is accumulated over time, while in the latter (\gamma > 0) this is prevented by the compensatory mutations which in this sensehalt the ratchet 
Edit intent: fluency
Original:  We show that the system under consideration (\gamma\geq 0) has a unique weak solution.
Revised:  We show that the system has a unique weak solution.
Edit intent: clarity
Original:  In particular, we show that the unique equilibrium state in the case \gamma>0 and N=\infty is the Poisson distribution with parameter \lambda/(\gamma + \alpha).
Revised:  In particular, we show that the unique equilibrium state is the Poisson distribution with parameter \lambda/(\gamma + \alpha).
Edit intent: coherence
Original: We consider an infinite dimensional system of stochastic differential equations describing the evolution of type frequencies in a large population.
Revised: We consider an infinite-dimensional system of stochastic differential equations describing the evolution of type frequencies in a large population.
Edit intent: fluency
Original:  While the case \gamma=0 is known as (the Fleming--Viot version of) %DIFDELCMD < {\em %%% Muller's ratchet , the case \gamma>0 is associated with %DIFDELCMD < {\em %%% compensatory mutations in the biological literature.
Revised:  While the case \gamma=0 is known as (the Fleming-Viot version of) %DIFDELCMD < {\em %%% Muller's ratchet , the case \gamma>0 is associated with %DIFDELCMD < {\em %%% compensatory mutations in the biological literature.
Edit intent: fluency
Original:  While the case \gamma=0 is known as (the Fleming--Viot version of) %DIFDELCMD < {\em %%% Muller's ratchet , the case \gamma>0 is associated with %DIFDELCMD < {\em %%% compensatory mutations in the biological literature.
Revised:  While the case \gamma=0 is known as (the Fleming--Viot version of) %DIFDELCMD < {\em %%% Muller's ratchet , the case \gamma>0 is associated with %DIFDELCMD < {\em %%% compensatory mutations in the biological literature.
Edit intent: fluency
Original:  In the absence of random fluctuations in type frequencies ( that is for the so-called infinite-population limit) we obtain the solution in a closed form by analyzing a probabilistic particle system and show that for \gamma>0  the unique equilibrium state is the Poisson distribution with parameter \lambda/(\gamma+\alpha).
Revised:  In the absence of random fluctuations in type frequencies ( i.e., for the so-called infinite-population limit) we obtain the solution in a closed form by analyzing a probabilistic particle system and show that for \gamma>0 the unique equilibrium state is the Poisson distribution with parameter \lambda/(\gamma+\alpha).
Edit intent: coherence
Original:  In the absence of random fluctuations in type frequencies ( that is for the so-called infinite-population limit) we obtain the solution in a closed form by analyzing a probabilistic particle system and show that for \gamma>0  the unique equilibrium state is the Poisson distribution with parameter \lambda/(\gamma+\alpha).
Revised:  In the absence of random fluctuations in type frequencies ( that is for the so-called infinite population limit) we obtain the solution in a closed form by analyzing a probabilistic particle system and show that for \gamma>0 the unique equilibrium state is the Poisson distribution with parameter \lambda/(\gamma+\alpha).
Edit intent: fluency
Original:  In the absence of random fluctuations in type frequencies ( that is for the so-called infinite-population limit) we obtain the solution in a closed form by analyzing a probabilistic particle system and show that for \gamma>0  the unique equilibrium state is the Poisson distribution with parameter \lambda/(\gamma+\alpha).
Revised:  In the absence of random fluctuations in type frequencies ( that is for the so-called infinite-population limit) we obtain the solution in a closed form by analyzing a probabilistic particle system and show that for \gamma>0 , the unique equilibrium state is the Poisson distribution with parameter \lambda/(\gamma+\alpha).
Edit intent: fluency
Original:  We study the implications of this phenomenon by analyzing two asymptotic regimes of the peer-to-peer network: the fluid regime and the hard--core regime.
Revised:  We analyze two asymptotic regimes of the peer-to-peer network: the fluid regime and the hard--core regime.
Edit intent: clarity
Original:  This segregated structure is identical to what is found in gene regulatory networks, where only a much smaller subset of genes - those responsible for transcription factors - are responsible for global regulation.
Revised:  This segregated structure is identical to what is found in gene regulatory networks, where only a much smaller subset of genes - those responsible for transcription factors - is responsible for global regulation.
Edit intent: fluency
Original: As a model of evolved genetic regulatory systems, we investigate the evolution of Boolean networks , which are subject to a selective pressure which favors robustness against noise  .
Revised: We investigate the evolution of Boolean networks , which are subject to a selective pressure which favors robustness against noise .
Edit intent: clarity
Original: As a model of evolved genetic regulatory systems, we investigate the evolution of Boolean networks , which are subject to a selective pressure which favors robustness against noise  .
Revised: As a model of evolved genetic regulatory systems, we investigate the evolution of Boolean networks subject to a selective pressure which favors robustness against noise .
Edit intent: coherence
Original: As a model of evolved genetic regulatory systems, we investigate the evolution of Boolean networks , which are subject to a selective pressure which favors robustness against noise  .
Revised: As a model of evolved genetic regulatory systems, we investigate the evolution of Boolean networks , which are subject to a selective pressure which favors robustness against noise , as a model of evolved genetic regulatory systems .
Edit intent: fluency
Original:  We compare the theoretical predictions with Monte-Carlo simulations of actual networks, and find an excellent agreement .
Revised:  We compare the theoretical predictions with Monte-Carlo simulations of evolved networks, and find an excellent agreement .
Edit intent: clarity
Original:  This segregated structure is identical to what is found in gene regulatory networks, where only a much smaller subset of genes - those responsible for transcription factors - is responsible for global regulation.
Revised:  This segregated structure is identical to what is found in gene regulatory networks, where only a much smaller subset of genes --- those responsible for transcription factors - is responsible for global regulation.
Edit intent: fluency
Original:  This segregated structure is identical to what is found in gene regulatory networks, where only a much smaller subset of genes - those responsible for transcription factors - is responsible for global regulation.
Revised:  This segregated structure is identical to what is found in gene regulatory networks, where only a much smaller subset of genes - those responsible for transcription factors --- is responsible for global regulation.
Edit intent: fluency
Original:  We compare the theoretical predictions with Monte-Carlo simulations of evolved networks , and with empirical data for Saccharomyces cerevisiae and Escherichi coli.
Revised:  We compare the theoretical predictions with Monte Carlo simulations of evolved networks , and with empirical data for Saccharomyces cerevisiae and Escherichi coli.
Edit intent: fluency
Original:  We compare the theoretical predictions with Monte-Carlo simulations of evolved networks , and with empirical data for Saccharomyces cerevisiae and Escherichi coli.
Revised:  We compare the theoretical predictions with Monte-Carlo simulations of evolved networks and with empirical data for Saccharomyces cerevisiae and Escherichi coli.
Edit intent: fluency
Original:  We compare the theoretical predictions with Monte-Carlo simulations of evolved networks , and with empirical data for Saccharomyces cerevisiae and Escherichi coli.
Revised:  We compare the theoretical predictions with Monte-Carlo simulations of evolved networks , and with empirical data for Saccharomyces cerevisiae and Escherichia coli.
Edit intent: fluency
Original: The Possible Winner problem asks, given an election where the voters' preferences over the candidates are specified only partially, whether a designated candidate can be made win . Betzler and Dorn [1 ,2 ] proved a result that is only one step away from a full dichotomy of this problem for the important class of pure scoring rules in the case of unweighted voters and an unbounded number of candidates:
Revised: The Possible Winner problem asks, given an election where the voters' preferences over the candidates are specified only partially, whether a designated candidate can be made win . Betzler and Dorn [1 ] proved a result that is only one step away from a full dichotomy of this problem for the important class of pure scoring rules in the case of unweighted voters and an unbounded number of candidates:
Edit intent: coherence
Original:  In fact, we consider a more refined version of {\sc MaxSat-AA ,{\sc Max-r(n)-Sat-AA , where r_j\le r(n) for each j. Alon {\em et al. (SODA 2010) proved that if r=r(n) is a constant, then {\sc Max-r-Sat-AA is fixed-parameter tractable.
Revised:  In fact, we consider a more refined version of {\sc MaxSat-AA ,{\sc Max-r(n)-Sat-AA , where r_j\le r(n) for each j. Alon {\em et al. (SODA 2010) proved that if r=r(n) is a constant, then {\sc Max-r-Sat-AA is fixed-parameter tractable.
Edit intent: fluency
Original:  More precisely, we derive degree bounding formulas which allow to estimate the degree of the output equations of creative telescoping as a function of the order.
Revised:  More precisely, we derive degree bounding formulas which allow to estimate the degree of the output equations from creative telescoping as a function of the order.
Edit intent: fluency
Original:  As an application, we show how the knowledge of this formula can be used to optimize the runtime of creative telescoping implementations, and we deduce bounds on the asymptotic complexity of creative telescoping for hyperexponential terms.
Revised:  As an application, we show how the knowledge of these formulas can be used to optimize the runtime of creative telescoping implementations, and we deduce bounds on the asymptotic complexity of creative telescoping for hyperexponential terms.
Edit intent: fluency
Original:  As an application, we show how the knowledge of this formula can be used to optimize the runtime of creative telescoping implementations, and we deduce bounds on the asymptotic complexity of creative telescoping for hyperexponential terms.
Revised:  As an application, we show how the knowledge of this formula can be used to improve, at least in principle, the performance of creative telescoping implementations, and we deduce bounds on the asymptotic complexity of creative telescoping for hyperexponential terms.
Edit intent: clarity
Original: Population-based Random Search (RS) algorithms , such as Evolutionary Algorithms (EAs), Ant Colony Optimization (ACO), Artificial Immune Systems (AIS) and Particle Swarm Optimization (PSO), have been widely applied to solving discrete optimization problems.
Revised: Population-based evolutionary algorithms have been widely applied to solving discrete optimization problems.
Edit intent: clarity
Original:  A common belief in this area is that the performance of a population-based RS algorithm may improve if increasing its population size.
Revised:  A common belief in this area is that the performance of a population-based EA and its population size.
Edit intent: clarity
Original:  Through analyzing fundamental matrix associated with ARS, several general results have been proven: (1) increasing population size may increase population scalability ;
Revised:  Through analyzing fundamental matrix associated with ARS, several general results have been proven: (1) Increasing the population size may increase population scalability ;
Edit intent: fluency
Original:  (2) no super linear scalability is available on any regular monotonic fitness landscape ;
Revised:  (2) no super linear scalability is available on any regular monotonic fitness function ;
Edit intent: clarity
Original:  (3) potential super linear scalability may exist on deceptive fitness landscapes ;
Revised:  (3) Potential super linear speedup may happen when elitist EAs are used to maximize deceptive fitness functions ;
Edit intent: clarity
Original:  (4) " bridgeable point " and "diversity preservation" are two necessary conditions for super linear scalability on all fitness landscapes;
Revised:  (4) " Bridgeable state " and "diversity preservation" are two necessary conditions for super linear scalability on all fitness landscapes;
Edit intent: clarity