wanyu/IteraTeR_v2 huggingface dataset:
Original:  An important aspect of these studies is discerning which evolutionary pressures favor the formation of swarms.
Revised:  An important goal of these studies is discerning which evolutionary pressures favor the formation of swarms.
Edit intent: clarity
Original:  Using a model of a predator-prey system with evolving predator and prey behaviors , we show that predator confusion provides a sufficient selection pressure to evolve swarming behavior in prey.
Revised:  Using an evolutionary model of a predator-prey system with evolving predator and prey behaviors , we show that predator confusion provides a sufficient selection pressure to evolve swarming behavior in prey.
Edit intent: clarity
Original:  Using a model of a predator-prey system with evolving predator and prey behaviors , we show that predator confusion provides a sufficient selection pressure to evolve swarming behavior in prey.
Revised:  Using a model of a predator-prey system , we show that predator confusion provides a sufficient selection pressure to evolve swarming behavior in prey.
Edit intent: clarity
Original:  Furthermore, we demonstrate that the evolutionary effect of predator confusion on prey could in turn exert evolutionary pressure on the structure of the predator's visual field, favoring the frontally oriented, high resolution visual systems commonly observed in predators that feed on swarming animals.
Revised:  Furthermore, we demonstrate that the evolutionary effect of predator confusion on prey could in turn exert pressure on the structure of the predator's visual field, favoring the frontally oriented, high resolution visual systems commonly observed in predators that feed on swarming animals.
Edit intent: fluency
Original:  Furthermore, we demonstrate that the evolutionary effect of predator confusion on prey could in turn exert evolutionary pressure on the structure of the predator's visual field, favoring the frontally oriented, high resolution visual systems commonly observed in predators that feed on swarming animals.
Revised:  Furthermore, we demonstrate that the evolutionary effect of predator confusion on prey could in turn exert evolutionary pressure on the structure of the predator's visual field, favoring the frontally oriented, high-resolution visual systems commonly observed in predators that feed on swarming animals.
Edit intent: fluency
Original:  Thus, we show that a relatively simple perceptual constraint--predator confusion--could have pervasive evolutionary effects on prey behavior, predator sensory mechanisms, and the ecological interactions between predators and prey 
Revised:  Thus, we show that a relatively simple perceptual constraint--predator confusion--could have pervasive evolutionary effects on prey behavior, predator sensory mechanisms, and the ecological interactions between predators and prey .
Edit intent: fluency
Original: In a recent paper, Girard uses the geometry of interaction in the hyperfinite factor in an innovative way to characterize complexity classes.
Revised: In a recent paper, Girard proposes to use his recent construction of a geometry of interaction in the hyperfinite factor in an innovative way to characterize complexity classes.
Edit intent: clarity
Original:  The purpose of this paper is two-fold: to give a detailed explanation of both the choices and the motivations of Girard's definitions , and - since Girard's paper skips over some non-trivial details and only sketches one half of the proof - to provide a complete proof that  co-NL can be characterized by an action of the group of finite permutations .
Revised:  We begin by giving a detailed explanation of both the choices and the motivations of Girard's definitions , and - since Girard's paper skips over some non-trivial details and only sketches one half of the proof - to provide a complete proof that co-NL can be characterized by an action of the group of finite permutations .
Edit intent: coherence
Original:  The purpose of this paper is two-fold: to give a detailed explanation of both the choices and the motivations of Girard's definitions , and - since Girard's paper skips over some non-trivial details and only sketches one half of the proof - to provide a complete proof that  co-NL can be characterized by an action of the group of finite permutations .
Revised:  The purpose of this paper is two-fold: to give a detailed explanation of both the choices and the motivations of Girard's definitions . We then provide a complete proof that co-NL can be characterized by an action of the group of finite permutations .
Edit intent: coherence
Original:  We introduce as a technical tool the non-deterministic pointer machine, a concrete model that computes the algorithmsrepresented in this setting .
Revised:  We introduce as a technical tool the non-deterministic pointer machine, a concrete model to computes algorithms .
Edit intent: clarity
Original:  To describe TE dynamics in populations, many previous studies have relied on the assumption of equilibrium between transpositionand selection .
Revised:  To describe TE dynamics in populations, many previous studies have relied on the assumption of a constant rate of transposition .
Edit intent: clarity
Original:  Here we derive a test of neutrality for TE insertions that does not rely on the assumption of transpositional equilibrium .
Revised:  Here we propose a test of neutrality for TE insertions that does not rely on the assumption of transpositional equilibrium .
Edit intent: clarity
Original:  Here we derive a test of neutrality for TE insertions that does not rely on the assumption of transpositional equilibrium .
Revised:  Here we derive a test of neutrality for TE insertions that does not rely on the assumption of a constant transposition rate .
Edit intent: clarity
Original:  We consider the case of TE insertions that have been ascertained from a single haploid reference genome sequence and have  had their allele frequency estimated in a population sample.
Revised:  We consider the case of TE insertions that have been ascertained from a single haploid reference genome sequence and have subsequently had their allele frequency estimated in a population sample.
Edit intent: clarity
Original:  By conditioning on age information provided within the sequence of a TE insertion in the form of the number of substitutions that have occurred within the fragment since insertioninto a reference genome, we derive the probability distribution for the TE allele frequency in a population sample under neutrality.
Revised:  By conditioning on the form of the number of substitutions that have occurred within the fragment since insertioninto a reference genome, we derive the probability distribution for the TE allele frequency in a population sample under neutrality.
Edit intent: clarity
Original:  By conditioning on age information provided within the sequence of a TE insertion in the form of the number of substitutions that have occurred within the fragment since insertioninto a reference genome, we derive the probability distribution for the TE allele frequency in a population sample under neutrality.
Revised:  By conditioning on age information provided within the sequence of a TE insertion in the form of the number of substitutions that have occurred within the TE sequence since insertion), we determine the probability distribution for the TE allele frequency in a population sample under neutrality.
Edit intent: clarity
Original:  By conditioning on age information provided within the sequence of a TE insertion in the form of the number of substitutions that have occurred within the fragment since insertioninto a reference genome, we derive the probability distribution for the TE allele frequency in a population sample under neutrality.
Revised:  By conditioning on age information provided within the sequence of a TE insertion in the form of the number of substitutions that have occurred within the fragment since insertioninto a reference genome, we derive the probability distribution for the insertion allele frequency in a population sample under neutrality.
Edit intent: fluency
Original:  Taking models of population fluctuation into account, we then test the fit of predictions of our model to allele frequency data from 190 retrotransposon insertion loci in North American and African populations of Drosophila melanogaster.
Revised:  Taking models of varying population size into account, we then test the fit of predictions of our model to allele frequency data from 190 retrotransposon insertion loci in North American and African populations of Drosophila melanogaster.
Edit intent: clarity
Original:  Taking models of population fluctuation into account, we then test the fit of predictions of our model to allele frequency data from 190 retrotransposon insertion loci in North American and African populations of Drosophila melanogaster.
Revised:  Taking models of population fluctuation into account, we then evaluate predictions of our model to allele frequency data from 190 retrotransposon insertion loci in North American and African populations of Drosophila melanogaster.
Edit intent: clarity
Original:  Taking models of population fluctuation into account, we then test the fit of predictions of our model to allele frequency data from 190 retrotransposon insertion loci in North American and African populations of Drosophila melanogaster.
Revised:  Taking models of population fluctuation into account, we then test the fit of predictions of our model against allele frequency data from 190 retrotransposon insertion loci in North American and African populations of Drosophila melanogaster.
Edit intent: clarity
Original:  Taking models of population fluctuation into account, we then test the fit of predictions of our model to allele frequency data from 190 retrotransposon insertion loci in North American and African populations of Drosophila melanogaster.
Revised:  Taking models of population fluctuation into account, we then test the fit of predictions of our model to allele frequency data from 190 retrotransposon insertions sampled from North American and African populations of Drosophila melanogaster.
Edit intent: clarity
Original:  Using this non-equilibrium model, we are able to explain about 80\% of the variance in TE insertion allele frequencies . Controlling for nonequilibrium dynamics of transposition and host demography, we demonstrate how one may detect negative selection acting against most TEs as well as evidence for a small subset of TEs being driven to high frequency by positive selection .
Revised:  Using this non-equilibrium model, we are able to explain about 80\% of the variance in TE insertion allele frequencies . Controlling for nonequilibrium dynamics of transposition and host demography, we provide evidence for negative selection acting against most TEs as well as evidence for a small subset of TEs being driven to high frequency by positive selection .
Edit intent: clarity
Original:  Using this non-equilibrium model, we are able to explain about 80\% of the variance in TE insertion allele frequencies . Controlling for nonequilibrium dynamics of transposition and host demography, we demonstrate how one may detect negative selection acting against most TEs as well as evidence for a small subset of TEs being driven to high frequency by positive selection .
Revised:  Using this non-equilibrium model, we are able to explain about 80\% of the variance in TE insertion allele frequencies . Controlling for nonequilibrium dynamics of transposition and host demography, we demonstrate how one may detect negative selection acting against most TEs as well as for positive selection acting on a small subset of TEs being driven to high frequency by positive selection .
Edit intent: clarity
Original:  Using this non-equilibrium model, we are able to explain about 80\% of the variance in TE insertion allele frequencies . Controlling for nonequilibrium dynamics of transposition and host demography, we demonstrate how one may detect negative selection acting against most TEs as well as evidence for a small subset of TEs being driven to high frequency by positive selection .
Revised:  Using this non-equilibrium model, we are able to explain about 80\% of the variance in TE insertion allele frequencies . Controlling for nonequilibrium dynamics of transposition and host demography, we demonstrate how one may detect negative selection acting against most TEs as well as evidence for a small subset of TEs .
Edit intent: clarity
Original:  The algorithm that achieves this ratio is the simple improved Christofides (1976) algorithm suggested by An, Kleinberg and Shmoys (2012)  which consists in determining the best Christofides {s,t}-tour out of those constructed from active trees of\Fscr a convex combination for the optimal fractional relaxation.
Revised:  The algorithm that achieves this ratio is the simple improved Christofides (1976) , suggested by An, Kleinberg and Shmoys (2012) which consists in determining the best Christofides {s,t}-tour out of those constructed from active trees of\Fscr a convex combination for the optimal fractional relaxation.
Edit intent: coherence
Original:  The algorithm that achieves this ratio is the simple improved Christofides (1976) algorithm suggested by An, Kleinberg and Shmoys (2012)  which consists in determining the best Christofides {s,t}-tour out of those constructed from active trees of\Fscr a convex combination for the optimal fractional relaxation.
Revised:  The algorithm that achieves this ratio is the simple improved Christofides (1976) algorithm suggested by An, Kleinberg and Shmoys (2012) , which consists in determining the best Christofides {s,t}-tour out of those constructed from active trees of\Fscr a convex combination for the optimal fractional relaxation.
Edit intent: fluency
Original:  The algorithm that achieves this ratio is the simple improved Christofides (1976) algorithm suggested by An, Kleinberg and Shmoys (2012)  which consists in determining the best Christofides {s,t}-tour out of those constructed from active trees of\Fscr a convex combination for the optimal fractional relaxation.
Revised:  The algorithm that achieves this ratio is the simple improved Christofides (1976) algorithm suggested by An, Kleinberg and Shmoys (2012) which consists in determining the best Christofides {s,t}-tour out of those constructed from a family\Fscr a convex combination for the optimal fractional relaxation.
Edit intent: clarity
Original:  The length of such an {s,t}-tour is guaranteed to be at most \sqrt{5+1 , which is the first improvement after the 5/3 guarantee of Hoogeveen's Christofides type algorithm (1991).
Revised:  The length of such an {s,t}-tour , which is the first improvement after the 5/3 guarantee of Hoogeveen's Christofides type algorithm (1991).
Edit intent: clarity
Original:  Along the way we also obtain a collection of 3'241 planar graphs that do not admit a simultaneous (plane straight-line) embedding without mapping.
Edit intent: coherence
Original:  In this paper, we present algorithms for integrating machine learning algorithms for acquiring labeled data into crowd-sourced databases .
Revised: Crowd-sourcing has become a popular means of acquiring labeled data for a wide variety of tasks where humans are more accurate than computers, e.g., labeling images, matching objects, or analyzing sentiment. However, relying solely on the crowd is often impractical even for data sets with thousands of items, due to time and cost constraints of acquiring human input (which cost pennies and minutes per label). In this paper, we present algorithms for integrating machine learning algorithms for acquiring labeled data into crowd-sourced databases .
Edit intent: coherence
Original:  In this paper, we present algorithms for integrating machine learning algorithms for acquiring labeled data into crowd-sourced databases .
Revised:  In this paper, we propose algorithms for integrating machine learning algorithms for acquiring labeled data into crowd-sourced databases .
Edit intent: clarity
Original:  In this paper, we present algorithms for integrating machine learning algorithms for acquiring labeled data into crowd-sourced databases .
Revised:  In this paper, we present algorithms for integrating machine learning into crowd-sourced databases .
Edit intent: clarity
Original:  The key observation is that there are a number of tasks for which humans and machine learning algorithms can be complementary, e.g., at labeling images where humans generally provide more accurate labels but are slow and expensive, while algorithms are usually less accurate  but faster and cheaper.
Revised:  The key observation is that , in many of the above tasks, humans and machine learning algorithms can be complementary, e.g., at labeling images where humans generally provide more accurate labels but are slow and expensive, while algorithms are usually less accurate but faster and cheaper.
Edit intent: clarity
Original:  The key observation is that there are a number of tasks for which humans and machine learning algorithms can be complementary, e.g., at labeling images where humans generally provide more accurate labels but are slow and expensive, while algorithms are usually less accurate  but faster and cheaper.
Revised:  The key observation is that there are a number of tasks for which humans and machine learning algorithms can be complementary, as humans are often more accurate but slow and expensive, while algorithms are usually less accurate but faster and cheaper.
Edit intent: clarity
Original:  The key observation is that there are a number of tasks for which humans and machine learning algorithms can be complementary, e.g., at labeling images where humans generally provide more accurate labels but are slow and expensive, while algorithms are usually less accurate  but faster and cheaper.
Revised:  The key observation is that there are a number of tasks for which humans and machine learning algorithms can be complementary, e.g., at labeling images where humans generally provide more accurate labels but are slow and expensive, while algorithms are usually less accurate , but faster and cheaper.
Edit intent: fluency
Original:  Based on this  , we present two  active learning algorithms designed to decide how to use humans and algorithms together in a crowd-sourced database.
Revised:  Based on this , we present two active learning algorithms to combine humans and algorithms together in a crowd-sourced database.
Edit intent: clarity
Original:  We also look at a range of issues specific to crowds, such as the fact that crowd-generated labels can be incorrect, and that multiple crowd workers can be "batched" to perform labeling on several different items simultaneously.
Edit intent: coherence
Original:  Our results, on three data sets collected with Amazon's Mechanical Turk, and on 15 data sets from the UCI KDD archive , show that our methods on average ask  one to two orders of magnitude fewer questions than a random baseline , and two to eight times fewer questions than previous active learning schemes.
Revised:  Our results, on three real-life datasets collected with Amazon's Mechanical Turk, and on 15 data sets from the UCI KDD archive , show that our methods on average ask one to two orders of magnitude fewer questions than a random baseline , and two to eight times fewer questions than previous active learning schemes.
Edit intent: clarity
Original:  Our results, on three data sets collected with Amazon's Mechanical Turk, and on 15 data sets from the UCI KDD archive , show that our methods on average ask  one to two orders of magnitude fewer questions than a random baseline , and two to eight times fewer questions than previous active learning schemes.
Revised:  Our results, on three data sets collected with Amazon's Mechanical Turk, and on 15 well-known UCI data sets , show that our methods on average ask one to two orders of magnitude fewer questions than a random baseline , and two to eight times fewer questions than previous active learning schemes.
Edit intent: clarity
Original:  The approximation factors are shown to be 1/3 and \max , respectively, where c is the curvature of the submodular function{2+\kappa} and \max}\frac{2{3(2+\kappa)},2/3(1-\kappa)} . We also look at how these algorithms perform for a directed graph and investigate a method to consider edge costs in addition to rewards.
Revised:  The approximation factors are shown to be , respectively, where c is the curvature of the submodular function{2+\kappa} and \max}\frac{2{3(2+\kappa)},2/3(1-\kappa)} . We also look at how these algorithms perform for a directed graph and investigate a method to consider edge costs in addition to rewards.
Edit intent: clarity
Original:  The approximation factors are shown to be 1/3 and \max , respectively, where c is the curvature of the submodular function{2+\kappa} and \max}\frac{2{3(2+\kappa)},2/3(1-\kappa)} . We also look at how these algorithms perform for a directed graph and investigate a method to consider edge costs in addition to rewards.
Revised:  The approximation factors are shown to be 1/3 and \max \frac{1{2+\kappa} and \max}\frac{2{3(2+\kappa)},2/3(1-\kappa)} . We also look at how these algorithms perform for a directed graph and investigate a method to consider edge costs in addition to rewards.
Edit intent: clarity
Original:  The lower bound combines a recent method of Blais, Brody and Matulef [BBM11] to get testing lower bounds from communication complexity , with a new Theta(k log k) bound for the one-way communication complexity of k-disjointness.
Revised:  The lower bound combines a recent method of Blais, Brody and Matulef [BBM11] to get lower bounds for testing from communication complexity , with a new Theta(k log k) bound for the one-way communication complexity of k-disjointness.
Edit intent: fluency
Original:  The lower bound combines a recent method of Blais, Brody and Matulef [BBM11] to get testing lower bounds from communication complexity , with a new Theta(k log k) bound for the one-way communication complexity of k-disjointness.
Revised:  The lower bound combines a recent method of Blais, Brody and Matulef [BBM11] to get testing lower bounds from communication complexity with an Omega(k \log k) lower bound for the one-way communication complexity of k-disjointness.
Edit intent: clarity
Original: The aim of this paper is to highlight and explore a traditional problem, which are the minimum spanning tree, and finding the shortest-path in network routing, by using biological inspired way (Swarm Intelligence).
Revised: The aim of this paper is to highlight and explore a traditional problem, which is the minimum spanning tree, and finding the shortest-path in network routing, by using biological inspired way (Swarm Intelligence).
Edit intent: fluency
Original:  In other words this work to be considered as a computer science investigation topic with combination between operations research, discrete mathematics, and new branch of computer science called evolutionary computing aiming to solve one of networking problems.
Revised:  In other words this work to be considered as an investigation topic with combination between operations research, discrete mathematics, and new branch of computer science called evolutionary computing aiming to solve one of networking problems.
Edit intent: clarity
Original:  In other words this work to be considered as a computer science investigation topic with combination between operations research, discrete mathematics, and new branch of computer science called evolutionary computing aiming to solve one of networking problems.
Revised:  In other words this work to be considered as a computer science investigation topic with combination between operations research, discrete mathematics, and evolutionary computing aiming to solve one of networking problems.
Edit intent: clarity
Original: In the recent years, we have linked the largest corpus of formal mathematics with ATP tools, and started to develop combined AI/ATP systems working in this setting.
Revised: In the recent years, we have linked a large corpus of formal mathematics with ATP tools, and started to develop combined AI/ATP systems working in this setting.
Edit intent: fluency