wanyu/IteraTeR_v2 huggingface dataset:
Original:  We } {\bf  ) is tractable for any, possibly NP-hard, }{\bf  {  prove that CSP({\bf \Gamma}^{\mathcal B}) can be reduced to a certain fragment of CSP({\bf \Gamma}) , under natural assumptions on \mathcal B .
Revised:  We } {\bf \Gamma ) is tractable for any, possibly NP-hard, }{\bf { prove that CSP({\bf \Gamma}^{\mathcal B}) can be reduced to a certain fragment of CSP({\bf \Gamma}) , under natural assumptions on \mathcal B .
Edit intent: fluency
Original:  We } {\bf  ) is tractable for any, possibly NP-hard, }{\bf  {  prove that CSP({\bf \Gamma}^{\mathcal B}) can be reduced to a certain fragment of CSP({\bf \Gamma}) , under natural assumptions on \mathcal B .
Revised:  We } {\bf ) is tractable for any, possibly NP-hard, }{\bf \Gamma { prove that CSP({\bf \Gamma}^{\mathcal B}) can be reduced to a certain fragment of CSP({\bf \Gamma}) , under natural assumptions on \mathcal B .
Edit intent: fluency
Original:  We } {\bf  ) is tractable for any, possibly NP-hard, }{\bf  {  prove that CSP({\bf \Gamma}^{\mathcal B}) can be reduced to a certain fragment of CSP({\bf \Gamma}) , under natural assumptions on \mathcal B .
Revised:  We } {\bf ) is tractable for any, possibly NP-hard, }{\bf { , we prove that CSP({\bf \Gamma}^{\mathcal B}) can be reduced to a certain fragment of CSP({\bf \Gamma}) , under natural assumptions on \mathcal B .
Edit intent: coherence
Original:  We } {\bf  ) is tractable for any, possibly NP-hard, }{\bf  {  prove that CSP({\bf \Gamma}^{\mathcal B}) can be reduced to a certain fragment of CSP({\bf \Gamma}) , under natural assumptions on \mathcal B .
Revised:  We } {\bf ) is tractable for any, possibly NP-hard, }{\bf { prove that CSP({\bf \Gamma}^{\mathcal B}) can be reduced to a certain fragment of CSP({\bf \Gamma}) , under natural assumptions on .
Edit intent: fluency
Original:  It turns out that if \mathcal{B} is preserved by some p\in Pol({\bf \Gamma}), then p can be extended to a polymorphism of {\bf \Gamma}^{\mathcal B} {\bf  {\bf   .
Revised:  It turns out that if \mathcal{B} is preserved by p\in Pol({\bf \Gamma}), then p can be extended to a polymorphism of {\bf \Gamma}^{\mathcal B} {\bf {\bf .
Edit intent: clarity
Original:  It turns out that if \mathcal{B} is preserved by some p\in Pol({\bf \Gamma}), then p can be extended to a polymorphism of {\bf \Gamma}^{\mathcal B} {\bf  {\bf   .
Revised:  It turns out that if \mathcal{B} is preserved by some p\in Pol({\bf \Gamma}), then p can be extended to a polymorphism of {\bf \Gamma}^{\mathcal B} {\bf \Gamma {\bf .
Edit intent: fluency
Original:  It turns out that if \mathcal{B} is preserved by some p\in Pol({\bf \Gamma}), then p can be extended to a polymorphism of {\bf \Gamma}^{\mathcal B} {\bf  {\bf   .
Revised:  It turns out that if \mathcal{B} is preserved by some p\in Pol({\bf \Gamma}), then p can be extended to a polymorphism of {\bf \Gamma}^{\mathcal B} {\bf {\bf \Gamma .
Edit intent: fluency
Original:  It turns out that if \mathcal{B} is preserved by some p\in Pol({\bf \Gamma}), then p can be extended to a polymorphism of {\bf \Gamma}^{\mathcal B} {\bf  {\bf   .
Revised:  It turns out that if \mathcal{B} is preserved by some p\in Pol({\bf \Gamma}), then p can be extended to a polymorphism of {\bf \Gamma}^{\mathcal B} {\bf {\bf ^{\mathcal B .
Edit intent: fluency
Original: Community detection is the task of identifying clusters or groups of nodes in a network where nodes within  same group are more connected with each other than with nodes in different groups.
Revised: Community detection is the task of identifying clusters or groups of nodes in a network where nodes within the same group are more connected with each other than with nodes in different groups.
Edit intent: fluency
Original:  In this paper, we propose a new community detection algorithm  which has a local approach and is able to run on large networks.
Revised:  In this paper, we propose a new community detection algorithm , which has a local approach and is able to run on large networks.
Edit intent: fluency
Original:  given a network, we construct a preference network with same set of nodes and each node has a single outgoing edge showing its preferred node to be in  same community with.
Revised:  given a network, algorithm constructs a preference network with same set of nodes and each node has a single outgoing edge showing its preferred node to be in same community with.
Edit intent: clarity
Original:  given a network, we construct a preference network with same set of nodes and each node has a single outgoing edge showing its preferred node to be in  same community with.
Revised:  given a network, we construct a preference network of nodes where each node has a single outgoing edge showing its preferred node to be in same community with.
Edit intent: clarity
Original:  given a network, we construct a preference network with same set of nodes and each node has a single outgoing edge showing its preferred node to be in  same community with.
Revised:  given a network, we construct a preference network with same set of nodes and each node has a single outgoing edge showing its preferred node to be in the same community with.
Edit intent: fluency
Original:  In construction of preference network, preferred node selection is critical and we used similarity based metrics  for this purpose .
Revised:  Selection of the preferred node is performed using similarity based metrics for this purpose .
Edit intent: clarity
Original:  first one is the number of common neighbors of selector node and its neighbors and, second one is the spread capability of neighbors around the selector node which is calculated by the gossip algorithm of Lind et.al.
Revised:  first one is the number of common neighbors of selector node and its neighbors and, the spread capability of neighbors around the selector node which is calculated by the gossip algorithm of Lind et.al.
Edit intent: coherence
Original:  The proposed algorithm decides the existence of a satisfying assignment in all assignments for a given formula by using a 2-dimensional binary search method without constructing all assignments.
Revised:  The proposed algorithm can efficiently decide the existence of a satisfying assignment in all assignments for a given formula by using a 2-dimensional binary search method without constructing all assignments.
Edit intent: clarity
Original: The research community working on species-habitat associations in animals is currently facing a paralysing methodological conundrum, because its two dominant analytical approaches have been shown to reach divergent conclusions.
Revised: The two dominant approaches for the analysis of species-habitat associations in animals is currently facing a paralysing methodological conundrum, because its two dominant analytical approaches have been shown to reach divergent conclusions.
Edit intent: clarity
Original: The research community working on species-habitat associations in animals is currently facing a paralysing methodological conundrum, because its two dominant analytical approaches have been shown to reach divergent conclusions.
Revised: The research community working on species-habitat associations in animals have been shown to reach divergent conclusions.
Edit intent: clarity
Original:  We therefore propose an analogy between the movements of an animal and the movements of an MCMC sampler, to guarantee convergence of the step selection rules to the parameters underlying the population's utilisation distribution.
Revised:  We therefore propose an analogy between the movements of an animal and the movements of a MCMC sampler, to guarantee convergence of the step selection rules to the parameters underlying the population's utilisation distribution.
Edit intent: fluency
Original:  We illustrate our method with simulations on a known utilisation distribution, and show theoretically and empirically that locations simulated from the local Gibbs sampler arise from the correct resource selection function  .
Revised:  We illustrate our method with simulations on a known utilisation distribution, and show theoretically and empirically that locations simulated from the local Gibbs sampler give rise to the correct resource selection function .
Edit intent: clarity
Original: Analyzing contention for resources in a cluster computing environment accurately is critical in order to understand the performance interferences faced by a query due to concurrent query executions , and to better manage the workload in the cluster.
Revised: There are many approaches is use today to either prevent or minimize the impact of inter-query interactions on a shared cluster. Despite these measures, performance issues due to concurrent query executions , and to better manage the workload in the cluster.
Edit intent: clarity
Original:  We perform an extensive experimental evaluation using ProtoXplore to analyze the query interactions of TPCDS queries on Apache Spark using microbenchmarks illustrating the effectiveness of our approach, and illustrate how the output from ProtoXplore can be used by alternate scheduling and task placement strategies to help improve the performance of affected queries in recurring executions .
Revised:  We perform an extensive experimental evaluation using ProtoXplore to analyze the interactions between TPC-DS queries on Apache Spark using microbenchmarks illustrating the effectiveness of our approach, and illustrate how the output from ProtoXplore can be used by alternate scheduling and task placement strategies to help improve the performance of affected queries in recurring executions .
Edit intent: clarity
Original:  Similar to the challenges faced by massive graphs, computational methods that process simplicial complexes are severely limited by computational costs associated with massive datasets.
Edit intent: coherence
Original:  To apply spectral methods in learning to massive datasets modeled as simplicial complexes, we work towards the sparsification of simplicial complexesbased on preserving the spectrum of the associated Laplacian operators.
Revised:  To apply spectral methods in learning to massive datasets modeled as simplicial complexes, we sparsify simplicial complexes, while preserving the spectrum of the associated Laplacian operators.
Edit intent: clarity
Original:  In particular, we introduce a generalized effective resistance for simplexes ;
Revised:  In particular, we introduce a generalized effective resistance for simplexes ,
Edit intent: fluency
Original:  provide an algorithm for sparsifying simplicial complexes at a fixed dimension ;
Revised:  provide an algorithm for sparsifying simplicial complexes at a fixed dimension ,
Edit intent: fluency
Original:  Finally, we  demonstrate via experiments the preservation of the up Laplacian during sparsification, as well as the utility of sparsification with respect to spectral clustering .
Revised:  Finally, we demonstrate via experiments the utility of the proposed spectral sparsification method for these applications .
Edit intent: clarity
Original:  In particular, spectral learning methods (e.g. label propagation and clustering) that directly operate on simplicial complexes represent a new direction emerging from the confluence of computational topology and machine learning .
Revised:  In particular, spectral learning methods (e.g. label propagation and clustering) that directly operate on simplicial complexes represent a new direction for analyzing such complex datasets .
Edit intent: clarity
Original:  To apply spectral methods in learning to massive datasets modeled as simplicial complexes, we sparsify simplicial complexes , while preserving the spectrum of the associated Laplacian operators .
Revised:  To apply spectral learning methods to massive datasets modeled as simplicial complexes, we sparsify simplicial complexes , while preserving the spectrum of the associated Laplacian operators .
Edit intent: clarity
Original:  To apply spectral methods in learning to massive datasets modeled as simplicial complexes, we sparsify simplicial complexes , while preserving the spectrum of the associated Laplacian operators .
Revised:  To apply spectral methods in learning to massive datasets modeled as simplicial complexes, we develop a method for sparsifying simplicial complexes that preserves the spectrum of the associated Laplacian operators .
Edit intent: clarity
Original:  To apply spectral methods in learning to massive datasets modeled as simplicial complexes, we sparsify simplicial complexes , while preserving the spectrum of the associated Laplacian operators .
Revised:  To apply spectral methods in learning to massive datasets modeled as simplicial complexes, we sparsify simplicial complexes , while preserving the spectrum of the associated Laplacian matrices .
Edit intent: clarity
Original: We consider the problem of constructing a physical system that evolves according to some specified conditional distribution .
Revised: We consider the problem of constructing a physical system whose evolution implements some specified stochastic matrix .
Edit intent: clarity
Original:  Examples range from constructing a logical gate to be used in a digital circuit to constructing an entire digital computer.
Edit intent: clarity
Original:  It is known that many conditional distributions over a space X cannot be implemented by any CTMC , even approximately.
Revised:  It is known that many conditional distributions over a space X cannot be implemented by any master equation , even approximately.
Edit intent: clarity
Original:  Here we focus on the case where the conditional distribution is a (single-valued) function f.
Revised:  Here we investigate this question for the special case where the conditional distribution is a (single-valued) function f.
Edit intent: clarity
Original:  Any f over a set of "visible" states X can be implemented --- if the system has access to some additional "hidden" states not in X.
Revised:  Any f over a set of "visible" states X can be implemented--if the master equation implementing f has access to some additional "hidden" states not in X.
Edit intent: clarity
Original:  Motivated by engineering considerations, we  consider a natural decomposition of any such CTMC into a sequence of "hidden" timesteps, demarcated by changes in the set of allowed transitions between states.
Revised:  Motivated by engineering considerations, we then consider a natural decomposition of any such CTMC into a sequence of "hidden" timesteps, demarcated by changes in the set of allowed transitions between states.
Edit intent: coherence
Original:  As we show , any function f over a set of "visible" states X can be implemented--if the master equation implementing f has access to some additional "hidden" states not in X.
Revised:  As we show , any function f over a set of "visible" states can be implemented with a master equation--but only if additional "hidden" states not in X.
Edit intent: clarity
Original:  We demonstrate a fundamental tradeoff between the number of hidden states and the number of hidden timesteps needed to implement any given f using master equation dynamics.
Revised:  We demonstrate a fundamental tradeoff between the numbers of hidden states and the number of hidden timesteps needed to implement any given f using master equation dynamics.
Edit intent: fluency
Original:  We demonstrate a fundamental tradeoff between the number of hidden states and the number of hidden timesteps needed to implement any given f using master equation dynamics.
Revised:  We demonstrate a fundamental tradeoff between the number of hidden states and of hidden timesteps needed to implement any given f using master equation dynamics.
Edit intent: fluency
Original:  We show that any deterministic map over some "visible" states can be implemented with a master equation--but only if additional "hidden" states are dynamically coupled to those visible states .
Revised:  We show that any deterministic map over some "visible" states can be implemented with a master equation--but only if additional "hidden" states at intermediate times .
Edit intent: clarity
Original:  We also show that any master equation implementing a given map can be decomposed into a sequence of "hidden" timesteps, demarcated by changes in what transitions are allowed under the rate matrix.
Revised:  We also show that any master equation implementing a function can be decomposed into a sequence of "hidden" timesteps, demarcated by changes in what transitions are allowed under the rate matrix.
Edit intent: clarity
Original:  We derive the associated " space/time " tradeoff between the numbers of hidden states and  of hidden timesteps needed to implement any given f(x) .
Revised:  We derive the associated " space-time " tradeoff between the numbers of hidden states and of hidden timesteps needed to implement any given f(x) .
Edit intent: clarity
Original:  We derive the associated " space/time " tradeoff between the numbers of hidden states and  of hidden timesteps needed to implement any given f(x) .
Revised:  We derive the associated " space/time " tradeoff between the number of hidden states and of hidden timesteps needed to implement any given f(x) .
Edit intent: fluency
Original:  We derive the associated " space/time " tradeoff between the numbers of hidden states and  of hidden timesteps needed to implement any given f(x) .
Revised:  We derive the associated " space/time " tradeoff between the numbers of hidden states and the number of hidden timesteps needed to implement any given f(x) .
Edit intent: fluency
Original:  We derive the associated " space/time " tradeoff between the numbers of hidden states and  of hidden timesteps needed to implement any given f(x) .
Revised:  We derive the associated " space/time " tradeoff between the numbers of hidden states and of hidden timesteps needed to implement any given function .
Edit intent: clarity
Original:  To measure the individual variability in its process components, a large variety of non-compartmental parameters ( e.g., the area under the concentration-time curve ;
Revised:  To assess its process components, a large variety of non-compartmental parameters ( e.g., the area under the concentration-time curve ;
Edit intent: clarity
Original:  To measure the individual variability in its process components, a large variety of non-compartmental parameters ( e.g., the area under the concentration-time curve ;
Revised:  To measure the individual variability in its process components, a large variety of non-compartmental parameters ( e.g., the area under the concentration-time curve (
Edit intent: fluency
Original:  AUC) are commonly utilized.
Revised:  AUC) are utilized.
Edit intent: clarity
Original: This work deals with a system of three distinct species that changes in time under the presence of mobility, reproduction and predation , as in the popular rock-paper-scissors game.
Revised: This work deals with a system of three distinct species that changes in time under the presence of mobility, selection, and reproduction , as in the popular rock-paper-scissors game.
Edit intent: clarity
Original:  The novelty of the current study is the modification of the mobility rule to the case of directional mobility  in which the species move following the direction associated to a larger (averaged) number density of preys in the surrounding neighborhood.
Revised:  The novelty of the current study is the modification of the mobility rule to the case of directional mobility , in which the species move following the direction associated to a larger (averaged) number density of preys in the surrounding neighborhood.
Edit intent: fluency
Original:  The novelty of the current study is the modification of the mobility rule to the case of directional mobility  in which the species move following the direction associated to a larger (averaged) number density of preys in the surrounding neighborhood.
Revised:  The novelty of the current study is the modification of the mobility rule to the case of directional mobility in which the species move following the direction associated to a larger (averaged) number density of selection targets in the surrounding neighborhood.
Edit intent: clarity
Original:  Directional mobility can be used to simulate eyes that see or a nose that smells, and we show how it may contribute to limit biodiversity .
Revised:  Directional mobility can be used to simulate eyes that see or a nose that smells, and we show how it may contribute to reduce the probability of coexistence .
Edit intent: clarity
Original: Relationship lending is conventionally interpreted as a strong partnership between a lender and a borrower.
Revised: Relationship lending is broadly interpreted as a strong partnership between a lender and a borrower.
Edit intent: clarity
Original:  Nevertheless, we still lack consensus regarding how to quantify  a lending relationship  while simple statistics such as the frequency and volume of loans have been frequently used .
Revised:  Nevertheless, we still lack consensus regarding how to quantify a lending relationship , while simple statistics such as the frequency and volume of loans have been frequently used .
Edit intent: fluency
Original:  In this paper, first given a directed network G and a vertex r \in V(G), we propose a new exact algorithm to compute betweenness score of r.
Revised:  In this paper, first given a directed network G and a vertex r \in V(G), we propose an exact algorithm to compute betweenness score of r.
Edit intent: clarity
Original:  Our algorithm pre-computes a set RV(r), which is used to prune a huge amount of computations that do not contribute in the betweenness score of r.
Revised:  Our algorithm pre-computes a set RV(r), which is used to prune a huge amount of computations that do not contribute to the betweenness score of r.
Edit intent: fluency
Original:  Time complexity of our exact algorithm depends on |RV(r)| and it is respectively \Theta(|RV(r)|\cdot|E(G)|) and \Theta(|RV(r)|\cdot|E(G)|+|RV(r)|\cdot|V(G)|\log |V(G)|) for unweighted graphs and weighted graphs with positive weights.
Revised:  Time complexity of our algorithm depends on |RV(r)| and it is respectively \Theta(|RV(r)|\cdot|E(G)|) and \Theta(|RV(r)|\cdot|E(G)|+|RV(r)|\cdot|V(G)|\log |V(G)|) for unweighted graphs and weighted graphs with positive weights.
Edit intent: clarity
Original:  We show that this algorithm provides an (\epsilon,\delta)-approximation of the betweenness score of r. Finally, we perform extensive experiments over several real-world datasets from different domains for several randomly chosen vertices as well as for the vertices with the highest betweenness scores.
Revised:  We show that this algorithm provides an (\epsilon,\delta)-approximation to the betweenness score of r. Finally, we perform extensive experiments over several real-world datasets from different domains for several randomly chosen vertices as well as for the vertices with the highest betweenness scores.
Edit intent: fluency
Original:  We also study the more general version of the problem where only a set of vertices is given and we need to determine whether they can be collinear.
Revised:  We also study the less restrictive version of the problem where only a set of vertices is given and we need to determine whether they can be collinear.
Edit intent: clarity
Original:  We show that the problem is \mathcal{NP but fixed-parameter tractable.
Revised:  We show that the problem is NP-complete but fixed-parameter tractable.
Edit intent: clarity
Original: This work underlies the poster presented at the XVIII Workshop on Quantitative Finance (QFW2017) in Milano on January 25-27, 2017.
Edit intent: coherence
Original:  In particular, Datamaran can successfully extract structured information from all datasets used in prior work, and can achieve 95\% extraction accuracy on automatically collected log datasets from GitHub - a substantial 66\% increase of accuracy compared to unsupervised schemes from prior work  .
Revised:  Datamaran can successfully extract structured information from all datasets used in prior work, and can achieve 95\% extraction accuracy on automatically collected log datasets from GitHub - a substantial 66\% increase of accuracy compared to unsupervised schemes from prior work .
Edit intent: coherence