wanyu/IteraTeR_v2 huggingface dataset:
Original: The concepts of quantum correlation and communication complexity have been proposed to quantify the worst-case computational resources needed in generating bipartite classical distributions or bipartite quantum states in the single-shot setting  by Zhang ({\em Proc.
Revised: The concepts of quantum correlation and communication complexity have been proposed to quantify the worst-case computational resources needed in generating bipartite classical or quantum states in the single-shot setting by Zhang ({\em Proc.
Edit intent: clarity
Original: The concepts of quantum correlation and communication complexity have been proposed to quantify the worst-case computational resources needed in generating bipartite classical distributions or bipartite quantum states in the single-shot setting  by Zhang ({\em Proc.
Revised: The concepts of quantum correlation and communication complexity have been proposed to quantify the worst-case computational resources needed in generating bipartite classical distributions or bipartite quantum states in the single-shot setting . \% by Zhang ({\em Proc.
Edit intent: fluency
Original:  The former characterizes the minimal size of the initial shared state needed if no communicationis allowed , and the latter characterizes the minimal amount of communication needed if sharing nothingat the beginning .
Revised:  The former is the minimum size of the initial shared state needed if no communicationis allowed , and the latter characterizes the minimal amount of communication needed if sharing nothingat the beginning .
Edit intent: fluency
Original:  The former characterizes the minimal size of the initial shared state needed if no communicationis allowed , and the latter characterizes the minimal amount of communication needed if sharing nothingat the beginning .
Revised:  The former characterizes the minimal size of the initial shared state needed if no communicationis allowed , and the latter is the minimum amount of communication needed if sharing nothingat the beginning .
Edit intent: clarity
Original:  The former characterizes the minimal size of the initial shared state needed if no communicationis allowed , and the latter characterizes the minimal amount of communication needed if sharing nothingat the beginning .
Revised:  The former characterizes the minimal size of the initial shared state needed if no communicationis allowed , and the latter characterizes the minimal amount of communication needed when initially sharing nothing .
Edit intent: clarity
Original:  (1)  To generate a bipartite classical distribution P(x,y) approximately, we show that the minimal amount of quantum communication needed can be characterized completely by the approximate PSD-rank of P.
Revised:  (1) To generate a bipartite classical distribution P(x,y) approximately, the quantum communication complexity is completely characterized by the approximate PSD-rank of P.
Edit intent: clarity
Original:  \% by Zhang (%DIFDELCMD < {\em %%% Proc.
Revised:  %DIFDELCMD < {\em %%% Proc.
Edit intent: coherence
Original:  3rd Innov Theor Comput., pp. 39-59, 2012 ).
Revised:  3rd Innov Theor Comput., pp. 39-59, 2012 
Edit intent: fluency
Original:  The quantum correlation complexity of approximately generating multipartite pure states is bounded by approximate local ranks . \%This result is obtained based on the fact that the cost to approximate a bipartite quantum state equals that to approximate its exact purifications, which also implies a result for a general bipartite quantum state .
Revised:  The quantum correlation complexity of approximately generating multipartite pure states is bounded by approximate local ranks .
Edit intent: clarity
Original:  We show that within this framework it is possible to simulate on a small computer cluster conformational reactions as complex as protein folding, using state-of-the-art all-atom force fields in explicit solvent.
Revised:  Within this framework it is possible to simulate on a small computer cluster conformational reactions as complex as protein folding, using state-of-the-art all-atom force fields in explicit solvent.
Edit intent: coherence
Original:  We show that within this framework it is possible to simulate on a small computer cluster conformational reactions as complex as protein folding, using state-of-the-art all-atom force fields in explicit solvent.
Revised:  We show that within this framework it is possible to simulate on a small computer cluster reactions as complex as protein folding, using state-of-the-art all-atom force fields in explicit solvent.
Edit intent: clarity
Original:  We show that within this framework it is possible to simulate on a small computer cluster conformational reactions as complex as protein folding, using state-of-the-art all-atom force fields in explicit solvent.
Revised:  We show that within this framework it is possible to simulate on a small computer cluster conformational reactions as complex as protein folding, using state of the art all-atom force fields in explicit solvent.
Edit intent: fluency
Original:  For illustration and validation purposes, we test this method against the results of protein folding MDsimulations which were obtained on the Anton supercomputer, using the same all-atom force field  .
Revised:  For illustration and validation purposes, we test this method against the results of protein folding MDsimulations which were obtained on the Anton supercomputer, using the same all-atom force field on the Anton supercomputer .
Edit intent: clarity
Original:  We find that our approach yields consistent results at a computational cost which is many orders of magnitude smaller than that required by standard MD simulations .
Revised:  We find that our approach yields results consistent with those of MD simulations, at a computational cost which is many orders of magnitude smaller than that required by standard MD simulations .
Edit intent: clarity
Original:  We find that our approach yields consistent results at a computational cost which is many orders of magnitude smaller than that required by standard MD simulations .
Revised:  We find that our approach yields consistent results at a computational cost orders of magnitude smaller than that required by standard MD simulations .
Edit intent: clarity
Original:  We find that our approach yields consistent results at a computational cost which is many orders of magnitude smaller than that required by standard MD simulations .
Revised:  We find that our approach yields consistent results at a computational cost which is many orders of magnitude smaller .
Edit intent: clarity
Original: The zero forcing number is a graph invariant introduced in order to study the minimum rank of the graph.
Revised: The zero forcing number is a graph invariant introduced to study the minimum rank of the graph.
Edit intent: coherence
Original:  In  the first part of this paper, we first highlight that the computation of the zero forcing number of any directed graph (allowing loops ) is NP-hard.
Revised:  In the zero forcing number of any directed graph (allowing loops ) is NP-hard.
Edit intent: clarity
Original:  In  the first part of this paper, we first highlight that the computation of the zero forcing number of any directed graph (allowing loops ) is NP-hard.
Revised:  In the first part of this paper, we first highlight that the computation of the zero forcing number of a directed graph allowing loops is also NP-hard.
Edit intent: clarity
Original:  The second part of the paper is an application of the notion of zero forcing set in the study of the strong structural controllability of networked systems .
Revised:  The second part of the paper is devoted to the strong controllability of a networked system .
Edit intent: clarity
Original:  This kind of controllability takes into account only the structure of the interconnection graph, but not the interaction strengths along the edges.
Revised:  This kind of controllability takes into account only the structure of the interconnection graph, but not the interconnection strengths along the edges.
Edit intent: fluency
Original:  We present the first efficient algorithm providing a minimum-size input set for the strong structural controllability of a self-damped or an undamped system with a tree structure .
Revised:  We present the first efficient algorithm providing a minimum-size input set for the strong controllability of a self-damped or an undamped system with a tree structure .
Edit intent: clarity
Original:  We present the first efficient algorithm providing a minimum-size input set for the strong structural controllability of a self-damped or an undamped system with a tree structure .
Revised:  We present the first efficient algorithm providing a minimum-size input set for the strong structural controllability of a self-damped system with a tree structure .
Edit intent: clarity
Original:  We present the first efficient algorithm providing a minimum-size input set for the strong structural controllability of a self-damped or an undamped system with a tree structure .
Revised:  We present the first efficient algorithm providing a minimum-size input set for the strong structural controllability of a self-damped or an undamped system with a tree-structure .
Edit intent: fluency
Original: One of the most fundamental problems in the realm of peer-to-peer systems consists of determining their service capacity .
Revised: One of the fundamental problems in the realm of peer-to-peer systems consists of determining their service capacity .
Edit intent: clarity
Original: One of the most fundamental problems in the realm of peer-to-peer systems consists of determining their service capacity .
Revised: One of the most fundamental problems in the realm of peer-to-peer systems is that of determining their service capacity .
Edit intent: clarity
Original: One of the most fundamental problems in the realm of peer-to-peer systems consists of determining their service capacity .
Revised: One of the most fundamental problems in the realm of peer-to-peer systems consists of determining their service capacities .
Edit intent: fluency
Original:  From these models, we obtain novel insights on the behavior of p2p swarming systems that motivate new mechanisms for publishers and peers to improve the overall performance.
Revised:  From these models, we obtain novel insights on the behavior of P2P swarming systems that motivate new mechanisms for publishers and peers to improve the overall performance.
Edit intent: fluency
Original:  In particular, we  show that system capacity can significantly increase if publishers adopt the most deprived peer selection and peers reduce their service rate when they have all the file blocks but one.
Revised:  In particular, we show that system capacity significantly increases if publishers adopt the most deprived peer selection and peers reduce their service rate when they have all the file blocks but one.
Edit intent: clarity
Original:  In particular, we  show that system capacity can significantly increase if publishers adopt the most deprived peer selection and peers reduce their service rate when they have all the file blocks but one.
Revised:  In particular, we show that system capacity can significantly increase if publishers adopt the most deprived peer selection and peers reduce their service rates when they have all the file blocks but one.
Edit intent: fluency
Original:  We introduce an empirically grounded, spatially explicit, agent-based model, InnoMIND ( Innovation diffusion driven by changing MINDs ), to simulate the effects of policy inter-ventions and social influence on consumers transport mode choices .
Revised:  We introduce an empirically grounded, spatially explicit, agent-based model, InnoMind Innovation diffusion driven by changing MINDs ), to simulate the effects of policy inter-ventions and social influence on consumers transport mode choices .
Edit intent: fluency
Original:  We introduce an empirically grounded, spatially explicit, agent-based model, InnoMIND ( Innovation diffusion driven by changing MINDs ), to simulate the effects of policy inter-ventions and social influence on consumers transport mode choices .
Revised:  We introduce an empirically grounded, spatially explicit, agent-based model, InnoMIND ( Innovation diffusion driven by changing Minds ), to simulate the effects of policy inter-ventions and social influence on consumers transport mode choices .
Edit intent: fluency
Original:  We introduce an empirically grounded, spatially explicit, agent-based model, InnoMIND ( Innovation diffusion driven by changing MINDs ), to simulate the effects of policy inter-ventions and social influence on consumers transport mode choices .
Revised:  We introduce an empirically grounded, spatially explicit, agent-based model, InnoMIND ( Innovation diffusion driven by changing MINDs ), to simulate the effects of policy interventions and social influence on consumers transport mode choices .
Edit intent: fluency
Original:  We introduce an empirically grounded, spatially explicit, agent-based model, InnoMIND ( Innovation diffusion driven by changing MINDs ), to simulate the effects of policy inter-ventions and social influence on consumers transport mode choices .
Revised:  We introduce an empirically grounded, spatially explicit, agent-based model, InnoMIND ( Innovation diffusion driven by changing MINDs ), to simulate the effects of policy inter-ventions and social influence on consumers transport mode preferences .
Edit intent: clarity
Original:  The results illustrate the varying effectiveness of measures in different market segments and the need for appropriate policies tailored to the heterogeneous needs of  travelers.
Revised:  The results illustrate the varying effectiveness of measures in different market segments and the need for appropriate policies tailored to the heterogeneous needs of different travelers.
Edit intent: fluency
Original:  This principle has been generalised to networks being constructed from a set of more exotic subgraphs.
Revised:  This approach has been generalised to networks being constructed from a set of more exotic subgraphs.
Edit intent: clarity
Original:  In this paper, we provide a general and automated approach to deriving a set of ordinary differential equations, or mean-field model, that describe , to a high degree of accuracy, the expected values of system-level quantities .
Revised:  In this paper, we provide a general and automated approach to deriving a set of ordinary differential equations, or mean-field model, that describes , to a high degree of accuracy, the expected values of system-level quantities .
Edit intent: fluency
Original:  The combination of these features makes it possible to generate families of networks with different subgraphs composition while keeping classical network indicators constant.
Revised:  The combination of these features makes it possible to generate families of networks with different subgraph compositions while keeping classical network indicators constant.
Edit intent: fluency
Original:  The combination of these features makes it possible to generate families of networks with different subgraphs composition while keeping classical network indicators constant.
Revised:  The combination of these features makes it possible to generate families of networks with different subgraphs composition while keeping classical network metrics constant.
Edit intent: clarity
Original:  Using our approach, we show that higher-order structure realised either through the introduction of loops of different size or by generating clustered networks with or without overlapping triangles, lead to significant differences in epidemic dynamics despite controlling for the basic network metrics .
Revised:  Using our approach, we show that higher-order structure realised either through the introduction of loops of different sizes or by generating clustered networks with or without overlapping triangles, lead to significant differences in epidemic dynamics despite controlling for the basic network metrics .
Edit intent: fluency
Original:  Using our approach, we show that higher-order structure realised either through the introduction of loops of different size or by generating clustered networks with or without overlapping triangles, lead to significant differences in epidemic dynamics despite controlling for the basic network metrics .
Revised:  Using our approach, we show that higher-order structure realised either through the introduction of loops of different size or by generating clustered networks with or without overlapping triangles, lead to significant differences in epidemic dynamics despite controlling for basic network metrics .
Edit intent: fluency
Original:  For a PSLG with m connected components and r reflex vertices, we gain a speed up from O(n^{1 + \epsilon} + n^{8/11 + \epsilon}r^{9/11+\epsilon}) to } O(n ^{4/3+\epsilon) in the non-degenerate case and from O(n^{1 + \epsilon} + n^{8/11 + \epsilon}r^{9/11+\epsilon}) to O(n(\log n)\log m + r^{17/11 + \epsilon}) in the degenerate case} .
Revised:  For a PSLG with m connected components and r reflex vertices, we gain a speed up from O(n^{1 + \epsilon} + n^{8/11 + \epsilon}r^{9/11+\epsilon}) to } O(n (\log n)\log m + r^{4/3 + \epsilon) in the non-degenerate case and from O(n^{1 + \epsilon} + n^{8/11 + \epsilon}r^{9/11+\epsilon}) to O(n(\log n)\log m + r^{17/11 + \epsilon}) in the degenerate case} .
Edit intent: clarity
Original: We investigate the role of networks of military alliances in preventing or encouraging wars between groups of countries.
Revised: We investigate the role of networks of alliances in preventing or encouraging wars between groups of countries.
Edit intent: clarity
Original:  One basic operation in OSN is to ?nd chains of relations,such as k-Hop friends.
Revised:  One basic operation in OSN is to find chains of relations,such as k-Hop friends.
Edit intent: fluency
Original:  Property path query in SPARQL can express this type of operation, but its implementation su?ers from performance problem considering the ever growing data size and complexity of OSN.
Revised:  Property path query in SPARQL can express this type of operation, but its implementation suffers from performance problem considering the ever growing data size and complexity of OSN.
Edit intent: fluency
Original: Most classical phylogenetic reconstruction methods fail to  infer the underlying topology  because they assume oversimplified models.
Revised: Most classical phylogenetic reconstruction methods fail to correctly infer the underlying topology because they assume oversimplified models.
Edit intent: clarity
Original: Most classical phylogenetic reconstruction methods fail to  infer the underlying topology  because they assume oversimplified models.
Revised: Most classical phylogenetic reconstruction methods fail to infer the underlying topology is because they assume oversimplified models.
Edit intent: clarity
Original:  In this paper we present Erik+2, a topology reconstruction method based on an idea of Eriksson on using phylogenetic invariants.
Revised:  In this paper we propose a topology reconstruction method based on an idea of Eriksson on using phylogenetic invariants.
Edit intent: clarity
Original:  Erik+2 assumes the most general Markov model of nucleotide substitution and also allows data coming from mixtures on the same tree topology.
Revised:  Erik+2 assumes the most general Markov model of nucleotide substitution , which can also deal with data coming from mixtures on the same tree topology.
Edit intent: clarity
Original:  We study its performance on quartets on a wide range of simulated data: both time homogeneous and heterogeneous , with or without among-site  heterogeneity, and with different branch lengths settings .
Revised:  We study its performance on real data and on a wide range of simulated data: both time homogeneous and heterogeneous , with or without among-site heterogeneity, and with different branch lengths settings .
Edit intent: clarity
Original:  We study its performance on quartets on a wide range of simulated data: both time homogeneous and heterogeneous , with or without among-site  heterogeneity, and with different branch lengths settings .
Revised:  We study its performance on quartets on a wide range of simulated data: both time homogeneous and heterogeneous , with or without among-site heterogeneity, and with different branch length settings) .
Edit intent: fluency
Original:  We compare it to the classical methods  neighbor-joining (with paralinear distance), maximum likelihood (with different underlying models), and maximum parsimony.
Revised:  We compare it to the classical methods of neighbor-joining (with paralinear distance), maximum likelihood (with different underlying models), and maximum parsimony.
Edit intent: fluency
Original:  Our results show that Erik+2 is an accurate and robust method, which has a similar performance as ML when data satisfies the assumptions of both methods, and outperforms the other methods when these are based on inappropriate substitution models or when long branches are present.
Revised:  Our results show that this method is accurate and robust method, which has a similar performance as ML when data satisfies the assumptions of both methods, and outperforms the other methods when these are based on inappropriate substitution models or when long branches are present.
Edit intent: clarity
Original:  Our results show that Erik+2 is an accurate and robust method, which has a similar performance as ML when data satisfies the assumptions of both methods, and outperforms the other methods when these are based on inappropriate substitution models or when long branches are present.
Revised:  Our results show that Erik+2 is an accurate and robust , has a similar performance as ML when data satisfies the assumptions of both methods, and outperforms the other methods when these are based on inappropriate substitution models or when long branches are present.
Edit intent: coherence
Original:  Our results show that Erik+2 is an accurate and robust method, which has a similar performance as ML when data satisfies the assumptions of both methods, and outperforms the other methods when these are based on inappropriate substitution models or when long branches are present.
Revised:  Our results show that Erik+2 is an accurate and robust method, which has a similar performance to ML when data satisfies the assumptions of both methods, and outperforms the other methods when these are based on inappropriate substitution models or when long branches are present.
Edit intent: fluency
Original:  Our results show that Erik+2 is an accurate and robust method, which has a similar performance as ML when data satisfies the assumptions of both methods, and outperforms the other methods when these are based on inappropriate substitution models or when long branches are present.
Revised:  Our results show that Erik+2 is an accurate and robust method, which has a similar performance as ML when data satisfies the assumptions of both methods, and outperforms all methods when these are based on inappropriate substitution models or when long branches are present.
Edit intent: clarity
Original:  If alignments are long enough, then Erik+2 also outperforms other methods when some of its assumptions are violated . Erik+2 provides also a system of weights that can be used as input of quartet-based methods .
Revised:  If alignments are long enough, then it also outperforms other methods when some of its assumptions are violated . Erik+2 provides also a system of weights that can be used as input of quartet-based methods .
Edit intent: coherence
Original:  If alignments are long enough, then Erik+2 also outperforms other methods when some of its assumptions are violated . Erik+2 provides also a system of weights that can be used as input of quartet-based methods .
Revised:  If alignments are long enough, then Erik+2 also outperforms other methods when some of its assumptions are violated .
Edit intent: coherence