wanyu/IteraTeR_v2 huggingface dataset:
Original: Give a graph class%DIFDELCMD < \cal %%% G and a nonnegative integer k, we use%DIFDELCMD < \cal %%% G + k v, %DIFDELCMD < \cal %%% G + k e, and %DIFDELCMD < \cal %%% G - k e to denote the classes of graphs that can be obtained from some graph in%DIFDELCMD < \cal %%% G by adding k vertices, adding k edges, and deleting k edges, respectively.
Revised: %DIFDELCMD < \cal %%% G and a nonnegative integer k, we use%DIFDELCMD < \cal %%% G + k v, %DIFDELCMD < \cal %%% G + k e, and %DIFDELCMD < \cal %%% G - k e to denote the classes of graphs that can be obtained from some graph in%DIFDELCMD < \cal %%% G by adding k vertices, adding k edges, and deleting k edges, respectively.
Edit intent: clarity
Original: Give a graph class%DIFDELCMD < \cal %%% G and a nonnegative integer k, we use%DIFDELCMD < \cal %%% G + k v, %DIFDELCMD < \cal %%% G + k e, and %DIFDELCMD < \cal %%% G - k e to denote the classes of graphs that can be obtained from some graph in%DIFDELCMD < \cal %%% G by adding k vertices, adding k edges, and deleting k edges, respectively.
Revised: Give a graph class%DIFDELCMD < \cal %%% %DIFDELCMD < \cal %%% G + k v, %DIFDELCMD < \cal %%% G + k e, and %DIFDELCMD < \cal %%% G - k e to denote the classes of graphs that can be obtained from some graph in%DIFDELCMD < \cal %%% G by adding k vertices, adding k edges, and deleting k edges, respectively.
Edit intent: coherence
Original: Give a graph class%DIFDELCMD < \cal %%% G and a nonnegative integer k, we use%DIFDELCMD < \cal %%% G + k v, %DIFDELCMD < \cal %%% G + k e, and %DIFDELCMD < \cal %%% G - k e to denote the classes of graphs that can be obtained from some graph in%DIFDELCMD < \cal %%% G by adding k vertices, adding k edges, and deleting k edges, respectively.
Revised: Give a graph class%DIFDELCMD < \cal %%% G and a nonnegative integer k, we use%DIFDELCMD < \cal %%% G + k v, %DIFDELCMD < \cal %%% G + k e, and %DIFDELCMD < \cal %%% G - k e denote the classes of graphs that can be obtained from some graph in%DIFDELCMD < \cal %%% G by adding k vertices, adding k edges, and deleting k edges, respectively.
Edit intent: coherence
Original: Give a graph class%DIFDELCMD < \cal %%% G and a nonnegative integer k, we use%DIFDELCMD < \cal %%% G + k v, %DIFDELCMD < \cal %%% G + k e, and %DIFDELCMD < \cal %%% G - k e to denote the classes of graphs that can be obtained from some graph in%DIFDELCMD < \cal %%% G by adding k vertices, adding k edges, and deleting k edges, respectively.
Revised: Give a graph class%DIFDELCMD < \cal %%% G and a nonnegative integer k, we use%DIFDELCMD < \cal %%% G + k v, %DIFDELCMD < \cal %%% G + k e, and %DIFDELCMD < \cal %%% G - k e to denote the classes of graphs that can be obtained from some %DIFDELCMD < \cal %%% G by adding k vertices, adding k edges, and deleting k edges, respectively.
Edit intent: clarity
Original:  They are called\emph{almost (unit) interval graphs if%DIFDELCMD < \cal %%% G is the class of (unit) interval graphs.
Revised:  \emph{almost (unit) interval graphs if%DIFDELCMD < \cal %%% G is the class of (unit) interval graphs.
Edit intent: coherence
Original:  They are called\emph{almost (unit) interval graphs if%DIFDELCMD < \cal %%% G is the class of (unit) interval graphs.
Revised:  They are called\emph{ if%DIFDELCMD < \cal %%% G is the class of (unit) interval graphs.
Edit intent: clarity
Original:  They are called\emph{almost (unit) interval graphs if%DIFDELCMD < \cal %%% G is the class of (unit) interval graphs.
Revised:  They are called\emph{almost (unit) interval graphs %DIFDELCMD < \cal %%% G is the class of (unit) interval graphs.
Edit intent: fluency
Original:  Almost (unit) interval graphs are well motivated from computational biology, where the data ought to be represented by a (unit) interval graph while we can only expect an almost (unit) interval graph for the best.
Revised:  They are well motivated from computational biology, where the data ought to be represented by a (unit) interval graph while we can only expect an almost (unit) interval graph for the best.
Edit intent: clarity
Original:  Almost (unit) interval graphs are well motivated from computational biology, where the data ought to be represented by a (unit) interval graph while we can only expect an almost (unit) interval graph for the best.
Revised:  Almost (unit) interval graphs are well motivated from computational biology, where the data ought to be represented by an interval graph while we can only expect an almost (unit) interval graph for the best.
Edit intent: fluency
Original:  Almost (unit) interval graphs are well motivated from computational biology, where the data ought to be represented by a (unit) interval graph while we can only expect an almost (unit) interval graph for the best.
Revised:  Almost (unit) interval graphs are well motivated from computational biology, where the data ought to be represented by a (unit) interval graph while we can only expect an almost interval graph for the best.
Edit intent: fluency
Original:  For any fixed k, we give linear-time algorithms for recognizing all these classes, and in the case of membership, our algorithms provide also a specific (unit) interval graph as evidence.
Revised:  For any fixed k, we give linear-time algorithms for recognizing all these classes, and in the case of membership, our algorithms provide also a specific interval graph as evidence.
Edit intent: clarity
Original:  Our results imply that all of them are fixed-parameter tractable parameterized by k, thereby resolving the long-standing open problem on the parameterized complexity of recognizing \mbox{ (unit)-interval }+ k e, first asked by Bodlaender et al.
Revised:  Our results imply that they are fixed-parameter tractable parameterized by k, thereby resolving the long-standing open problem on the parameterized complexity of recognizing \mbox{ (unit)-interval }+ k e, first asked by Bodlaender et al.
Edit intent: clarity
Original:  Moreover, our algorithms for recognizing \mbox{ (unit-) interval}+ k v and \mbox{ (unit-) interval}- k e have single-exponential dependence on k and linear dependence on the graph size,which significantly improve all previous algorithms for recognizing the same classes:\cdot n^3m)-time algorithm of } Heggernes et al. , STOC 2007] ;
Revised:  Moreover, our algorithms for recognizing \mbox{ interval}+ k v and \mbox{ (unit-) interval}- k e have single-exponential dependence on k and linear dependence on the graph size,which significantly improve all previous algorithms for recognizing the same classes:\cdot n^3m)-time algorithm of } Heggernes et al. , STOC 2007] ;
Edit intent: fluency
Original:  Moreover, our algorithms for recognizing \mbox{ (unit-) interval}+ k v and \mbox{ (unit-) interval}- k e have single-exponential dependence on k and linear dependence on the graph size,which significantly improve all previous algorithms for recognizing the same classes:\cdot n^3m)-time algorithm of } Heggernes et al. , STOC 2007] ;
Revised:  Moreover, our algorithms for recognizing \mbox{ (unit-) interval}+ k v and \mbox{ interval}- k e have single-exponential dependence on k and linear dependence on the graph size,which significantly improve all previous algorithms for recognizing the same classes:\cdot n^3m)-time algorithm of } Heggernes et al. , STOC 2007] ;
Edit intent: fluency
Original:  Moreover, our algorithms for recognizing \mbox{ (unit-) interval}+ k v and \mbox{ (unit-) interval}- k e have single-exponential dependence on k and linear dependence on the graph size,which significantly improve all previous algorithms for recognizing the same classes:\cdot n^3m)-time algorithm of } Heggernes et al. , STOC 2007] ;
Revised:  Moreover, our algorithms for recognizing \mbox{ (unit-) interval}+ k v and \mbox{ (unit-) interval}- k e have single-exponential dependence on k and linear dependence on the graph size,which significantly improve all previous algorithms for recognizing the same classes:\cdot n^3m)-time algorithm of } Heggernes et al. STOC 2007] ;
Edit intent: fluency
Original:  Moreover, our algorithms for recognizing \mbox{ (unit-) interval}+ k v and \mbox{ (unit-) interval}- k e have single-exponential dependence on k and linear dependence on the graph size,which significantly improve all previous algorithms for recognizing the same classes:\cdot n^3m)-time algorithm of } Heggernes et al. , STOC 2007] ;
Revised:  Moreover, our algorithms for recognizing \mbox{ (unit-) interval}+ k v and \mbox{ (unit-) interval}- k e have single-exponential dependence on k and linear dependence on the graph size,which significantly improve all previous algorithms for recognizing the same classes:\cdot n^3m)-time algorithm of } Heggernes et al. , STOC 2007] 
Edit intent: fluency
Original:  Kaplan et al., FOCS 1994%DIFDELCMD < ]%%% ;
Revised:  %DIFDELCMD < ]%%% ;
Edit intent: coherence
Original:  Cao and Marx , SODA 2014] ;
Revised:  Cao and Marx SODA 2014] ;
Edit intent: fluency
Original:  and Villanger, IPEC 2010 .
Revised:  and respectively .
Edit intent: coherence
Original:  Finally, a scan of Gmin across the X ~ chromosome of Drosophila melanogaster identifies candidate regions of introgression between sub-Saharan African and cosmopolitan populations that were previously missed by other methods.
Revised:  Finally, a scan of Gmin across the X chromosome of Drosophila melanogaster identifies candidate regions of introgression between sub-Saharan African and cosmopolitan populations that were previously missed by other methods.
Edit intent: fluency
Original:  We present a simple sequence measure, Gmin which is specifically designed to identify regions of diverging genomes as candidates for experiencing recent gene flow . Gmin is defined as the ratio of the minimum  number of nucleotide differences between sequences from two different populations to the average number of between-population differences.
Revised:  We present a simple sequence measure, called Gmin, which is designed to identify regions of diverging genomes as candidates for experiencing recent gene flow . Gmin is defined as the ratio of the minimum number of nucleotide differences between sequences from two different populations to the average number of between-population differences.
Edit intent: coherence
Original:  We present a simple sequence measure, Gmin which is specifically designed to identify regions of diverging genomes as candidates for experiencing recent gene flow . Gmin is defined as the ratio of the minimum  number of nucleotide differences between sequences from two different populations to the average number of between-population differences.
Revised:  We present a simple sequence measure, Gmin which is specifically designed to identify regions of diverging genomes as candidates for experiencing recent gene flow . Gmin is defined as the ratio of the minimum between-population number of nucleotide differences between sequences from two different populations to the average number of between-population differences.
Edit intent: fluency
Original:  We present a simple sequence measure, Gmin which is specifically designed to identify regions of diverging genomes as candidates for experiencing recent gene flow . Gmin is defined as the ratio of the minimum  number of nucleotide differences between sequences from two different populations to the average number of between-population differences.
Revised:  We present a simple sequence measure, Gmin which is specifically designed to identify regions of diverging genomes as candidates for experiencing recent gene flow . Gmin is defined as the ratio of the minimum number of nucleotide differences to the average number of between-population differences.
Edit intent: clarity
Original:   We compare the sensitivity  of Gmin to that of the widely used index of population differentiation, Fst  . Extensive computer simulations demonstrate that Gmin has  greater sensitivity and specificity to detect gene flow than Fst.
Revised:  We compare the sensitivity and specificity of Gmin to that of the widely used index of population differentiation, Fst . Extensive computer simulations demonstrate that Gmin has greater sensitivity and specificity to detect gene flow than Fst.
Edit intent: fluency
Original:   We compare the sensitivity  of Gmin to that of the widely used index of population differentiation, Fst  . Extensive computer simulations demonstrate that Gmin has  greater sensitivity and specificity to detect gene flow than Fst.
Revised:  We compare the sensitivity of Gmin to those of the widely used index of population differentiation, Fst . Extensive computer simulations demonstrate that Gmin has greater sensitivity and specificity to detect gene flow than Fst.
Edit intent: fluency
Original:  Additionally, the sensitivity of Gmin to detect gene flow is robust with respect to both the population mutation and recombination rates , suggesting that it is flexible and can be applied to a variety of biological scenarios .
Revised:  Additionally, the sensitivity of Gmin is robust with respect to both the population mutation and recombination rates , suggesting that it is flexible and can be applied to a variety of biological scenarios .
Edit intent: clarity
Original:  Additionally, the sensitivity of Gmin to detect gene flow is robust with respect to both the population mutation and recombination rates , suggesting that it is flexible and can be applied to a variety of biological scenarios .
Revised:  Additionally, the sensitivity of Gmin to detect gene flow is robust with respect to both the population mutation and recombination rates .
Edit intent: coherence
Original:  These results demonstrate that Gmin is a biologically straightforward, yet powerful, alternative to Fst, as well as to more computationally intensive model-based methods for detecting gene flow.
Revised:  These results show that Gmin is a biologically straightforward, yet powerful, alternative to Fst, as well as to more computationally intensive model-based methods for detecting gene flow.
Edit intent: fluency
Original:  The  fitness distribution is characterized by a power law, but the power law distribution in number of customers still holds , implying the robustness of the model.
Revised:  The fitness distribution is characterized by a power law, but the distribution in number of customers still holds , implying the robustness of the model.
Edit intent: fluency
Original:  The  fitness distribution is characterized by a power law, but the power law distribution in number of customers still holds , implying the robustness of the model.
Revised:  The fitness distribution is characterized by a power law, but the power law distribution in number of customers is still given by power law , implying the robustness of the model.
Edit intent: clarity
Original:  We consider a system of many restaurants where customers compete, as in a game, to get the best services out of many choices using iterative learning . Results for the case with uniform attractiveness are reported.
Revised:  We consider a system of many restaurants where customers compete, as in a game, to attract customers using an iterative learning process . Results for the case with uniform attractiveness are reported.
Edit intent: clarity
Original:  When attractiveness is uniformly distributed, it gives rise to a Zipf-Pareto law for the number of customers.
Revised:  When attractiveness is uniformly distributed, it gives rise to a Zipf law for the number of customers.
Edit intent: clarity
Original:  We perform an exact calculation for the utilization fraction for the case when choices are made independent of attractiveness .
Revised:  We perform an exact calculation for the utilization fraction for the case when choices are made independent of fitness .
Edit intent: clarity
Original:  A variant of the model is also introduced where the attractiveness can be treated as a fitness to stay in the business.
Revised:  A variant of the model is also introduced where the fitness can be treated as a fitness to stay in the business.
Edit intent: clarity
Original:  A variant of the model is also introduced where the attractiveness can be treated as a fitness to stay in the business.
Revised:  A variant of the model is also introduced where the attractiveness can be treated as an ability to stay in the business.
Edit intent: clarity
Original:  The steady state fitness distribution is characterized by a power law, but the distribution in number of customers is still given by power law, implying the robustness of the model.
Revised:  The steady state fitness distribution is characterized by a power law, while the distribution of the number of customers is still given by power law, implying the robustness of the model.
Edit intent: clarity
Original:  Our model serves as a paradigm for city size distribution and the emergence of Zipf law  .
Revised:  Our model serves as a paradigm for the emergence of Zipf law .
Edit intent: clarity
Original:  Our model serves as a paradigm for city size distribution and the emergence of Zipf law  .
Revised:  Our model serves as a paradigm for city size distribution and the emergence of Zipf law in city size distribution .
Edit intent: clarity
Original:  We show that a solution to the eikonal equation is obtained in the limit as Planck's constant \hbar (treated as a free parameter) tends to zero of the solution to the corresponding linear Schr\"odinger equation.
Revised:  In our framework, a solution to the eikonal equation is obtained in the limit as Planck's constant \hbar (treated as a free parameter) tends to zero of the solution to the corresponding linear Schr\"odinger equation.
Edit intent: clarity
Original:  Aleksi Saarela has introduced a new approach to word equations that is based on linear-algebraic properties of polynomials encoding the equations and their solutions.
Revised:  Aleksi Saarela has introduced a new approach to the problem that is based on linear-algebraic properties of polynomials encoding the equations and their solutions.
Edit intent: clarity
Original:  This, in particular, allows to improve the bound for the number of independent equations with minimal defect effect from quadratic to linear.
Revised:  This, in particular, allows to improve the bound for the number of independent equations with maximal rank from quadratic to linear.
Edit intent: clarity
Original:  Based on asynchronous weak commitment search we distribute the task of QoS provision  among different network nodes.
Revised:  Based on asynchronous weak commitment search the task of QoS provision among different network nodes.
Edit intent: clarity
Original:  Based on asynchronous weak commitment search we distribute the task of QoS provision  among different network nodes.
Revised:  Based on asynchronous weak commitment search we distribute the task of QoS provision is distributed among different network nodes.
Edit intent: clarity
Original: A new generation of security solutions is attempting to actively predict future cyber-attacks by profiling offensive practices . Since prediction accuracy can improve with more information about attackers, data sharing among organizations is often advocated.
Revised: In recent years, security solutions have increasingly focused on actively predicting future attacks . Since prediction accuracy can improve with more information about attackers, data sharing among organizations is often advocated.
Edit intent: clarity
Original: A new generation of security solutions is attempting to actively predict future cyber-attacks by profiling offensive practices . Since prediction accuracy can improve with more information about attackers, data sharing among organizations is often advocated.
Revised: A new generation of security solutions is attempting to actively predict future cyber-attacks by profiling offensive practices . Since prediction accuracy can improve with more information about the attackers, sharing security-relevant data among organizations is often advocated.
Edit intent: clarity
Original:  This paperinvestigates a novel approach to collaborative threat mitigation where organizations identify suitable partners for data sharing in a privacy-preserving way .
Revised:  This paperinvestigates a novel approach to collaborative threat mitigation where organizations estimate the benefits of data sharing with potential partners in a privacy-preserving way .
Edit intent: clarity
Original:  We focus on collaborative predictive blacklisting, and consider organizations that predict future attacks based on their own data , and that of a few selected partners.
Revised:  We focus on collaborative predictive blacklisting, i.e., predicting sources of future attacks based on their own data , and that of a few selected partners.
Edit intent: clarity
Original:  We focus on collaborative predictive blacklisting, and consider organizations that predict future attacks based on their own data , and that of a few selected partners.
Revised:  We focus on collaborative predictive blacklisting, and consider organizations that predict future attacks based on both one's own data and that of a few selected partners.
Edit intent: clarity
Original:  We study how  collaboration strategies affect prediction accuracy  on a real-world dataset of suspicious IP addresses , and observe up to a 105\% prediction improvementin blacklisting .
Revised:  We study how collaboration strategies affect prediction accuracy on a real-world dataset of suspicious IP addresses , and observe up to a 105\% improvement .
Edit intent: clarity
Original:  However, collaborative security approaches are rarely implemented due to related trust, privacy, and liability concerns  .
Revised:  However, collaborative security faces a number of important challenges, including privacy, trust, and liability concerns .
Edit intent: clarity
Original:  We study how different collaboration strategies affect prediction accuracy by experimenting on a real-world dataset of 2 billion IP addresses and observe up to a 105\%  improvement.
Revised:  We study how different partner selection strategies affect prediction accuracy by experimenting on a real-world dataset of 2 billion IP addresses and observe up to a 105\% improvement.
Edit intent: clarity
Original:  We evaluate MaxSSmap's accuracy and runtime when mapping simulated E.coli and human reads of 10 \% to 30\% \\%DIF > to 30\\% mismatches with gaps of various lengths to the E.coli genome and human chromosome one respectively.
Revised:  We evaluate MaxSSmap's accuracy and runtime when mapping simulated E.coli and human reads of 10 \\%DIF > to 30\\% mismatches with gaps of various lengths to the E.coli genome and human chromosome one respectively.
Edit intent: coherence
Original:  Similar to mainstream approaches MaxSSmap identifies a local region of the genome followed by exact alignment.
Revised:  Similar to most programs MaxSSmap identifies a local region of the genome followed by exact alignment.
Edit intent: clarity
Original:  We evaluate MaxSSmap's accuracy and runtime when mapping simulated  E.coli and human reads of 10\\%DIF < to 30\\% mismatches with gaps of various lengths to the E.coli genome %DIF > to 30\\% mismatches with gaps to the E.coli genome and human chromosome one respectively.
Revised:  We evaluate MaxSSmap's accuracy and runtime when mapping simulated E.coli and human reads of 10\\%DIF < to 30\\% %DIF > to 30\\% mismatches with gaps to the E.coli genome and human chromosome one respectively.
Edit intent: clarity
Original:  We also show that MaxSSmap can map reads rejected by fast mappers with high accuracy and low error much faster than if Smith-Waterman were used  .
Revised:  We show that MaxSSmap can map reads rejected by fast mappers with high accuracy and low error much faster than if Smith-Waterman were used .
Edit intent: clarity
Original:  This keeps statistical errors high.
Revised:  These small sizes keep the statistical errors of comparisons high.
Edit intent: clarity
Original:  With the growing availability of bibliographic data these statistical errors can be reduced by merging methods of thematic grouping, citation networks and keyword co-usage networks .
Revised:  With the growing availability of bibliographic data such statistical errors can be reduced by merging methods of thematic grouping, citation networks and keyword co-usage networks .
Edit intent: fluency