wanyu/IteraTeR_v2 huggingface dataset:
Original:  Core vertices are well-connected both among themselves and to peripheral vertices, which are not well-connected to any vertices.
Revised:  Core vertices tend to be well-connected both among themselves and to peripheral vertices, which are not well-connected to any vertices.
Edit intent: clarity
Original:  Core vertices are well-connected both among themselves and to peripheral vertices, which are not well-connected to any vertices.
Revised:  Core vertices are well-connected both among themselves and to peripheral vertices, which tend not to be well-connected to any vertices.
Edit intent: clarity
Original:  Core vertices are well-connected both among themselves and to peripheral vertices, which are not well-connected to any vertices.
Revised:  Core vertices are well-connected both among themselves and to peripheral vertices, which are not well-connected to other vertices.
Edit intent: clarity
Original:  Our first method, which is based on transportation in networks, aggregates information from many geodesic paths in a network and yields a score for each vertex that reflects the likelihood that that vertex is a core vertex.
Revised:  Our first method, which is based on transportation in networks, aggregates information from many geodesic paths in a network and yields a score for each vertex that reflects the likelihood that vertex is a core vertex.
Edit intent: fluency
Original:  Our first method, which is based on transportation in networks, aggregates information from many geodesic paths in a network and yields a score for each vertex that reflects the likelihood that  vertex is a core vertex.
Revised:  Our first method, which is based on transportation in networks, aggregates information from many geodesic paths in a network and yields a score for each vertex that reflects the likelihood that a vertex is a core vertex.
Edit intent: fluency
Original:  Additionally, we design an objective function to (1) help classify vertices into core or peripheral vertices and (2) provide a goodness-of-fit criterion for classifications into core versus peripheral vertices.
Revised:  We also design an objective function to (1) help classify vertices into core or peripheral vertices and (2) provide a goodness-of-fit criterion for classifications into core versus peripheral vertices.
Edit intent: coherence
Original:  To examine the performance of our methods, we apply our algorithms to both synthetically-generated networks and a variety of  real-world data sets.
Revised:  To examine the performance of our methods, we apply our algorithms to both synthetically-generated networks and a variety of networks constructed from real-world data sets.
Edit intent: clarity
Original:  In this paper, we introduce novel machine learning based methods for solving the Entity Resolution (ER), a problem for matching user profiles across multiple OSNs.
Revised:  In this paper, we introduce novel machine learning based methods for solving Entity Resolution (ER), a problem for matching user profiles across multiple OSNs.
Edit intent: fluency
Original:  How people react to these social influence signals, in combination with other effects, such as the quality of content and its presentation order, determines how many recommendations content receives.
Revised:  How people react to social signals, in combination with other effects, such as the quality of content and its presentation order, determines how many recommendations content receives.
Edit intent: clarity
Original:  How people react to these social influence signals, in combination with other effects, such as the quality of content and its presentation order, determines how many recommendations content receives.
Revised:  How people react to these social influence signals, in combination with content quality and its presentation order, determines how many recommendations content receives.
Edit intent: clarity
Original:  Using Amazon Mechanical Turk, we experimentally measure the effects of social influence on user decisions to recommend content . Specifically, after controlling for variation in content quality and position, we find that social influence affects outcomes of peer recommendation about half as much as position and quality do.
Revised:  Using Amazon Mechanical Turk, we experimentally measure the effects of social signals in peer recommendation . Specifically, after controlling for variation in content quality and position, we find that social influence affects outcomes of peer recommendation about half as much as position and quality do.
Edit intent: clarity
Original:  Using Amazon Mechanical Turk, we experimentally measure the effects of social influence on user decisions to recommend content . Specifically, after controlling for variation in content quality and position, we find that social influence affects outcomes of peer recommendation about half as much as position and quality do.
Revised:  Using Amazon Mechanical Turk, we experimentally measure the effects of social influence on user decisions to recommend content . Specifically, after controlling for variation due to item content and its position, we find that social influence affects outcomes of peer recommendation about half as much as position and quality do.
Edit intent: clarity
Original:  Using Amazon Mechanical Turk, we experimentally measure the effects of social influence on user decisions to recommend content . Specifically, after controlling for variation in content quality and position, we find that social influence affects outcomes of peer recommendation about half as much as position and quality do.
Revised:  Using Amazon Mechanical Turk, we experimentally measure the effects of social influence on user decisions to recommend content . Specifically, after controlling for variation in content quality and position, we find that social signals affect item popularity about half as much as position and quality do.
Edit intent: clarity
Original:  Using Amazon Mechanical Turk, we experimentally measure the effects of social influence on user decisions to recommend content . Specifically, after controlling for variation in content quality and position, we find that social influence affects outcomes of peer recommendation about half as much as position and quality do.
Revised:  Using Amazon Mechanical Turk, we experimentally measure the effects of social influence on user decisions to recommend content . Specifically, after controlling for variation in content quality and position, we find that social influence affects outcomes of peer recommendation about half as much as position and content do.
Edit intent: clarity
Original:  These effects are somewhat correlated, increasing the inequality of popularity in the presence of social influence .
Revised:  These effects are somewhat correlated, so social signals exacerbate the "rich get richer" phenomenon, which results in a wider variance of popularity .
Edit intent: clarity
Original:  Further, we find that social influence changes people's preferences, creating a "herding" effect that biases their judgements about the content.
Revised:  Further, social signals change individual preferences, creating a "herding" effect that biases their judgements about the content.
Edit intent: clarity
Original:  Further, we find that social influence changes people's preferences, creating a "herding" effect that biases their judgements about the content.
Revised:  Further, we find that social influence changes people's preferences, creating a "herding" effect that biases people's judgments about the content.
Edit intent: fluency
Original:  While similar adverse outcomes have been noted in previous studies, we demonstrate a benefit of social influence: namely, it reduces the effort devoted to evaluating content without significantly diminishing the performance of peer recommendation .
Revised:  Despite this, we find that social signals improve the efficiency of peer recommendation by reducing the effort devoted to evaluating content without significantly diminishing the performance of peer recommendation .
Edit intent: clarity
Original:  While similar adverse outcomes have been noted in previous studies, we demonstrate a benefit of social influence: namely, it reduces the effort devoted to evaluating content without significantly diminishing the performance of peer recommendation .
Revised:  While similar adverse outcomes have been noted in previous studies, we demonstrate a benefit of social influence: namely, it reduces the effort devoted to evaluating content while maintaining recommendation quality .
Edit intent: clarity
Original: Increased coupling between critical infrastructure networks, such as power and communications, has important implications for the reliability and security of these networks .
Revised: Increased coupling between critical infrastructure networks, such as power and communication systems, will have important implications for the reliability and security of these networks .
Edit intent: clarity
Original: Increased coupling between critical infrastructure networks, such as power and communications, has important implications for the reliability and security of these networks .
Revised: Increased coupling between critical infrastructure networks, such as power and communications, has important implications for the reliability and security of these systems .
Edit intent: clarity
Original:  To understand the implications of power-communications coupling, researchers have studied interdependent network models and reported that increased coupling can increase system vulnerability 1, 2 . However, these results come from models that have substantially different mechanisms of cascading, relative to those found in actual power and communications networks.
Revised:  To understand the effects of power-communication coupling, several have studied interdependent network models and reported that increased coupling can increase system vulnerability 1, 2 . However, these results come from models that have substantially different mechanisms of cascading, relative to those found in actual power and communications networks.
Edit intent: clarity
Original:  To understand the implications of power-communications coupling, researchers have studied interdependent network models and reported that increased coupling can increase system vulnerability 1, 2 . However, these results come from models that have substantially different mechanisms of cascading, relative to those found in actual power and communications networks.
Revised:  To understand the implications of power-communications coupling, researchers have studied interdependent network models and reported that increased coupling can increase system vulnerability . However, these results come from models that have substantially different mechanisms of cascading, relative to those found in actual power and communications networks.
Edit intent: coherence
Original:  To understand the implications of power-communications coupling, researchers have studied interdependent network models and reported that increased coupling can increase system vulnerability 1, 2 . However, these results come from models that have substantially different mechanisms of cascading, relative to those found in actual power and communications networks.
Revised:  To understand the implications of power-communications coupling, researchers have studied interdependent network models and reported that increased coupling can increase system vulnerability 1, 2 . However, these results come from models that have substantially different mechanisms of cascading, relative to those found in actual power and communication networks.
Edit intent: fluency
Original:  In the first set of experiments , we compare a simple model of intra-network cascading to a power grid model and find that the power grid model reveals that power grids have a higher level of vulnerability, relative to what would be inferred from a topological contagion model.
Revised:  First , we compare a simple model of intra-network cascading to a power grid model and find that the power grid model reveals that power grids have a higher level of vulnerability, relative to what would be inferred from a topological contagion model.
Edit intent: clarity
Original:  In the first set of experiments , we compare a simple model of intra-network cascading to a power grid model and find that the power grid model reveals that power grids have a higher level of vulnerability, relative to what would be inferred from a topological contagion model.
Revised:  In the first set of experiments , we compare a simple model of topological contagion to a model of cascading in power systems and find that the power grid model reveals that power grids have a higher level of vulnerability, relative to what would be inferred from a topological contagion model.
Edit intent: clarity
Original:  In the first set of experiments , we compare a simple model of intra-network cascading to a power grid model and find that the power grid model reveals that power grids have a higher level of vulnerability, relative to what would be inferred from a topological contagion model.
Revised:  In the first set of experiments , we compare a simple model of intra-network cascading to a power grid model and find that the power grid shows a much higher level of vulnerability, relative to what would be inferred from a topological contagion model.
Edit intent: clarity
Original:  In the first set of experiments , we compare a simple model of intra-network cascading to a power grid model and find that the power grid model reveals that power grids have a higher level of vulnerability, relative to what would be inferred from a topological contagion model.
Revised:  In the first set of experiments , we compare a simple model of intra-network cascading to a power grid model and find that the power grid model reveals that power grids have a higher level of vulnerability, relative to the contagion model.
Edit intent: clarity
Original:  In a second set of experiments , we compare the coupled topological model from 1 to three different physics-based models of power grids coupled to communication networks.
Revised:  Second , we compare the coupled topological model from 1 to three different physics-based models of power grids coupled to communication networks.
Edit intent: clarity
Original:  In a second set of experiments , we compare the coupled topological model from 1 to three different physics-based models of power grids coupled to communication networks.
Revised:  In a second set of experiments , we compare 1 to three different physics-based models of power grids coupled to communication networks.
Edit intent: coherence
Original:  Again, the results show that more accurate models lead to very different conclusions.
Revised:  Again, the more accurate models lead to very different conclusions.
Edit intent: clarity
Original:  Again, the results show that more accurate models lead to very different conclusions.
Revised:  Again, the results show that more accurate models suggest very different conclusions.
Edit intent: clarity
Original:  In all but the most extreme case, the physics-based power grid models suggest that increased power-communications coupling decreases vulnerability.
Revised:  In all but the most extreme case, the physics-based power grid models indicate that increased power-communication coupling decreases vulnerability.
Edit intent: fluency
Original:  This is opposite from what one would conclude from the model in 1 , in which zero coupling is optimal.
Revised:  This is opposite from what one would conclude from the 1 , in which zero coupling is optimal.
Edit intent: clarity
Original:  Finally, an extreme case , in which communications failures immediately cause grid failures, suggests that if systems are poorly designed, increased coupling can be harmful  .
Revised:  Finally, an extreme case in which communication failures immediately cause grid failures, suggests that if systems are poorly designed, increased coupling can be harmful .
Edit intent: fluency
Original:  It includes a spike train generator and an event detector which makes it capable of analyzing continuous data.
Revised:  It also comprises a spike train generator and an event detector which makes it capable of analyzing continuous data.
Edit intent: clarity
Original:  Numerical experiments illustrate that this second heuristic performs extremely well, and allows us to compute optimal semi-NMF decompositions in many situations.
Revised:  Numerical experiments illustrate that this second approach performs extremely well, and allows us to compute optimal semi-NMF decompositions in many situations.
Edit intent: clarity
Original:  Finally, we analyze the computational complexity of semi-NMF and prove that it is NP-hard , already in the rank-one case (that is, for r = 1  ).
Revised:  Finally, we analyze the computational complexity of semi-NMF proving its NP-hardness , already in the rank-one case (that is, for r = 1 ).
Edit intent: clarity
Original: Taylors Law (TL) describes the scaling relationship between the mean and variance of one or more populations as a power-law.
Revised: Taylors Law (TL) describes the scaling relationship between the mean and variance of populations as a power-law.
Edit intent: clarity
Original:  These algorithms the provide first fundamental steps necessary for solving the following problems: parameter estimation in this ERGM, extending the model to its Bayesian relative, and developing a rigorous methodology for testing goodness of fit of the model and model selection.
Revised:  These algorithms provide first fundamental steps necessary for solving the following problems: parameter estimation in this ERGM, extending the model to its Bayesian relative, and developing a rigorous methodology for testing goodness of fit of the model and model selection.
Edit intent: fluency
Original:  We propose to resolve these features using anisotropic mesh adaptation.
Revised:  We propose to resolve these small length scales using anisotropic mesh adaptation.
Edit intent: clarity
Original:  We demonstrate this combination for the  L-bracket problem with p=10, and we are able to investigate mesh dependence.
Revised:  We demonstrate this combination for the L-bracket problems with p=10, and we are able to investigate mesh dependence.
Edit intent: fluency
Original:  We conducted a detailed investigation of the real time unfolding of human entrainment , as expressed by the content and patterns of hundreds of thousands of messages on Twitter , during the 2012 US presidential debates.
Revised:  We conducted a detailed investigation of the unfolding of human entrainment , as expressed by the content and patterns of hundreds of thousands of messages on Twitter , during the 2012 US presidential debates.
Edit intent: clarity
Original:  We conducted a detailed investigation of the real time unfolding of human entrainment , as expressed by the content and patterns of hundreds of thousands of messages on Twitter , during the 2012 US presidential debates.
Revised:  We conducted a detailed investigation of the real time unfolding of human entrainment - as expressed by the content and patterns of hundreds of thousands of messages on Twitter , during the 2012 US presidential debates.
Edit intent: fluency
Original:  We conducted a detailed investigation of the real time unfolding of human entrainment , as expressed by the content and patterns of hundreds of thousands of messages on Twitter , during the 2012 US presidential debates.
Revised:  We conducted a detailed investigation of the real time unfolding of human entrainment , as expressed by the content and patterns of hundreds of thousands of messages on Twitter - during the 2012 US presidential debates.
Edit intent: fluency
Original:  By time locking these data sources, we quantify the real time impact of the  debate on human attention.
Revised:  By time locking these data sources, we quantify the impact of the debate on human attention.
Edit intent: clarity
Original:  By time locking these data sources, we quantify the real time impact of the  debate on human attention.
Revised:  By time locking these data sources, we quantify the real time impact of the unfolding debate on human attention.
Edit intent: clarity
Original:  We show that  social behavior covaries second by second to the interactional dynamics of the debates: A candidate speaking induces rapid increases in mentions of his name on social media and decreases in mentions of the other candidate.
Revised:  We show that collective social behavior covaries second by second to the interactional dynamics of the debates: A candidate speaking induces rapid increases in mentions of his name on social media and decreases in mentions of the other candidate.
Edit intent: clarity
Original:  We show that  social behavior covaries second by second to the interactional dynamics of the debates: A candidate speaking induces rapid increases in mentions of his name on social media and decreases in mentions of the other candidate.
Revised:  We show that social behavior covaries second-by-second to the interactional dynamics of the debates: A candidate speaking induces rapid increases in mentions of his name on social media and decreases in mentions of the other candidate.
Edit intent: fluency
Original:  We also highlight a distinct time scale for the impact of salient moments in the debate: Mentions in social media start within 5 to 10 seconds after the moment;
Revised:  We also highlight a distinct time scale for the impact of salient moments in the debate: Mentions in social media start within 5-10 seconds after the moment;
Edit intent: fluency
Original:  and slowly decay in a consistent fashion across well known events during the debates.
Revised:  and slowly decay in a consistent fashion across well-known events during the debates.
Edit intent: fluency
Original:   The methods and results pave the way for careful real time study of the dynamics and mechanisms of large scale human entrainment.
Revised:  The methods and results pave the way for careful study of the dynamics and mechanisms of large scale human entrainment.
Edit intent: clarity
Original:   The methods and results pave the way for careful real time study of the dynamics and mechanisms of large scale human entrainment.
Revised:  The methods and results pave the way for careful real time study of the dynamics and mechanisms of large-scale human entrainment.
Edit intent: fluency
Original:  To find out who transmits infection to whom , and how that affects the spread and containment of infection , remains a major empirical challenge in the epidemiology of HIV/AIDS. Here we develop a technique, based on the routine sampling of infection in linked population groups, which shows how an Asian HIV/AIDS epidemic began in FSW, was propagated mainly by IDU, and ultimately generated most cases among the female partners of MCF (FPM).
Revised:  To find out who transmits infection to whom and how that affects the spread and containment of infection , remains a major empirical challenge in the epidemiology of HIV/AIDS. Here we develop a technique, based on the routine sampling of infection in linked population groups, which shows how an Asian HIV/AIDS epidemic began in FSW, was propagated mainly by IDU, and ultimately generated most cases among the female partners of MCF (FPM).
Edit intent: fluency
Original:  To find out who transmits infection to whom , and how that affects the spread and containment of infection , remains a major empirical challenge in the epidemiology of HIV/AIDS. Here we develop a technique, based on the routine sampling of infection in linked population groups, which shows how an Asian HIV/AIDS epidemic began in FSW, was propagated mainly by IDU, and ultimately generated most cases among the female partners of MCF (FPM).
Revised:  To find out who transmits infection to whom , and how that affects the spread and containment of infection remains a major empirical challenge in the epidemiology of HIV/AIDS. Here we develop a technique, based on the routine sampling of infection in linked population groups, which shows how an Asian HIV/AIDS epidemic began in FSW, was propagated mainly by IDU, and ultimately generated most cases among the female partners of MCF (FPM).
Edit intent: fluency