wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We apply our simulation framework to study hybrid SIR processes that  describe infections as discrete-time Markovian and recovery events as continuous-time non-Markovian processes, which mimic the distribution of cell-cycle times .
Revised:  We apply our simulation framework to study hybrid SIR processes that we study in this paper describe infections as discrete-time Markovian and recovery events as continuous-time non-Markovian processes, which mimic the distribution of cell-cycle times .
Edit intent: meaning-changed
Original:  We apply our simulation framework to study hybrid SIR processes that  describe infections as discrete-time Markovian and recovery events as continuous-time non-Markovian processes, which mimic the distribution of cell-cycle times .
Revised:  We apply our simulation framework to study hybrid SIR processes that describe infections as discrete-time Markovian and recovery events as continuous-time non-Markovian processes, which mimic the distribution of cell cycles .
Edit intent: clarity
Original:  This yields a simple analytical formula for the model assortativity, and opens up ways to analyze rank correlation coefficients suitable for random graphs with heavy-tailed  degree distributions.
Revised:  This yields a simple analytical formula for the model assortativity, and opens up ways to analyze rank correlation coefficients suitable for random graphs with heavy-tailed degree distributions. We also study the effects of power laws on the asymptotic joint degree distributions.
Edit intent: meaning-changed
Original:  In light of these developments, we set out to understand how and why a highly educated population, university students, chooses and uses VPNs, whether they are aware of data leakages by VPNs, and how their awareness and attitudes about VPNs affects their decisions to use them.
Revised:  In light of these developments, we aim understand how and why a highly educated population, university students, chooses and uses VPNs, whether they are aware of data leakages by VPNs, and how their awareness and attitudes about VPNs affects their decisions to use them.
Edit intent: clarity
Original:  In light of these developments, we set out to understand how and why a highly educated population, university students, chooses and uses VPNs, whether they are aware of data leakages by VPNs, and how their awareness and attitudes about VPNs affects their decisions to use them.
Revised:  In light of these developments, we set out to understand how and why a technically savvy population---university students---chooses and uses VPNs, whether they are aware of data leakages by VPNs, and how their awareness and attitudes about VPNs affects their decisions to use them.
Edit intent: clarity
Original:  In light of these developments, we set out to understand how and why a highly educated population, university students, chooses and uses VPNs, whether they are aware of data leakages by VPNs, and how their awareness and attitudes about VPNs affects their decisions to use them.
Revised:  In light of these developments, we set out to understand how and why a highly educated population, university students, chooses and uses VPNs, whether they are aware of data leakages by VPNs, and how awareness and attitudes about VPNs affects their decisions to use them.
Edit intent: fluency
Original:  In light of these developments, we set out to understand how and why a highly educated population, university students, chooses and uses VPNs, whether they are aware of data leakages by VPNs, and how their awareness and attitudes about VPNs affects their decisions to use them.
Revised:  In light of these developments, we set out to understand how and why a highly educated population, university students, chooses and uses VPNs, whether they are aware of data leakages by VPNs, and how their awareness and attitudes about VPNs affects decisions to use them.
Edit intent: fluency
Original:  To answer these questions, we conducted 32 in-person interviews , a survey with 350 respondents, and a controlled lab study with 11 users, all university students in the United States.
Revised:  To answer these questions, we conducted 32 in-person interviews and a survey with 350 respondents, and a controlled lab study with 11 users, all university students in the United States.
Edit intent: meaning-changed
Original:  We found that , surprisingly, access , cost, and performance trumped privacy concerns for many users ; many users even expected that their VPNs were collecting data about them .
Revised:  We found that students were mostly concerned with access to content, and privacy concerns were often secondary. They made tradeoffs to achieve a particular goal, such as using a free commercial VPN that potentially collects their online activities simply to access an online service in a geographically restricted area. Many users even expected that their VPNs were collecting data about them .
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Original:  many users even expected that their VPNs were collecting data about them . Even presenting information to users about the data leaked from VPNs to third-party trackers had little effect on their attitudes, suggesting that more work is needed to improve ways to show users what can be inferred from data collected about them .
Revised:  many users even expected that their VPNs were collecting data about them , although they did not understand how VPNs work. Given the proliferation of choices among VPN providers, it is technically possible to achieve both access and privacy. We conclude with a discussion, informed by our results, concerning ways to help users make choices about VPNs that help them access content without compromising privacy .
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Original:  Recent studies have shown that some commercial VPNs leak user traffic to third-party trackers. In light of these developments , we aim  understand how and why a technically savvy population---university students---chooses and uses VPNs,  whether they are aware of data leakages by VPNs, and how awareness and attitudes about VPNs affects decisions to use them .
Revised:  Recent studies have shown that some commercial VPNs leak user traffic to third parties. In this paper , we aim understand how and why a technically savvy population---university students---chooses and uses VPNs, whether they are aware of data leakages by VPNs, and how awareness and attitudes about VPNs affects decisions to use them .
Edit intent: clarity
Original:  In light of these developments , we aim  understand how and why a technically savvy population---university students---chooses and uses VPNs,  whether they are aware of data leakages by VPNs, and how awareness and attitudes about VPNs affects decisions to use them .
Revised:  In light of these developments , we aim to understand how and why a technically savvy population---university students---chooses and uses VPNs, whether they are aware of data leakages by VPNs, and how awareness and attitudes about VPNs affects decisions to use them .
Edit intent: fluency
Original:  In light of these developments , we aim  understand how and why a technically savvy population---university students---chooses and uses VPNs,  whether they are aware of data leakages by VPNs, and how awareness and attitudes about VPNs affects decisions to use them .
Revised:  In light of these developments , we aim understand how and why a population of VPN users---university students---chooses and uses VPNs, whether they are aware of data leakages by VPNs, and how awareness and attitudes about VPNs affects decisions to use them .
Edit intent: clarity
Original:  In light of these developments , we aim  understand how and why a technically savvy population---university students---chooses and uses VPNs,  whether they are aware of data leakages by VPNs, and how awareness and attitudes about VPNs affects decisions to use them .
Revised:  In light of these developments , we aim understand how and why a technically savvy population---university students---chooses and uses VPNs, and whether they are aware of data leakages by VPNs, and how awareness and attitudes about VPNs affects decisions to use them .
Edit intent: fluency
Original:  In light of these developments , we aim  understand how and why a technically savvy population---university students---chooses and uses VPNs,  whether they are aware of data leakages by VPNs, and how awareness and attitudes about VPNs affects decisions to use them .
Revised:  In light of these developments , we aim understand how and why a technically savvy population---university students---chooses and uses VPNs, whether they are aware of the security and privacy risks that VPNs pose .
Edit intent: clarity
Original: Recent studies have shown that some commercial VPNs leak user traffic to third parties. In this paper, we aim to understand how and why a population of VPN users---university students---chooses and uses VPNs, and whether they are aware of the security and privacy risks that VPNs pose.
Revised: We study how and why a population of VPN users---university students---chooses and uses VPNs, and whether they are aware of the security and privacy risks that VPNs pose.
Edit intent: coherence
Original:  As an extension of deep learning , Graph neural networks (GNNs) are designed to solve the non-Euclidean problems on graph-structured data which can hardly be handled by general deep learning techniques.
Revised:  As an extension of deep learning beyond these domains, graph neural networks (GNNs) are designed to solve the non-Euclidean problems on graph-structured data which can hardly be handled by general deep learning techniques.
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Original:  As an extension of deep learning , Graph neural networks (GNNs) are designed to solve the non-Euclidean problems on graph-structured data which can hardly be handled by general deep learning techniques.
Revised:  As an extension of deep learning , Graph neural networks (GNNs) are designed to handle the non-Euclidean problems on graph-structured data which can hardly be handled by general deep learning techniques.
Edit intent: clarity
Original:  Existing GNNs under various mechanisms, such as random walk, PageRank, graph convolution, and heat diffusion, are designed for different types of graphs and problems, which makes it difficult to compare them directly. Previous GNN surveys focus on categorizing current models into independent groups, lacking analysis regarding their internal connection. This paper proposes a unified framework and provides a novel perspective that can widely fit existing GNNsinto our framework methodologically. Specifically, we survey and categorize existing GNN models into the spatial and spectral domains, and reveal connections among subcategories in each domain.
Revised:  Existing GNNs are presented using various techniques, making direct comparison and cross-reference more complex. Although existing studies categorize GNNs into spatial-based and spectral-based techniques, there hasn't been a thorough examination of their relationship. To close this gap, this study presents a single framework that systematically incorporates most GNNs. URLanize existing GNNs into spatial and spectral domains, and reveal connections among subcategories in each domain.
Edit intent: coherence
Original: {\em   Based on some novel insights in geometry, we prove that the complexities of these two problems can be significantly reduced through JL transform .
Revised: {\em is one of the most popular methods for dimension reduction. Though the JL transform has been widely studied in the past decades, its effectiveness for dealing with outliers has never been investigated before (to the best of our knowledge). Based on some novel insights in geometry, we prove that the complexities of these two problems can be significantly reduced through JL transform .
Edit intent: meaning-changed
Original: {\em   Based on some novel insights in geometry, we prove that the complexities of these two problems can be significantly reduced through JL transform .
Revised: {\em Based on some novel insights in geometry, we prove that the complexities of these two problems can be significantly reduced through the JL transform. Moreover, we prove that the solution in the dimensionality-reduced space can be efficiently recovered in the original \mathbb{R .
Edit intent: meaning-changed
Original:  Conversely, the visual disconnection of the artificial hands did not significantly influence physiological responding to a virtual threat and motor performance in a quick task. These results are in accordance with past evidence , indicating that observation of body discontinuity negatively impacts the sense of embodiment.
Revised:  Contradictory evidence was found in relation to motor performance, where further research is needed to understand the role of visual body discontinuity in motor tasks. Preliminary findings on physiological reactions to a threat were also assessed, indicating that body visual discontinuity does not differently impact threat-related skin conductance responses. The present results are in accordance with past evidence , indicating that observation of body discontinuity negatively impacts the sense of embodiment.
Edit intent: meaning-changed
Original:  In addition, regardless of the synaptic strengths, shorter electrical synaptic delays are found to be better optimizers of the phenomenon than shorter chemical synaptic delays, while longer chemical synaptic delays are a better optimizers than longer electrical synaptic delays - in both cases, the poorer optimizers are in fact worst.
Revised:  In addition, regardless of the synaptic strengths, shorter electrical synaptic delays are found to be better optimizers of the phenomenon than shorter chemical synaptic delays, while longer chemical synaptic delays are better optimizers than longer electrical synaptic delays - in both cases, the poorer optimizers are in fact worst.
Edit intent: fluency
Original:  In addition, regardless of the synaptic strengths, shorter electrical synaptic delays are found to be better optimizers of the phenomenon than shorter chemical synaptic delays, while longer chemical synaptic delays are a better optimizers than longer electrical synaptic delays - in both cases, the poorer optimizers are in fact worst.
Revised:  In addition, regardless of the synaptic strengths, shorter electrical synaptic delays are found to be better optimizers of the phenomenon than shorter chemical synaptic delays, while longer chemical synaptic delays are a better optimizers than longer electrical synaptic delays -- in both cases, the poorer optimizers are in fact worst.
Edit intent: fluency
Original:  This allows us to represent the generators of a steady state ideal as sums of binomials, which yields a corresponding coefficient matrix.
Revised:  This leads us to the concept of unconditional binomiality, meaning binomiality for all values of the rate constants. This concept is different from conditional binomiality that applies when rate constant values or relations among rate constants are given. We start by representing the generators of a steady state ideal as sums of binomials, which yields a corresponding coefficient matrix.
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Original:  Eiben et al. (ESA 2019) improved the running time to O(8^tt^5n^2\log n)  . Moreover, they showed that there is no O(n^{2-\varepsilon})-time algorithm for trees under some reasonable complexity assumption.
Revised:  Eiben et al. (ESA 2019) improved the dependency of n in the running time by giving an O(8^tt^5n^2\log n) . Moreover, they showed that there is no O(n^{2-\varepsilon})-time algorithm for trees under some reasonable complexity assumption.
Edit intent: meaning-changed
Original:  Eiben et al. (ESA 2019) improved the running time to O(8^tt^5n^2\log n)  . Moreover, they showed that there is no O(n^{2-\varepsilon})-time algorithm for trees under some reasonable complexity assumption.
Revised:  Eiben et al. (ESA 2019) improved the running time to O(8^tt^5n^2\log n) -time algorithm . Moreover, they showed that there is no O(n^{2-\varepsilon})-time algorithm for trees under some reasonable complexity assumption.
Edit intent: meaning-changed
Original: The phenomena of community structure in real-world networks has been central to network science's success. While much attention has been paid to community detectionalgorithms and evaluation criteria, a related question has been overlooked: what would the change in modularity be if a node were to be deleted from a network ? In this work, we first show that the answer to this question can be calculated for all nodes in the network in O(m) time. Then ,  we show that this quantity has implications in three research areas within network science: community analysis, network robustness, and community deception.
Revised: Group-centrality is an emerging area in Network Science which seeks to quantify the importance of nodes within and between communities. Previous group-centrality measures either have strong theoretical underpinnings in community detection , we show that this quantity has implications in three research areas within network science: community analysis, network robustness, and community deception.
Edit intent: clarity
Original:  Then ,  we show that this quantity has implications in three research areas within network science: community analysis, network robustness, and community deception.
Revised:  Then , or scale well, but not both. We propose an extremely scalable measure, modularity-impact, that is directly derived from Newman's modularity. Unlike other measures, modularity-impact is signed, providing additional information about each node's role: hub within its community, or bridge between communities. We analyze our results in a network robustness framework, as it is a generalized version of immunization under SIR models, which prior works have used for evaluation. We show that our measure out-performs previous measures for immunizing strategies in generated networks with strong community structure. Additionally, we show that this quantity has implications in three research areas within network science: community analysis, network robustness, and community deception.
Edit intent: meaning-changed
Original:  Then ,  we show that this quantity has implications in three research areas within network science: community analysis, network robustness, and community deception. Modularity-deltas are useful in community analysis as they are a semi-global measure , scalable like local measures but using global information through the network's partition.In robustness, modularity maximizing attack strategies are more efficient than betweenness-based ones, and are shown to damage the US Powergrid and European Road networks more effectively than degree based attacks .
Revised:  Then , we show that our measure gives the most effective attack strategy for a million-node infrastructure network, fragmenting it while removing less than 2\% of nodes. This is over 8 times more fragile than can be seen with previous group-centralities. We further find that the social media communication network demonstrates extreme robustness to group-centrality attacks, suggesting that user-based interventions to mitigate misinformation diffusion will be ineffective .
Edit intent: meaning-changed
Original: Group-centrality is an emerging area in Network Science which seeks to quantify the importance of nodes within and between communities. Previous group-centrality measures either have strong theoretical underpinnings in community detection, or scale well, but not both.We propose an extremely scalable measure, modularity-impact, that is directly derived from Newman's modularity. Unlike other measures, modularity-impact is signed, providing additional information about each node's role : hub within its community , or bridge between communities.
Revised: Group-centrality is an emerging area in Network Science which seeks to quantify the importance of nodes in relation to community structure. Measures are a function of a network's structure and a given partition. Previous approaches extend classical centrality measures to account for community structure with little connection to community detection theory. In contrast, we propose cluster-quality vitality measures, i.e., modularity vitality, a community-aware measure which is well-grounded in both centrality and community detection theory. Modularity vitality quantifies positive and negative contributions to community structure, which indicate a node's role : hub within its community , or bridge between communities.
Edit intent: meaning-changed
Original:  Unlike other measures, modularity-impact is signed, providing additional information about each node's role : hub within its community , or bridge between communities. We analyze our results in a network robustness framework, as it is a generalized version of immunization under SIR models, which prior works have used for evaluation. We show that our measure out-performs previous measures for immunizing strategies in generated networks with strong community structure. Additionally, we show that our measure gives the most effective attack strategy for a million-node infrastructure network, fragmenting it while removing less than 2\% of nodes. This is over 8 times more fragile than can be seen with previous group-centralities.
Revised:  Unlike other measures, modularity-impact is signed, providing additional information about each node's role as a community bridge or hub. We derive a computationally efficient method of calculating modularity vitality for all nodes in O(M + NC) time, where C is the number of communities. We systematically fragment networks by removing central nodes, and find that modularity vitality consistently outperforms existing community-aware centrality measures. Modularity vitality is over 8 times more fragile than can be seen with previous group-centralities.
Edit intent: meaning-changed
Original:  This is over 8 times more fragile than can be seen with previous group-centralities. We further find that the social media communication network demonstrates extreme robustness to group-centrality attacks, suggesting that user-based interventions to mitigate misinformation diffusion will be ineffective.
Revised:  This is over 8 times more effective than the next-best method on a million-node infrastructure network. This result does not generalize to social media communication network demonstrates extreme robustness to group-centrality attacks, suggesting that user-based interventions to mitigate misinformation diffusion will be ineffective.
Edit intent: meaning-changed
Original:  We further find that the social media communication network demonstrates extreme robustness to group-centrality attacks, suggesting that user-based interventions to mitigate misinformation diffusion will be ineffective.
Revised:  We further find that the social media communication network demonstrates extreme robustness to all community-aware centrality attacks. This robustness suggests that user-based interventions to mitigate misinformation diffusion will be ineffective.
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Original:   Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples.
Revised:  Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples.
Edit intent: meaning-changed