wanyu/IteraTeR_v2 huggingface dataset:
Original:  Here, we connect the two problems - the influential spreaders identification and spreading processes' hitting time estimation - by introducing a centrality metric, called ViralRank, which quantifies how close a node is , on average, to the other nodes in terms of the random-walk effective distance .
Revised:  Here, we connect the two problems - the influential spreaders identification and spreading processes' hitting time estimation - by introducing a centrality metric, called ViralRank, which quantifies how close a node is , on average, to the other nodes .
Edit intent: clarity
Original:  We show that ViralRank significantly outperforms state-of-the-art centrality metrics in identifying influential spreaders for super-critical contact-network processes and for metapopulation global contagion processes.
Revised:  We show that ViralRank significantly outperforms state-of-the-art metrics in detecting the influencers for global contagion processes.
Edit intent: clarity
Original: Unraveling bacterial strategies for spatial exploration is crucial to understand the complexity of the organi- zation of life.
Revised: Unraveling bacterial strategies for spatial exploration is crucial for understanding the complexity in the organization of life.
Edit intent: clarity
Original:  Currently, a cornerstone for quantitative modeling of bacterial transport , is their run-and-tumble strategy to explore their environment .
Revised:  Bacterial motility determines the spatio-temporal structure of microbial communities, controls infection spreading and the microbiota organization in guts or in soils. Most theoretical approaches for modeling bacterial transport rely on their run-and-tumble strategy to explore their environment .
Edit intent: clarity
Original:  Currently, a cornerstone for quantitative modeling of bacterial transport , is their run-and-tumble strategy to explore their environment .
Revised:  Currently, a cornerstone for quantitative modeling of bacterial transport , is their run-and-tumble motion .
Edit intent: clarity
Original:  Direct measurements on flagellar motors show , on the contrary, heavy-tailed distributions of rotation times stemming from the intrinsic noise in the chemotactic mechanism.
Revised:  Direct measurements on flagellar motors show heavy-tailed distributions of rotation times stemming from the intrinsic noise in the chemotactic mechanism.
Edit intent: coherence
Original:  The crucial role of stochasticity on the chemotactic response has also been highlighted by recent modeling, suggesting its determinant influence on motility .
Revised:  Currently, there is no direct experimental evidence that the stochasticity in the chemotactic machinery affect the macroscopic motility of bacteria .
Edit intent: clarity
Original:  In stark contrast with the accepted vision of run-and-tumble, here we report a large behavioral variability of wild-type E. coli , revealed in their three-dimensional trajectories.
Revised:  In stark contrast with the accepted vision of run-and-tumble, here we report a large behavioral variability of wild-type E. coli , revealed in their three-dimensional trajectories.
Edit intent: fluency
Original:  At short  times, a broad distribution of run times is measured on a population and attributed to the slow fluctuations of a signaling protein triggering the flagellar motor reversal.
Revised:  At short observation times, a broad distribution of run times is measured on a population and attributed to the slow fluctuations of a signaling protein triggering the flagellar motor reversal.
Edit intent: clarity
Original:  At short  times, a broad distribution of run times is measured on a population and attributed to the slow fluctuations of a signaling protein triggering the flagellar motor reversal.
Revised:  At short times, a large distribution of run times is measured on a population and attributed to the slow fluctuations of a signaling protein triggering the flagellar motor reversal.
Edit intent: clarity
Original:  We demonstrate that such a large distribution  introduces measurement biases in most practical situations.
Revised:  We demonstrate that such a large distribution of run times introduces measurement biases in most practical situations.
Edit intent: clarity
Original:  These results reconcile the notorious conundrum between run time observations and motor switching statistics.
Revised:  Our results reconcile the notorious conundrum between run time observations and motor switching statistics.
Edit intent: fluency
Original:  We finally propose that statistical modeling of transport properties currently undertaken in the emerging framework of active matter studies  should be reconsidered under the scope of this large variability of motility features.
Revised:  We finally propose that statistical modeling of transport properties currently undertaken in the emerging framework of active matter studies , should be reconsidered under the scope of this large variability of motility features.
Edit intent: fluency
Original:  Strong stability is more restricted than the notion of stability introduced by Jerrum and Sinclair ( TCS, 1990) .
Revised:  Strong stability is more restricted than the notion of stability introduced by Jerrum and Sinclair ( 1990) .
Edit intent: clarity
Original:  In particular, our work resolves an open problem posed by Greenhill and Sfragara ( TCS, 2017) regarding the switch chain for undirected graphs.
Revised:  In particular, our work resolves an open problem posed by Greenhill and Sfragara ( 2017) regarding the switch chain for undirected graphs.
Edit intent: clarity
Original:  In particular, our work resolves an open problem posed by Greenhill and Sfragara ( TCS, 2017) regarding the switch chain for undirected graphs.
Revised:  In particular, our work resolves an open problem posed by Greenhill and Sfragara ( TCS, 2017) regarding the switch chain on undirected graphs.
Edit intent: fluency
Original:  This comparison provides a much shorter proof for the currently known rapid mixing results of the switch chain and extends them up to sharp characterizations of P-stability in terms of certain graph parameters .
Revised:  This comparison provides a much shorter proof for the currently known rapid mixing results of the switch chain and extends them up to sharp characterizations of P-stability .
Edit intent: clarity
Original: Past. Data curation - the process of discovering, integrating, and cleaning data - is one of the oldest  data management problems.
Revised:  Data curation - the process of discovering, integrating, and cleaning data - is one of the oldest data management problems.
Edit intent: coherence
Original:  Unfortunately , it is still  the most time consuming and least enjoyable work of data scientists.
Revised:  Unfortunately , it is still one of the most time consuming and least enjoyable work of data scientists.
Edit intent: clarity
Original:  Present. The power of current data curation  solutions are not keeping up with the ever changing data ecosystemin terms of volume, velocity, variety and veracity, mainly due to the high human cost , instead of machine cost, needed for providing the ad-hoc solutions mentioned above .
Revised:  Present. The power of current data curation solutions are not keeping up with the ever-changing data ecosystem, because they often require substantially high human cost , instead of machine cost, needed for providing the ad-hoc solutions mentioned above .
Edit intent: clarity
Original:  Present. The power of current data curation  solutions are not keeping up with the ever changing data ecosystemin terms of volume, velocity, variety and veracity, mainly due to the high human cost , instead of machine cost, needed for providing the ad-hoc solutions mentioned above .
Revised:  Present. The power of current data curation solutions are not keeping up with the ever changing data ecosystemin terms of volume, velocity, variety and veracity, mainly due to the high human cost .
Edit intent: coherence
Original:  Meanwhile, deep learning is making strides in achieving remarkable successes in areas such as image recognition, natural language processing, and speech recognition.
Revised:  Meanwhile, deep learning is making strides in achieving remarkable successes in multiple areas, such as image recognition, natural language processing, and speech recognition.
Edit intent: clarity
Original: The paper is devoted to the solution of a weighted non-linear least-squares problem for low-rank signal estimation, which is related  Hankel structured low-rank approximation problems.
Revised: The paper is devoted to the solution of a weighted nonlinear least squares problem for low-rank signal estimation, which is related Hankel structured low-rank approximation problems.
Edit intent: fluency
Original: The paper is devoted to the solution of a weighted non-linear least-squares problem for low-rank signal estimation, which is related  Hankel structured low-rank approximation problems.
Revised: The paper is devoted to the solution of a weighted non-linear least-squares problem for low-rank signal estimation, which is related to Hankel structured low-rank approximation problems.
Edit intent: fluency
Original:  The solution is constructed by a modified weighted Gauss-Newton method  .
Revised:  A modified weighted Gauss-Newton method .
Edit intent: coherence
Original:  The advantage of the suggested method is the possibility of its  stable and fast implementation.
Revised:  The advantage of the proposed method is the possibility of its stable and fast implementation.
Edit intent: fluency
Original:  For the weighting matrix, which corresponds to autoregressive processes of order p, the computational cost  is O(N r^2 + N p^2 + r N \log N), where N is the time series length, r is the rank of  approximating time series.
Revised:  For the weighting matrix, which corresponds to an autoregressive process of order p, the computational cost is O(N r^2 + N p^2 + r N \log N), where N is the time series length, r is the rank of approximating time series.
Edit intent: fluency
Original:  For the weighting matrix, which corresponds to autoregressive processes of order p, the computational cost  is O(N r^2 + N p^2 + r N \log N), where N is the time series length, r is the rank of  approximating time series.
Revised:  For the weighting matrix, which corresponds to autoregressive processes of order p, the computational cost is O(N r^2 + N p^2 + r N \log N), where N is the time series length, r is the rank of the approximating time series.
Edit intent: fluency
Original: The paper is devoted to the solution of a weighted nonlinear least squares problem for low-rank signal estimation, which is related to Hankel structured low-rank approximation problems.
Revised: The paper is devoted to the solution of a weighted nonlinear least-squares problem for low-rank signal estimation, which is related to Hankel structured low-rank approximation problems.
Edit intent: fluency
Original:  A modified weighted Gauss-Newton method (MGN) , which uses projection on the image space of the signal, is proposed to solve this problem.
Revised:  A modified weighted Gauss-Newton method , which uses projection on the image space of the signal, is proposed to solve this problem.
Edit intent: clarity
Original:  A modified weighted Gauss-Newton method (MGN) , which uses projection on the image space of the signal, is proposed to solve this problem.
Revised:  A modified weighted Gauss-Newton method (MGN) , which uses projecting on the image space of the signal, is proposed to solve this problem.
Edit intent: fluency
Original:  For a weight matrix, which corresponds to an autoregressive process of order p, the computational cost of iterations is O(N r^2 + N p^2 + r N \log N), where N is the time series length, r is the rank of the approximating time series . Since the proposed algorithms can be naturally extended to the case of rank-deficient weight matrices, the method MGN can be applied to time series with missing data .
Revised:  For a weight matrix, which corresponds to an autoregressive process of order p, the computational cost of iterations is O(N r^2 + N p^2 + r N \log N), where N is the time series length, r is the rank of the approximating time series .
Edit intent: coherence
Original: The lack of a comprehensive decision-making approach at the community-level is of significant importance that must be garnered immediate attention.
Revised: The lack of a comprehensive decision-making approach at the community level is an important problem that warrants immediate attention.
Edit intent: clarity
Original:  This research introduces a sequential discrete optimization approach, as a decision-making framework at the community-level for recovery management.
Revised:  This research introduces a sequential discrete optimization approach, as a decision-making framework at the community level for recovery management.
Edit intent: fluency
Original:  We also provide computational results which suggest that our methodology not only incorporates recovery policies of responsible public and private entities within the community , but also substantially enhances the performance of their underlying strategies with limited resources.
Revised:  We also provide computational results showing that our methodology not only incorporates recovery policies of responsible public and private entities within the community , but also substantially enhances the performance of their underlying strategies with limited resources.
Edit intent: clarity
Original:  We also provide computational results which suggest that our methodology not only incorporates recovery policies of responsible public and private entities within the community , but also substantially enhances the performance of their underlying strategies with limited resources.
Revised:  We also provide computational results which suggest that our methodology not only incorporates recovery policies of responsible public and private entities within the community but also substantially enhances the performance of their underlying strategies with limited resources.
Edit intent: fluency
Original:  The methodology can be implemented efficiently to identify near-optimal recovery decisions following a severe earthquake based on multiple objectives for an modeled Electrical Power Network of a testbed community coarsely modeled after Gilroy, California, United States.
Revised:  The methodology can be implemented efficiently to identify near-optimal recovery decisions following a severe earthquake based on multiple objectives for an electrical power network of a testbed community coarsely modeled after Gilroy, California, United States.
Edit intent: fluency
Original:  The proposed optimization method supports risk-informed community decision-makers within chaotic post-hazard circumstances.
Revised:  The proposed optimization method supports risk-informed community decision makers within chaotic post-hazard circumstances.
Edit intent: fluency
Original:  In fact, stable matchings are popular matchings of minimum size, and a maximum size popular matching can be as large as twice the size of a(ny) stable matching of a given instance.
Revised:  In fact, stable matchings are popular matchings of minimum size, and a maximum size popular matching can be as large as twice the size of a(ny) stable matching in a given instance.
Edit intent: clarity
Original: We explore geometry of London's streets using computational mode of an excitable chemical system, Belousov-Zhanotinsky (BZ) medium.
Revised: We explore geometry of London's streets using computational mode of an excitable chemical system, Belousov-Zhabotinsky (BZ) medium.
Edit intent: fluency
Original:  This index makes sense for polytomic trees and it can be computed in time linear in the number of leaves.
Revised:  This index makes sense for multifurcating trees and it can be computed in time linear in the number of leaves.
Edit intent: clarity
Original:  We compute its maximum and minimum values for arbitrary and bifurcating trees, and we provide exact formulas for its expected value and variance for bifurcating trees under Ford's \alpha-model and Aldous' \beta-model and for arbitrary trees under the \alpha-\gamma-model.
Revised:  We determine its maximum and minimum values for arbitrary and bifurcating trees, and we provide exact formulas for its expected value and variance for bifurcating trees under Ford's \alpha-model and Aldous' \beta-model and for arbitrary trees under the \alpha-\gamma-model.
Edit intent: clarity
Original:  We compute its maximum and minimum values for arbitrary and bifurcating trees, and we provide exact formulas for its expected value and variance for bifurcating trees under Ford's \alpha-model and Aldous' \beta-model and for arbitrary trees under the \alpha-\gamma-model.
Revised:  We compute its maximum and minimum values for arbitrary and bifurcating trees, and we provide exact formulas for its expected value and variance on bifurcating trees under Ford's \alpha-model and Aldous' \beta-model and for arbitrary trees under the \alpha-\gamma-model.
Edit intent: clarity
Original:  We compute its maximum and minimum values for arbitrary and bifurcating trees, and we provide exact formulas for its expected value and variance for bifurcating trees under Ford's \alpha-model and Aldous' \beta-model and for arbitrary trees under the \alpha-\gamma-model.
Revised:  We compute its maximum and minimum values for arbitrary and bifurcating trees, and we provide exact formulas for its expected value and variance for bifurcating trees under Ford's \alpha-model and Aldous' \beta-model and on arbitrary trees under the \alpha-\gamma-model.
Edit intent: clarity
Original: Homophily --- our tendency to surround ourselves with others who share our perspectives and opinions about the world --- is both a part of human nature and an organizing principle underpinning many of our digital social networks.
Revised: Homophily -- our tendency to surround ourselves with others who share our perspectives and opinions about the world --- is both a part of human nature and an organizing principle underpinning many of our digital social networks.
Edit intent: fluency
Original: Homophily --- our tendency to surround ourselves with others who share our perspectives and opinions about the world --- is both a part of human nature and an organizing principle underpinning many of our digital social networks.
Revised: Homophily --- our tendency to surround ourselves with others who share our perspectives and opinions about the world -- is both a part of human nature and an organizing principle underpinning many of our digital social networks.
Edit intent: fluency
Original:  Connections to the pancake graph for the hyperoctahedral group are also drawn.
Revised:  Connections to the pancake graph of the hyperoctahedral group are also drawn.
Edit intent: fluency
Original:  We validate the hypothesis using voting record data of the social game shows  Survivor and Big Brother.
Revised:  Our focus is in the present study is to validate it on competitive networks arising from social game shows Survivor and Big Brother.
Edit intent: clarity
Original:  We validate the hypothesis using voting record data of the social game shows  Survivor and Big Brother.
Revised:  We validate the hypothesis using voting record data of the social game shows such as Survivor and Big Brother.
Edit intent: clarity
Original: Interactive analytics increasingly involves querying for quantiles over specific sub-populations and time windows of high cardinality datasets.
Revised: Interactive analytics increasingly involves querying for quantiles over sub-populations and time windows of high cardinality datasets.
Edit intent: clarity
Original: Interactive analytics increasingly involves querying for quantiles over specific sub-populations and time windows of high cardinality datasets.
Revised: Interactive analytics increasingly involves querying for quantiles over specific sub-populations of high cardinality datasets.
Edit intent: clarity
Original:  Data processing engines such as Druid and Spark use mergeable summaries to estimate quantiles on these large datasets , but summary merge times are a bottleneck during high-cardinality aggregation.
Revised:  Data processing engines such as Druid and Spark use mergeable summaries to estimate quantiles , but summary merge times are a bottleneck during high-cardinality aggregation.
Edit intent: clarity
Original:  Data processing engines such as Druid and Spark use mergeable summaries to estimate quantiles on these large datasets , but summary merge times are a bottleneck during high-cardinality aggregation.
Revised:  Data processing engines such as Druid and Spark use mergeable summaries to estimate quantiles on these large datasets , but summary merge times can be a bottleneck during high-cardinality aggregation.
Edit intent: clarity
Original:  Data processing engines such as Druid and Spark use mergeable summaries to estimate quantiles on these large datasets , but summary merge times are a bottleneck during high-cardinality aggregation.
Revised:  Data processing engines such as Druid and Spark use mergeable summaries to estimate quantiles on these large datasets , but summary merge times are a bottleneck during aggregation.
Edit intent: coherence
Original: Aims : The arrhythmogenic role of atrial complex morphology such as left atrial appendage (LAA) has not yet been clearly elucidated.
Revised: Background : The arrhythmogenic role of atrial complex morphology such as left atrial appendage (LAA) has not yet been clearly elucidated.
Edit intent: clarity
Original: Aims : The arrhythmogenic role of atrial complex morphology such as left atrial appendage (LAA) has not yet been clearly elucidated.
Revised: Aims : The arrhythmogenic role of atrial complex morphology has not yet been clearly elucidated.
Edit intent: clarity
Original:  Methods  : We simulated 2D-bumpy atrial model by varying the degree of bumpiness  and 3D-left atrial (LA) model of 14 patients with persistent AF.
Revised:  Methods : We simulated 2D-bumpy atrial model by varying the degree of bumpiness , and 3D-left atrial (LA) model of 14 patients with persistent AF.
Edit intent: fluency
Original:  Results: In 2D-bumpy model, APD dispersion increased with increasing surface bumpiness (p<0.001) , and wavebreak occurred spontaneously when the surface bumpiness was higher, showing phase transition-like behavior (p<0.001).
Revised:  Results: In 2D-bumpy model, APD dispersion increased (p<0.001) , and wavebreak occurred spontaneously when the surface bumpiness was higher, showing phase transition-like behavior (p<0.001).
Edit intent: clarity
Original:  Results: In 2D-bumpy model, APD dispersion increased with increasing surface bumpiness (p<0.001) , and wavebreak occurred spontaneously when the surface bumpiness was higher, showing phase transition-like behavior (p<0.001).
Revised:  Results: In 2D-bumpy model, APD dispersion increased with increasing surface bumpiness (p<0.001) and wavebreak occurred spontaneously when the surface bumpiness was higher, showing phase transition-like behavior (p<0.001).
Edit intent: fluency
Original:  Additionally, 2D-bumpy model with bumpiness gradient showed that spiral wave drifted in the direction of higher bumpiness, and phase singularity (PS) points were mostly located in areas with higher bumpiness.
Revised:  Bumpiness gradient 2D-model showed that spiral wave drifted in the direction of higher bumpiness, and phase singularity (PS) points were mostly located in areas with higher bumpiness.
Edit intent: coherence
Original:  Conclusion: The bumpy tissue geometry promotes APD dispersion, wavebreak, and spiral wave drift in in-silico human atrial tissue .
Revised:  Conclusion: The bumpy tissue geometry promotes APD dispersion, wavebreak, and spiral wave drift in in silico human atrial tissue .
Edit intent: fluency