wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We show that the catchbond behaviour of individual dynein motors manifests as a generic mechanism of generating spontaneous oscillations in such complexes. The results reported here elucidate the central role of catchbond mediated mechanism in fashioning a distinctive stability behaviour and oscillations in motor-microtubule complexes , such as mitotic spindles .
Revised:  We show that the catchbond behaviour of individual dynein motors manifests as a generic mechanism of generating spontaneous oscillations in motor-cytoskeletal filament complexes. We obtain the phase diagram to characterize how this novel catch bond mediated mechanism can give rise to bistability and sustained limit cycle oscillations and results in very distinctive stability behaviour and oscillations in motor-microtubule complexes , such as mitotic spindles .
Edit intent: meaning-changed
Original:  The results reported here elucidate the central role of catchbond mediated mechanism in fashioning a distinctive stability behaviour and oscillations in motor-microtubule complexes , such as mitotic spindles .
Revised:  The results reported here elucidate the central role of catchbond mediated mechanism in fashioning a distinctive stability behaviour , including bistable and non-linearly stabilised in motor-microtubule complexes , such as mitotic spindles .
Edit intent: meaning-changed
Original:  The results reported here elucidate the central role of catchbond mediated mechanism in fashioning a distinctive stability behaviour and oscillations in motor-microtubule complexes , such as mitotic spindles .
Revised:  The results reported here elucidate the central role of catchbond mediated mechanism in fashioning a distinctive stability behaviour and oscillations in motor-microtubule complexes in biologically relevant regimes. Hitherto, it was thought that the primary functional role of the biological catchbond was to improve surface adhesion of bacteria and cell when subjected to external forces or flow field. Instead our theoretical study shows that the imprint of this catch bond mediated physical mechanism would have ramifications for whole gamut of intracellular processes ranging from oscillations in mitotic spindle oscillations to activity in muscle fibres .
Edit intent: meaning-changed
Original:  The standard "compartment" models of the spread of disease through a population follow the law of mass action; that is, the rate of conversion of individuals from one compartment to another is directly proportional to the number of individuals in the former.
Revised:  The standard "compartment" models of the spread of disease through a population using compartmentalized differential equations, but do not incorporate population-level heterogeneity in disease susceptibility. We show that variation leads to the natural emergence of a power law in the force of infection (\beta I S^p), where the order p is a simple function of the rate of conversion of individuals from one compartment to another is directly proportional to the number of individuals in the former.
Edit intent: meaning-changed
Original:  that is, the rate of conversion of individuals from one compartment to another is directly proportional to the number of individuals in the former. The basic S-I-R model, which tracks susceptible, infectious, and recovered people, is functionally identical to autocatalytic reactions studied in chemical kinetics. However, if we account for heterogeneity in individuals' susceptibility to catch the disease, then the most susceptible individuals are likely to be removed from the pool early; this is analogous to evaporative cooling in chemical kinetics. Here, we use standard tools from statistical physics to account for this effect, which suggest that the rate of appearance of new cases should be proportional to thesquare of the number of susceptible people in the population, not the first power.
Revised:  that is, the square of the number of susceptible people in the population, not the first power.
Edit intent: clarity
Original:  Here, we use standard tools from statistical physics to account for this effect, which suggest that the rate of appearance of new cases should be proportional to thesquare of the number of susceptible people in the population, not the first power.
Revised:  Here, we use standard tools from statistical physics to account for this effect, which suggest that the rate of appearance of new cases should be proportional to the of the number of susceptible people in the population, not the first power.
Edit intent: coherence
Original:  Here, we use standard tools from statistical physics to account for this effect, which suggest that the rate of appearance of new cases should be proportional to thesquare of the number of susceptible people in the population, not the first power. We validate this finding with both Monte Carlo (stochastic) and many-compartment differential equation (deterministic) simulations. This leads to quite different predictions of the peak infection rateand final outbreak size, with the second-order model giving milder predictions of each.
Revised:  Here, we use standard tools from statistical physics to account for this effect, which suggest that the rate of appearance of new cases should be proportional to thesquare distribution shape. p is significantly greater than one for reasonable variances, suggesting that conventional epidemic models make extreme assumptions about the absence of variance in susceptibility. The power-law behavior fundamentally alters predictions of the peak infection rateand final outbreak size, with the second-order model giving milder predictions of each.
Edit intent: clarity
Original:  This leads to quite different predictions of the peak infection rateand final outbreak size, with the second-order model giving milder predictions of each. This has dramatic effects on the extrapolation of a disease's progression when the data collected comes from the early, exponential-like region, and may remove a systematic bias from such models .
Revised:  This leads to quite different predictions of the long-term infection rate, and suggests that first-order models that are parameterized in the exponential-like region, and may remove a systematic bias from such models .
Edit intent: clarity
Original:  We show that variation leads to the natural emergence of a power law in the force of infection (\beta I S^p), where the order p is a simple function of the distribution shape. p is significantly greater than one for reasonable variances, suggesting that conventional epidemic models make extreme assumptions about the absence of variance in susceptibility .
Revised:  We show that variation strongly influences the rate of infection, while the infection process simultaneously sculpts the susceptibility distribution. These joint dynamics influence the force of infection (\beta I S^p), where the order p is a simple function of the distribution shape. p is significantly greater than one for reasonable variances, suggesting that conventional epidemic models make extreme assumptions about the absence of variance in susceptibility .
Edit intent: meaning-changed
Original:  We show that variation leads to the natural emergence of a power law in the force of infection (\beta I S^p), where the order p is a simple function of the distribution shape. p is significantly greater than one for reasonable variances, suggesting that conventional epidemic models make extreme assumptions about the absence of variance in susceptibility .
Revised:  We show that variation leads to the natural emergence of a power law in the force of infection and are, in turn, influenced by the shape of the initial variability. Intriguingly, we find that certain susceptibility distributions (the exponential and the gamma) are unchanged through the course of the outbreak, and lead naturally to power-law behavior in the force of infection; other distributions often tend towards these "eigen-distributions" through the process of contagion .
Edit intent: meaning-changed
Original:  The power-law behavior fundamentally alters predictions of the long-term infection rate, and suggests that first-order  models that are parameterized in the exponential-like phase may systematically and significantly over-estimate the final severity of the outbreak.
Revised:  The power-law behavior fundamentally alters predictions of the long-term infection rate, and suggests that first-order epidemic models that are parameterized in the exponential-like phase may systematically and significantly over-estimate the final severity of the outbreak.
Edit intent: clarity
Original:  To improve this, we propose CONE-Align, which matches nodes based on embeddings that model intra-network proximity and are aligned to be comparable across networks . Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains up to 49 \% greater accuracy than the state-of-the-art graph alignment algorithms .
Revised:  To improve this, we propose CONE-Align, which models intra-network proximity and are aligned to be comparable across networks . Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains up to 49 \% greater accuracy than the state-of-the-art graph alignment algorithms .
Edit intent: clarity
Original:  To improve this, we propose CONE-Align, which matches nodes based on embeddings that model intra-network proximity and are aligned to be comparable across networks . Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains up to 49 \% greater accuracy than the state-of-the-art graph alignment algorithms .
Revised:  To improve this, we propose CONE-Align, which matches nodes based on embeddings that model intra-network proximity with node embeddings and uses them to match nodes across networks after aligning the embedding subspaces . Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains up to 49 \% greater accuracy than the state-of-the-art graph alignment algorithms .
Edit intent: meaning-changed
Original:  To improve this, we propose CONE-Align, which matches nodes based on embeddings that model intra-network proximity and are aligned to be comparable across networks . Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains up to 49 \% greater accuracy than the state-of-the-art graph alignment algorithms .
Revised:  To improve this, we propose CONE-Align, which matches nodes based on embeddings that model intra-network proximity and are aligned to be comparable across networks . Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains 19.25 \% greater accuracy than the state-of-the-art graph alignment algorithms .
Edit intent: meaning-changed
Original:  To improve this, we propose CONE-Align, which matches nodes based on embeddings that model intra-network proximity and are aligned to be comparable across networks . Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains up to 49 \% greater accuracy than the state-of-the-art graph alignment algorithms .
Revised:  To improve this, we propose CONE-Align, which matches nodes based on embeddings that model intra-network proximity and are aligned to be comparable across networks . Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains up to 49 \% greater accuracy on average than the best-performing state-of-the-art graph alignment algorithms .
Edit intent: clarity
Original:  To improve this, we propose CONE-Align, which matches nodes based on embeddings that model intra-network proximity and are aligned to be comparable across networks . Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains up to 49 \% greater accuracy than the state-of-the-art graph alignment algorithms .
Revised:  To improve this, we propose CONE-Align, which matches nodes based on embeddings that model intra-network proximity and are aligned to be comparable across networks . Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains up to 49 \% greater accuracy than the state-of-the-art graph alignment algorithm in highly noisy settings .
Edit intent: meaning-changed
Original:  We derive an integral equation for the distribution of the waiting time for an individual to get infected. In a special case the integral equation is equivalent to the Bernoulli differential equation and implies the classic SIR model under an appropriate scaling  .
Revised:  We derive an integral equation for the distribution of the time before infection can be expressed in terms of the solution of an integral equation, special cases of which are equivalent to the Bernoulli differential equation and implies the classic SIR model under an appropriate scaling .
Edit intent: meaning-changed
Original: We introduce a graph clustering algorithm that generalizes k-means to graphs. Our method utilizes PageRank measures on graphs to quickly and robustly compute centrality of nodes in a given graph.
Revised: We utilize the PageRank vector to generalize the k-means clustering algorithm to directed and undirected graphs. We demonstrate that PageRank and other centrality measures can be used in our setting to robustly compute centrality of nodes in a given graph.
Edit intent: meaning-changed
Original:  A choice p\in X is called a \beta-plurality point for V, if for any other choice q\in X it holds that  \{v\in V\mid \beta\cdot d(p,v)\le d(q,v)\}  \ge|V|{2}.
Revised:  A choice p\in X is called a \beta-plurality point for V, if for any other choice q\in X it holds that | \{v\in V\mid \beta\cdot d(p,v)\le d(q,v)\} \ge|V|{2}.
Edit intent: fluency
Original:  A choice p\in X is called a \beta-plurality point for V, if for any other choice q\in X it holds that  \{v\in V\mid \beta\cdot d(p,v)\le d(q,v)\}  \ge|V|{2}.
Revised:  A choice p\in X is called a \beta-plurality point for V, if for any other choice q\in X it holds that \{v\in V\mid \beta\cdot d(p,v)\le d(q,v)\} | \ge|V|{2}.
Edit intent: coherence
Original:  In other words, at least half of the voters "prefer" p over q, when an extra factor of \beta is taken in favor of p. Define \beta^*_{(X,d) = \sup\{\beta\mid \mbox{every finite multiset V in X admits a \beta-plurality point\}.
Revised:  In other words, at least half of the voters "prefer" p over q, when an extra factor of \beta is taken in favor of p. For \beta = \sup\{\beta\mid \mbox{every finite multiset V in X admits a \beta-plurality point\}.
Edit intent: meaning-changed
Original:  In other words, at least half of the voters "prefer" p over q, when an extra factor of \beta is taken in favor of p. Define \beta^*_{(X,d) = \sup\{\beta\mid \mbox{every finite multiset V in X admits a \beta-plurality point\}.
Revised:  In other words, at least half of the voters "prefer" p over q, when an extra factor of \beta is taken in favor of p. Define \beta^*_{(X,d) = \}.
Edit intent: meaning-changed
Original:  }  Aronov, de Berg, Gudmundsson, and Horton [SoCG 2020] ,=\sup\{\beta\mid every finite multiset V in X admits a \beta-plurality point\}.
Revised:  } 1, this is equivalent to Condorcet winner, which rarely exists. The concept of \beta-plurality was suggested by Aronov, de Berg, Gudmundsson, and Horton [SoCG 2020] ,=\sup\{\beta\mid every finite multiset V in X admits a \beta-plurality point\}.
Edit intent: meaning-changed
Original:  }  Aronov, de Berg, Gudmundsson, and Horton [SoCG 2020] ,=\sup\{\beta\mid every finite multiset V in X admits a \beta-plurality point\}.
Revised:  } Aronov, de Berg, Gudmundsson, and Horton [SoCG 2020] as a relaxation of the Condorcet criterion. Let \beta^*_{(X,d)=\sup\{\beta\mid every finite multiset V in X admits a \beta-plurality point\}.
Edit intent: meaning-changed
Original:  In addition, we prove that for every metric space (X,d) it holds that \sqrt{2}-1\le\beta^*_{(X,d)}, and show that there exist a metric space for which \beta^*_{(X,d)}\le \frac12.
Revised:  In addition, we prove that for every metric space (X,d) it holds that \sqrt{2}-1\le\beta^*_{(X,d)}, and show that there exists a metric space for which \beta^*_{(X,d)}\le \frac12.
Edit intent: fluency
Original:  For simple graphs, we demonstrate that learning algorithms defined onLE s tie with their performance on the original graphs, implying that no loss of information occurs in the expansion . For hypergraphs, we show that learning over the new representation leads to algorithms that beat all prior state-of-the-art hypergraph learning baselines .
Revised:  For simple graphs, we demonstrate that learning algorithms defined onLE We evaluate the proposed line expansion on five hypergraph datasets, the results show that our method beats SOTA baselines by a significant margin .
Edit intent: meaning-changed
Original:  The quantiles weighting structure is parsimoniously parameterized by means of a Beta  function whose coefficients are optimized by minimizing a joint VaR and ES loss function of the Fissler-Ziegel class.
Revised:  The quantiles weighting structure is parsimoniously parameterized by means of a Beta weight function whose coefficients are optimized by minimizing a joint VaR and ES loss function of the Fissler-Ziegel class.
Edit intent: meaning-changed
Original:  The proposed models are applied to 7 stock market indices and their forecasting performances are compared to those of a range of parametric, non-parametric and semi-parametric models, including GARCH, Conditional AutoRegressive Expectile (CARE , Taylor 2008 ), joint VaR and ES quantile regression models (Taylor, 2019) and simple average of quantiles.
Revised:  The proposed models are applied to 7 stock market indices and their forecasting performances are compared to those of a range of parametric, non-parametric and semi-parametric models, including GARCH, Conditional AutoRegressive Expectile (CARE ), joint VaR and ES quantile regression models (Taylor, 2019) and simple average of quantiles.
Edit intent: coherence
Original:  The proposed models are applied to 7 stock market indices and their forecasting performances are compared to those of a range of parametric, non-parametric and semi-parametric models, including GARCH, Conditional AutoRegressive Expectile (CARE , Taylor 2008 ), joint VaR and ES quantile regression models (Taylor, 2019) and simple average of quantiles.
Revised:  The proposed models are applied to 7 stock market indices and their forecasting performances are compared to those of a range of parametric, non-parametric and semi-parametric models, including GARCH, Conditional AutoRegressive Expectile (CARE , Taylor 2008 ), joint VaR and ES quantile regression models and simple average of quantiles.
Edit intent: coherence
Original:  These algorithms all solve the challenges in fMRI by resorting to the same solution : they start from making clear statements of assumptions about neural data and existing domain knowledge, incorporating those assumptions and domain knowledge into probabilistic graphical models, and using those models to estimate properties of interest or latent structures in the data.
Revised:  These algorithms all tackle the challenges in fMRI by resorting to the same solution : they start from making clear statements of assumptions about neural data and existing domain knowledge, incorporating those assumptions and domain knowledge into probabilistic graphical models, and using those models to estimate properties of interest or latent structures in the data.
Edit intent: clarity
Original:  These algorithms all solve the challenges in fMRI by resorting to the same solution : they start from making clear statements of assumptions about neural data and existing domain knowledge, incorporating those assumptions and domain knowledge into probabilistic graphical models, and using those models to estimate properties of interest or latent structures in the data.
Revised:  These algorithms all solve the challenges in fMRI similarly : they start from making clear statements of assumptions about neural data and existing domain knowledge, incorporating those assumptions and domain knowledge into probabilistic graphical models, and using those models to estimate properties of interest or latent structures in the data.
Edit intent: clarity
Original:  These algorithms all solve the challenges in fMRI by resorting to the same solution : they start from making clear statements of assumptions about neural data and existing domain knowledge, incorporating those assumptions and domain knowledge into probabilistic graphical models, and using those models to estimate properties of interest or latent structures in the data.
Revised:  These algorithms all solve the challenges in fMRI by resorting to the same solution : they start by making clear statements of assumptions about neural data and existing domain knowledge, incorporating those assumptions and domain knowledge into probabilistic graphical models, and using those models to estimate properties of interest or latent structures in the data.
Edit intent: fluency
Original:  Previous works typically focus on understanding the temporal dependency of stock price movement based on the history of individual stock movement, but they do not take the complex relationships among involved stocks into consideration. However it is well known that an individual stock price is correlated with prices of other stocks .
Revised:  Previous works typically focus on understanding the temporal dependency of stock price movement based on the history of individual stock movement, but they do not take the complex relationships among involved stocks . However, it is well known that an individual stock price is correlated with prices of other stocks .
Edit intent: coherence
Original:  However it is well known that an individual stock price is correlated with prices of other stocks . To address that , we propose a deep learning-based framework, which utilizes recurrent neural network (RNN) and graph convolutional network (GCN)  to predict stock movement.
Revised:  However it is well known that an individual stock price is correlated with prices of other stocks in complex ways. To take the cross effect into consideration , we propose a deep learning-based framework, which utilizes recurrent neural network (RNN) and graph convolutional network (GCN) to predict stock movement.
Edit intent: meaning-changed
Original:  To address that , we propose a deep learning-based framework, which utilizes recurrent neural network (RNN) and graph convolutional network (GCN)  to predict stock movement.
Revised:  To address that , we propose a deep learning-based framework, which utilizes recurrent neural network (RNN) and graph convolutional network (GCN) and gated recurrent units (GRU) to predict stock movement.
Edit intent: meaning-changed
Original:  Specifically, we first use RNN to model the temporal dependency of each related stock' price movement based on their own information of the past time slices, then we employ GCN to model the influence from involved stock based on three novel graphs which represent the shareholder relationship, industry relationship and concept relationship among stocks based on investment decisions . Experiments on two stock indexes in China market show that our model outperforms other baselines.
Revised:  Specifically, we first encode multiple relationships among stocks into graphs based on financial domain knowledge and utilize GCN to extract the cross effect based on these pre-defined graphs. To further get rid of prior knowledge, we explore an adaptive relationship learned by data automatically. The cross-correlation features produced by GCN is concatenated with historical records and fed into GRU to model the influence from involved stock based on three novel graphs which represent the shareholder relationship, industry relationship and concept relationship among stocks based on investment decisions . Experiments on two stock indexes in China market show that our model outperforms other baselines.
Edit intent: meaning-changed