wanyu/IteraTeR_v2 huggingface dataset:
Original:  For a suitable parameter regime in which parasite reproduction is faster than reinfection and reinfection is faster than host replacement, we show that in the limit of large host and parasite populations the system is driven by a dynamical system with a globally stable equilibrium, guaranteeing that both types are maintained in the parasite population for a long time on the host time scale.
Revised:  For a suitable parameter regime we show that in the limit of large host and parasite populations the system is driven by a dynamical system with a globally stable equilibrium, guaranteeing that both types are maintained in the parasite population for a long time on the host time scale.
Edit intent: clarity
Original:  In this paper, we extend the notion of CFCC to a group of vertices in a weighted graph . For a graph with n vertices and m edges, the CFCC C(S) for a vertex group S is equal to the ratio of n to the sum of effective resistances from S to all other vertices.
Revised:  In this paper, we extend this notion to a group of vertices in a weighted graph . For a graph with n vertices and m edges, the CFCC C(S) for a vertex group S is equal to the ratio of n to the sum of effective resistances from S to all other vertices.
Edit intent: clarity
Original:  We then study the problem of finding a group S ^* of k vertices , so that the CFCC C( S^* )is maximized.
Revised:  We then study the problem of finding a group S ^* of k vertices to maximize its CFCC C( S^* )is maximized.
Edit intent: clarity
Original:  We then study the problem of finding a group S ^* of k vertices , so that the CFCC C( S^* )is maximized.
Revised:  We then study the problem of finding a group S ^* of k vertices , so that the CFCC C( )is maximized.
Edit intent: clarity
Original:  The first is a deterministic algorithm with an approximation factor %DIFDELCMD < \left(%%%  1- \frac{1{e}}%DIFDELCMD < \right) %%% and O(n^3){k-1}\cdot1{e}) and cubic } running time;
Revised:  The first is a deterministic algorithm with an approximation factor %DIFDELCMD < \left(%%% ( 1- \frac{1{e}}%DIFDELCMD < \right) %%% and O(n^3){k-1}\cdot1{e}) and cubic } running time;
Edit intent: fluency
Original:  The first is a deterministic algorithm with an approximation factor %DIFDELCMD < \left(%%%  1- \frac{1{e}}%DIFDELCMD < \right) %%% and O(n^3){k-1}\cdot1{e}) and cubic } running time;
Revised:  The first is a deterministic algorithm with an approximation factor %DIFDELCMD < \left(%%% 1- {e}}%DIFDELCMD < \right) %%% and O(n^3){k-1}\cdot1{e}) and cubic } running time;
Edit intent: fluency
Original:  while the second is a randomized algorithm with a %DIFDELCMD < \left(%%%  1- {k-1}\cdot} \frac{1}{e}-\epsilon %DIFDELCMD < \right)%%%  -approximation and %DIFDELCMD < \widetilde{O} %%% (k m\epsilon^{-2 running time for any small \epsilon > 0, where the%DIFDELCMD < \widetilde{O} %%% (\cdot) notation hides the%DIFDELCMD < {\rm %%% poly \log factors.
Revised:  while the second is a randomized algorithm with a %DIFDELCMD < \left(%%% ( 1- {k-1}\cdot} \frac{1}{e}-\epsilon %DIFDELCMD < \right)%%% -approximation and %DIFDELCMD < \widetilde{O} %%% (k m\epsilon^{-2 running time for any small \epsilon > 0, where the%DIFDELCMD < \widetilde{O} %%% (\cdot) notation hides the%DIFDELCMD < {\rm %%% poly \log factors.
Edit intent: fluency
Original:  while the second is a randomized algorithm with a %DIFDELCMD < \left(%%%  1- {k-1}\cdot} \frac{1}{e}-\epsilon %DIFDELCMD < \right)%%%  -approximation and %DIFDELCMD < \widetilde{O} %%% (k m\epsilon^{-2 running time for any small \epsilon > 0, where the%DIFDELCMD < \widetilde{O} %%% (\cdot) notation hides the%DIFDELCMD < {\rm %%% poly \log factors.
Revised:  while the second is a randomized algorithm with a %DIFDELCMD < \left(%%% 1- \frac{k{k-1}\cdot} \frac{1}{e}-\epsilon %DIFDELCMD < \right)%%% -approximation and %DIFDELCMD < \widetilde{O} %%% (k m\epsilon^{-2 running time for any small \epsilon > 0, where the%DIFDELCMD < \widetilde{O} %%% (\cdot) notation hides the%DIFDELCMD < {\rm %%% poly \log factors.
Edit intent: fluency
Original:  while the second is a randomized algorithm with a %DIFDELCMD < \left(%%%  1- {k-1}\cdot} \frac{1}{e}-\epsilon %DIFDELCMD < \right)%%%  -approximation and %DIFDELCMD < \widetilde{O} %%% (k m\epsilon^{-2 running time for any small \epsilon > 0, where the%DIFDELCMD < \widetilde{O} %%% (\cdot) notation hides the%DIFDELCMD < {\rm %%% poly \log factors.
Revised:  while the second is a randomized algorithm with a %DIFDELCMD < \left(%%% 1- {k-1}\cdot} \frac{1}{e}-\epsilon %DIFDELCMD < \right)%%% ) -approximation and %DIFDELCMD < \widetilde{O} %%% (k m\epsilon^{-2 running time for any small \epsilon > 0, where the%DIFDELCMD < \widetilde{O} %%% (\cdot) notation hides the%DIFDELCMD < {\rm %%% poly \log factors.
Edit intent: fluency
Original:  while the second is a randomized algorithm with a %DIFDELCMD < \left(%%%  1- {k-1}\cdot} \frac{1}{e}-\epsilon %DIFDELCMD < \right)%%%  -approximation and %DIFDELCMD < \widetilde{O} %%% (k m\epsilon^{-2 running time for any small \epsilon > 0, where the%DIFDELCMD < \widetilde{O} %%% (\cdot) notation hides the%DIFDELCMD < {\rm %%% poly \log factors.
Revised:  while the second is a randomized algorithm with a %DIFDELCMD < \left(%%% 1- {k-1}\cdot} \frac{1}{e}-\epsilon %DIFDELCMD < \right)%%% -approximation and %DIFDELCMD < \widetilde{O} %%% (k m\epsilon^{-2 running time for any \epsilon > 0, where the%DIFDELCMD < \widetilde{O} %%% (\cdot) notation hides the%DIFDELCMD < {\rm %%% poly \log factors.
Edit intent: clarity
Original:  while the second is a randomized algorithm with a %DIFDELCMD < \left(%%%  1- {k-1}\cdot} \frac{1}{e}-\epsilon %DIFDELCMD < \right)%%%  -approximation and %DIFDELCMD < \widetilde{O} %%% (k m\epsilon^{-2 running time for any small \epsilon > 0, where the%DIFDELCMD < \widetilde{O} %%% (\cdot) notation hides the%DIFDELCMD < {\rm %%% poly \log factors.
Revised:  while the second is a randomized algorithm with a %DIFDELCMD < \left(%%% 1- {k-1}\cdot} \frac{1}{e}-\epsilon %DIFDELCMD < \right)%%% -approximation and %DIFDELCMD < \widetilde{O} %%% (k m\epsilon^{-2 running time for any small \epsilon > %DIFDELCMD < \widetilde{O} %%% (\cdot) notation hides the%DIFDELCMD < {\rm %%% poly \log factors.
Edit intent: coherence
Original:  while the second is a randomized algorithm with a %DIFDELCMD < \left(%%%  1- {k-1}\cdot} \frac{1}{e}-\epsilon %DIFDELCMD < \right)%%%  -approximation and %DIFDELCMD < \widetilde{O} %%% (k m\epsilon^{-2 running time for any small \epsilon > 0, where the%DIFDELCMD < \widetilde{O} %%% (\cdot) notation hides the%DIFDELCMD < {\rm %%% poly \log factors.
Revised:  while the second is a randomized algorithm with a %DIFDELCMD < \left(%%% 1- {k-1}\cdot} \frac{1}{e}-\epsilon %DIFDELCMD < \right)%%% -approximation and %DIFDELCMD < \widetilde{O} %%% (k m\epsilon^{-2 running time for any small \epsilon > 0, where the%DIFDELCMD < \widetilde{O} %%% (\cdot) notation hides the%DIFDELCMD < {\rm %%% \log factors.
Edit intent: fluency
Original:  Extensive experiments on models and real networks demonstrate that our algorithms are effective and efficient, with the second algorithm being scalable to massive networks with more than a million vertices.
Revised:  Extensive experiments on model and real networks demonstrate that our algorithms are effective and efficient, with the second algorithm being scalable to massive networks with more than a million vertices.
Edit intent: fluency
Original: Digital data archives play essential roles in knowledge infrastructures by mediating access to data within and between communities.
Revised: Digital archives are the preferred means for open access to research data. They play essential roles in knowledge infrastructures by mediating access to data within and between communities.
Edit intent: clarity
Original:  Most existing works on SG assume that the underlying problem is unconstrained or has an easy-to-project constraint set.
Revised:  Most existing works on SGMs assume that the underlying problem is unconstrained or has an easy-to-project constraint set.
Edit intent: clarity
Original:  Within every iteration, it inquires a stochastic subgradient of the objective,  a subgradient and  function value of one randomly sampled constraint function , and function value of another sampled constraint function .
Revised:  Within every iteration, it inquires a stochastic subgradient of the objective, and a subgradient and function value of one randomly sampled constraint function , and function value of another sampled constraint function .
Edit intent: fluency
Original:  Within every iteration, it inquires a stochastic subgradient of the objective,  a subgradient and  function value of one randomly sampled constraint function , and function value of another sampled constraint function .
Revised:  Within every iteration, it inquires a stochastic subgradient of the objective, a subgradient and the function value of one randomly sampled constraint function , and function value of another sampled constraint function .
Edit intent: fluency
Original:  Within every iteration, it inquires a stochastic subgradient of the objective,  a subgradient and  function value of one randomly sampled constraint function , and function value of another sampled constraint function .
Revised:  Within every iteration, it inquires a stochastic subgradient of the objective, a subgradient and function value of one randomly sampled constraint function .
Edit intent: coherence
Original:  We establish its convergence rate for convex and also strongly convex problems .
Revised:  We establish its convergence rate for convex problems and also problems with strongly convex objective .
Edit intent: clarity
Original: Most epidemic spreading models assume memoryless agents and independent transmissions along different infection channels.
Revised: Albeit epidemic models have evolved into powerful predictive tools, most assume memoryless agents and independent transmissions along different infection channels.
Edit intent: clarity
Original: This paper focuses on fully understanding the figure skating sport videos.
Revised: This paper targets at learning to score the figure skating sport videos.
Edit intent: clarity
Original:  On average, the length of each video is 2 minute and 50 seconds.
Revised:  On average, the length of each video is 2 minutes and 50 seconds.
Edit intent: fluency
Original:  Each video is annotated by three scores from nine different referees, i.e., Total Element Score(TES) , Total Program Component Score (PCS) , and Total Deductions(DED).
Revised:  Each video is annotated by three scores from nine different referees, i.e., Total Element Score(TES) and Total Program Component Score (PCS) , and Total Deductions(DED).
Edit intent: coherence
Original:  As to the 5-boson sampling  , the running times of the Store-reuse algorithm for a related permanent on ENIAC and TRADIC are each lower than the that of the sampling operation on the multiphoton boson sampling machine (shortly MPBSM), and thus MPBSM does not beat the early classical computers .
Revised:  As to the 5-boson sampling task , the running times of the Store-reuse algorithm for a related permanent on ENIAC and TRADIC are each lower than the that of the sampling operation on the multiphoton boson sampling machine (shortly MPBSM), and thus MPBSM does not beat the early classical computers .
Edit intent: clarity
Original:  As to the 5-boson sampling  , the running times of the Store-reuse algorithm for a related permanent on ENIAC and TRADIC are each lower than the that of the sampling operation on the multiphoton boson sampling machine (shortly MPBSM), and thus MPBSM does not beat the early classical computers .
Revised:  As to the 5-boson sampling , the running time of the Store-zechin algorithm computing the correspondent permanent on ENIAC and TRADIC are each lower than the that of the sampling operation on the multiphoton boson sampling machine (shortly MPBSM), and thus MPBSM does not beat the early classical computers .
Edit intent: clarity
Original:  As to the 5-boson sampling  , the running times of the Store-reuse algorithm for a related permanent on ENIAC and TRADIC are each lower than the that of the sampling operation on the multiphoton boson sampling machine (shortly MPBSM), and thus MPBSM does not beat the early classical computers .
Revised:  As to the 5-boson sampling , the running times of the Store-reuse algorithm for a related permanent on ENIAC as well as TRADIC is lower than that of the sampling operation on the multiphoton boson sampling machine (shortly MPBSM), and thus MPBSM does not beat the early classical computers .
Edit intent: clarity
Original:  As to the 5-boson sampling  , the running times of the Store-reuse algorithm for a related permanent on ENIAC and TRADIC are each lower than the that of the sampling operation on the multiphoton boson sampling machine (shortly MPBSM), and thus MPBSM does not beat the early classical computers .
Revised:  As to the 5-boson sampling , the running times of the Store-reuse algorithm for a related permanent on ENIAC and TRADIC are each lower than the that of the sampling operation on a multiphoton boson sampling machine (shortly MPBSM), and thus MPBSM does not beat the early classical computers .
Edit intent: fluency
Original:  However, thisdoes not mean that a quantum boson sampling machine will be beaten by a classical computer when n gets large enough .
Revised:  However, thisdoes not mean that a quantum boson sampling machine will beat a classical computer when n gets large enough .
Edit intent: clarity
Original:  However, thisdoes not mean that a quantum boson sampling machine will be beaten by a classical computer when n gets large enough .
Revised:  However, thisdoes not mean that a quantum boson sampling machine will be beaten by a classical computer ) .
Edit intent: coherence
Original: Let A_0,  \ldots, A_n be m \times m symmetric matrices with entries in Q, and let A(x) be the linear pencil A_0 + x_1 A_1 + \cdots + x_n A_n , where x=(x_1,\ldots,x_n) are unknowns.
Revised: Given symmetric matrices A_0, \ldots, A_n be m \times m symmetric matrices with entries in Q, and let A(x) be the linear pencil A_0 + x_1 A_1 + \cdots + x_n A_n , where x=(x_1,\ldots,x_n) are unknowns.
Edit intent: fluency
Original: Let A_0,  \ldots, A_n be m \times m symmetric matrices with entries in Q, and let A(x) be the linear pencil A_0 + x_1 A_1 + \cdots + x_n A_n , where x=(x_1,\ldots,x_n) are unknowns.
Revised: Let A_0, A_1, \ldots, A_n be m \times m symmetric matrices with entries in Q, and let A(x) be the linear pencil A_0 + x_1 A_1 + \cdots + x_n A_n , where x=(x_1,\ldots,x_n) are unknowns.
Edit intent: fluency
Original:  The minimization of linear functions over spectrahedra is called semidefinite programming (SDP) . Such problems appear frequently in control theory and real algebra, especially in the context of nonnegativity certificates for multivariate polynomials based on sums of squares.
Revised:  The minimization of linear functions over spectrahedra is called semidefinite programming . Such problems appear frequently in control theory and real algebra, especially in the context of nonnegativity certificates for multivariate polynomials based on sums of squares.
Edit intent: clarity
Original:  Numerical software for solving SDP are mostly based on the interior point method, assuming some non-degeneracy properties such as the existence of interior points in the admissible set .
Revised:  Numerical software for semidefinite programming are mostly based on the interior point method, assuming some non-degeneracy properties such as the existence of interior points in the admissible set .
Edit intent: clarity
Original:  Numerical software for solving SDP are mostly based on the interior point method, assuming some non-degeneracy properties such as the existence of interior points in the admissible set .
Revised:  Numerical software for solving SDP are mostly based on interior point methods, assuming non-degeneracy properties such as the existence of interior points in the admissible set .
Edit intent: clarity
Original:  Numerical software for solving SDP are mostly based on the interior point method, assuming some non-degeneracy properties such as the existence of interior points in the admissible set .
Revised:  Numerical software for solving SDP are mostly based on the interior point method, assuming some non-degeneracy properties such as the existence of an interior point in the spectrahedron .
Edit intent: clarity
Original:  Because of the exactness of the output, it cannot compete with numerical routines in practice but we prove that solving such problems can be done in polynomial time if either n or m is fixed.
Revised:  Because of the exactness of the output, it cannot compete with numerical routines in practice . However, we prove that solving such problems can be done in polynomial time if either n or m is fixed.
Edit intent: coherence
Original:  Our protein-specific model requires experimental data only regarding the protein of interest , and performs well even with few experimental measurements.
Revised:  Our protein-specific model requires experimental data only regarding the protein of interest and performs well even with few experimental measurements.
Edit intent: fluency
Original:  Although these neonatal transfer networks are important  both economically and in terms of infant morbidity and mortality, the national-level pattern of neonatal transfers is largely unknown.
Revised:  Although these neonatal transfer networks are important , both economically and in terms of infant morbidity and mortality, the national-level pattern of neonatal transfers is largely unknown.
Edit intent: fluency
Original:  Although these neonatal transfer networks are important  both economically and in terms of infant morbidity and mortality, the national-level pattern of neonatal transfers is largely unknown.
Revised:  Although these neonatal transfer networks are important both economically and for infant morbidity and mortality, the national-level pattern of neonatal transfers is largely unknown.
Edit intent: clarity
Original:  Using data  on 44,753 infants , 2,122 hospitals, and 9,722 inter-hospital infant transfers from 2015, we performed the largest analysis to date on the inter-hospital transfer network for VLBW infants in the U.S. We found that transfers are organized around regional communities, that, despite being largely within state boundaries, most  often contain at least two hospitals in different states.
Revised:  Using data on 44,753 infants , 2,122 hospitals, and 9,722 inter-hospital infant transfers from 2015, we performed the largest analysis to date on the inter-hospital transfer network for VLBW infants in the U.S. We find that transfers are organized around regional communities, that, despite being largely within state boundaries, most often contain at least two hospitals in different states.
Edit intent: fluency
Original:  Using data  on 44,753 infants , 2,122 hospitals, and 9,722 inter-hospital infant transfers from 2015, we performed the largest analysis to date on the inter-hospital transfer network for VLBW infants in the U.S. We found that transfers are organized around regional communities, that, despite being largely within state boundaries, most  often contain at least two hospitals in different states.
Revised:  Using data on 44,753 infants , 2,122 hospitals, and 9,722 inter-hospital infant transfers from 2015, we performed the largest analysis to date on the inter-hospital transfer network for VLBW infants in the U.S. We found that transfers are organized around regional communities, but that despite being largely within state boundaries, most often contain at least two hospitals in different states.
Edit intent: coherence
Original:  Using data  on 44,753 infants , 2,122 hospitals, and 9,722 inter-hospital infant transfers from 2015, we performed the largest analysis to date on the inter-hospital transfer network for VLBW infants in the U.S. We found that transfers are organized around regional communities, that, despite being largely within state boundaries, most  often contain at least two hospitals in different states.
Revised:  Using data on 44,753 infants , 2,122 hospitals, and 9,722 inter-hospital infant transfers from 2015, we performed the largest analysis to date on the inter-hospital transfer network for VLBW infants in the U.S. We found that transfers are organized around regional communities, that, despite being largely within state boundaries, most communities often contain at least two hospitals in different states.
Edit intent: fluency
Original:  Our results have a practical implication of characterizing neonatal care in the U.S. and may broadly apply to the role of hierarchical forces in organizing complex adaptive systems.
Revised:  Our results have a practical implication of characterizing neonatal care in the U.S. , and may apply more broadly to the role of hierarchical forces in organizing complex adaptive systems.
Edit intent: clarity
Original:  Our results have a practical implication of characterizing neonatal care in the U.S. and may broadly apply to the role of hierarchical forces in organizing complex adaptive systems.
Revised:  Our results have a practical implication of characterizing neonatal care in the U.S. and may broadly apply to the role of centralizing forces in organizing complex adaptive systems.
Edit intent: clarity
Original:  To classify the structural variation in transfer pattern amongst these communities, we applied a spectral measure for regionalization and found a positive correlation between a community's degree of regionalization and their infant transfer rate, which was not utilized in detecting communities.
Revised:  To classify the structural variation in transfer pattern amongst these communities, we applied a spectral measure for regionalization and found an association between a community's degree of regionalization and their infant transfer rate, which was not utilized in detecting communities.
Edit intent: clarity
Original:  We also demonstrate that the established measures of network centrality and hierarchy, e.g., the community-wide entropy in PageRank or Betweenness centrality and number of distinct `layers' within a community, correlate weakly with our regionalization index and were not significantly associated with metrics on infant transfer rate.
Revised:  We also demonstrate that the established measures of network centrality and hierarchy, e.g., the community-wide entropy in PageRank or betweenness centrality and number of distinct `layers' within a community, correlate weakly with our regionalization index and were not significantly associated with metrics on infant transfer rate.
Edit intent: fluency
Original: The performance of existing coded caching schemes is sensitive to the worst channel quality, a problem which is exacerbated when communicating over fading channels.
Revised: The performance of existing coded caching schemes is sensitive to the worst channel quality, a problem which is exacerbated when communicating over fading channels.
Edit intent: fluency
Original:  In this paper, we address this limitation in the following manner: in short-term , we allow transmissions to subsets of users with good channel quality, avoiding users with fades, while in long-term we ensure fairness among users.
Revised:  In this paper, we address this limitation in the following manner: in short-term , we allow transmissions to subsets of users with good channel quality, avoiding users with fades, while in long-term we ensure fairness among users.
Edit intent: fluency
Original:  Our online scheme combines  the classical decentralized coded caching scheme maddah2013decentralized with ( i ) joint scheduling and power control for the fading broadcast channel, as well as ( ii ) congestion control for ensuring the optimal long-term average performance.
Revised:  Our online scheme combines (i) the classical decentralized coded caching scheme maddah2013decentralized with ( i ) joint scheduling and power control for the fading broadcast channel, as well as ( ii ) congestion control for ensuring the optimal long-term average performance.
Edit intent: fluency