wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, we clarify the relationship between this non-trivial scaling and the structure of the network by applying mean-field approximation of  diffusion in a complex network to a money-transport model , which has been numerically shown to reproduce this empirical scaling.
Revised:  In a previous study using numerical simulations, we found that this scaling can be explained by both the money-transport model, where a firm (i.e. customer) distributes money to its out-edges (suppliers) in proportion to the in-degree of destinations, and by the correlations among the Japanese inter-firm trading network. However, in this previous study, we could not specifically identify what types of structure properties (or correlations) of the network determine the 1.3 exponent. In the present study, we more clearly elucidate the relationship between this non-trivial scaling and the structure of the network by applying mean-field approximation of diffusion in a complex network to a money-transport model , which has been numerically shown to reproduce this empirical scaling.
Edit intent: meaning-changed
Original:  In this paper, we clarify the relationship between this non-trivial scaling and the structure of the network by applying mean-field approximation of  diffusion in a complex network to a money-transport model , which has been numerically shown to reproduce this empirical scaling.
Revised:  In this paper, we clarify the relationship between this nonlinear scaling and the structure of the network by applying mean-field approximation of diffusion in a complex network to a money-transport model , which has been numerically shown to reproduce this empirical scaling.
Edit intent: clarity
Original:  In this paper, we clarify the relationship between this non-trivial scaling and the structure of the network by applying mean-field approximation of  diffusion in a complex network to a money-transport model , which has been numerically shown to reproduce this empirical scaling.
Revised:  In this paper, we clarify the relationship between this non-trivial scaling and the network structure by applying mean-field approximation of diffusion in a complex network to a money-transport model , which has been numerically shown to reproduce this empirical scaling.
Edit intent: clarity
Original:  In this paper, we clarify the relationship between this non-trivial scaling and the structure of the network by applying mean-field approximation of  diffusion in a complex network to a money-transport model , which has been numerically shown to reproduce this empirical scaling.
Revised:  In this paper, we clarify the relationship between this non-trivial scaling and the structure of the network by applying mean-field approximation of the diffusion in a complex network to a money-transport model , which has been numerically shown to reproduce this empirical scaling.
Edit intent: fluency
Original:  In this paper, we clarify the relationship between this non-trivial scaling and the structure of the network by applying mean-field approximation of  diffusion in a complex network to a money-transport model , which has been numerically shown to reproduce this empirical scaling.
Revised:  In this paper, we clarify the relationship between this non-trivial scaling and the structure of the network by applying mean-field approximation of diffusion in a complex network to this money-transport model , which has been numerically shown to reproduce this empirical scaling.
Edit intent: fluency
Original:  In this paper, we clarify the relationship between this non-trivial scaling and the structure of the network by applying mean-field approximation of  diffusion in a complex network to a money-transport model , which has been numerically shown to reproduce this empirical scaling. By theoretical analysis, we obtain the mean-field solution of money-transport models and find thatthe scaling exponent can be determined from the  average degree of the nearest neighbours , which is one of the cardinal features of a network .
Revised:  In this paper, we clarify the relationship between this non-trivial scaling and the structure of the network by applying mean-field approximation of diffusion in a complex network to a money-transport model . Using theoretical analysis, we obtain the mean-field solution of money-transport models and find thatthe scaling exponent can be determined from the average degree of the nearest neighbours , which is one of the cardinal features of a network .
Edit intent: coherence
Original:  By theoretical analysis, we obtain the mean-field solution of money-transport models and find thatthe scaling exponent can be determined from the  average degree of the nearest neighbours , which is one of the cardinal features of a network .
Revised:  By theoretical analysis, we obtained the mean-field solution of money-transport models and find thatthe scaling exponent can be determined from the average degree of the nearest neighbours , which is one of the cardinal features of a network .
Edit intent: fluency
Original:  By theoretical analysis, we obtain the mean-field solution of money-transport models and find thatthe scaling exponent can be determined from the  average degree of the nearest neighbours , which is one of the cardinal features of a network .
Revised:  By theoretical analysis, we obtain the mean-field solution of the model and found that, in the case of the Japanese firms, the scaling exponent of 1.3 can be determined from the average degree of the nearest neighbours , which is one of the cardinal features of a network .
Edit intent: meaning-changed
Original:  By theoretical analysis, we obtain the mean-field solution of money-transport models and find thatthe scaling exponent can be determined from the  average degree of the nearest neighbours , which is one of the cardinal features of a network .
Revised:  By theoretical analysis, we obtain the mean-field solution of money-transport models and find thatthe scaling exponent can be determined from the power law of the average degree of the nearest neighbours , which is one of the cardinal features of a network .
Edit intent: meaning-changed
Original:  By theoretical analysis, we obtain the mean-field solution of money-transport models and find thatthe scaling exponent can be determined from the  average degree of the nearest neighbours , which is one of the cardinal features of a network .
Revised:  By theoretical analysis, we obtain the mean-field solution of money-transport models and find thatthe scaling exponent can be determined from the average degree of the nearest neighbours of the network with an exponent of -0.7 .
Edit intent: meaning-changed
Original:  A Wiener-Hopf argument allows one to prove that this system (in which the kernel matrix is the unknown) possesses a unique causal solution and consequently that the second-order properties fully characterize Hawkes processes .
Revised:  A Wiener-Hopf argument allows one to prove that this system (in which the kernel matrix is the unknown) possesses a unique causal solution and consequently that the second-order properties fully characterize a Hawkes process .
Edit intent: fluency
Original:  We discuss the estimation error and provide some numerical examples . Applications to high frequency trading events in financial markets and to earthquakes occurrence dynamics are  considered.
Revised:  In this paper, we perform a systematic study of this non-parametric estimation procedure in the general framework of marked Hawkes processes. We describe precisely this procedure step by step. We discuss the estimation error and provide some numerical examples . Applications to high frequency trading events in financial markets and to earthquakes occurrence dynamics are considered.
Edit intent: meaning-changed
Original:  We discuss the estimation error and provide some numerical examples . Applications to high frequency trading events in financial markets and to earthquakes occurrence dynamics are  considered.
Revised:  We discuss the estimation error and explain how the values for the main parameters should be chosen. Various numerical examples are given in order to illustrate the broad possibilities of this estimation procedure ranging from 1-dimensional (power-law or non positive kernels) up to 3-dimensional (circular dependence) processes. A comparison to other non-parametric estimation procedures is made. Applications to high frequency trading events in financial markets and to earthquakes occurrence dynamics are considered.
Edit intent: meaning-changed
Original: In Part III of this paper , we apply the price dynamical model with big buyers and big sellers developed in Part I of this paper to the daily closing data of the top 20 stocks in Hang Seng Index in Hong Kong Stock Exchange.
Revised: In Part III of this study , we apply the price dynamical model with big buyers and big sellers developed in Part I of this paper to the daily closing data of the top 20 stocks in Hang Seng Index in Hong Kong Stock Exchange.
Edit intent: clarity
Original: In Part III of this paper , we apply the price dynamical model with big buyers and big sellers developed in Part I of this paper to the daily closing data of the top 20 stocks in Hang Seng Index in Hong Kong Stock Exchange.
Revised: In Part III of this paper , we apply the price dynamical model with big buyers and big sellers developed in Part I of this paper to the daily closing prices of the top 20 stocks in Hang Seng Index in Hong Kong Stock Exchange.
Edit intent: clarity
Original: In Part III of this paper , we apply the price dynamical model with big buyers and big sellers developed in Part I of this paper to the daily closing data of the top 20 stocks in Hang Seng Index in Hong Kong Stock Exchange.
Revised: In Part III of this paper , we apply the price dynamical model with big buyers and big sellers developed in Part I of this paper to the daily closing data of the top 20 banking and real estate stocks listed in the Hong Kong Stock Exchange.
Edit intent: meaning-changed
Original:  We develop two trading strategies: (i) Follow-the-Big-Buyer which buys when big buyer begins to appear and there is no sign of big sellers, holds the stock as long as the big buyer is still there, and sells all holdings of this stock once the big buyer disappears;
Revised:  We propose two trading strategies: (i) Follow-the-Big-Buyer which buys when big buyer begins to appear and there is no sign of big sellers, holds the stock as long as the big buyer is still there, and sells all holdings of this stock once the big buyer disappears;
Edit intent: clarity
Original:  We develop two trading strategies: (i) Follow-the-Big-Buyer which buys when big buyer begins to appear and there is no sign of big sellers, holds the stock as long as the big buyer is still there, and sells all holdings of this stock once the big buyer disappears;
Revised:  We develop two trading strategies: (i) Follow-the-Big-Buyer which buys when big buyer begins to appear and there is no sign of big sellers, holds the stock as long as the big buyer is still there, and sells the stock once the big buyer disappears;
Edit intent: clarity
Original:  and (ii) Ride-the-Mood which buys as soon as the big buyer strength begins to surpass the big seller strength and sells all holdings of the stock once the big seller strength is larger than the big buyer strength . Based on the testing over 198 two-year intervals uniformly distributed across the six-year period from 03-July-2007 to 28-June-2013 which includes a variety of scenarios,
Revised:  and (ii) Ride-the-Mood which buys as soon as the big buyer strength begins to surpass the big seller strength , and sells the stock once the big seller strength is larger than the big buyer strength . Based on the testing over 198 two-year intervals uniformly distributed across the six-year period from 03-July-2007 to 28-June-2013 which includes a variety of scenarios,
Edit intent: clarity
Original:  and (ii) Ride-the-Mood which buys as soon as the big buyer strength begins to surpass the big seller strength and sells all holdings of the stock once the big seller strength is larger than the big buyer strength . Based on the testing over 198 two-year intervals uniformly distributed across the six-year period from 03-July-2007 to 28-June-2013 which includes a variety of scenarios,
Revised:  and (ii) Ride-the-Mood which buys as soon as the big buyer strength begins to surpass the big seller strength and sells all holdings of the stock once the opposite happens . Based on the testing over 198 two-year intervals uniformly distributed across the six-year period from 03-July-2007 to 28-June-2013 which includes a variety of scenarios,
Edit intent: clarity
Original:  and (ii) Ride-the-Mood which buys as soon as the big buyer strength begins to surpass the big seller strength and sells all holdings of the stock once the big seller strength is larger than the big buyer strength . Based on the testing over 198 two-year intervals uniformly distributed across the six-year period from 03-July-2007 to 28-June-2013 which includes a variety of scenarios,
Revised:  and (ii) Ride-the-Mood which buys as soon as the big buyer strength begins to surpass the big seller strength and sells all holdings of the stock once the big seller strength is larger than the big buyer strength . Based on the testing over 245 two-year intervals uniformly distributed across the six-year period from 03-July-2007 to 28-June-2013 which includes a variety of scenarios,
Edit intent: meaning-changed
Original:  and (ii) Ride-the-Mood which buys as soon as the big buyer strength begins to surpass the big seller strength and sells all holdings of the stock once the big seller strength is larger than the big buyer strength . Based on the testing over 198 two-year intervals uniformly distributed across the six-year period from 03-July-2007 to 28-June-2013 which includes a variety of scenarios,
Revised:  and (ii) Ride-the-Mood which buys as soon as the big buyer strength begins to surpass the big seller strength and sells all holdings of the stock once the big seller strength is larger than the big buyer strength . Based on the testing over 198 two-year intervals uniformly distributed across the seven years from 03-July-2007 to 28-June-2013 which includes a variety of scenarios,
Edit intent: meaning-changed
Original:  and (ii) Ride-the-Mood which buys as soon as the big buyer strength begins to surpass the big seller strength and sells all holdings of the stock once the big seller strength is larger than the big buyer strength . Based on the testing over 198 two-year intervals uniformly distributed across the six-year period from 03-July-2007 to 28-June-2013 which includes a variety of scenarios,
Revised:  and (ii) Ride-the-Mood which buys as soon as the big buyer strength begins to surpass the big seller strength and sells all holdings of the stock once the big seller strength is larger than the big buyer strength . Based on the testing over 198 two-year intervals uniformly distributed across the six-year period from 03-July-2007 to 02-July-2014 which includes a variety of scenarios,
Edit intent: meaning-changed
Original:  the net profits would increase 47\% or 64 \% on average if an investor switched from the benchmark Buy-and-Hold strategy to the Follow-the-Big-Buyer or Ride-the-Mood strategies during this period, respectively.
Revised:  the net profits would increase 67\% or 120 \% on average if an investor switched from the benchmark Buy-and-Hold strategy to the Follow-the-Big-Buyer or Ride-the-Mood strategies during this period, respectively.
Edit intent: meaning-changed
Original: We propose a "sugar" coarse-grained (CG) DNA model capable of simulating both biologically significant B- and A-DNA forms. The number of degrees of freedom is reduced to six grains per nucleotide.
Revised: We propose a "sugar" coarse-grained (CG) DNA model capable of simulating both biologically significant B- and A-DNA . The model also demonstrates both the A to B and the B to A transitions. The number of degrees of freedom is reduced to six grains per nucleotide.
Edit intent: meaning-changed
Original:  The key features of the sugar CG DNA model are : (1) simulation of sugar repuckering between C2'-endo and C3'-endo by the use of one non-harmonic potential and one three-particle potential, (2) explicit representation of sodium counterions and (3) implicit solvent approach . Effects of solvation and of partial charge screening at small distances are taken into account through the shape of potentials of interactions between charged particles .
Revised:  The key features of the model are (1) simulation of sugar repuckering between C2'-endo and C3'-endo by the use of one non-harmonic potential and one three-particle potential, (2) explicit representation of sodium counterions and (3) implicit solvent approach . Effects of solvation and of partial charge screening at small distances are taken into account through the shape of potentials of interactions between charged particles .
Edit intent: clarity
Original:  The key features of the sugar CG DNA model are : (1) simulation of sugar repuckering between C2'-endo and C3'-endo by the use of one non-harmonic potential and one three-particle potential, (2) explicit representation of sodium counterions and (3) implicit solvent approach . Effects of solvation and of partial charge screening at small distances are taken into account through the shape of potentials of interactions between charged particles .
Revised:  The key features of the sugar CG DNA model are : (1) simulation of sugar repuckering between C2'-endo and C3'-endo by the use of one nonharmonic potential and one three-particle potential, (2) explicit representation of sodium counterions and (3) implicit solvent approach . Effects of solvation and of partial charge screening at small distances are taken into account through the shape of potentials of interactions between charged particles .
Edit intent: fluency
Original:  The key features of the sugar CG DNA model are : (1) simulation of sugar repuckering between C2'-endo and C3'-endo by the use of one non-harmonic potential and one three-particle potential, (2) explicit representation of sodium counterions and (3) implicit solvent approach . Effects of solvation and of partial charge screening at small distances are taken into account through the shape of potentials of interactions between charged particles .
Revised:  The key features of the sugar CG DNA model are : (1) simulation of sugar repuckering between C2'-endo and C3'-endo by the use of one non-harmonic potential and one three-particle potential, (2) explicit representation of ions in solution around the DNA, (3) implicit solvent approach . Effects of solvation and of partial charge screening at small distances are taken into account through the shape of potentials of interactions between charged particles .
Edit intent: clarity
Original:  The key features of the sugar CG DNA model are : (1) simulation of sugar repuckering between C2'-endo and C3'-endo by the use of one non-harmonic potential and one three-particle potential, (2) explicit representation of sodium counterions and (3) implicit solvent approach . Effects of solvation and of partial charge screening at small distances are taken into account through the shape of potentials of interactions between charged particles .
Revised:  The key features of the sugar CG DNA model are : (1) simulation of sugar repuckering between C2'-endo and C3'-endo by the use of one non-harmonic potential and one three-particle potential, (2) explicit representation of sodium counterions and (3) implicit solvent approach and (4) sequence dependence .
Edit intent: meaning-changed
Original:  We obtain parameters of the sugar CG DNA model from the all-atom AMBER model. The suggested model allows adequate simulation of the transitions between A- and B-DNA forms, as well as of large deformations of long DNA molecules , for example, in binding with proteins . Small modifications of the model can provide the possibility of introducing sequence dependence, as well as of modeling base pairs openings .
Revised:  We obtain parameters of the sugar CG DNA model from the all atom AMBER force field. The model can be used to study large local deformations of long DNA molecules , for example, in binding with proteins . Small modifications of the model can provide the possibility of introducing sequence dependence, as well as of modeling base pairs openings .
Edit intent: clarity
Original:  The suggested model allows adequate simulation of the transitions between A- and B-DNA forms, as well as of large deformations of long DNA molecules , for example, in binding with proteins . Small modifications of the model can provide the possibility of introducing sequence dependence, as well as of modeling base pairs openings .
Revised:  The suggested model allows adequate simulation of the transitions between A- and B-DNA forms, as well as of large deformations of long DNA molecules ( for example, in binding with proteins . Small modifications of the model can provide the possibility of introducing sequence dependence, as well as of modeling base pairs openings .
Edit intent: fluency
Original:  The suggested model allows adequate simulation of the transitions between A- and B-DNA forms, as well as of large deformations of long DNA molecules , for example, in binding with proteins . Small modifications of the model can provide the possibility of introducing sequence dependence, as well as of modeling base pairs openings .
Revised:  The suggested model allows adequate simulation of the transitions between A- and B-DNA forms, as well as of large deformations of long DNA molecules , for example, in binding with proteins ). Small modification of the model can provide the possibility of introducing sequence dependence, as well as of modeling base pairs openings .
Edit intent: fluency
Original:  The suggested model allows adequate simulation of the transitions between A- and B-DNA forms, as well as of large deformations of long DNA molecules , for example, in binding with proteins . Small modifications of the model can provide the possibility of introducing sequence dependence, as well as of modeling base pairs openings .
Revised:  The suggested model allows adequate simulation of the transitions between A- and B-DNA forms, as well as of large deformations of long DNA molecules , for example, in binding with proteins . Small modifications of the model can provide the possibility of modeling base pairs openings .
Edit intent: coherence
Original:  The suggested model allows adequate simulation of the transitions between A- and B-DNA forms, as well as of large deformations of long DNA molecules , for example, in binding with proteins . Small modifications of the model can provide the possibility of introducing sequence dependence, as well as of modeling base pairs openings . One can also study other, than sodium, ions as well as different solvents, and model the corresponding electrostatic effects .
Revised:  The suggested model allows adequate simulation of the transitions between A- and B-DNA forms, as well as of large deformations of long DNA molecules , for example, in binding with proteins . Small modifications of the model can provide the possibility of introducing sequence dependence, as well as of modeling base pairs openings in melting, transcription and replication. And one can also simulate the interactions of the DNA molecule with different types of ions in different kinds of solutions .
Edit intent: clarity
Original: We propose a "sugar" coarse-grained (CG) DNA model capable of simulating both biologically significant B- and A-DNA. The model also demonstrates both the A to B and the B to A transitions.
Revised: We propose a "sugar" coarse-grained (CG) DNA models have been developed for simulating the behavior of this molecule under various conditions, including those required for nanotechnology. However, none of these models reproduces the DNA polymorphism associated with conformational changes in the ribose rings of the DNA backbone. These changes make an essential contribution to the DNA local deformability and provide the possibility of the transition of the DNA double helix from the B-form to the B to A transitions.
Edit intent: meaning-changed