wanyu/IteraTeR_full_sent huggingface dataset:
Original:  As  numerical illustration, the model is simultaneously calibrated to Stoxx50E call options at four different maturities and subsequently prices and Greeks of down-and-in digital and down-and-in call options are calculated . Comparison with Monte Carlo simulation results shows that the method is fast, accurate, and stable.
Revised:  As numerical illustration, the prices and Greeks of down-and-in digital and down-and-in call options are calculated . Comparison with Monte Carlo simulation results shows that the method is fast, accurate, and stable.
Edit intent: clarity
Original:  As  numerical illustration, the model is simultaneously calibrated to Stoxx50E call options at four different maturities and subsequently prices and Greeks of down-and-in digital and down-and-in call options are calculated . Comparison with Monte Carlo simulation results shows that the method is fast, accurate, and stable.
Revised:  As numerical illustration, the model is simultaneously calibrated to Stoxx50E call options at four different maturities and subsequently prices and Greeks of down-and-in digital and down-and-in call options are calculated for a set of parameters obtained by a simultaneous calibration to Stoxx50E call options across strikes and four different maturities. By comparing the results with Monte-Carlo simulations, we show that the method is fast, accurate, and stable.
Edit intent: meaning-changed
Original:   ]  We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch .
Revised:  This work generalises the model of ] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch .
Edit intent: meaning-changed
Original:   ]  We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch .
Revised:  Phys. Rev. Lett., 101, 118104] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch .
Edit intent: meaning-changed
Original:   ]  We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch .
Revised:  ] to a broader class of linear feedback systems. We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch .
Edit intent: meaning-changed
Original:   ]  We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch .
Revised:  ] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, for the general case where the enzyme can mediate flipping in either direction. For this general case we also solve for the flip time distribution, making a connection to first passage and persistence problems in statistical physics .
Edit intent: meaning-changed
Original:  We also show that this model can exhibit long-lived temporal correlations, thus providing a primitive form of cellular memory.
Revised:  The occurrence of such a peak is analysed as a function of the parameter space. We present a new relation between the flip time distributions measured for two relevant choices of initial condition. We also introduce a new correlation measure to show that this model can exhibit long-lived temporal correlations, thus providing a primitive form of cellular memory.
Edit intent: meaning-changed
Original: Sorting permutations by reversals and/or transpositions is an important genome rearrangement problem in computational molecular biology. From theoretical point of view, finding efficient algorithms for this problem and its variations are quite challenging. In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation.
Revised: In this paper we study several variations of the the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation.
Edit intent: clarity
Original:  In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation.
Revised:  In this paper we considerpancake flipping problem the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation.
Edit intent: meaning-changed
Original:  In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation.
Revised:  In this paper we consider, which is also well known as the problem of the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation.
Edit intent: meaning-changed
Original:  In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation.
Revised:  In this paper we considersorting by prefix reversals the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation.
Edit intent: meaning-changed
Original:  In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation.
Revised:  In this paper we consider. We consider the variations in the sorting process by adding with prefix reversals other similar operations such as prefix transpositions and prefix transreversals. These type of sorting problems have applications in interconnection networks and computational biology. We first study the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation.
Edit intent: meaning-changed
Original:  In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation.
Revised:  In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions and present a 3-approximation algorithm for this problem. Then we give a 2-approximation algorithm for sorting by prefix reversals and prefix transreversals. We also provide a 3-approximation algorithm for sorting by prefix reversals and prefix transpositions where the operations are always applied at the unsorted suffix of the given permutation.
Edit intent: meaning-changed
Original:  In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. For this problem we first present a 3-approximation algorithm, which performs close to 2 in practice.
Revised:  In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the permutation.
Edit intent: coherence
Original:  We further analyze the problem in more practical way and consider the variation where a certain number of prefix reversals are forced to happen in the sorting process. Here we achieve an improved approximation ratio of (3-\frac{3r{b(\pi)+r}), where r is the }  number of prefix reversals that must happen in the sorting, when possible, and b(\pi)\geq 2r is the number of breakpoints in the given permutation.
Revised:  We further analyze the problem in more practical way and {b(\pi)+r}), where r is the } number of prefix reversals that must happen in the sorting, when possible, and b(\pi)\geq 2r is the number of breakpoints in the given permutation.
Edit intent: clarity
Original:  Here we achieve an improved approximation ratio of (3-\frac{3r{b(\pi)+r}), where r is the }  number of prefix reversals that must happen in the sorting, when possible, and b(\pi)\geq 2r is the number of breakpoints in the given permutation.
Revised:  Here we achieve an improved approximation ratio of (3-\frac{3r{b(\pi)+r}), where r is the } show quantitatively how approximation ratios of our algorithms improve with the increase of number of prefix reversals that must happen in the sorting, when possible, and b(\pi)\geq 2r is the number of breakpoints in the given permutation.
Edit intent: meaning-changed
Original:  Here we achieve an improved approximation ratio of (3-\frac{3r{b(\pi)+r}), where r is the }  number of prefix reversals that must happen in the sorting, when possible, and b(\pi)\geq 2r is the number of breakpoints in the given permutation. Again, in this variation our algorithm performs close to 1.6 in practice .
Revised:  Here we achieve an improved approximation ratio of (3-\frac{3r{b(\pi)+r}), where r is the } number of prefix reversals applied by optimal algorithms. Finally, we present experimental results to support our analysis .
Edit intent: clarity
Original:  Here, we present a stochastic simulation method for determining the kinetics of multivalent biomolecular interactions , which has a computational cost independent of reaction network size . The method is based on sampling a set of chemical transformation classes that characterize the interactions in a system.
Revised:  Here, we present a stochastic simulation method for determining the kinetics of multivalent biomolecular interactions , which has a computational cost independent of reaction network size , and it is based on sampling a set of chemical transformation classes that characterize the interactions in a system.
Edit intent: coherence
Original:  We apply the method to simulate multivalent ligand-receptor interaction systems . Simulation results reveal insights into ligand-receptor binding kinetics that are not available from previously developed equilibrium models .
Revised:  We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results reveal insights into ligand-receptor binding kinetics that are not available from previously developed equilibrium models .
Edit intent: meaning-changed
Original:  Here, we present and evaluate a rejection-free stochastic simulation method for determining the kinetics of multivalent biomolecular interactions . The method has a computational cost independent of reaction network size, and it is based on sampling a set of chemical transformation classes defined by formal rules that characterize the interactions in a system. We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that  the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling .
Revised:  Here, we present and evaluate a rejection-free stochastic simulation method for determining the kinetics of multivalent biomolecular interactions , and we apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling .
Edit intent: coherence
Original:  We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that  the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling .
Revised:  We apply the method to simulate simple models for ligand-receptor interactions . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling .
Edit intent: clarity
Original:  We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that  the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling .
Revised:  We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that performance of the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling .
Edit intent: clarity
Original:  We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that  the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling .
Revised:  We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is equal to or better than that of the rejection method over wide parameter ranges than a related method that relies on rejection sampling .
Edit intent: meaning-changed
Original:  We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that  the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling .
Revised:  We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is more efficient over wide parameter ranges , and the rejection-free method is more efficient for simulating systems in which aggregation is extensive. The rejection-free method reported here should be useful for simulating a variety of systems in which multisite molecular interactions yield large molecular aggregates .
Edit intent: meaning-changed
Original: RNA polymerase (RNAP) is like a mobile molecular workshop that polymerizes a RNA molecule by adding monomeric subunits one by one, while moving step by step on the DNA template itself.
Revised: RNA polymerase (RNAP) is a mobile molecular workshop that polymerizes a RNA molecule by adding monomeric subunits one by one, while moving step by step on the DNA template itself.
Edit intent: coherence
Original:  We consider a company whose cumulative net revenues evolve as a drifted Brownian motion modulated by a finite state Markov chain, and model the discount rate as a deterministic function of the current state of the chain.
Revised:  We consider a company whose cumulative net revenues evolve as a Brownian motion with positive drift that is modulated by a finite state Markov chain, and model the discount rate as a deterministic function of the current state of the chain.
Edit intent: meaning-changed
Original:  The objective is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which occurs the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) hit zero.
Revised:  In this setting the objective of the company is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which occurs the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) hit zero.
Edit intent: meaning-changed
Original:  The objective is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which occurs the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) hit zero.
Revised:  The objective is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which is taken to be the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) hit zero.
Edit intent: clarity
Original:  We show that, if the drift is positive in each regime , it is optimal to adopt a barrier strategy at certain positive regime-dependent levels, and explicitly characterize the value function as the fixed point of a contraction.
Revised:  We show that, if the drift is positive in each state , it is optimal to adopt a barrier strategy at certain positive regime-dependent levels, and explicitly characterize the value function as the fixed point of a contraction.
Edit intent: clarity
Original:  We show that, if the drift is positive in each regime , it is optimal to adopt a barrier strategy at certain positive regime-dependent levels, and explicitly characterize the value function as the fixed point of a contraction.
Revised:  We show that, if the drift is positive in each regime , it is optimal to adopt a barrier strategy at certain positive regime-dependent levels, and provide an explicit characterization of the value function as the fixed point of a contraction.
Edit intent: clarity
Original:  In this paper , we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed l_1-regularized logistic regression formalism that can be cast as  standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks.
Revised:  In this paper we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed l_1-regularized logistic regression formalism that can be cast as standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks.
Edit intent: fluency
Original:  In this paper , we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed l_1-regularized logistic regression formalism that can be cast as  standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks.
Revised:  In this paper , we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed l_1-regularized logistic regression formalism that can be cast as a standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks.
Edit intent: fluency
Original:  For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course.
Revised:  For real data sets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course.
Edit intent: clarity
Original:  For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course.
Revised:  For real datasets , we reverse engineer the latent sequence of temporally rewiring political networks between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course.
Edit intent: fluency
Original:  For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course.
Revised:  For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US Senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course.
Edit intent: fluency
Original:  For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course.
Revised:  For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving regulatory networks underlying 588 genes across the life cycle of Drosophila melanogaster from microarray time course.
Edit intent: meaning-changed
Original:  For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course.
Revised:  For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of from microarray time course.
Edit intent: clarity
Original:  For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course.
Revised:  For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster Drosophila melanogaster from the microarray time course.
Edit intent: meaning-changed