wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Interestingly, miRNAs predominately target and repress DNA methyltransferases, the core enzymes for DNA methylation , and decreasing methylation in turn activates the top miRNAs . This system-wide circuit between up-regulated miRNAs and the repressed DNA methylation system eventually activates the pluripotent core factors for pluripotency. Therefore, miRNAs systematically repress development and inhibit the epigenetic system to dynamically modulate stem cell pluripotency .
Revised:  Interestingly, miRNAs predominately target and repress DNA methyltransferases, the core enzymes for DNA methylation , and decreasing methylation in turn activates the top miRNAs . This system-wide circuit between up-regulated miRNAs and the repressed DNA methylation system eventually activates the pluripotent core factors , creating an active circuit system to modulate pluripotency. MiRNAs vary their functions with different stem cell states. While miRNAs directly repress pluripotent core factors to facilitate the differentiation during cell differentiation, they also help stem cells to maintain pluripotency by activating pluripotent cores through directly repressing DNA methylation systems and primarily inhibiting development .
Edit intent: meaning-changed
Original: In this paper we derive a new handy integral equation for the free boundary of infinite time horizon, continuous time, stochastic, irreversible investment problems with uncertainty modeled as a one-dimensional, regular diffusion X.
Revised: In this paper we derive a new handy integral equation for the free-boundary of infinite time horizon, continuous time, stochastic, irreversible investment problems with uncertainty modeled as a one-dimensional, regular diffusion X.
Edit intent: fluency
Original: In this paper we derive a new handy integral equation for the free boundary of infinite time horizon, continuous time, stochastic, irreversible investment problems with uncertainty modeled as a one-dimensional, regular diffusion X.
Revised: In this paper we derive a new handy integral equation for the free boundary of infinite time horizon, continuous time, stochastic, irreversible investment problems with uncertainty mo-deled as a one-dimensional, regular diffusion X.
Edit intent: fluency
Original:  The new integral equation allows to explicitly find the free boundary, b , in some so far unsolved cases, as when  X is a three-dimensional Bessel process or a CEV process.
Revised:  The new integral equation allows to explicitly find the free boundary, b , in some so far unsolved cases, as when the operating profit function is not multiplicatively separable and X is a three-dimensional Bessel process or a CEV process.
Edit intent: meaning-changed
Original: In this paper  we derive a new handy integral equation for the free-boundary of infinite time horizon, continuous time, stochastic, irreversible investment problems with uncertainty mo-deled as a one-dimensional, regular diffusion X.
Revised: In this paper , we derive a new handy integral equation for the free-boundary of infinite time horizon, continuous time, stochastic, irreversible investment problems with uncertainty mo-deled as a one-dimensional, regular diffusion X.
Edit intent: fluency
Original: In this paper  we derive a new handy integral equation for the free-boundary of infinite time horizon, continuous time, stochastic, irreversible investment problems with uncertainty mo-deled as a one-dimensional, regular diffusion X.
Revised: In this paper we derive a new handy integral equation for the free-boundary of infinite time horizon, continuous time, stochastic, irreversible investment problems with uncertainty modeled as a one-dimensional, regular diffusion X.
Edit intent: fluency
Original:  The new integral equation allows to explicitly find the free-boundary b  in some so far unsolved cases, as when the operating profit function is not multiplicatively separable and X is a three- dimensional Bessel process or a CEV process.
Revised:  The new integral equation allows to explicitly find the free-boundary b (\cdot) in some so far unsolved cases, as when the operating profit function is not multiplicatively separable and X is a three- dimensional Bessel process or a CEV process.
Edit intent: meaning-changed
Original:  The new integral equation allows to explicitly find the free-boundary b  in some so far unsolved cases, as when the operating profit function is not multiplicatively separable and X is a three- dimensional Bessel process or a CEV process.
Revised:  The new integral equation allows to explicitly find the free-boundary b in some so far unsolved cases, as when the operating profit function is not multiplicatively separable and X is a three-dimensional Bessel process or a CEV process.
Edit intent: fluency
Original:  Indeed, we first show that b(X(t))=l  (t), with l  the unique optional solution of a representation problem in the spirit of Bank-El Karoui  (2004)  ;
Revised:  Indeed, we first show that b(X(t))=l ^* (t), with l the unique optional solution of a representation problem in the spirit of Bank-El Karoui (2004) ;
Edit intent: meaning-changed
Original:  Indeed, we first show that b(X(t))=l  (t), with l  the unique optional solution of a representation problem in the spirit of Bank-El Karoui  (2004)  ;
Revised:  Indeed, we first show that b(X(t))=l (t), with l ^* the unique optional solution of a representation problem in the spirit of Bank-El Karoui (2004) ;
Edit intent: meaning-changed
Original:  Indeed, we first show that b(X(t))=l  (t), with l  the unique optional solution of a representation problem in the spirit of Bank-El Karoui  (2004)  ;
Revised:  Indeed, we first show that b(X(t))=l (t), with l the unique optional solution of a representation problem in the spirit of Bank-El Karoui Ann. Probab. 32 (2004) ;
Edit intent: meaning-changed
Original:  Indeed, we first show that b(X(t))=l  (t), with l  the unique optional solution of a representation problem in the spirit of Bank-El Karoui  (2004)  ;
Revised:  Indeed, we first show that b(X(t))=l (t), with l the unique optional solution of a representation problem in the spirit of Bank-El Karoui (2004) 1030-1067 ;
Edit intent: meaning-changed
Original:  When the future is unknown, the optimal average queue length diverges at rate \log_{1/(1-p) 1/(1-\lambda)} 1{1-\lambda}} , as \lambda\to 1.
Revised:  When the future is unknown, the optimal average queue length diverges at rate 1/(1-\lambda)} 1{1-\lambda}} , as \lambda\to 1.
Edit intent: meaning-changed
Original:  When the future is unknown, the optimal average queue length diverges at rate \log_{1/(1-p) 1/(1-\lambda)} 1{1-\lambda}} , as \lambda\to 1.
Revised:  When the future is unknown, the optimal average queue length diverges at rate \log_{1/(1-p) 1/(1-\lambda)} \sim\log_{1/(1-p)1{1-\lambda}} , as \lambda\to 1.
Edit intent: meaning-changed
Original:  the optimal average queue length converges to a finite constant, (1-p)/p, as \lambda %DIFDELCMD < \to %%% 1. \to1 We further show that the finite limit of (1-p)/p can be achieved using only a finite lookahead window starting from the current time frame, whose length scales as \log 1/(1-\lambda(\log1{1-\lambda}} ), as \lambda %DIFDELCMD < \to %%% 1. \to1 This leads to the conjecture of an interesting duality between queuing delay and the amount of information about the future.
Revised:  the optimal average queue length converges to a finite constant, (1-p)/p, as \lambda %DIFDELCMD < \to %%% \to1 We further show that the finite limit of (1-p)/p can be achieved using only a finite lookahead window starting from the current time frame, whose length scales as \log 1/(1-\lambda(\log1{1-\lambda}} ), as \lambda %DIFDELCMD < \to %%% 1. \to1 This leads to the conjecture of an interesting duality between queuing delay and the amount of information about the future.
Edit intent: fluency
Original:  the optimal average queue length converges to a finite constant, (1-p)/p, as \lambda %DIFDELCMD < \to %%% 1. \to1 We further show that the finite limit of (1-p)/p can be achieved using only a finite lookahead window starting from the current time frame, whose length scales as \log 1/(1-\lambda(\log1{1-\lambda}} ), as \lambda %DIFDELCMD < \to %%% 1. \to1 This leads to the conjecture of an interesting duality between queuing delay and the amount of information about the future.
Revised:  the optimal average queue length converges to a finite constant, (1-p)/p, as \lambda %DIFDELCMD < \to %%% 1. \to1. We further show that the finite limit of (1-p)/p can be achieved using only a finite lookahead window starting from the current time frame, whose length scales as \log 1/(1-\lambda(\log1{1-\lambda}} ), as \lambda %DIFDELCMD < \to %%% 1. \to1 This leads to the conjecture of an interesting duality between queuing delay and the amount of information about the future.
Edit intent: fluency
Original:  the optimal average queue length converges to a finite constant, (1-p)/p, as \lambda %DIFDELCMD < \to %%% 1. \to1 We further show that the finite limit of (1-p)/p can be achieved using only a finite lookahead window starting from the current time frame, whose length scales as \log 1/(1-\lambda(\log1{1-\lambda}} ), as \lambda %DIFDELCMD < \to %%% 1. \to1 This leads to the conjecture of an interesting duality between queuing delay and the amount of information about the future.
Revised:  the optimal average queue length converges to a finite constant, (1-p)/p, as \lambda %DIFDELCMD < \to %%% 1. \to1 We further show that the finite limit of (1-p)/p can be achieved using only a finite lookahead window starting from the current time frame, whose length scales as \mathcal{O(\log1{1-\lambda}} ), as \lambda %DIFDELCMD < \to %%% 1. \to1 This leads to the conjecture of an interesting duality between queuing delay and the amount of information about the future.
Edit intent: meaning-changed
Original:  the optimal average queue length converges to a finite constant, (1-p)/p, as \lambda %DIFDELCMD < \to %%% 1. \to1 We further show that the finite limit of (1-p)/p can be achieved using only a finite lookahead window starting from the current time frame, whose length scales as \log 1/(1-\lambda(\log1{1-\lambda}} ), as \lambda %DIFDELCMD < \to %%% 1. \to1 This leads to the conjecture of an interesting duality between queuing delay and the amount of information about the future.
Revised:  the optimal average queue length converges to a finite constant, (1-p)/p, as \lambda %DIFDELCMD < \to %%% 1. \to1 We further show that the finite limit of (1-p)/p can be achieved using only a finite lookahead window starting from the current time frame, whose length scales as \log 1/(1-\lambda(\log1{1-\lambda}} ), as \lambda %DIFDELCMD < \to %%% \to1 This leads to the conjecture of an interesting duality between queuing delay and the amount of information about the future.
Edit intent: fluency
Original:  the optimal average queue length converges to a finite constant, (1-p)/p, as \lambda %DIFDELCMD < \to %%% 1. \to1 We further show that the finite limit of (1-p)/p can be achieved using only a finite lookahead window starting from the current time frame, whose length scales as \log 1/(1-\lambda(\log1{1-\lambda}} ), as \lambda %DIFDELCMD < \to %%% 1. \to1 This leads to the conjecture of an interesting duality between queuing delay and the amount of information about the future.
Revised:  the optimal average queue length converges to a finite constant, (1-p)/p, as \lambda %DIFDELCMD < \to %%% 1. \to1 We further show that the finite limit of (1-p)/p can be achieved using only a finite lookahead window starting from the current time frame, whose length scales as \log 1/(1-\lambda(\log1{1-\lambda}} ), as \lambda %DIFDELCMD < \to %%% 1. \to1. This leads to the conjecture of an interesting duality between queuing delay and the amount of information about the future.
Edit intent: fluency
Original:  The pricing kernel, giving rise to the price system in an incomplete market, is modelled by weighted heat kernels that are driven by multivariate Markov processes and that provide enough degrees of freedom in order to calibrate to relevant data, e.g. to the term structure of bond prices.
Revised:  The pricing kernel, giving rise to the price system in an incomplete market, is modelled by weighted heat kernels which are driven by multivariate Markov processes and that provide enough degrees of freedom in order to calibrate to relevant data, e.g. to the term structure of bond prices.
Edit intent: fluency
Original:  The pricing kernel, giving rise to the price system in an incomplete market, is modelled by weighted heat kernels that are driven by multivariate Markov processes and that provide enough degrees of freedom in order to calibrate to relevant data, e.g. to the term structure of bond prices.
Revised:  The pricing kernel, giving rise to the price system in an incomplete market, is modelled by weighted heat kernels that are driven by multivariate Markov processes and which provide enough degrees of freedom in order to calibrate to relevant data, e.g. to the term structure of bond prices.
Edit intent: fluency
Original: Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on  previously unseen input data , using machine learning techniques to build a model of the algorithm's runtime as a function of domain-specific problem features.
Revised: Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a previously unseen input data , using machine learning techniques to build a model of the algorithm's runtime as a function of domain-specific problem features.
Edit intent: fluency
Original: Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on  previously unseen input data , using machine learning techniques to build a model of the algorithm's runtime as a function of domain-specific problem features.
Revised: Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on previously unseen input , using machine learning techniques to build a model of the algorithm's runtime as a function of domain-specific problem features.
Edit intent: clarity
Original: Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on  previously unseen input data , using machine learning techniques to build a model of the algorithm's runtime as a function of domain-specific problem features.
Revised: Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on previously unseen input data , using machine learning techniques to build a model of the algorithm's runtime as a function of problem-specific instance features.
Edit intent: clarity
Original:  We also describe novel features for predicting algorithm runtime for the propositional satisfiability (SAT),  mixed integer programming (MIP) , and travelling salesperson (TSP) problems.
Revised:  We also comprehensively describe new and existing features for predicting algorithm runtime for the propositional satisfiability (SAT), mixed integer programming (MIP) , and travelling salesperson (TSP) problems.
Edit intent: meaning-changed
Original:  We also describe novel features for predicting algorithm runtime for the propositional satisfiability (SAT),  mixed integer programming (MIP) , and travelling salesperson (TSP) problems.
Revised:  We also describe novel features for predicting algorithm runtime for the propositional satisfiability (SAT), travelling salesperson (TSP) and mixed integer programming (MIP) , and travelling salesperson (TSP) problems.
Edit intent: meaning-changed
Original: In this paper, we create a model of unexpected fluctuation of the long-term real interest rate. This model is based on our new version of IS-LM model. The new IS-LM model eliminates two main deficiencies of the original model: assumptions of constant price level and of strictly exogenous money supply. The unexpected fluctuations of the long-term real interest rate can be explained by existence of special type of cycle called relaxation oscillation on money (or financial assets) market.
Revised: In this paper, we present own point of view how the unexpected fluctuations of the long-term real interest rate can be explained by existence of special type of cycle called relaxation oscillation on money (or financial assets) market.
Edit intent: clarity
Original:  The unexpected fluctuations of the long-term real interest rate can be explained by existence of special type of cycle called relaxation oscillation on money (or financial assets) market. Relaxation oscillations include some short parts looking like "jumps". These "jumps" can be interpreted like unexpected. In other words, we try to explain these "unexpected" fluctuations of long-term real interest rate and show that these fluctuations can be only seemingly unexpected.
Revised:  The unexpected fluctuations of the long-term real interest rate can be explained . We describe a macroeconomic environment by the modification of the fundamental macroeconomic equilibrium model called the IS-LM model.
Edit intent: clarity
Original:  Our model uses a combina- tion of analytic techniques as well as computational simulations to calculate the mean-expression and noise profiles of genes regu- lated by both sRNAs and microRNAs . We find that despite very different molecular machinery and modes of action (catalytic vs stoichiometric), the mean expression levels and noise profiles of microRNA-regulated genes are almost identical to genes regu- lated by prokaryotic sRNAs.
Revised:  Our model uses a combination of analytic techniques as well as computational simulations to calculate the mean-expression and noise profiles of genes regu- lated by both sRNAs and microRNAs . We find that despite very different molecular machinery and modes of action (catalytic vs stoichiometric), the mean expression levels and noise profiles of microRNA-regulated genes are almost identical to genes regu- lated by prokaryotic sRNAs.
Edit intent: fluency
Original:  Our model uses a combina- tion of analytic techniques as well as computational simulations to calculate the mean-expression and noise profiles of genes regu- lated by both sRNAs and microRNAs . We find that despite very different molecular machinery and modes of action (catalytic vs stoichiometric), the mean expression levels and noise profiles of microRNA-regulated genes are almost identical to genes regu- lated by prokaryotic sRNAs.
Revised:  Our model uses a combina- tion of analytic techniques as well as computational simulations to calculate the mean-expression and noise profiles of genes regulated by both microRNAs and sRNAs . We find that despite very different molecular machinery and modes of action (catalytic vs stoichiometric), the mean expression levels and noise profiles of microRNA-regulated genes are almost identical to genes regu- lated by prokaryotic sRNAs.
Edit intent: fluency
Original:  Our model uses a combina- tion of analytic techniques as well as computational simulations to calculate the mean-expression and noise profiles of genes regu- lated by both sRNAs and microRNAs . We find that despite very different molecular machinery and modes of action (catalytic vs stoichiometric), the mean expression levels and noise profiles of microRNA-regulated genes are almost identical to genes regu- lated by prokaryotic sRNAs.
Revised:  Our model uses a combina- tion of analytic techniques as well as computational simulations to calculate the mean-expression and noise profiles of genes regu- lated by both sRNAs and microRNAs . We find that despite very different molecular machinery and modes of action (catalytic vs stoichiometric), the mean expression levels and noise profiles of microRNA-regulated genes are almost identical to genes regulated by prokaryotic sRNAs.
Edit intent: fluency
Original:  We extend our model to study crosstalk be- tween multiple mRNAs that are regulated by a single microRNA and show that noise is a sensitive measure of microRNA-mediated interaction between mRNAs.
Revised:  We extend our model to study crosstalk between multiple mRNAs that are regulated by a single microRNA and show that noise is a sensitive measure of microRNA-mediated interaction between mRNAs.
Edit intent: fluency
Original:  Our model suggests that partially reprogrammed cells are a natural consequence of high-dimensional landscapes  and predicts that partially reprogrammed cells should be hybrids that co-express genes from multiple cell fates.
Revised:  Our model suggests that partially reprogrammed cells are a natural consequence of high-dimensional landscapes , and predicts that partially reprogrammed cells should be hybrids that co-express genes from multiple cell fates.
Edit intent: fluency
Original:  The first complex is related to Golgi transport and vesicle coating. Several viral proteins go through this complex before viral budding, the knock-down of Golgi related proteins might have prevented viral production. The second complex is related to ATP dependent proton transport. The uncoating of the  Influenza virus is dependent on pH changes within the endosome, knock-down of these genes may changes in the endosomal pH inhibiting the uncoating of the virus .
Revised:  The biological function and the Influenza virus is dependent on pH changes within the endosome, knock-down of these genes may changes in the endosomal pH inhibiting the uncoating of the virus .
Edit intent: clarity
Original:  The uncoating of the  Influenza virus is dependent on pH changes within the endosome, knock-down of these genes may changes in the endosomal pH inhibiting the uncoating of the virus .
Revised:  The uncoating of the relevance of each identified protein complex in the Influenza virus is dependent on pH changes within the endosome, knock-down of these genes may changes in the endosomal pH inhibiting the uncoating of the virus .
Edit intent: meaning-changed
Original: The fundamental theorem of utility maximization (called FTUM hereafter) says that the utility maximization admits solution if and only if there exists an equivalent martingale measure.
Revised: This paper proposes two approaches that quantify the exact relationship among the viability, the absence of arbitrage, and/or the existence of the num\'eraire portfolio under minimal assumptions and for general continuous-time market models. Precisely, our first and principal contribution proves the equivalence among the utility maximization admits solution if and only if there exists an equivalent martingale measure.
Edit intent: meaning-changed
Original: The fundamental theorem of utility maximization (called FTUM hereafter) says that the utility maximization admits solution if and only if there exists an equivalent martingale measure. This theorem is true for discrete market models (where the number of scenarios is finite), andremains valid for general discrete-time market modelswhen the utility is smooth enough. However, this theorem --in this current formulation-- fails in continuous-time framework even with nice utility function, where there might exist arbitrage opportunities and optimal portfolio. This paper addresses the question how far we can weaken the non-arbitrage condition as well as the utility maximization problem to preserve their complete and strong relationship described by the FTUM. As application of our version of the FTUM, we establish equivalence between the No-Unbounded-Profit-with-Bounded-Risk condition  , the existence of  num\'eraire portfolio, and the existence of solution to the utility maximization under equivalent probability measure  .
Revised: The fundamental theorem of utility maximization (called FTUM hereafter) says that the No-Unbounded-Profit-with-Bounded-Risk condition , the existence of num\'eraire portfolio, and the existence of solution to the utility maximization under equivalent probability measure .
Edit intent: coherence