wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this model  the value of the option is given by a linear (1 + 2) evolution partial differential equation , in which the price of the option depends on two independent variables, the value of the underlying asset S and a new variable, y , which follow from the Orstein-Uhlenbeck process .
Revised:  In this model , the value of the option is given by a linear (1 + 2) evolution partial differential equation , in which the price of the option depends on two independent variables, the value of the underlying asset S and a new variable, y , which follow from the Orstein-Uhlenbeck process .
Edit intent: fluency
Original:  In this model  the value of the option is given by a linear (1 + 2) evolution partial differential equation , in which the price of the option depends on two independent variables, the value of the underlying asset S and a new variable, y , which follow from the Orstein-Uhlenbeck process .
Revised:  In this model the value of the option is given by a linear (1 + 2) evolution partial differential equation in which the price of the option depends on two independent variables, the value of the underlying asset S and a new variable, y , which follow from the Orstein-Uhlenbeck process .
Edit intent: fluency
Original:  In this model  the value of the option is given by a linear (1 + 2) evolution partial differential equation , in which the price of the option depends on two independent variables, the value of the underlying asset S and a new variable, y , which follow from the Orstein-Uhlenbeck process .
Revised:  In this model the value of the option is given by a linear (1 + 2) evolution partial differential equation , in which the price of the option depends upon two independent variables, the value of the underlying asset S and a new variable, y , which follow from the Orstein-Uhlenbeck process .
Edit intent: fluency
Original:  In this model  the value of the option is given by a linear (1 + 2) evolution partial differential equation , in which the price of the option depends on two independent variables, the value of the underlying asset S and a new variable, y , which follow from the Orstein-Uhlenbeck process .
Revised:  In this model the value of the option is given by a linear (1 + 2) evolution partial differential equation , in which the price of the option depends on two independent variables, the value of the underlying asset , S, and a new variable, y , which follow from the Orstein-Uhlenbeck process .
Edit intent: fluency
Original:  In this model  the value of the option is given by a linear (1 + 2) evolution partial differential equation , in which the price of the option depends on two independent variables, the value of the underlying asset S and a new variable, y , which follow from the Orstein-Uhlenbeck process .
Revised:  In this model the value of the option is given by a linear (1 + 2) evolution partial differential equation , in which the price of the option depends on two independent variables, the value of the underlying asset S and a new variable, y .
Edit intent: coherence
Original:  We find that for arbitrary functional form of the volatility, \sigma(y), the (1 + 2) evolution equation admits always two Lie symmetries, plus the linear symmetry and the infinity number of solution symmetries.
Revised:  We find that for arbitrary functional form of the volatility, \sigma(y), the (1 + 2) evolution equation always admits two Lie point symmetries in addition to the automatic linear symmetry and the infinity number of solution symmetries.
Edit intent: meaning-changed
Original:  We find that for arbitrary functional form of the volatility, \sigma(y), the (1 + 2) evolution equation admits always two Lie symmetries, plus the linear symmetry and the infinity number of solution symmetries.
Revised:  We find that for arbitrary functional form of the volatility, \sigma(y), the (1 + 2) evolution equation admits always two Lie symmetries, plus the linear symmetry and the infinite number of solution symmetries.
Edit intent: fluency
Original:  However  when \sigma(y)=\sigma_{0} and since the price of the option depends on the second Brownian motion in which the volatility is defined, the (1 + 2) evolution is not reduced to the Black-Scholes-Merton equation , the model admits five Lie symmetries, plus the linear symmetry and the infinity number of solution symmetries.
Revised:  However , when \sigma(y)=\sigma_{0} and since the price of the option depends on the second Brownian motion in which the volatility is defined, the (1 + 2) evolution is not reduced to the Black-Scholes-Merton equation , the model admits five Lie symmetries, plus the linear symmetry and the infinity number of solution symmetries.
Edit intent: fluency
Original:  However  when \sigma(y)=\sigma_{0} and since the price of the option depends on the second Brownian motion in which the volatility is defined, the (1 + 2) evolution is not reduced to the Black-Scholes-Merton equation , the model admits five Lie symmetries, plus the linear symmetry and the infinity number of solution symmetries.
Revised:  However when \sigma(y)=\sigma_{0} and as the price of the option depends on the second Brownian motion in which the volatility is defined, the (1 + 2) evolution is not reduced to the Black-Scholes-Merton equation , the model admits five Lie symmetries, plus the linear symmetry and the infinity number of solution symmetries.
Edit intent: coherence
Original:  However  when \sigma(y)=\sigma_{0} and since the price of the option depends on the second Brownian motion in which the volatility is defined, the (1 + 2) evolution is not reduced to the Black-Scholes-Merton equation , the model admits five Lie symmetries, plus the linear symmetry and the infinity number of solution symmetries.
Revised:  However when \sigma(y)=\sigma_{0} and since the price of the option depends upon the second Brownian motion in which the volatility is defined, the (1 + 2) evolution is not reduced to the Black-Scholes-Merton equation , the model admits five Lie symmetries, plus the linear symmetry and the infinity number of solution symmetries.
Edit intent: fluency
Original:  However  when \sigma(y)=\sigma_{0} and since the price of the option depends on the second Brownian motion in which the volatility is defined, the (1 + 2) evolution is not reduced to the Black-Scholes-Merton equation , the model admits five Lie symmetries, plus the linear symmetry and the infinity number of solution symmetries.
Revised:  However when \sigma(y)=\sigma_{0} and since the price of the option depends on the second Brownian motion in which the volatility is defined, the (1 + 2) evolution is not reduced to the Black--Scholes--Merton Equation , the model admits five Lie symmetries, plus the linear symmetry and the infinity number of solution symmetries.
Edit intent: fluency
Original:  However  when \sigma(y)=\sigma_{0} and since the price of the option depends on the second Brownian motion in which the volatility is defined, the (1 + 2) evolution is not reduced to the Black-Scholes-Merton equation , the model admits five Lie symmetries, plus the linear symmetry and the infinity number of solution symmetries.
Revised:  However when \sigma(y)=\sigma_{0} and since the price of the option depends on the second Brownian motion in which the volatility is defined, the (1 + 2) evolution is not reduced to the Black-Scholes-Merton equation , the model admits five Lie point symmetries in addition to the linear symmetry and the infinity number of solution symmetries.
Edit intent: meaning-changed
Original:  However  when \sigma(y)=\sigma_{0} and since the price of the option depends on the second Brownian motion in which the volatility is defined, the (1 + 2) evolution is not reduced to the Black-Scholes-Merton equation , the model admits five Lie symmetries, plus the linear symmetry and the infinity number of solution symmetries.
Revised:  However when \sigma(y)=\sigma_{0} and since the price of the option depends on the second Brownian motion in which the volatility is defined, the (1 + 2) evolution is not reduced to the Black-Scholes-Merton equation , the model admits five Lie symmetries, plus the linear symmetry and the infinite number of solution symmetries.
Edit intent: fluency
Original:  We demonstrate that  structure-only methods fail to properly characterize controllability in these nonlinear systems; even in very simple networks, a large variation of possible dynamics can occur for the same structure, each with different control profiles. Our methodology is also used to characterize critical control variables in three models of biochemical regulation: the Drosophila melanogaster single-cell segment polarity network , the eukaryotic cell cycle of budding yeast Saccharomyces cerevisiae, and the URLan arrangement in Arabidopsis thaliana.
Revised:  We demonstrate that structure-only methods fail to characterize controllability when dynamics are introduced. We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the Drosophila melanogaster single-cell segment polarity network , the eukaryotic cell cycle of budding yeast Saccharomyces cerevisiae, and the URLan arrangement in Arabidopsis thaliana.
Edit intent: clarity
Original:  Our methodology is also used to characterize critical control variables in three models of biochemical regulation: the Drosophila melanogaster single-cell segment polarity network , the eukaryotic cell cycle of budding yeast Saccharomyces cerevisiae, and the URLan arrangement in Arabidopsis thaliana.
Revised:  Our methodology is also used to characterize critical control variables in three models of biochemical regulation: the segment polarity network , the eukaryotic cell cycle of budding yeast Saccharomyces cerevisiae, and the URLan arrangement in Arabidopsis thaliana.
Edit intent: coherence
Original:  Our methodology is also used to characterize critical control variables in three models of biochemical regulation: the Drosophila melanogaster single-cell segment polarity network , the eukaryotic cell cycle of budding yeast Saccharomyces cerevisiae, and the URLan arrangement in Arabidopsis thaliana.
Revised:  Our methodology is also used to characterize critical control variables in three models of biochemical regulation: the Drosophila melanogaster single-cell segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the URLan arrangement in Arabidopsis thaliana.
Edit intent: clarity
Original:  The challenge is linking hardware energy consumption to the high-level application source code, considering the complex run-time context , such as thread scheduling, user inputs and the abstraction of the virtual machine. Traditional energy modeling is bottom-to-top, but this approach faces obstacles when software consists of a number of abstract layers.
Revised:  The challenge is linking hardware energy consumption to the high-level application source code, considering the complex run-time context and software stack. Traditional techniques build the energy model by mapping a hardware energy model onto software constructs; this approach faces obstacles when software consists of a number of abstract layers.
Edit intent: clarity
Original:   In this paper, we propose a top-to-bottom view. We focus on identifying valuable information from the source code, which results in the idea of utilizing an intermediate representation, "energy operation " , to capture the energy characteristics.
Revised:  In this paper, we explain how to construct a fine-grained energy model for the source code, which results in the idea of utilizing an intermediate representation, "energy operation " , to capture the energy characteristics.
Edit intent: clarity
Original:  We focus on identifying valuable information from the source code, which results in the idea of utilizing an intermediate representation, "energy operation " , to capture the energy characteristics. The experiment results show that the energy model at such a high-level can reduce the error margin to within 10\% and enable energy breakdown at function-level, which helps developers understand the energy-related features of the code .
Revised:  We focus on identifying valuable information from the source code, which results in the idea of utilizing an intermediate representation, "energy operation " identified directly from the source code and able to provide more valuable information for code optimization. We apply the approach to a class of applications based on a game-engine, and explain the wider applicability of the method .
Edit intent: meaning-changed
Original:  The public information generated by the financial assets, and a larger flow of information containing additional knowledge about a death time(random time /horizon) of an insured. By expanding the filtration, the death uncertainty and its entailed risk are fully considered without any mathematical restriction.
Revised:  The public information generated by the financial assets, and a larger flow of information containing additional knowledge about a random time. This random time can represent many economic and financial settings, such as the default time of a firm for credit risk, and the death time of an insured for life insurance. By using the expansion of filtration, the death uncertainty and its entailed risk are fully considered without any mathematical restriction.
Edit intent: meaning-changed
Original:  In this context , which catches real features such as correlation between the market model and the time of death, we address the risk-minimization problem \`a la F\"ollmer-Sondermann for a large class of equity-linked mortality contracts. The challenge in this setting, when no modelspecification for these securities nor for the death timeis given, lies in finding the dynamics and the structures for the mortality / longevity securities used in the securitization.
Revised:  In this context with no model's specification for the random time, the main challenge lies in finding the dynamics and the structures for the mortality / longevity securities used in the securitization.
Edit intent: clarity
Original:  The challenge in this setting, when no modelspecification for these securities nor for the death timeis given, lies in finding the dynamics and the structures for the mortality / longevity securities used in the securitization.
Revised:  The challenge in this setting, when no modelspecification for these securities nor for the death timeis given, lies in finding the dynamics and the structures for the value processes of defaultable or mortality and / longevity securities used in the securitization.
Edit intent: clarity
Original:  The challenge in this setting, when no modelspecification for these securities nor for the death timeis given, lies in finding the dynamics and the structures for the mortality / longevity securities used in the securitization.
Revised:  The challenge in this setting, when no modelspecification for these securities nor for the death timeis given, lies in finding the dynamics and the structures for the mortality / or longevity securities which are vital for the insurance securitization.
Edit intent: meaning-changed
Original:  To overcome this obstacle, we elaborate our optional martingale representation results, which state that any local martingale in the large filtration stopped at the death time can be decomposed into several and precise orthogonal local martingales .
Revised:  To overcome this obstacle, we elaborate our optional martingale representation results, which state that any martingale in the large filtration stopped at the death time can be decomposed into several and precise orthogonal local martingales .
Edit intent: fluency
Original:  To overcome this obstacle, we elaborate our optional martingale representation results, which state that any local martingale in the large filtration stopped at the death time can be decomposed into several and precise orthogonal local martingales .
Revised:  To overcome this obstacle, we elaborate our optional martingale representation results, which state that any local martingale in the large filtration stopped at the random time can be decomposed into several and precise orthogonal local martingales .
Edit intent: meaning-changed
Original:  To overcome this obstacle, we elaborate our optional martingale representation results, which state that any local martingale in the large filtration stopped at the death time can be decomposed into several and precise orthogonal local martingales .
Revised:  To overcome this obstacle, we elaborate our optional martingale representation results, which state that any local martingale in the large filtration stopped at the death time can be decomposed into precise and unique orthogonal local martingales .
Edit intent: clarity
Original:  To overcome this obstacle, we elaborate our optional martingale representation results, which state that any local martingale in the large filtration stopped at the death time can be decomposed into several and precise orthogonal local martingales .
Revised:  To overcome this obstacle, we elaborate our optional martingale representation results, which state that any local martingale in the large filtration stopped at the death time can be decomposed into several and precise orthogonal local martingales (i.e. local martingales whose product remains a local martingale).
Edit intent: clarity
Original:  Hence, the dynamics of mortality and longevity securities used in the securitization is described without mortality specification, and this constitutes our second novel contribution. Our third main contribution resides in finding explicitly the risk-minimization strategy as well as the corresponding undiversified risk for a largest class of mortality/longevity linked liabilities with or without the mortality securitization .
Revised:  Hence, the dynamics of mortality and longevity securities used in the securitization is described without mortality specification, and this constitutes our second contribution .
Edit intent: clarity
Original: Classical molecular dynamics (MD) simulations, within the AMBER program package that runs entirely on a CUDA-enabled NVIDIA graphic processing unit (GPU), were employed to study the dynamics of the methane-thiosulfonate spin labelled (MTSL)  Aurora-A kinase activation loop in a very short time and with good quality of the sampling.
Revised: Classical molecular dynamics (MD) simulations, within the AMBER program package that runs entirely on a CUDA-enabled NVIDIA graphic processing unit (GPU), were employed to study with low computational cost and good quality of the sampling the dynamics of the methane-thiosulfonate spin labelled (MTSL) Aurora-A kinase activation loop in a very short time and with good quality of the sampling.
Edit intent: meaning-changed
Original: Classical molecular dynamics (MD) simulations, within the AMBER program package that runs entirely on a CUDA-enabled NVIDIA graphic processing unit (GPU), were employed to study the dynamics of the methane-thiosulfonate spin labelled (MTSL)  Aurora-A kinase activation loop in a very short time and with good quality of the sampling.
Revised: Classical molecular dynamics (MD) simulations, within the AMBER program package that runs entirely on a CUDA-enabled NVIDIA graphic processing unit (GPU), were employed to study the dynamics of the methane-thiosulfonate spin label (MTSL) Aurora-A kinase activation loop in a very short time and with good quality of the sampling.
Edit intent: fluency
Original: Classical molecular dynamics (MD) simulations, within the AMBER program package that runs entirely on a CUDA-enabled NVIDIA graphic processing unit (GPU), were employed to study the dynamics of the methane-thiosulfonate spin labelled (MTSL)  Aurora-A kinase activation loop in a very short time and with good quality of the sampling.
Revised: Classical molecular dynamics (MD) simulations, within the AMBER program package that runs entirely on a CUDA-enabled NVIDIA graphic processing unit (GPU), were employed to study the dynamics of the methane-thiosulfonate spin labelled (MTSL) attached to the activation loop of the Aurora-A kinase activation loop in a very short time and with good quality of the sampling.
Edit intent: meaning-changed
Original: Classical molecular dynamics (MD) simulations, within the AMBER program package that runs entirely on a CUDA-enabled NVIDIA graphic processing unit (GPU), were employed to study the dynamics of the methane-thiosulfonate spin labelled (MTSL)  Aurora-A kinase activation loop in a very short time and with good quality of the sampling. The MD simulation provided a wealth of information on the interactions between MTSL and protein residues, and on the different motional contributions to the overall dynamics of the MTSL that were validated using a multifrequency electron paramagnetic resonance (EPR) approach.
Revised: Classical molecular dynamics (MD) simulations, within the AMBER program package that runs entirely on a CUDA-enabled NVIDIA graphic processing unit (GPU), were employed to study the dynamics of the methane-thiosulfonate spin labelled (MTSL) Aurora-A kinase. MD provided a wealth of information on the interactions between MTSL and protein residues, and on the different motional contributions to the overall dynamics of the MTSL that were validated using a multifrequency electron paramagnetic resonance (EPR) approach.
Edit intent: clarity
Original:  The MD simulation provided a wealth of information on the interactions between MTSL and protein residues, and on the different motional contributions to the overall dynamics of the MTSL that were validated using a multifrequency electron paramagnetic resonance (EPR) approach.
Revised:  The MD simulation provided a wealth of information about the timescale of the different motional contributions to the overall dynamics of the MTSL that were validated using a multifrequency electron paramagnetic resonance (EPR) approach.
Edit intent: meaning-changed
Original:  The MD simulation provided a wealth of information on the interactions between MTSL and protein residues, and on the different motional contributions to the overall dynamics of the MTSL that were validated using a multifrequency electron paramagnetic resonance (EPR) approach.
Revised:  The MD simulation provided a wealth of information on the interactions between MTSL and protein residues, and on the different motional contributions to the overall dynamics of the spin label. These data were validated by multi-frequency continuous-wave electron paramagnetic resonance (EPR) approach.
Edit intent: meaning-changed
Original:  The MD simulation provided a wealth of information on the interactions between MTSL and protein residues, and on the different motional contributions to the overall dynamics of the MTSL that were validated using a multifrequency electron paramagnetic resonance (EPR) approach. The latter relayed on the frequency dependence of the resolution of the fast and slow motions of the spin probe and was used to distinguish the fast internal motion of the spin label from the slow protein tumbling.
Revised:  The MD simulation provided a wealth of information on the interactions between MTSL and protein residues, and on the different motional contributions to the overall dynamics of the MTSL that were validated using a multifrequency electron paramagnetic resonance (EPR) measurements, that relying on the frequency dependence of the resolution of the fast and slow motions of the spin probe and was used to distinguish the fast internal motion of the spin label from the slow protein tumbling.
Edit intent: clarity