wanyu/IteraTeR_full_sent huggingface dataset:
Original: In the present work, a novel second-order approximation for ATM option prices is derived for a large class of exponential L\'evy models . Our method of proof is based on an integral representation of the option price involving the tail probability of the log-return process under the share measure and a suitable change of probability measure under which the pure-jump componentof the log-return%DIFDELCMD < } %%% process becomes a Y-stable process.
Revised: In the present work, a novel second-order approximation for ATM option prices is derived for a large class of exponential L\'evy models pure-jump componentof the log-return%DIFDELCMD < } %%% process becomes a Y-stable process.
Edit intent: coherence
Original:  Our method of proof is based on an integral representation of the option price involving the tail probability of the log-return process under the share measure and a suitable change of probability measure under which the pure-jump componentof the log-return%DIFDELCMD < } %%% process becomes a Y-stable process.
Revised:  Our method of proof is based on an integral representation of the option price involving the tail probability of the log-return process under the share measure and a suitable change of probability measure under which the %DIFDELCMD < } %%% process becomes a Y-stable process.
Edit intent: clarity
Original:  Our method of proof is based on an integral representation of the option price involving the tail probability of the log-return process under the share measure and a suitable change of probability measure under which the pure-jump componentof the log-return%DIFDELCMD < } %%% process becomes a Y-stable process. Our approach is sufficiently general to cover a wide class of L\'evy processes which satisfy the latter property and whose L\'evy densities can be closely approximated by a stable density near the origin.
Revised:  Our method of proof is based on an integral representation of the option price involving the tail probability of the log-return process under the share measure and a suitable change of probability measure under which the pure-jump componentof the log-return%DIFDELCMD < } %%% with or without Brownian component.
Edit intent: meaning-changed
Original:  In the presence of an additional Brownian component, the second-order term, in time-t, is of the form d_{2} t^{(3-Y)/2}, with the coefficient d_{2} depending only on the overall jump intensity of the process and the tail-heaviness parameter Y.
Revised:  In the presence of a Brownian component, the second-order term, in time-t, is of the form d_{2} t^{(3-Y)/2}, with the coefficient d_{2} depending only on the overall jump intensity of the process and the tail-heaviness parameter Y.
Edit intent: clarity
Original:  In the presence of an additional Brownian component, the second-order term, in time-t, is of the form d_{2} t^{(3-Y)/2}, with the coefficient d_{2} depending only on the overall jump intensity of the process and the tail-heaviness parameter Y. This extends the already%DIFDELCMD < } %%% known result that the leading term is \sigma t^{1/2/2\pi, where }%DIFDELCMD < \sigmais %%%  the volatility of the continuous component  .
Revised:  In the presence of an additional Brownian component, the second-order term, in time-t, is of the form d_{2} t^{(3-Y)/2}, with the coefficient d_{2} depending only on already%DIFDELCMD < } %%% known result that the leading term is \sigma t^{1/2/2\pi, where }%DIFDELCMD < \sigmais %%% the volatility of the continuous component .
Edit intent: coherence
Original:  This extends the already%DIFDELCMD < } %%% known result that the leading term is \sigma t^{1/2/2\pi, where }%DIFDELCMD < \sigmais %%%  the volatility of the continuous component  .
Revised:  This extends the %DIFDELCMD < } %%% known result that the leading term is \sigma t^{1/2/2\pi, where }%DIFDELCMD < \sigmais %%% the volatility of the continuous component .
Edit intent: clarity
Original:  This extends the already%DIFDELCMD < } %%% known result that the leading term is \sigma t^{1/2/2\pi, where }%DIFDELCMD < \sigmais %%%  the volatility of the continuous component  .
Revised:  This extends the already%DIFDELCMD < } %%% /2\pi, where }%DIFDELCMD < \sigmais %%% the volatility of the continuous component .
Edit intent: clarity
Original:  This extends the already%DIFDELCMD < } %%% known result that the leading term is \sigma t^{1/2/2\pi, where }%DIFDELCMD < \sigmais %%%  the volatility of the continuous component  .
Revised:  This extends the already%DIFDELCMD < } %%% known result that the leading term is \sigma t^{1/2/2\pi, where }%DIFDELCMD < \sigmais %%% Y, the degree of jump activity, on \sigma, the volatility of the continuous component .
Edit intent: meaning-changed
Original:  This extends the already%DIFDELCMD < } %%% known result that the leading term is \sigma t^{1/2/2\pi, where }%DIFDELCMD < \sigmais %%%  the volatility of the continuous component  .
Revised:  This extends the already%DIFDELCMD < } %%% known result that the leading term is \sigma t^{1/2/2\pi, where }%DIFDELCMD < \sigmais %%% the volatility of the continuous component , and on an additional parameter controlling the intensity of the "small" jumps (regardless of their signs) .
Edit intent: meaning-changed
Original:  In contrast, under such a pure-jump model, the dependence on the overall jump intensity and Y is already reflected in the leading term, which is of the form d_{1}t^{1/Y}.
Revised:  In contrast, under a pure-jump model, the dependence on the overall jump intensity and Y is already reflected in the leading term, which is of the form d_{1}t^{1/Y}.
Edit intent: clarity
Original:  In contrast, under such a pure-jump model, the dependence on the overall jump intensity and Y is already reflected in the leading term, which is of the form d_{1}t^{1/Y}.
Revised:  In contrast, under such a pure-jump model, the dependence on Y and on the separate intensities of negative and positive small jumps are already reflected in the leading term, which is of the form d_{1}t^{1/Y}.
Edit intent: meaning-changed
Original:  The information on the relative frequency of negative and positive jumps appears only in the second-order term , which is shown to be of the form d_{2} t and whose order of decay turns out to be independent of Y.
Revised:  The information on the relative frequency of negative and positive jumps appears only in the second-order term is shown to be of the form d_{2} t and whose order of decay turns out to be independent of Y.
Edit intent: coherence
Original:  The information on the relative frequency of negative and positive jumps appears only in the second-order term , which is shown to be of the form d_{2} t and whose order of decay turns out to be independent of Y.
Revised:  The information on the relative frequency of negative and positive jumps appears only in the second-order term , which is shown to be of the form d_{2} t and , therefore, its order of decay turns out to be independent of Y.
Edit intent: coherence
Original:  Our  numerical results show that first-order term typically exhibits rather poor performance and that the second-order term significantly improves the approximation's accuracy.
Revised:  Our method of proof is based on an integral representation of the option price involving the tail probability of the log-return process under the share measure and a suitable change of probability measure under which the pure-jump component of the log-return process becomes a Y-stable process. Our approach is sufficiently general to cover a wide class of L\'evy processes which satisfy the latter property and whose L\'evy densitiy can be closely approximated by a stable density near the origin. Our numerical results show that first-order term typically exhibits rather poor performance and that the second-order term significantly improves the approximation's accuracy.
Edit intent: meaning-changed
Original: Multimodal oncological strategies which combine chemotherapy or radiotherapy with hyperthermia (i.e. raising the temperature of a region of the body affected by cancer) have a potential of improving the efficacy of the non-surgical methods of cancer treatment.
Revised: Multimodal oncological strategies which combine chemotherapy or radiotherapy with hyperthermia have a potential of improving the efficacy of the non-surgical methods of cancer treatment.
Edit intent: clarity
Original:  Traditional generation scheduling paradigms assuming perfect prediction of future electricity supply and demand  are no longer applicable to microgrids with unpredictable renewable energy supply and  co-generation (that depends on both electricity and heat demand).
Revised:  Traditional generation scheduling paradigms rely on perfect prediction of future electricity supply and demand are no longer applicable to microgrids with unpredictable renewable energy supply and co-generation (that depends on both electricity and heat demand).
Edit intent: clarity
Original:  Traditional generation scheduling paradigms assuming perfect prediction of future electricity supply and demand  are no longer applicable to microgrids with unpredictable renewable energy supply and  co-generation (that depends on both electricity and heat demand).
Revised:  Traditional generation scheduling paradigms assuming perfect prediction of future electricity supply and demand . They are no longer applicable to microgrids with unpredictable renewable energy supply and co-generation (that depends on both electricity and heat demand).
Edit intent: coherence
Original:  Traditional generation scheduling paradigms assuming perfect prediction of future electricity supply and demand  are no longer applicable to microgrids with unpredictable renewable energy supply and  co-generation (that depends on both electricity and heat demand).
Revised:  Traditional generation scheduling paradigms assuming perfect prediction of future electricity supply and demand are no longer applicable to microgrids with unpredictable renewable energy supply and with co-generation (that depends on both electricity and heat demand).
Edit intent: fluency
Original:  Traditional generation scheduling paradigms assuming perfect prediction of future electricity supply and demand  are no longer applicable to microgrids with unpredictable renewable energy supply and  co-generation (that depends on both electricity and heat demand).
Revised:  Traditional generation scheduling paradigms assuming perfect prediction of future electricity supply and demand are no longer applicable to microgrids with unpredictable renewable energy supply and co-generation (that needs to consider both electricity and heat demand).
Edit intent: clarity
Original:  Based on  insights from the structure of the offline optimal solution, we propose a class of competitive online algorithms, called CHASE (Competitive Heuristic Algorithm for Scheduling Energy-generation), that track the offline optimal in an online fashion.
Revised:  Based on the insights from the structure of the offline optimal solution, we propose a class of competitive online algorithms, called CHASE (Competitive Heuristic Algorithm for Scheduling Energy-generation), that track the offline optimal in an online fashion.
Edit intent: fluency
Original:  Under certain settings, we show that CHASE achieves the best competitive ratio of all deterministic online algorithms . We also extend our algorithms to intelligently leverage on limited prediction of the future, such as near-term demand or wind forecast. By extensive empirical evaluation using real-world traces, we show that our proposed algorithms can achieve near-offline-optimal performance. In a representative scenario, CHASE leads to around 20\% cost savings with no future look-ahead at all, and the cost-savings further increase with limited future look-ahead.
Revised:  Under certain settings, we show that CHASE achieves the best competitive ratio of all deterministic online algorithms , and the ratio is no larger than a small constant 3.
Edit intent: meaning-changed
Original: We consider the optimal problem \sup:=\sup} _{\tau\inT _{\eps,T } \mathbb{E[i=1^n (\tau-\eps^i)^+^i], where } B_{(\tau-\eps)^+} posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012.
Revised: We consider the optimal stopping problem v^{(\eps):=\sup} _{\tau\inT _{\eps,T } \mathbb{E[i=1^n (\tau-\eps^i)^+^i], where } B_{(\tau-\eps)^+} posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012.
Edit intent: meaning-changed
Original: We consider the optimal problem \sup:=\sup} _{\tau\inT _{\eps,T } \mathbb{E[i=1^n (\tau-\eps^i)^+^i], where } B_{(\tau-\eps)^+} posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012.
Revised: We consider the optimal problem \sup:=\sup} _{\tau\inT _{0,T } \mathbb{E[i=1^n (\tau-\eps^i)^+^i], where } B_{(\tau-\eps)^+} posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012.
Edit intent: meaning-changed
Original: We consider the optimal problem \sup:=\sup} _{\tau\inT _{\eps,T } \mathbb{E[i=1^n (\tau-\eps^i)^+^i], where } B_{(\tau-\eps)^+} posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012.
Revised: We consider the optimal problem \sup:=\sup} _{\tau\inT _{\eps,T } [i=1^n (\tau-\eps^i)^+^i], where } B_{(\tau-\eps)^+} posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012.
Edit intent: coherence
Original: We consider the optimal problem \sup:=\sup} _{\tau\inT _{\eps,T } \mathbb{E[i=1^n (\tau-\eps^i)^+^i], where } B_{(\tau-\eps)^+} posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012.
Revised: We consider the optimal problem \sup:=\sup} _{\tau\inT _{\eps,T } \mathbb{E[i=1^n (\tau-\eps^i)^+^i], where } \mathbb{EB_{(\tau-\eps)^+} posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012.
Edit intent: fluency
Original:  Here } T>0 is a fixed time horizon, ( \phi_t^i )_{0\leq t\leq T} is progressively measurable with respect to the Brownian filtration , \eps ^i \in[0,T] is a constant, i=1,...o,n, and T_{\eps,T} is the set of stopping times that lie between a constant \eps \in[0,T] and T.
Revised:  Here } T>0 is a fixed time horizon, ( B_t )_{0\leq t\leq T} is progressively measurable with respect to the Brownian filtration , \eps ^i \in[0,T] is a constant, i=1,...o,n, and T_{\eps,T} is the set of stopping times that lie between a constant \eps \in[0,T] and T.
Edit intent: meaning-changed
Original:  Here } T>0 is a fixed time horizon, ( \phi_t^i )_{0\leq t\leq T} is progressively measurable with respect to the Brownian filtration , \eps ^i \in[0,T] is a constant, i=1,...o,n, and T_{\eps,T} is the set of stopping times that lie between a constant \eps \in[0,T] and T.
Revised:  Here } T>0 is a fixed time horizon, ( \phi_t^i )_{0\leq t\leq T} is the Brownian motion , \eps ^i \in[0,T] is a constant, i=1,...o,n, and T_{\eps,T} is the set of stopping times that lie between a constant \eps \in[0,T] and T.
Edit intent: clarity
Original:  Here } T>0 is a fixed time horizon, ( \phi_t^i )_{0\leq t\leq T} is progressively measurable with respect to the Brownian filtration , \eps ^i \in[0,T] is a constant, i=1,...o,n, and T_{\eps,T} is the set of stopping times that lie between a constant \eps \in[0,T] and T.
Revised:  Here } T>0 is a fixed time horizon, ( \phi_t^i )_{0\leq t\leq T} is progressively measurable with respect to the Brownian filtration , \eps \in[0,T] is a constant, i=1,...o,n, and T_{\eps,T} is the set of stopping times that lie between a constant \eps \in[0,T] and T.
Edit intent: clarity
Original:  Here } T>0 is a fixed time horizon, ( \phi_t^i )_{0\leq t\leq T} is progressively measurable with respect to the Brownian filtration , \eps ^i \in[0,T] is a constant, i=1,...o,n, and T_{\eps,T} is the set of stopping times that lie between a constant \eps \in[0,T] and T.
Revised:  Here } T>0 is a fixed time horizon, ( \phi_t^i )_{0\leq t\leq T} is progressively measurable with respect to the Brownian filtration , \eps ^i \in[0,T] is a constant, and T_{\eps,T} is the set of stopping times that lie between a constant \eps \in[0,T] and T.
Edit intent: meaning-changed
Original:  Here } T>0 is a fixed time horizon, ( \phi_t^i )_{0\leq t\leq T} is progressively measurable with respect to the Brownian filtration , \eps ^i \in[0,T] is a constant, i=1,...o,n, and T_{\eps,T} is the set of stopping times that lie between a constant \eps \in[0,T] and T.
Revised:  Here } T>0 is a fixed time horizon, ( \phi_t^i )_{0\leq t\leq T} is progressively measurable with respect to the Brownian filtration , \eps ^i \in[0,T] is a constant, i=1,...o,n, and T_{\eps,T} is the set of stopping times taking values in \eps \in[0,T] and T.
Edit intent: clarity
Original:  Here } T>0 is a fixed time horizon, ( \phi_t^i )_{0\leq t\leq T} is progressively measurable with respect to the Brownian filtration , \eps ^i \in[0,T] is a constant, i=1,...o,n, and T_{\eps,T} is the set of stopping times that lie between a constant \eps \in[0,T] and T. We solve this problem by conditioning and then using the theory of  reflected backward stochastic differential equations (RBSDEs).
Revised:  Here } T>0 is a fixed time horizon, ( \phi_t^i )_{0\leq t\leq T} is progressively measurable with respect to the Brownian filtration , \eps ^i \in[0,T] is a constant, i=1,...o,n, and T_{\eps,T} is the set of stopping times that lie between a constant \eps ,T reflected backward stochastic differential equations (RBSDEs).
Edit intent: coherence
Original:  We solve this problem by conditioning and then using the theory of  reflected backward stochastic differential equations (RBSDEs). As a corollary, we provide the solution to the optimal stopping problem \sup_{\tau\in\mathcal{T_{0,T}}\mathbb{E}B_{(\tau-\eps)^+} recently posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012.
Revised:  We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations _{0,T}}\mathbb{E}B_{(\tau-\eps)^+} recently posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012.
Edit intent: meaning-changed
Original:  We present here a new method to interrogate affinity differences resulting from mutations in a host-virus protein--protein interface. Our method is based on extensive non-equilibrium all-atom simulations: We computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1) and estimate affinity using the maximum applied force during a pulling simulation and the area under the force-versus-distance curve.
Revised:  We present here a new method to interrogate affinity differences resulting from mutations in a host-virus protein-protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1) and estimate affinity using the maximum applied force during a pulling simulation and the area under the force-versus-distance curve.
Edit intent: clarity
Original:  Our method is based on extensive non-equilibrium all-atom simulations: We computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1) and estimate affinity using the maximum applied force during a pulling simulation and the area under the force-versus-distance curve.
Revised:  Our method is based on extensive non-equilibrium all-atom simulations: We computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1) . We then approximate affinity using the maximum applied force during a pulling simulation and the area under the force-versus-distance curve.
Edit intent: clarity
Original:  Our method is based on extensive non-equilibrium all-atom simulations: We computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1) and estimate affinity using the maximum applied force during a pulling simulation and the area under the force-versus-distance curve.
Revised:  Our method is based on extensive non-equilibrium all-atom simulations: We computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1) and estimate affinity using the maximum applied force of separation and the area under the force-versus-distance curve.
Edit intent: clarity
Original:   Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that only one of them is critical for the bindinginteraction .
Revised:  Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that only one of them is critical for the bindinginteraction .
Edit intent: meaning-changed
Original:   Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that only one of them is critical for the bindinginteraction .
Revised:  Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them is critical for the bindinginteraction .
Edit intent: clarity
Original:   Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that only one of them is critical for the bindinginteraction .
Revised:  Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that only one of them may not be important for tight binding .
Edit intent: clarity
Original:  We choose the lattice-Boltzmann method (LBM)  as a prototype for this scenario in order to show if and how single-chip performance and power characteristics can be generalized to the highly parallel case.
Revised:  We choose the lattice-Boltzmann method (LBM) on an Intel Sandy Bridge cluster as a prototype for this scenario in order to show if and how single-chip performance and power characteristics can be generalized to the highly parallel case.
Edit intent: meaning-changed
Original:  We choose the lattice-Boltzmann method (LBM)  as a prototype for this scenario in order to show if and how single-chip performance and power characteristics can be generalized to the highly parallel case.
Revised:  We choose the lattice-Boltzmann method (LBM) as a prototype scenario to investigate if and how single-chip performance and power characteristics can be generalized to the highly parallel case.
Edit intent: clarity