wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Applying the model to high frequent data test in China stock market, we have main findings as follows: 1) there are , in general, significant positive correlations between the rate of mean return and that of change in both trading conditioning intensity and the amount of transaction ;
Revised:  Applying the model to high frequent data test in China stock market, we have main findings as follows: 1) there are , in general, significant positive correlation between the rate of mean return and that of change in both trading conditioning intensity and the amount of transaction ;
Edit intent: fluency
Original:  Applying the model to high frequent data test in China stock market, we have main findings as follows: 1) there are , in general, significant positive correlations between the rate of mean return and that of change in both trading conditioning intensity and the amount of transaction ;
Revised:  Applying the model to high frequent data test in China stock market, we have main findings as follows: 1) there are , in general, significant positive correlations between the rate of mean return and that of change in trading conditioning intensity and the amount of transaction ;
Edit intent: clarity
Original:  Applying the model to high frequent data test in China stock market, we have main findings as follows: 1) there are , in general, significant positive correlations between the rate of mean return and that of change in both trading conditioning intensity and the amount of transaction ;
Revised:  Applying the model to high frequent data test in China stock market, we have main findings as follows: 1) there are , in general, significant positive correlations between the rate of mean return and that of change in both trading conditioning intensity ;
Edit intent: clarity
Original:  This yields a leading order expansion for the implied volatility at large strikes: BS( k,T)^{2}T\sim \Psi (s ^* -1) \times k (Roger Lee's moment formula).
Revised:  This yields a leading order expansion for the implied volatility at large strikes: BS( k,T)^{2}T\sim \Psi (s _+ -1) \times k (Roger Lee's moment formula).
Edit intent: meaning-changed
Original:  Motivated by recent "tail-wing" refinements of this moment formula, we first derive a novel tail expansion for the Heston density, sharpening previous work of Dr \u{a}%DIFDELCMD < }%%% gulescu and Yakovenko [Quant.
Revised:  Motivated by recent "tail-wing" refinements of this moment formula, we first derive a novel tail expansion for the Heston density, sharpening previous work of \u{a}%DIFDELCMD < }%%% gulescu and Yakovenko [Quant.
Edit intent: fluency
Original:  Motivated by recent "tail-wing" refinements of this moment formula, we first derive a novel tail expansion for the Heston density, sharpening previous work of Dr \u{a}%DIFDELCMD < }%%% gulescu and Yakovenko [Quant.
Revised:  Motivated by recent "tail-wing" refinements of this moment formula, we first derive a novel tail expansion for the Heston density, sharpening previous work of Dr }%DIFDELCMD < }%%% gulescu and Yakovenko [Quant.
Edit intent: coherence
Original:  Motivated by recent "tail-wing" refinements of this moment formula, we first derive a novel tail expansion for the Heston density, sharpening previous work of Dr \u{a}%DIFDELCMD < }%%% gulescu and Yakovenko [Quant.
Revised:  Motivated by recent "tail-wing" refinements of this moment formula, we first derive a novel tail expansion for the Heston density, sharpening previous work of Dr \u{a}%DIFDELCMD < }%%% Dragulescu and Yakovenko [Quant.
Edit intent: meaning-changed
Original:  Finance 2, 6 (2002), 443--453], and then show the validity of a refined expansion of the type \% BS( k,T) ^{2}T=( 1k^{1/2}+2+...
Revised:  Finance 2, 6 (2002), 443--453], and then show the validity of a refined expansion of the type BS( k,T) ^{2}T=( 1k^{1/2}+2+...
Edit intent: coherence
Original:  at no point do we need knowledge of the (explicit, but cumbersome) closed form expression of the Fourier transform of \log S_{T}  (equivalently: Mellin transform of S_{T} \% ) . Secondly, our analysis reveals a new parameter (" \textit{critical slope " \% ), defined in a model free manner, which drives the second and higher order terms in tail- and implied volatility expansions.
Revised:  at no point do we need knowledge of the (explicit, but cumbersome) closed form expression of the Fourier transform of \log S_{T} \ (equivalently: Mellin transform of S_{T} \% ) . Secondly, our analysis reveals a new parameter (" \textit{critical slope " \% ), defined in a model free manner, which drives the second and higher order terms in tail- and implied volatility expansions.
Edit intent: fluency
Original:  at no point do we need knowledge of the (explicit, but cumbersome) closed form expression of the Fourier transform of \log S_{T}  (equivalently: Mellin transform of S_{T} \% ) . Secondly, our analysis reveals a new parameter (" \textit{critical slope " \% ), defined in a model free manner, which drives the second and higher order terms in tail- and implied volatility expansions.
Revised:  at no point do we need knowledge of the (explicit, but cumbersome) closed form expression of the Fourier transform of \log S_{T} (equivalently: Mellin transform of S_{T} ); what matters is that these transforms satisfy ordinary differential equations of Riccati type . Secondly, our analysis reveals a new parameter (" \textit{critical slope " \% ), defined in a model free manner, which drives the second and higher order terms in tail- and implied volatility expansions.
Edit intent: meaning-changed
Original:  at no point do we need knowledge of the (explicit, but cumbersome) closed form expression of the Fourier transform of \log S_{T}  (equivalently: Mellin transform of S_{T} \% ) . Secondly, our analysis reveals a new parameter (" \textit{critical slope " \% ), defined in a model free manner, which drives the second and higher order terms in tail- and implied volatility expansions.
Revised:  at no point do we need knowledge of the (explicit, but cumbersome) closed form expression of the Fourier transform of \log S_{T} (equivalently: Mellin transform of S_{T} \% ) . Secondly, our analysis reveals a new parameter (" \textit{critical slope " \% ), defined in a model free manner, which drives the second and higher order terms in tail- and implied volatility expansions.
Edit intent: fluency
Original:  at no point do we need knowledge of the (explicit, but cumbersome) closed form expression of the Fourier transform of \log S_{T}  (equivalently: Mellin transform of S_{T} \% ) . Secondly, our analysis reveals a new parameter (" \textit{critical slope " \% ), defined in a model free manner, which drives the second and higher order terms in tail- and implied volatility expansions.
Revised:  at no point do we need knowledge of the (explicit, but cumbersome) closed form expression of the Fourier transform of \log S_{T} (equivalently: Mellin transform of S_{T} \% ) . Secondly, our analysis reveals a new parameter (" \textit{critical slope " ), defined in a model free manner, which drives the second and higher order terms in tail- and implied volatility expansions.
Edit intent: fluency
Original: The Wako-Sait\^o-Mu\~noz-Eaton } (WSME) model  initially introduced in the theory of protein folding can also model some epitaxial phenomena.
Revised: The Wako-Sait\^o-Mu\~noz-Eaton \^o} (WSME) model initially introduced in the theory of protein folding can also model some epitaxial phenomena.
Edit intent: fluency
Original: The Wako-Sait\^o-Mu\~noz-Eaton } (WSME) model  initially introduced in the theory of protein folding can also model some epitaxial phenomena.
Revised: The Wako-Sait\^o-Mu\~noz-Eaton }-Mu\~noz-Eaton (WSME) model initially introduced in the theory of protein folding can also model some epitaxial phenomena.
Edit intent: meaning-changed
Original: The Wako-Sait\^o-Mu\~noz-Eaton } (WSME) model  initially introduced in the theory of protein folding can also model some epitaxial phenomena.
Revised: The Wako-Sait\^o-Mu\~noz-Eaton } (WSME) model , initially introduced in the theory of protein folding can also model some epitaxial phenomena.
Edit intent: fluency
Original: The Wako-Sait\^o-Mu\~noz-Eaton } (WSME) model  initially introduced in the theory of protein folding can also model some epitaxial phenomena.
Revised: The Wako-Sait\^o-Mu\~noz-Eaton } (WSME) model initially introduced in the theory of protein folding , has also been used in modeling the RNA folding and some epitaxial phenomena.
Edit intent: meaning-changed
Original:  The advantage of the model is that it admits exact solution in the general inhomogeneous case (Bruscolini and Pelizzola, 2002) which facilitates the modeling of realistic systems.
Revised:  The advantage of this model is that it admits exact solution in the general inhomogeneous case (Bruscolini and Pelizzola, 2002) which facilitates the modeling of realistic systems.
Edit intent: clarity
Original:  The advantage of the model is that it admits exact solution in the general inhomogeneous case (Bruscolini and Pelizzola, 2002) which facilitates the modeling of realistic systems.
Revised:  The advantage of the model is that it admits exact solution in the general inhomogeneous case (Bruscolini and Pelizzola, 2002) which facilitates the study of realistic systems.
Edit intent: clarity
Original:  But due to the protein (or atomic chain) flexibility, the residues (atoms) separated by several non-native bonds along the sequence can become very close in the real space which usually causes their strong interaction.
Revised:  But due to the biopolymer ( atomic chain) flexibility, the residues (atoms) separated by several non-native bonds along the sequence can become very close in the real space which usually causes their strong interaction.
Edit intent: meaning-changed
Original:  But due to the protein (or atomic chain) flexibility, the residues (atoms) separated by several non-native bonds along the sequence can become very close in the real space which usually causes their strong interaction.
Revised:  But due to the protein (or atomic chain) flexibility, the monomers (atoms) separated by several non-native bonds along the sequence can become very close in the real space which usually causes their strong interaction.
Edit intent: meaning-changed
Original: We consider a stochastic volatility model with L\'evy jumps for a log-return process Z=(Z _t) _{t %DIFDELCMD < \ge %%%  0} of the form Z=U+X, where U=(U _t) _{t %DIFDELCMD < \ge %%%  0} is a classical stochastic volatility process and X=(X _t) _{t %DIFDELCMD < \ge %%%  0} is an independent L\'evy process with absolutely continuous L\'evy measure \nu.
Revised: We consider a stochastic volatility model with L\'evy jumps for a log-return process Z=(Z _{t _{t %DIFDELCMD < \ge %%% 0} of the form Z=U+X, where U=(U _t) _{t %DIFDELCMD < \ge %%% 0} is a classical stochastic volatility process and X=(X _t) _{t %DIFDELCMD < \ge %%% 0} is an independent L\'evy process with absolutely continuous L\'evy measure \nu.
Edit intent: fluency
Original: We consider a stochastic volatility model with L\'evy jumps for a log-return process Z=(Z _t) _{t %DIFDELCMD < \ge %%%  0} of the form Z=U+X, where U=(U _t) _{t %DIFDELCMD < \ge %%%  0} is a classical stochastic volatility process and X=(X _t) _{t %DIFDELCMD < \ge %%%  0} is an independent L\'evy process with absolutely continuous L\'evy measure \nu.
Revised: We consider a stochastic volatility model with L\'evy jumps for a log-return process Z=(Z _t) _{t %DIFDELCMD < \ge %%% \geq 0} of the form Z=U+X, where U=(U _t) _{t %DIFDELCMD < \ge %%% 0} is a classical stochastic volatility process and X=(X _t) _{t %DIFDELCMD < \ge %%% 0} is an independent L\'evy process with absolutely continuous L\'evy measure \nu.
Edit intent: clarity
Original: We consider a stochastic volatility model with L\'evy jumps for a log-return process Z=(Z _t) _{t %DIFDELCMD < \ge %%%  0} of the form Z=U+X, where U=(U _t) _{t %DIFDELCMD < \ge %%%  0} is a classical stochastic volatility process and X=(X _t) _{t %DIFDELCMD < \ge %%%  0} is an independent L\'evy process with absolutely continuous L\'evy measure \nu.
Revised: We consider a stochastic volatility model with L\'evy jumps for a log-return process Z=(Z _t) _{t %DIFDELCMD < \ge %%% 0} of the form Z=U+X, where U=(U _{t _{t %DIFDELCMD < \ge %%% 0} is a classical stochastic volatility process and X=(X _t) _{t %DIFDELCMD < \ge %%% 0} is an independent L\'evy process with absolutely continuous L\'evy measure \nu.
Edit intent: fluency
Original: We consider a stochastic volatility model with L\'evy jumps for a log-return process Z=(Z _t) _{t %DIFDELCMD < \ge %%%  0} of the form Z=U+X, where U=(U _t) _{t %DIFDELCMD < \ge %%%  0} is a classical stochastic volatility process and X=(X _t) _{t %DIFDELCMD < \ge %%%  0} is an independent L\'evy process with absolutely continuous L\'evy measure \nu.
Revised: We consider a stochastic volatility model with L\'evy jumps for a log-return process Z=(Z _t) _{t %DIFDELCMD < \ge %%% 0} of the form Z=U+X, where U=(U _t) _{t %DIFDELCMD < \ge %%% \geq 0} is a classical stochastic volatility process and X=(X _t) _{t %DIFDELCMD < \ge %%% 0} is an independent L\'evy process with absolutely continuous L\'evy measure \nu.
Edit intent: meaning-changed
Original: We consider a stochastic volatility model with L\'evy jumps for a log-return process Z=(Z _t) _{t %DIFDELCMD < \ge %%%  0} of the form Z=U+X, where U=(U _t) _{t %DIFDELCMD < \ge %%%  0} is a classical stochastic volatility process and X=(X _t) _{t %DIFDELCMD < \ge %%%  0} is an independent L\'evy process with absolutely continuous L\'evy measure \nu.
Revised: We consider a stochastic volatility model with L\'evy jumps for a log-return process Z=(Z _t) _{t %DIFDELCMD < \ge %%% 0} of the form Z=U+X, where U=(U _t) _{t %DIFDELCMD < \ge %%% 0} is a classical stochastic volatility process and X=(X _{t _{t %DIFDELCMD < \ge %%% 0} is an independent L\'evy process with absolutely continuous L\'evy measure \nu.
Edit intent: fluency
Original: We consider a stochastic volatility model with L\'evy jumps for a log-return process Z=(Z _t) _{t %DIFDELCMD < \ge %%%  0} of the form Z=U+X, where U=(U _t) _{t %DIFDELCMD < \ge %%%  0} is a classical stochastic volatility process and X=(X _t) _{t %DIFDELCMD < \ge %%%  0} is an independent L\'evy process with absolutely continuous L\'evy measure \nu.
Revised: We consider a stochastic volatility model with L\'evy jumps for a log-return process Z=(Z _t) _{t %DIFDELCMD < \ge %%% 0} of the form Z=U+X, where U=(U _t) _{t %DIFDELCMD < \ge %%% 0} is a classical stochastic volatility process and X=(X _t) _{t %DIFDELCMD < \ge %%% \geq 0} is an independent L\'evy process with absolutely continuous L\'evy measure \nu.
Edit intent: meaning-changed
Original:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%%  z} for smooth functions \phi and z>0.
Revised:  Small-time expansions, of arbitrary polynomial order , in time-t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%% z} for smooth functions \phi and z>0.
Edit intent: fluency
Original:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%%  z} for smooth functions \phi and z>0.
Revised:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z %DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%% z} for smooth functions \phi and z>0.
Edit intent: fluency
Original:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%%  z} for smooth functions \phi and z>0.
Revised:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% _{t\geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%% z} for smooth functions \phi and z>0.
Edit intent: meaning-changed
Original:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%%  z} for smooth functions \phi and z>0.
Revised:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _{t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%% z} for smooth functions \phi and z>0.
Edit intent: meaning-changed
Original:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%%  z} for smooth functions \phi and z>0.
Revised:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } -1)_{+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%% z} for smooth functions \phi and z>0.
Edit intent: fluency
Original:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%%  z} for smooth functions \phi and z>0.
Revised:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \varphi (x){\bf 1}_{x %DIFDELCMD < \ge %%% z} for smooth functions \phi and z>0.
Edit intent: fluency
Original:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%%  z} for smooth functions \phi and z>0.
Revised:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%% \geq{ z} for smooth functions \phi and z>0.
Edit intent: clarity
Original:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%%  z} for smooth functions \phi and z>0.
Revised:  Small-time expansions, of arbitrary polynomial order in time t , are obtained for the tails \bbp(Z _t%DIFDELCMD < \ge %%% \geq } z), z>0, and for the call-option prices \bbe(e^{z+Z _t } - 1)_+ , z\neq 0, assuming smoothness conditions on the L\'evy density away from the origin and a small-time large deviation principle on U. Our approach allows for a unified treatment of general payoff functions of the form \phi (x){\bf 1}_{x %DIFDELCMD < \ge %%% z} for smooth functions \varphi and z>0.
Edit intent: fluency
Original:  However, this queue-length-based approach could potentially suffer from large (even infinite) packet delays due to the well-known last packet problem, whereby packets may get excessively delayed due to lack of subsequent packet arrivals.
Revised:  However, these queue-length-based approach could potentially suffer from large (even infinite) packet delays due to the well-known last packet problem, whereby packets may get excessively delayed due to lack of subsequent packet arrivals.
Edit intent: fluency
Original:  However, this queue-length-based approach could potentially suffer from large (even infinite) packet delays due to the well-known last packet problem, whereby packets may get excessively delayed due to lack of subsequent packet arrivals.
Revised:  However, this queue-length-based schemes could potentially suffer from large (even infinite) packet delays due to the well-known last packet problem, whereby packets may get excessively delayed due to lack of subsequent packet arrivals.
Edit intent: clarity