wanyu/IteraTeR_full_sent huggingface dataset:
Original:  When the drift term is non-negligible, the corresponding \b{eta not follow a power law and becomes stationary after a certain characteristic time that depends on the values of the drift parameter and q. Neither of these behaviors is supported by the results of the empirical fit 
Revised:  When the drift term is non-negligible, the corresponding \b{eta not follow a power law and become stationary after certain characteristic times that depend on the values of the drift parameter and q. Neither of these behaviors is supported by the results of the empirical fit 
Edit intent: fluency
Original:  When the drift term is non-negligible, the corresponding \b{eta not follow a power law and becomes stationary after a certain characteristic time that depends on the values of the drift parameter and q. Neither of these behaviors is supported by the results of the empirical fit 
Revised:  When the drift term is non-negligible, the corresponding \b{eta not follow a power law and becomes stationary after a certain characteristic time that depends on the values of the drift parameter and q. Neither of these behaviors is supported by the results of the empirical fit .
Edit intent: fluency
Original: In this article, we consider the small-time asymptotics of options on a Leveraged Exchange-Traded Fund (LETF) when the underlying Exchange Traded Fund (ETF) exhibits both local volatility and jumps of either finite or infinite activity.
Revised: In this article, we consider the small-time asymptotics of options on a Leveraged Exchange-Traded Fund (LETF) when the underlying Exchange Traded Fund (ETF) exhibits both local volatility and jumps of either finite or infinite activity.
Edit intent: fluency
Original:  With this bigger picture in mind, we have initiated the characterization of a putative primase that is probably involved in the DNA replicationand/or repair in Mimivirus. Sequence alignment of gp0577 the protein encoded by gene L537 with other primases reveals that it contains the motifs common to the superfamily Archaeo-Eukaryotic Primase (AEP) and it aligns with Primpols, a novel type of primase which has RNA and DNA polymerase activities. Our initial analysis revealed that gp0577 probably exists as a dimer in its native state. We also identified the presence of conserved motifs: DxD, sxH, h- and Zn binding motif . Using Dpni mediated site directed mutagenesis we have generated active site mutants for all motifs. Templates of defined sequence were utilized to investigate the mechanism of primer synthesis by gp0577. Previous studies have suggested that the rate-limiting step of primer synthesis occurs during primer initiation during the formation of the dinucleotide. Thus we checked the effect of different concentrations of rNTPs involved in initial dinucleotide synthesis on primer synthesis. Consistent with this idea, increasing the concentration of NTPs required for dinucleotide synthesis increased the rate of primer synthesis, whereas increasing the concentration of NTPs not involved in dinucleotide synthesis inhibited primer synthesis .
Revised:  With this bigger picture in mind, we have initiated the characterization involved in DNA replication .
Edit intent: meaning-changed
Original:  Although it has been shown that a cascade arrangement of multiple ultrasensitive modules can produce an enhancement of the system's ultrasensitivity, how the combination of layers affects the cascade's ultrasensitivity remains an open-ended question for the general case.
Revised:  Although it has been shown that a cascade arrangement of multiple ultrasensitive modules can produce an enhancement of the system's ultrasensitivity, how the combination of layers affects the cascade's ultrasensitivity remains an open question for the general case.
Edit intent: clarity
Original:  Here we have developed a methodology that allowed us to quantify the effective contribution of each module to the overall cascade's ultrasensitivity and to determine the impact of sequestration effects in the overall system's ultrasensitivity .
Revised:  Here we introduced a methodology that allowed us to quantify the effective contribution of each module to the overall cascade's ultrasensitivity and to determine the impact of sequestration effects in the overall system's ultrasensitivity .
Edit intent: clarity
Original:  Here we have developed a methodology that allowed us to quantify the effective contribution of each module to the overall cascade's ultrasensitivity and to determine the impact of sequestration effects in the overall system's ultrasensitivity .
Revised:  Here we have developed a methodology that allowed us to determine the presence of sequestration effects and to quantify the relative contribution of each module to the overall cascade's ultrasensitivity and to determine the impact of sequestration effects in the overall system's ultrasensitivity .
Edit intent: meaning-changed
Original:  The proposed analysis framework provided a natural link between global and local ultrasensitivity descriptors and was particularly well-suited to study the ultrasensitivity in MAP kinase cascades. We used our methodology to revisit O'Shaughnessy et al.  tunable synthetic MAPK cascade, in which they claim to have found a new source of ultrasensitivity: ultrasensitivity generated de novo, which arises due to cascade structure itself.
Revised:  The proposed analysis framework provided a natural link between global and local ultrasensitivity descriptors and was particularly well-suited to characterize and better understand mathematical models used to study real biological systems. As a case study we considered three mathematical models introduced by O'Shaughnessy et al. tunable synthetic MAPK cascade, in which they claim to have found a new source of ultrasensitivity: ultrasensitivity generated de novo, which arises due to cascade structure itself.
Edit intent: meaning-changed
Original:  We used our methodology to revisit O'Shaughnessy et al.  tunable synthetic MAPK cascade, in which they claim to have found a new source of ultrasensitivity: ultrasensitivity generated de novo, which arises due to cascade structure itself.
Revised:  We used our methodology to revisit O'Shaughnessy et al. to study a tunable synthetic MAPK cascade, in which they claim to have found a new source of ultrasensitivity: ultrasensitivity generated de novo, which arises due to cascade structure itself.
Edit intent: clarity
Original:  We used our methodology to revisit O'Shaughnessy et al.  tunable synthetic MAPK cascade, in which they claim to have found a new source of ultrasensitivity: ultrasensitivity generated de novo, which arises due to cascade structure itself. In this respect, we showed that the system's ultrasensitivity in its single-step cascade did not come from a cascading effect but from a hidden first-order ultrasensitivity process in one of the cascade's layer. Our analysis also highlighted the impact of the detailed functional form of a module's response curve on the overall system's ultrasensitivity in cascade architectures. Local sensitivity features of the involved transfer functions were found to be of the uttermost importance in this kind of setting and could be at the core of non-trivial phenomenology associated to ultrasensitive motifs .
Revised:  We used our methodology to revisit O'Shaughnessy et al. tunable synthetic MAPK cascade, and showed how our methodology might help modelers to better understand modeling alternatives .
Edit intent: coherence
Original:  We apply the model to simulated data as well as daily log-returns of 300 S&P 500 stocks and demonstrate the effectiveness of the shrinkage prior to obtain sparse loadings matrices and more precise correlation estimates.
Revised:  We apply the model to simulated data as well as daily log-returns of 300 S&P 500 stocks and demonstrate its effectiveness to obtain sparse loadings matrices and more precise correlation estimates.
Edit intent: clarity
Original:  Moreover, we investigate predictive performance and discuss different choices for the number of latent factors . Additionally to being a stand-alone tool, the algorithm is designed to act as a "plug and play" extension for other MCMC samplers .
Revised:  Moreover, we investigate predictive performance and discuss different choices for the number of latent factors are discussed. In addition to serving as a stand-alone tool, the algorithm is designed to act as a "plug and play" extension for other MCMC samplers .
Edit intent: clarity
Original: Dynamic covariance estimation for multivariate time series suffers from the curse of dimensionality . Consequently, parsimonious estimation methods are essential for conducting reliable statistical inference. In the paper at hand, this issue is addressed by modeling the underlying co-volatility dynamics of a time series vector through a lower dimensional collection of latent time-varying stochastic factors.
Revised: Dynamic covariance estimation for multivariate time series suffers from the curse of dimensionality in dynamic covariance estimation by modeling the underlying co-volatility dynamics of a time series vector through a lower dimensional collection of latent time-varying stochastic factors.
Edit intent: clarity
Original:  Furthermore, we propose using a Normal-Gamma prior for the elements of the factor loadings matrix . This hierarchical shrinkage prior effectively pulls the factor loadings of unimportant factors towards zero , thereby increasing parsimony even more.
Revised:  Furthermore, we propose using a Normal-Gamma prior for the elements of the factor loadings matrix pulls loadings on superfluous factors towards zero , thereby increasing parsimony even more.
Edit intent: clarity
Original:  This hierarchical shrinkage prior effectively pulls the factor loadings of unimportant factors towards zero , thereby increasing parsimony even more. We apply the model to simulated data as well as  daily log-returns of 300 S&P 500 stocks and demonstrate its effectiveness to obtain sparse loadings matrices and more precise correlation estimates .
Revised:  This hierarchical shrinkage prior effectively pulls the factor loadings of unimportant factors towards zero . To demonstrate the merits of the proposed framework, the model is applied to simulated data as well as daily log-returns of 300 S&P 500 stocks and demonstrate its effectiveness to obtain sparse loadings matrices and more precise correlation estimates .
Edit intent: meaning-changed
Original:  We apply the model to simulated data as well as  daily log-returns of 300 S&P 500 stocks and demonstrate its effectiveness to obtain sparse loadings matrices and more precise correlation estimates . To assess predictive accuracy, our approach is compared to more traditional approaches via log predictive scores and implied minimum variance portfolio performance .
Revised:  We apply the model to simulated data as well as daily log-returns of 300 S&P 500 stocks and demonstrate its effectiveness to obtain sparse loadings matrices and more precise correlation estimates , strong implied minimum variance portfolio performance .
Edit intent: meaning-changed
Original:  To assess predictive accuracy, our approach is compared to more traditional approaches via log predictive scores and implied minimum variance portfolio performance . Thereby, different choices for the number of latent factors are discussed. In addition to serving as a stand-alone tool, the algorithm is designed to complement other MCMC samplers as a "plug and play" extension. It can easily be used by means of the R package factorstochvol .
Revised:  To assess predictive accuracy, our approach is compared to more traditional approaches via log predictive scores and implied minimum variance portfolio performance and superior forecasting accuracy in terms of log predictive scores when compared to typical benchmarks .
Edit intent: meaning-changed
Original: In this article, inspired by Shi, et al. we investigate the optimal portfolio selection with one risk-free asset and one risky asset in a multiple period set- ting under cumulative prospect theory (CPT).
Revised: In this article, inspired by Shi, et al. we investigate the optimal portfolio selection with one risk-free asset and one risky asset in a multiple period setting under cumulative prospect theory (CPT).
Edit intent: fluency
Original:  This paper presents a new deep learning method for contact prediction that predicts contacts by integrating both evolutionary coupling (EC) information and sequence conservation information through an ultra-deep neural network consisting of two deep residual neural networks.
Revised:  This paper presents a new deep learning method for contact prediction that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network consisting of two deep residual neural networks.
Edit intent: fluency
Original:  This paper presents a new deep learning method for contact prediction that predicts contacts by integrating both evolutionary coupling (EC) information and sequence conservation information through an ultra-deep neural network consisting of two deep residual neural networks.
Revised:  This paper presents a new deep learning method for contact prediction that predicts contacts by integrating both evolutionary coupling (EC) information and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks.
Edit intent: clarity
Original:  The two residual networks conduct a series of convolutional transformation of protein features including sequence profile, EC information and pairwise potential. This neural network allows us to model very complex relationship between sequence and contact map as well as long-range interdependency between contacts and thus, obtain high-quality contact prediction .
Revised:  This deep neural network allows us to model very complex relationship between sequence and contact map as well as long-range interdependency between contacts and thus, obtain high-quality contact prediction .
Edit intent: clarity
Original:  For example, on the 105 CASP11 test proteins, the L/10 long-range accuracy obtained by our methodis 83.3\% while that by CCMpred and MetaPSICOV ( the CASP11 winner ) is 43.4\% and 60.2\%, respectively.
Revised:  Tested on three datasets of 579 proteins, the L/10 long-range accuracy obtained by our methodis 83.3\% while that by CCMpred and MetaPSICOV ( the CASP11 winner ) is 43.4\% and 60.2\%, respectively.
Edit intent: meaning-changed
Original:  For example, on the 105 CASP11 test proteins, the L/10 long-range accuracy obtained by our methodis 83.3\% while that by CCMpred and MetaPSICOV ( the CASP11 winner ) is 43.4\% and 60.2\%, respectively.
Revised:  For example, on the 105 CASP11 test proteins, the average top L long-range accuracy obtained by our methodis 83.3\% while that by CCMpred and MetaPSICOV ( the CASP11 winner ) is 43.4\% and 60.2\%, respectively.
Edit intent: meaning-changed
Original:  For example, on the 105 CASP11 test proteins, the L/10 long-range accuracy obtained by our methodis 83.3\% while that by CCMpred and MetaPSICOV ( the CASP11 winner ) is 43.4\% and 60.2\%, respectively.
Revised:  For example, on the 105 CASP11 test proteins, the L/10 long-range prediction accuracy obtained our method, the representative EC method CCMpred and the CASP11 winner ) is 43.4\% and 60.2\%, respectively.
Edit intent: meaning-changed
Original:  For example, on the 105 CASP11 test proteins, the L/10 long-range accuracy obtained by our methodis 83.3\% while that by CCMpred and MetaPSICOV ( the CASP11 winner ) is 43.4\% and 60.2\%, respectively. On the 398 membrane proteins, the L/10 long-range accuracy obtained by our methodis 77.3\% while that by CCMpred and MetaPSICOV is 51.8\% and 61.2\% ,  respectively.
Revised:  For example, on the 105 CASP11 test proteins, the L/10 long-range accuracy obtained by our methodis 83.3\% while that by CCMpred and MetaPSICOV ( the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy obtained by our methodis 77.3\% while that by CCMpred and MetaPSICOV is 51.8\% and 61.2\% , respectively.
Edit intent: meaning-changed
Original:  On the 398 membrane proteins, the L/10 long-range accuracy obtained by our methodis 77.3\% while that by CCMpred and MetaPSICOV is 51.8\% and 61.2\% ,  respectively.
Revised:  On the 398 membrane proteins, the L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 51.8\% and 61.2\% , respectively.
Edit intent: clarity
Original:  On the 398 membrane proteins, the L/10 long-range accuracy obtained by our methodis 77.3\% while that by CCMpred and MetaPSICOV is 51.8\% and 61.2\% ,  respectively.
Revised:  On the 398 membrane proteins, the L/10 long-range accuracy obtained by our methodis 77.3\% while that by CCMpred and MetaPSICOV is 0.77 , respectively.
Edit intent: meaning-changed
Original:  On the 398 membrane proteins, the L/10 long-range accuracy obtained by our methodis 77.3\% while that by CCMpred and MetaPSICOV is 51.8\% and 61.2\% ,  respectively.
Revised:  On the 398 membrane proteins, the L/10 long-range accuracy obtained by our methodis 77.3\% while that by CCMpred and MetaPSICOV is 51.8\% and 61.2\% , 0.47 and 0.59, respectively.
Edit intent: meaning-changed
Original:  Ab initio folding guided by our predicted contacts  can yield correct folds (i.e., TMscore>0.6) for 224 of the 579 test proteins, while that by MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them , respectively.
Revised:  Ab initio folding using our predicted contacts can yield correct folds (i.e., TMscore>0.6) for 224 of the 579 test proteins, while that by MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them , respectively.
Edit intent: clarity
Original:  Ab initio folding guided by our predicted contacts  can yield correct folds (i.e., TMscore>0.6) for 224 of the 579 test proteins, while that by MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them , respectively.
Revised:  Ab initio folding guided by our predicted contacts as restraints can yield correct folds (i.e., TMscore>0.6) for 224 of the 579 test proteins, while that by MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them , respectively.
Edit intent: meaning-changed
Original:  Ab initio folding guided by our predicted contacts  can yield correct folds (i.e., TMscore>0.6) for 224 of the 579 test proteins, while that by MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them , respectively.
Revised:  Ab initio folding guided by our predicted contacts can yield correct folds (i.e., TMscore>0.6) for 203 test proteins, while that by MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them , respectively.
Edit intent: meaning-changed
Original:  Ab initio folding guided by our predicted contacts  can yield correct folds (i.e., TMscore>0.6) for 224 of the 579 test proteins, while that by MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them , respectively.
Revised:  Ab initio folding guided by our predicted contacts can yield correct folds (i.e., TMscore>0.6) for 224 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them , respectively.
Edit intent: fluency
Original:  Ab initio folding guided by our predicted contacts  can yield correct folds (i.e., TMscore>0.6) for 224 of the 579 test proteins, while that by MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them , respectively.
Revised:  Ab initio folding guided by our predicted contacts can yield correct folds (i.e., TMscore>0.6) for 224 of the 579 test proteins, while that by MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 proteins , respectively.
Edit intent: clarity
Original:  Further, our contact-assisted models also have much better quality (especially for membrane proteins ) than template-based models .
Revised:  Further, our contact-assisted models also have much better quality than template-based models. Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast, when the training proteins of our method are used as templates, homology modeling can only do so for 10 of them. One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method still works well on membrane protein prediction .
Edit intent: meaning-changed
Original:  One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method still works well on membrane protein prediction  .
Revised:  One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method still works well on membrane protein prediction . Finally, in recent CAMEO benchmark our method successfully folded a mainly-beta protein of 182 residues with a novel fold .
Edit intent: meaning-changed
Original: We establish an explicit pricing formula for a class of non-Gaussian models (the Levy-stable, or Log-Levy model with finite moments and stability parameter between 1 and 2) allowing a straightforward evaluation of an European option , without numerical simulations and with as much accuracy as one wishes .
Revised: We establish an explicit pricing formula for the class of L\'evy-stable models with maximal negative asymmetry (Log-L\'evy model with finite moments and stability parameter between 1 and 2) allowing a straightforward evaluation of an European option , without numerical simulations and with as much accuracy as one wishes .
Edit intent: meaning-changed
Original: We establish an explicit pricing formula for a class of non-Gaussian models (the Levy-stable, or Log-Levy model with finite moments and stability parameter between 1 and 2) allowing a straightforward evaluation of an European option , without numerical simulations and with as much accuracy as one wishes .
Revised: We establish an explicit pricing formula for a class of non-Gaussian models (the Levy-stable, or Log-Levy model with finite moments and stability parameter 1 and 2) allowing a straightforward evaluation of an European option , without numerical simulations and with as much accuracy as one wishes .
Edit intent: clarity
Original: We establish an explicit pricing formula for a class of non-Gaussian models (the Levy-stable, or Log-Levy model with finite moments and stability parameter between 1 and 2) allowing a straightforward evaluation of an European option , without numerical simulations and with as much accuracy as one wishes .
Revised: We establish an explicit pricing formula for a class of non-Gaussian models (the Levy-stable, or Log-Levy model with finite moments and stability parameter between 1 <\alpha\leq 2) allowing a straightforward evaluation of an European option , without numerical simulations and with as much accuracy as one wishes .
Edit intent: meaning-changed
Original: We establish an explicit pricing formula for a class of non-Gaussian models (the Levy-stable, or Log-Levy model with finite moments and stability parameter between 1 and 2) allowing a straightforward evaluation of an European option , without numerical simulations and with as much accuracy as one wishes .
Revised: We establish an explicit pricing formula for a class of non-Gaussian models (the Levy-stable, or Log-Levy model with finite moments and stability parameter between 1 and 2) in the form of rapidly converging series. The series is obtained with help of Mellin transform and the residue theory in \mathbb{C straightforward evaluation of an European option , without numerical simulations and with as much accuracy as one wishes .
Edit intent: meaning-changed