wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this article , we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (\DeltaCoD) measures , which contain the well-known conditional Value-at-Risk (CoVaR) , conditional Expected Shortfall (CoES) , and risk contribution measures in terms of the VaR and ES (%DIFDELCMD < \DeltaCoVaR %%% and%DIFDELCMD < \DeltaCoES%%% ) as special cases.
Revised:  In this article , we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (\DeltaCoD) measures , which contain the well-known conditional Value-at-Risk (CoVaR) , conditional Expected Shortfall , and risk contribution measures in terms of the VaR and ES (%DIFDELCMD < \DeltaCoVaR %%% and%DIFDELCMD < \DeltaCoES%%% ) as special cases.
Edit intent: fluency
Original:  In this article , we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (\DeltaCoD) measures , which contain the well-known conditional Value-at-Risk (CoVaR) , conditional Expected Shortfall (CoES) , and risk contribution measures in terms of the VaR and ES (%DIFDELCMD < \DeltaCoVaR %%% and%DIFDELCMD < \DeltaCoES%%% ) as special cases.
Revised:  In this article , we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (\DeltaCoD) measures , which contain the well-known conditional Value-at-Risk (CoVaR) , conditional Expected Shortfall (CoES) , and risk contribution measures in terms of the VaR and ES %DIFDELCMD < \DeltaCoVaR %%% and%DIFDELCMD < \DeltaCoES%%% ) as special cases.
Edit intent: fluency
Original:  In this article , we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (\DeltaCoD) measures , which contain the well-known conditional Value-at-Risk (CoVaR) , conditional Expected Shortfall (CoES) , and risk contribution measures in terms of the VaR and ES (%DIFDELCMD < \DeltaCoVaR %%% and%DIFDELCMD < \DeltaCoES%%% ) as special cases.
Revised:  In this article , we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (\DeltaCoD) measures , which contain the well-known conditional Value-at-Risk (CoVaR) , conditional Expected Shortfall (CoES) , and risk contribution measures in terms of the VaR and ES (%DIFDELCMD < \DeltaCoVaR %%% %DIFDELCMD < \DeltaCoES%%% ) as special cases.
Edit intent: coherence
Original:  In this article , we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (\DeltaCoD) measures , which contain the well-known conditional Value-at-Risk (CoVaR) , conditional Expected Shortfall (CoES) , and risk contribution measures in terms of the VaR and ES (%DIFDELCMD < \DeltaCoVaR %%% and%DIFDELCMD < \DeltaCoES%%% ) as special cases.
Revised:  In this article , we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (\DeltaCoD) measures , which contain the well-known conditional Value-at-Risk (CoVaR) , conditional Expected Shortfall (CoES) , and risk contribution measures in terms of the VaR and ES (%DIFDELCMD < \DeltaCoVaR %%% and%DIFDELCMD < \DeltaCoES%%% as special cases.
Edit intent: fluency
Original:  These conditions are stated in terms of the stochastic dominance, increasing convex/concave, dispersive order , and excess wealth orders of the marginals under some assumptions of positive/negative stochastic dependence  .
Revised:  These conditions are expressed using the conventional stochastic dominance, increasing convex/concave, dispersive order , and excess wealth orders of the marginals under some assumptions of positive/negative stochastic dependence .
Edit intent: clarity
Original:  These conditions are stated in terms of the stochastic dominance, increasing convex/concave, dispersive order , and excess wealth orders of the marginals under some assumptions of positive/negative stochastic dependence  .
Revised:  These conditions are stated in terms of the stochastic dominance, increasing convex/concave, dispersive , and excess wealth orders of the marginals under some assumptions of positive/negative stochastic dependence .
Edit intent: fluency
Original:  These conditions are stated in terms of the stochastic dominance, increasing convex/concave, dispersive order , and excess wealth orders of the marginals under some assumptions of positive/negative stochastic dependence  .
Revised:  These conditions are stated in terms of the stochastic dominance, increasing convex/concave, dispersive order , and excess wealth orders of the marginals and canonical positive/negative stochastic dependence .
Edit intent: clarity
Original:  These conditions are stated in terms of the stochastic dominance, increasing convex/concave, dispersive order , and excess wealth orders of the marginals under some assumptions of positive/negative stochastic dependence  .
Revised:  These conditions are stated in terms of the stochastic dominance, increasing convex/concave, dispersive order , and excess wealth orders of the marginals under some assumptions of positive/negative stochastic dependence notions .
Edit intent: clarity
Original:  In Dhaene et al. (2018)}\hspace{0pt}%DIFAUXCMD } , we introduce and analyze some new stochastic orders related to systemic risk.
Revised:  In \mbox{%DIFAUXCMD \cite{DLZorder2018a}\hspace{0pt}%DIFAUXCMD } , we introduce and analyze some new stochastic orders related to systemic risk.
Edit intent: meaning-changed
Original: We extend a recently introduced framework for transient compartmentalization of replicators with selection dynamics, by including the effect of mutations and noise in such systems. In the presence of mutations, functional replicators (ribozymes) are turned into non-functional ones (parasites). We evaluate the phase diagram of a system undergoing transient compartmentalization with selection. The system can exhibit either coexistence of ribozymes and parasites, or a pure parasite phase. If the mutation rate exceeds a certain level called the error threshold, the only stable phase is the pure parasite one. Transient compartmentalization with selection can relax this error treshold with respect to a bulk quasispecies case, and even allow ribozymes to coexist with faster growing parasites. In order to analyze the role of noise, we also introduce a model for the replication of a template by an enzyme. This model admits two regimes: a diffusion limited regimewhich generates a high noise, and a replication limited regime, which generates a low noise at the population level. Based on this model, we find that , since the ribozyme dynamics belongs to the replication limited regime, the effects of noise on the phase diagram of the system are mostly negligible. Our results underlines the importance of transient compartmentalization for prebiotic scenarios, and may have implications for directed evolution experiments .
Revised: We extend a recently introduced framework for transient compartmentalization of replicators with selection dynamics, by including the effect of mutations and noise in replication, as both can be detrimental the survival of compartments. Our study comprises situations where compartments contain uncoupled autocatalytic reactions feeding on a common resource, and systems based on RNA molecules copied by replicases, following a recent experimental study. Using the theory of branching processes, we show analytically that two regimes are possible. In the diffusion-limited regime, replication is asynchronous which leads to a large variability in the composition of compartments. In contrast, in a replication-limited regime, the growth is synchronous and thus the compositional variability is low. Typically, simple autocatalysts are in the former regime, while polymeric replicators can access the latter. For deterministic growth dynamics, we introduce mutations that turn functional replicators into parasites. We derive the phase boundary separating coexistence or parasite dominance as a function of relative growth, inoculation size and mutation rate. We show that transient compartmentalization allows coexistence beyond the classical error threshold, above which the parasite dominates. Our findings invite to revisit major prebiotic transitions, notably the transitions towards cooperation, complex polymers and cell division .
Edit intent: meaning-changed
Original:  Though this strategy has indeed led to improved processing time efficiency in machine algorithms,  at least one set of critical tests of this idea has never been performed with respect to the role of early selection in human vision.
Revised:  This strategy has led to a plethora of saliency algorithms that have indeed improved processing time efficiency in machine algorithms, at least one set of critical tests of this idea has never been performed with respect to the role of early selection in human vision.
Edit intent: meaning-changed
Original:  Though this strategy has indeed led to improved processing time efficiency in machine algorithms,  at least one set of critical tests of this idea has never been performed with respect to the role of early selection in human vision.
Revised:  Though this strategy has indeed led to improved processing time efficiency in machine algorithms, which in turn have strengthened the suggestion that human vision also employs a similar early selection strategy. However, at least one set of critical tests of this idea has never been performed with respect to the role of early selection in human vision.
Edit intent: meaning-changed
Original: The relation between volatility and the predicted average returns of portfolios in excess of market returnsis investigated using four risk measures: 1) Tsallis q-Gaussian relative entropy, 2) Kullback-Leibler relative entropy, 3) the parameter 'beta' of the Capital Asset Pricing Model (CAPM), and 4) relative standard deviation.
Revised: Tsallis relative entropy, which is the generalization of Kullback-Leibler relative entropy to non-extensive systems, is investigated as a possible risk measure in constructing risk optimal portfolios whose returns beat market returns. The results are compared with those from three other risk measures: 1) Tsallis q-Gaussian relative entropy, 2) Kullback-Leibler relative entropy, 3) the parameter 'beta' of the Capital Asset Pricing Model (CAPM), and 4) relative standard deviation.
Edit intent: meaning-changed
Original: The relation between volatility and the predicted average returns of portfolios in excess of market returnsis investigated using four risk measures: 1) Tsallis q-Gaussian relative entropy, 2) Kullback-Leibler relative entropy, 3) the parameter 'beta' of the Capital Asset Pricing Model (CAPM), and 4) relative standard deviation.
Revised: The relation between volatility and the predicted average returns of portfolios in excess of market returnsis investigated using four risk measures: 1) the commonly used 'beta' of the Capital Asset Pricing Model (CAPM), and 4) relative standard deviation.
Edit intent: clarity
Original: The relation between volatility and the predicted average returns of portfolios in excess of market returnsis investigated using four risk measures: 1) Tsallis q-Gaussian relative entropy, 2) Kullback-Leibler relative entropy, 3) the parameter 'beta' of the Capital Asset Pricing Model (CAPM), and 4) relative standard deviation.
Revised: The relation between volatility and the predicted average returns of portfolios in excess of market returnsis investigated using four risk measures: 1) Tsallis q-Gaussian relative entropy, 2) Kullback-Leibler relative entropy, 3) the parameter 'beta' of the Capital Asset Pricing Model (CAPM), 2) Kullback-Leibler relative entropy, and 3) the relative standard deviation.
Edit intent: clarity
Original: Tsallis relative entropy , which is the generalization of Kullback-Leibler relative entropy  to non-extensive systems, is investigated as a possible risk measure in constructing risk optimal portfolios whose returns beat market returns.
Revised: Earlier studies have shown that stock market distributions can be well described by distributions derived from Tsallis entropy, which is a generalization of Shannon entropy to non-extensive systems. In this paper, Tsallis relative entropy (TRE) , which is the generalization of Kullback-Leibler relative entropy to non-extensive systems, is investigated as a possible risk measure in constructing risk optimal portfolios whose returns beat market returns.
Edit intent: meaning-changed
Original: Tsallis relative entropy , which is the generalization of Kullback-Leibler relative entropy  to non-extensive systems, is investigated as a possible risk measure in constructing risk optimal portfolios whose returns beat market returns.
Revised: Tsallis relative entropy , which is the generalization of Kullback-Leibler relative entropy (KLRE) to non-extensive systems, is investigated as a possible risk measure in constructing risk optimal portfolios whose returns beat market returns.
Edit intent: meaning-changed
Original:  The results are compared with those from three other risk measures: 1) the commonly used 'beta' of the Capital Asset Pricing Model (CAPM), 2) Kullback-Leibler relative entropy, and 3) the relative standard deviation.
Revised:  Portfolios are constructed by binning the risk values and allocating the stocks to bins according to their risk values. The average return in excess of market returns for each bin is calculated to get the risk-return patterns of the portfolios. The results are compared with those from three other risk measures: 1) the commonly used 'beta' of the Capital Asset Pricing Model (CAPM), 2) Kullback-Leibler relative entropy, and 3) the relative standard deviation.
Edit intent: meaning-changed
Original:  Portfolios are constructed by binning the securities according to their risk values. The mean risk value and the mean return in excess of market returns for each bin is calculated to get the risk-return patterns of the portfolios. The investigations have been carried out for both long (~18 years) and shorter  (~9 years) terms that include the dot-com bubble and the 2008 crash periods .
Revised:  Tests carried out for both long (~18 years) and shorter (~9 years) terms that include the dot-com bubble and the 2008 crash periods .
Edit intent: coherence
Original:  The investigations have been carried out for both long (~18 years) and shorter  (~9 years) terms that include the dot-com bubble and the 2008 crash periods . In all cases, a linear fit can be obtained for the risk and excess return profiles , both for long and shorter periods.
Revised:  The investigations have been carried out for both long (~18 years) and shorter (~9 years) terms that include the dot-com bubble and the 2008 crash periods , show that a linear fit can be obtained for the risk and excess return profiles , both for long and shorter periods.
Edit intent: clarity
Original:  In all cases, a linear fit can be obtained for the risk and excess return profiles , both for long and shorter periods. For longer periods, the linear fits have a positive slope, with Tsallis relative entropy giving the best goodness of fit. For shorter periods, the risk-return profiles from Tsallis relative entropy show a more consistent behavior in terms of  goodness of fit  than the other three risk measures 
Revised:  In all cases, a linear fit can be obtained for the risk-excess return profiles of all four risk measures. However, in all cases, the profiles from Tsallis relative entropy show a more consistent behavior in terms of goodness of fit than the other three risk measures 
Edit intent: meaning-changed
Original:  We show that the cell's electrical opacitywhich is a metric that reveals the properties of the cell membrane is strongly influenced by the physical design parameters of the cytometer and should therefore be carefully defined and interpreted in experiments .
Revised:  In particular, the area of microelectrodes can have significant effect on cells electrical opacity( the ratio of the cell membrane is strongly influenced by the physical design parameters of the cytometer and should therefore be carefully defined and interpreted in experiments .
Edit intent: meaning-changed
Original:  We show that the cell's electrical opacitywhich is a metric that reveals the properties of the cell membrane is strongly influenced by the physical design parameters of the cytometer and should therefore be carefully defined and interpreted in experiments .
Revised:  We show that the cell's electrical opacitywhich is a metric that reveals the properties of the cell impedance at high to low frequency) which is commonly used to distinguish between sub-population of the target cells( e.g. lymphocytes vs monocytes when counting white blood cells).Moreover, we highlight that the opacity response vs frequency can significantly vary depending upon whether the absolute cell impedance or the differential output impedance is used in the calculation .
Edit intent: meaning-changed
Original:  In parallel, sophisticated tools  to process, visualize and analyze tracking data have been developed in abundance. Within the R software alone, we listed 57 focused on these tasks, called here tracking packages.
Revised:  In parallel, sophisticated tools to process, visualize and analyze tracking data , however many of these tools have proliferated in isolation, making it challenging for users to select the most appropriate method for the question in hand. Indeed, within the R software alone, we listed 57 focused on these tasks, called here tracking packages.
Edit intent: meaning-changed
Original:  Within the R software alone, we listed 57 focused on these tasks, called here tracking packages. Here, we reviewed these tracking packages, as an introduction to this set of packages for researchers, and to provide feedback and recommendations to package developers, from a user perspective. We described each package based on a workflow centered around tracking data (i.e.  (x,y,t)), broken down in three stages: pre-processing, post-processing , and analysis( data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others ) . Supporting documentation is key to the accessibility of a package for users.
Revised:  Within the R software alone, we listed 59 packages created to deal with tracking data or 'tracking packages'. Here we reviewed and described each tracking package based on a workflow centered around tracking data (i.e. (x,y,t)), broken down in three stages: pre-processing, post-processing , and analysis( data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others ) . Supporting documentation is key to the accessibility of a package for users.
Edit intent: clarity
Original:  We described each package based on a workflow centered around tracking data (i.e.  (x,y,t)), broken down in three stages: pre-processing, post-processing , and analysis( data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others ) . Supporting documentation is key to the accessibility of a package for users.
Revised:  We described each package based on a workflow centered around tracking data (i.e. spatio-temporal locations (x,y,t)), broken down in three stages: pre-processing, post-processing , and analysis( data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others ) . Supporting documentation is key to the accessibility of a package for users.
Edit intent: meaning-changed
Original:  We described each package based on a workflow centered around tracking data (i.e.  (x,y,t)), broken down in three stages: pre-processing, post-processing , and analysis( data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others ) . Supporting documentation is key to the accessibility of a package for users.
Revised:  We described each package based on a workflow centered around tracking data (i.e. (x,y,t)), broken down into three stages: pre-processing, post-processing , and analysis( data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others ) . Supporting documentation is key to the accessibility of a package for users.
Edit intent: fluency
Original:  We described each package based on a workflow centered around tracking data (i.e.  (x,y,t)), broken down in three stages: pre-processing, post-processing , and analysis( data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others ) . Supporting documentation is key to the accessibility of a package for users.
Revised:  We described each package based on a workflow centered around tracking data (i.e. (x,y,t)), broken down in three stages: pre-processing, post-processing and analysis, the latter consisting of data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others ) . Supporting documentation is key to the accessibility of a package for users.
Edit intent: clarity
Original:  We described each package based on a workflow centered around tracking data (i.e.  (x,y,t)), broken down in three stages: pre-processing, post-processing , and analysis( data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others ) . Supporting documentation is key to the accessibility of a package for users.
Revised:  We described each package based on a workflow centered around tracking data (i.e. (x,y,t)), broken down in three stages: pre-processing, post-processing , and analysis( data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others . Supporting documentation is key to the accessibility of a package for users.
Edit intent: fluency
Original:  We described each package based on a workflow centered around tracking data (i.e.  (x,y,t)), broken down in three stages: pre-processing, post-processing , and analysis( data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others ) . Supporting documentation is key to the accessibility of a package for users.
Revised:  We described each package based on a workflow centered around tracking data (i.e. (x,y,t)), broken down in three stages: pre-processing, post-processing , and analysis( data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others ) . Supporting documentation is key to render a package accessible for users.
Edit intent: clarity
Original:  Although a large group of packages shows some degree of connectivity (either depending on functions or suggesting the use of another tracking package), a third of tracking packages work on isolation, reflecting a fragmentation in the R Movement-Ecology programming community.
Revised:  Although a large group of packages showed some degree of connectivity (either depending on functions or suggesting the use of another tracking package), a third of tracking packages work on isolation, reflecting a fragmentation in the R Movement-Ecology programming community.
Edit intent: fluency