wanyu/IteraTeR_full_sent huggingface dataset:
Original:  From the local scaling exponents calculated for our nine peak regions we have defined what we named the Development Index (H_{DI , which proved, at least in the case of our dataset, to be suitable to rank the SMI series that we have analyzed in three distinct groups .
Revised:  From the local scaling exponents calculated for our nine peak regions we have defined what we named the Development Index , which proved, at least in the case of our dataset, to be suitable to rank the SMI series that we have analyzed in three distinct groups .
Edit intent: clarity
Original:  From the local scaling exponents calculated for our nine peak regions we have defined what we named the Development Index (H_{DI , which proved, at least in the case of our dataset, to be suitable to rank the SMI series that we have analyzed in three distinct groups . Further verification of this method remains open for future research .
Revised:  From the local scaling exponents calculated for our nine peak regions we have defined what we named the Development Index (H_{DI , which proved, at least in the case of our dataset, to be suitable to rank the SMI series that we have analyzed in three distinct groups .
Edit intent: clarity
Original: A binding potential of mean force (BPMF) is a free energy of noncovalent association in which one binding partner is flexible and the other is rigid .
Revised: Alchemical Grid Dock (AlGDock) is open-source software designed to compute the binding potential of mean force (BPMF) is a free energy of noncovalent association in which one binding partner is flexible and the other is rigid .
Edit intent: meaning-changed
Original: A binding potential of mean force (BPMF) is a free energy of noncovalent association in which one binding partner is flexible and the other is rigid . I have developed a method to calculate BPMFs for protein-ligand systems. The method is based on replica exchange sampling from multiple thermodynamic states at different temperatures and protein-ligand interaction strengths.
Revised: A binding potential of mean force (BPMF) - the binding free energy between a flexible ligand and a rigid receptor - for a URLanic ligand and a biological macromolecule. Multiple BPMFs can be used to rigorously compute binding affinities between flexible partners. AlGDock uses replica exchange between thermodynamic states at different temperatures and protein-ligand interaction strengths.
Edit intent: clarity
Original:  Protein-ligand interactions are represented by interpolating precomputed electrostatic and van der Waals grids. Using a simple estimator for thermodynamic length, thermodynamic states are initialized at approximately equal intervals. The method is demonstrated on the Astex diverse set, a database of 85 protein-ligand complexes relevant to pharmacy or agriculture. Fifteen independent simulations of each complex were started using poses from crystallography, docking, or the lowest-energy pose observed in the other simulations. Benchmark simulations completed within three days on a single processor. Overall, protocols initialized using the thermodynamic length estimator were system-specific, robust, and led to approximately even replica exchange acceptance probabilities between neighboring states. In most systems, the standard deviation of the BPMF converges to within 5 kT . Even with low variance, however, the mean BPMF was sometimes dependent on starting conditions, implying inadequate sampling. Within the thermodynamic cycle, free energies estimated based on multiple intermediate states were more precise, and those estimated by single-step perturbation were less precise. The results demonstrate that the method is promising, but that ligand pose sampling and phase space overlap can sometimes prevent precise BPMF estimation. The software used to perform these calculations, Alchemical Grid Dock (AlGDock), is available under the open-source MIT license at URL
Revised:  Protein-ligand interactions are represented by interpolating precomputed grids. Thermodynamic states are adaptively initialized and adjusted on-the-fly to maintain replica exchange rates. In demonstrative calculations, when the bound ligand is treated as fully solvated, AlGDock estimates BPMFs with a precision within 4 kT in 65\% and within 8 kT for 91\% of systems. It correctly identifies the native binding pose in 83\% of simulations. Performance is sometimes limited by subtle differences in the important configuration space of sampled and targeted thermodynamic states.
Edit intent: meaning-changed
Original: In his celebrated book "What is Life?" Schrodinger proposed using the properties of living systems to constrain unknown features of life. Here we propose an inverse approach and suggest using biology as a means to constrain unknown physics. We focus on information and causation, as their widespread use in biology is the most problematic aspect of life from the perspective of fundamental physics. Our proposal is cast as a methodology for identifying potentially distinctive features of the informational architecture of biological systems, as compared to other classes of physical system.
Revised: We compare the informational architecture of biological systems, as compared to other classes of physical system.
Edit intent: coherence
Original:  Our proposal is cast as a methodology for identifying potentially distinctive features of the informational architecture of biological systems, as compared to other classes of physical system. To illustrate our approach, we use as a case study a Boolean network model for  the cell cycle regulation of the single-celled fission yeast (Schizosaccharomyces Pombe) and compare its informational properties to two classes of null model that share commonalities in their causal structure .
Revised:  Our proposal is cast as a methodology for identifying potentially distinctive features of the informational architecture of biological and random networks to identify informational features that may distinguish biological networks from random. The study presented here focuses on the Boolean network model for the cell cycle regulation of the single-celled fission yeast (Schizosaccharomyces Pombe) and compare its informational properties to two classes of null model that share commonalities in their causal structure .
Edit intent: meaning-changed
Original:  To illustrate our approach, we use as a case study a Boolean network model for  the cell cycle regulation of the single-celled fission yeast (Schizosaccharomyces Pombe) and compare its informational properties to two classes of null model that share commonalities in their causal structure .
Revised:  To illustrate our approach, we use as a case study a Boolean network model for regulation of the cell cycle regulation of the single-celled fission yeast (Schizosaccharomyces Pombe) and compare its informational properties to two classes of null model that share commonalities in their causal structure .
Edit intent: clarity
Original:  To illustrate our approach, we use as a case study a Boolean network model for  the cell cycle regulation of the single-celled fission yeast (Schizosaccharomyces Pombe) and compare its informational properties to two classes of null model that share commonalities in their causal structure .
Revised:  To illustrate our approach, we use as a case study a Boolean network model for the cell cycle of null model that share commonalities in their causal structure .
Edit intent: clarity
Original:  To illustrate our approach, we use as a case study a Boolean network model for  the cell cycle regulation of the single-celled fission yeast (Schizosaccharomyces Pombe) and compare its informational properties to two classes of null model that share commonalities in their causal structure .
Revised:  To illustrate our approach, we use as a case study a Boolean network model for the cell cycle regulation of the single-celled fission yeast (Schizosaccharomyces Pombe) and compare its informational properties to two classes of the fission yeast Schizosaccharomyces Pombe. We compare calculated values of local and global information measures for the fission yeast cell cycle to the same measures as applied to two different classes of random networks: random and scale-free .
Edit intent: meaning-changed
Original:  We report patterns in local information processing and storage that do indeed distinguish biological from random  . Conversely, we find that integrated information, which serves as a  measure of "emergent" information processing, does not differ from random for the case presented.
Revised:  We report patterns in local information processing and storage that do indeed distinguish biological from random , associated with control nodes that regulate the function of the fission yeast cell cycle network . Conversely, we find that integrated information, which serves as a measure of "emergent" information processing, does not differ from random for the case presented.
Edit intent: meaning-changed
Original:  We report patterns in local information processing and storage that do indeed distinguish biological from random  . Conversely, we find that integrated information, which serves as a  measure of "emergent" information processing, does not differ from random for the case presented.
Revised:  We report patterns in local information processing and storage that do indeed distinguish biological from random . Conversely, we find that integrated information, which serves as a global measure of "emergent" information processing, does not differ from random for the case presented.
Edit intent: meaning-changed
Original: We present a mathematical solution to the insurance puzzle. Our solution only uses time-average growth rates and makes no reference to risk preferences. The insurance puzzle is this: according to the expectation value of wealth, buying insurance is only rational at a price that makes it irrational to sell insurance.
Revised: Voluntary insurance contracts constitute a puzzle because they increase the expectation value of wealth, buying insurance is only rational at a price that makes it irrational to sell insurance.
Edit intent: clarity
Original:  The insurance puzzle is this: according to the expectation value of wealth, buying insurance is only rational at a price that makes it irrational to sell insurance. There is no price that is beneficial to both the buyer and the seller of an insurance contract. The puzzle why insurance contracts exist is traditionally resolved by appealing to utility theory, asymmetric information, or a mix of both .
Revised:  The insurance puzzle is this: according to the expectation value of one party's wealth, whereas both parties must sign for such contracts to exist. Classically, the puzzle is resolved by introducing non-linear utility functions, which encode asymmetric risk preferences; or by assuming the parties have asymmetric information .
Edit intent: clarity
Original:  Here we note that the expectation value is the wrong starting point -- a legacy from the early days of probability theory. It is the wrong starting point because not even the most basic models of wealth (random walks) are stationary, and what the individual experiences over time is not the expectation value. We use the standard model of noisy exponential growth and compute time-average growth rates instead of expectation values of wealth .
Revised:  Here we show the puzzle goes away if contracts are evaluated by their effect on the time-average growth rates instead of expectation values of wealth .
Edit intent: meaning-changed
Original:  We use the standard model of noisy exponential growth and compute time-average growth rates instead of expectation values of wealth . In this new paradigm insurance contracts exist that are beneficial for both parties .
Revised:  We use the standard model of noisy exponential growth and compute time-average growth rate of wealth. Our solution assumes only knowledge of wealth dynamics. Time averages and expectation values differ because wealth changes are non-ergodic. Our reasoning is generalisable: business happens when both parties grow faster .
Edit intent: meaning-changed
Original: MTRV ( A Modern Theory of Random Variation , Wiley, 2012) provides an alternative approach to derivative asset pricing.
Revised: An analytic method for pricing American call options is provided; followed by an empirical method for pricing Asian call options. The methodology is the pricing theory presented in " A Modern Theory of Random Variation , Wiley, 2012) provides an alternative approach to derivative asset pricing.
Edit intent: meaning-changed
Original: The ongoing concern about systemic risk since the outburst of the global financial crisis has highlighted the need for risk measures at the level of sets of interconnected financial components, such as portfolios, institutions or members of clearinghouses .
Revised: The ongoing concern about systemic risk since the outburst of the global financial crisis has highlighted the need for risk measures at the level of sets of interconnected financial components, such as portfolios, institutions or members of clearing houses .
Edit intent: fluency
Original:  In particular, we study the interplay between the loss function and the dependence structure of the components , that provides valuable insights into the properties of good loss functions. Moreover, we provide numerical schemes to assess the risk allocation in high dimensions .
Revised:  In particular, we study the interplay between the loss function and the dependence structure of the components . Moreover, we address the computational aspects of risk allocation. Finally, we apply this methodology to the allocation of the default fund of a CCP on real data .
Edit intent: coherence
Original:  The presented forward PDs can be used for the derivation of  lifetime credit losses  required by the new accounting standard IFRS 9. In doing so, the presented approach is endogenous, as it does not require any exogenous macroeconomic forecasts  which are notoriously unreliable and often subjective  .
Revised:  The resulting PD forecasts can be used for the derivation of lifetime credit losses required by the new accounting standard IFRS 9. In doing so, the presented approach is endogenous, as it does not require any exogenous macroeconomic forecasts which are notoriously unreliable and often subjective .
Edit intent: clarity
Original:  The presented forward PDs can be used for the derivation of  lifetime credit losses  required by the new accounting standard IFRS 9. In doing so, the presented approach is endogenous, as it does not require any exogenous macroeconomic forecasts  which are notoriously unreliable and often subjective  .
Revised:  The presented forward PDs can be used for the derivation of expected lifetime credit losses required by the new accounting standard IFRS 9. In doing so, the presented approach is endogenous, as it does not require any exogenous macroeconomic forecasts which are notoriously unreliable and often subjective .
Edit intent: clarity
Original:  The presented forward PDs can be used for the derivation of  lifetime credit losses  required by the new accounting standard IFRS 9. In doing so, the presented approach is endogenous, as it does not require any exogenous macroeconomic forecasts  which are notoriously unreliable and often subjective  .
Revised:  The presented forward PDs can be used for the derivation of lifetime credit losses as required by the new accounting standard IFRS 9. In doing so, the presented approach is endogenous, as it does not require any exogenous macroeconomic forecasts which are notoriously unreliable and often subjective .
Edit intent: clarity
Original:  The presented forward PDs can be used for the derivation of  lifetime credit losses  required by the new accounting standard IFRS 9. In doing so, the presented approach is endogenous, as it does not require any exogenous macroeconomic forecasts  which are notoriously unreliable and often subjective  .
Revised:  The presented forward PDs can be used for the derivation of lifetime credit losses required by the newly adopted accounting standard IFRS 9. In doing so, the presented approach is endogenous, as it does not require any exogenous macroeconomic forecasts which are notoriously unreliable and often subjective .
Edit intent: clarity
Original:  The presented forward PDs can be used for the derivation of  lifetime credit losses  required by the new accounting standard IFRS 9. In doing so, the presented approach is endogenous, as it does not require any exogenous macroeconomic forecasts  which are notoriously unreliable and often subjective  .
Revised:  The presented forward PDs can be used for the derivation of lifetime credit losses required by the new accounting standard IFRS 9. In doing so, the presented approach is endogenous, as it does not require any exogenous macroeconomic forecasts , which are notoriously unreliable and often subjective .
Edit intent: fluency
Original:  The presented forward PDs can be used for the derivation of  lifetime credit losses  required by the new accounting standard IFRS 9. In doing so, the presented approach is endogenous, as it does not require any exogenous macroeconomic forecasts  which are notoriously unreliable and often subjective  .
Revised:  The presented forward PDs can be used for the derivation of lifetime credit losses required by the new accounting standard IFRS 9. In doing so, the presented approach is endogenous, as it does not require any exogenous macroeconomic forecasts which are notoriously unreliable and often subjective . Also, it does not require any dependency modeling between PDs and macroeconomic variables, which often proves to be cumbersome and unstable .
Edit intent: meaning-changed
Original:  Most of the existing literature focuses on providing inferences on the mean output response with respect to input uncertainty , including point estimation and confidence interval construction of the mean response. However, risk assessment of the mean response with respect to input uncertainty often plays an important role in system evaluation /controlbecause it quantifies the behavior of the mean response under extreme input models.
Revised:  Most of the existing literature focuses on providing inferences on the mean output response with respect to input uncertainty , including point estimation and confidence interval construction . Risk quantification of mean response under input uncertainty often plays an important role in system evaluation /controlbecause it quantifies the behavior of the mean response under extreme input models.
Edit intent: clarity
Original:  In the present paper, we will fill in the gap and introduce risk measures for input uncertaintyin output analysis. We develop nested Monte Carlo estimators and construct (asymptotically valid) confidence intervals for risk measures of mean response.
Revised:  In the present paper, we will fill in the gap and introduce risk measures of mean response under input uncertainty, and propose a nested Monte Carlo estimators and construct (asymptotically valid) confidence intervals for risk measures of mean response.
Edit intent: clarity
Original:  We develop nested Monte Carlo estimators and construct (asymptotically valid) confidence intervals for risk measures of mean response. We further study the associated budget allocation problem for more efficient nested simulation of the estimators, and propose a novel method to solve the problem .
Revised:  We develop nested Monte Carlo simulation approach to estimate them. Then we develop asymptotical properties such as consistency and asymptotic normality for the proposed nested risk estimators. Finally we study the associated budget allocation problem for more efficient nested simulation of the estimators, and propose a novel method to solve the problem .
Edit intent: meaning-changed
Original: The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 40 stock markets from 2nd January 2006 to 31st December 2013.
Revised: Using a rolling windows analysis of filtered and aligned stock index returns from 40 countries during the period 2006-2014, we construct Granger causality networks using daily closing prices of 40 stock markets from 2nd January 2006 to 31st December 2013.
Edit intent: meaning-changed
Original: The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 40 stock markets from 2nd January 2006 to 31st December 2013. The data is properly aligned to take into account non-synchronous trading effects.By conducting a rolling window spatial probit analysis on the set of edges of Granger causality networks , we confirm the significance of temporal proximity and preferential attachment on edge creation.
Revised: The structure of return spillovers is examined by constructing Granger causality networks and investigate the ensuing structure of the relationships by studying network properties and fitting spatial probit models. We provide evidence that stock market volatility and market size increases, while foreign exchange volatility decreases the probability of return spillover from a given market. We also show that market development and returns on the foreign exchange market and stock market also matter, but they exhibit significant time-varying behaviour with alternating effects. These results suggest that higher market integration periods are alternated with periods where investors appear to be chasing returns. Despite the significance of market characteristics and market conditions, what in reality matters for information propagation is the temporal distance between closing hours, i.e. the temporal proximity effect. This implies that choosing markets which trade in similar hours bears additional costs to investors, as the probability of return spillovers increases. The same effect was observed with regard to the temporal distance to the US market. Finally , we confirm the significance of temporal proximity and preferential attachment on edge creation.
Edit intent: meaning-changed
Original: By conducting a rolling window spatial probit analysis on the set of edges of Granger causality networks , we confirm the significance of temporal proximity and preferential attachment on edge creation.We extend the analysis by incorporating market specific factors, such as market capitalization, turnover and volatility .
Revised: By conducting a rolling window spatial probit analysis on the set of edges of Granger causality networks , we confirm the existence of the preferential attachment effect, i.e. the probability of a given market to propagate return spillovers to a new market depends endogenously and positively on the existing number of return spillovers from that market .
Edit intent: meaning-changed
Original:  while whether such defects can be excited under the level of DNA bending in those experiments has remained unclearand been debated. Interestingly, due to experimental design DNA contained pre-existing nicks in nearly all those experiments, while the potential effect of nicks have never been considered  .
Revised:  while whether such defects can be excited under the level of DNA bending in these experiments remains unclear. Intriguingly, the DNA contained preexisting nicks in most of these experiments but whether nicks might play a role in flexibility has never been considered .
Edit intent: clarity
Original:   Overall, our results challenge the interpretations of previous sharp DNA bending experiments and highlight that the micromechanics of sharply bent DNA still remains an open question.
Revised:  Therefore, basepair disruption inside B-form DNA can be suppressed if the DNA contains preexisting nicks. Overall, our results challenge the interpretations of previous sharp DNA bending experiments and highlight that the micromechanics of sharply bent DNA still remains an open question.
Edit intent: meaning-changed