wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To reduce the complexity of the model and the number of fit parameters, we focus on three scenarios and carry out numerical simulations. We also introduce a diffusion function that measures the correlated motion of prices from different stocks to test our simulated results.
Revised:  To quantify the self- and cross-impacts, we propose a construction to fix the parameters in the impact functions. These parameters are further corroborated by a diffusion function that measures the correlated motion of prices from different stocks to test our simulated results.
Edit intent: meaning-changed
Original:   In recent years, computational methods have made huge progress due to advance in computation speed and machine learning methods.
Revised:  Its importance comes from the relationship between the 3D structure and the function of a given protein or RNA. 3D structures also help to find the ligands of the protein, which are usually small molecules, a key step in drug design. Unfortunately, there is no shortcut to accurately obtain the 3D structure of a macromolecule. Many physical measurements of macromolecular 3D structures cannot scale up, due to their large labor costs and the requirements for lab conditions. In recent years, computational methods have made huge progress due to advance in computation speed and machine learning methods.
Edit intent: meaning-changed
Original:  The computing speed is not nearly enough to simulate the atom-level fold process when computing the physical energy among all the atoms. The two obstacles can be removed by knowledge-based methods, which combine knowledge learned from the known structures and biologists  knowledge of the folding process of protein or RNA. In the dissertation, I will present my results in building a knowledge-based method by using machine learning methods to tackle this problem.
Revised:  The two obstacles can be removed by knowledge-based methods, which combine knowledge learned from the known structures and biologists knowledge of the folding process of protein or RNA. In the dissertation, I will present my results in building a knowledge-based method by using machine learning methods to tackle this problem.
Edit intent: coherence
Original:  The two obstacles can be removed by knowledge-based methods, which combine knowledge learned from the known structures and biologists  knowledge of the folding process of protein or RNA. In the dissertation, I will present my results in building a knowledge-based method by using machine learning methods to tackle this problem.
Revised:  The two obstacles can be removed by knowledge-based methods, which combine knowledge learned from the known structures and biologists ' knowledge of the folding process of protein or RNA. In the dissertation, I will present my results in building a knowledge-based method by using machine learning methods to tackle this problem.
Edit intent: fluency
Original: Genetically identical microbial cells typically display significant variability in every measurable property. In particular, highly abundant proteins - which can determine cellular behavior - exhibit large variability in copy number among individuals. Their distribution has a universal shape common to different proteins and URLanisms; the same distribution shape is measured both in populations and in single-cell temporal traces. Moreover, different highly expressed proteins are statistically correlated with cell size and with cell-cycle time.
Revised: Cellular phenotype is characterized by different components such as cell size, protein content and cell cycle time. These are global variables that are the outcome of multiple internal microscopic processes. Accordingly, they display some universal statistical properties and scaling relations, such as distribution collapse and relation between moments. Cell size statistics and its relation to growth and division has been mostly studied separately from proteins and other cellular variables. Here we present experimental and theoretical analyses of these phenotype components in a unified framework that reveals their correlations and interactions inside the cell. We measure these components simultaneously in single cells over dozens of generations, quantify their correlations, and compare population to temporal statistics. We find that cell size and highly expressed proteins are statistically correlated with cell size and with cell-cycle time.
Edit intent: meaning-changed
Original:  Moreover, different highly expressed proteins are statistically correlated with cell size and with cell-cycle time. These results indicate coupling between measurable properties in the cell and buffering of their statistics from the microscopic scale. We propose a modeling framework in which the complex intracellular processes produce a phenotype composed of many effectively interacting components . These interactions, as well as the imperfect nature of cell division events, provide a simple model that reconstructs many properties of phenotypic variability on several timescales. These include fluctuating accumulation rates along consecutive cell-cycles, with correlations among the rates of phenotype components; universal and non-universal properties of distributions; correlations between cell-cycle time and different phenotype components; and temporally structured autocorrelation functions with long (\sim 10 generation) timescales .
Revised:  Moreover, different highly expressed proteins have very similar dynamics over growth and division cycles, which result in parallel statistical properties, both universal and individual. In particular, while distribution shapes of fluctuations along time are common to all cells and components, other properties are variable and remain distinct in individual cells for a surprisingly large number of generations. These include temporal averages of cell size and protein content, and the structure of their auto-correlation functions. We explore possible roles of the different components in controlling cell growth and division. We find that in order to stabilize exponential accumulation and division of all components across generations, coupled dynamics among them is required. Finally, we incorporate effective coupling within the cell cycle with a phenomenological mapping across consecutive cycles, and show that this model reproduces the entire array of experimental observations .
Edit intent: clarity
Original:  Secondly, given separation, more work is always necessary to create a specific copy than a non-specific polymer of the same length. Finally, the mixing of copies from distinct templates makes correlations between template and copy sequences unexploitable, combining with copying inaccuracy to reduce the free energy stored in a polymer ensemble. This lower stored free energy in turn reduces the minimal entropy generation during non-specific depolymerization .
Revised:  Secondly, given separation, more work is always necessary to create a specific copy than a canonical biochemical readout network generates correlations; extracting no work from these correlations sets a lower bound on dissipation. For general input, the biochemical network cannot reach this bound, even with arbitrarily slow reactions or weak thermodynamic driving. It faces an accuracy-dissipation trade-off that is qualitatively distinct from and worse than implied by the bound, and more complex steady-state copy processes cannot perform better. Nonetheless, the cost remains close to the thermodynamic bound unless accuracy is extremely high. Additionally, we show that biomolecular reactions could be used in thermodynamically optimal devices under exogenous manipulation of chemical fuels, suggesting an experimental system for testing computational thermodynamics .
Edit intent: meaning-changed
Original:  Concerning the asymptotic behavior of the MLE in the supercritical case, we derive a stochastic representation of the limiting mixed normal distribution containing a jump-type supercritical CIR process , which is a new phenomena, compared to the critical case, where a diffusion-type critical CIR process comes into play .
Revised:  Concerning the asymptotic behavior of the MLE in the supercritical case, we derive a stochastic representation of the limiting mixed normal distribution , where the almost sure limit of an appropriately scaled jump-type supercritical CIR process , which is a new phenomena, compared to the critical case, where a diffusion-type critical CIR process comes into play .
Edit intent: meaning-changed
Original:  Concerning the asymptotic behavior of the MLE in the supercritical case, we derive a stochastic representation of the limiting mixed normal distribution containing a jump-type supercritical CIR process , which is a new phenomena, compared to the critical case, where a diffusion-type critical CIR process comes into play .
Revised:  Concerning the asymptotic behavior of the MLE in the supercritical case, we derive a stochastic representation of the limiting mixed normal distribution containing a jump-type supercritical CIR process comes into play. This is a new phenomena, compared to the critical case, where a diffusion-type critical CIR process comes into play .
Edit intent: coherence
Original:  Concerning the asymptotic behavior of the MLE in the supercritical case, we derive a stochastic representation of the limiting mixed normal distribution containing a jump-type supercritical CIR process , which is a new phenomena, compared to the critical case, where a diffusion-type critical CIR process comes into play .
Revised:  Concerning the asymptotic behavior of the MLE in the supercritical case, we derive a stochastic representation of the limiting mixed normal distribution containing a jump-type supercritical CIR process , which is a new phenomena, compared to the critical case, where a diffusion-type critical CIR process plays a role .
Edit intent: clarity
Original: We consider a jump-type Cox-Ingersoll-Ross (CIR) process driven by  a subordinator, and we study asymptotic properties of the maximum likelihood estimator (MLE) for its growth rate.
Revised: We consider a jump-type Cox--Ingersoll--Ross (CIR) process driven by a subordinator, and we study asymptotic properties of the maximum likelihood estimator (MLE) for its growth rate.
Edit intent: fluency
Original: We consider a jump-type Cox-Ingersoll-Ross (CIR) process driven by  a subordinator, and we study asymptotic properties of the maximum likelihood estimator (MLE) for its growth rate.
Revised: We consider a jump-type Cox-Ingersoll-Ross (CIR) process driven by a standard Wiener process and a subordinator, and we study asymptotic properties of the maximum likelihood estimator (MLE) for its growth rate.
Edit intent: meaning-changed
Original:  Inspired by the empirical findings, we introduce a new class of continuous-time stochastic volatility models, capable of decoupling roughness ( fine properties ) from long memory and persistence (long-term behavior) in a simple and parsimonious way, which allows us to successfully model volatility at all intraday time scales.
Revised:  Inspired by the empirical findings, we introduce a new class of continuous-time stochastic volatility models, capable of decoupling roughness ( short-term behavior ) from long memory and persistence (long-term behavior) in a simple and parsimonious way, which allows us to successfully model volatility at all intraday time scales.
Edit intent: meaning-changed
Original:  Finally, in a forecasting study, we find that our new models outperform a wide array of benchmarks considerably, indicating that it pays off to exploit both roughness and persistence in volatility forecasting.
Revised:  As an illustration of the usefulness our new models, we conduct an extensive forecasting study; we find that our new models outperform a wide array of benchmarks considerably, indicating that it pays off to exploit both roughness and persistence in volatility forecasting.
Edit intent: coherence
Original:  Finally, in a forecasting study, we find that our new models outperform a wide array of benchmarks considerably, indicating that it pays off to exploit both roughness and persistence in volatility forecasting.
Revised:  Finally, in a forecasting study, we find that the models proposed in this paper outperform a wide array of benchmarks considerably, indicating that it pays off to exploit both roughness and persistence in volatility forecasting.
Edit intent: meaning-changed
Original: We study the empirical properties of realized volatility of the E-mini S P 500 futures contract at various time scales, ranging from a few minutes to one day. Our main finding is that intraday volatility is remarkably rough and persistent. What is more, by further studying daily realized volatility measures of close to two thousand individual US equities, we find that both roughness and persistence appear to be universal properties of volatility. Inspired by the empirical findings, we introduce a new class of continuous-time stochastic volatility models, capable of decoupling roughness (short-term behavior) from long memoryand persistence (long-term behavior) in a simple and parsimonious way, which allows us to successfully model volatility at all intraday time scales .
Revised: We study the empirical properties of realized volatility of the E-mini S introduce a new class of continuous-time stochastic volatility models, capable of decoupling roughness (short-term behavior) from long memoryand persistence (long-term behavior) in a simple and parsimonious way, which allows us to successfully model volatility at all intraday time scales .
Edit intent: coherence
Original:  Inspired by the empirical findings, we introduce a new class of continuous-time stochastic volatility models, capable of decoupling roughness (short-term behavior) from long memoryand persistence (long-term behavior) in a simple and parsimonious way, which allows us to successfully model volatility at all intraday time scales .
Revised:  Inspired by the empirical findings, we introduce a new class of continuous-time models of the stochastic volatility of asset prices. The models can simultaneously incorporate roughness and slowly decaying autocorrelations, including proper long memory, which are two stylized facts often found in volatility data .
Edit intent: meaning-changed
Original:  Here, we propose a deterministic method for high-resolution reconstruction given a very low resolution initial guess, that requires a predictable and relatively modest amount of computational effort  .
Revised:  Here, we propose a deterministic method for high-resolution reconstruction given a very low resolution initial guess, that requires a predictable and relatively modest amount of computational effort , by marching out radially in the Fourier domain from low to high frequency, increasing the resolution by a fixed increment at each step .
Edit intent: meaning-changed
Original:  Balanced fairness is known to be both insensitive and Pareto-efficient in such networks , which ensures that the performance metrics, when computable, will provide robust insights into the real performance of the system considered.
Revised:  Balanced fairness is known to be both insensitive and Pareto-efficient in such systems , which ensures that the performance metrics, when computable, will provide robust insights into the real performance of the system considered.
Edit intent: clarity
Original:  We first show that these performance metrics can be evaluated with a complexity that is polynomial in the system size when we allow for some controlled asymmetry, in the sense that the network contains a fixed number of parts wherein all queues are `exchangeable ' . This in turn allows us to derive stochastic bounds for a larger class of networks which satisfy less restrictive symmetry assumptions.
Revised:  We first show that these performance metrics can be evaluated with a complexity that is polynomial in the system size if the system is partitioned into a finite number of parts wherein all queues are `exchangeable ' . This in turn allows us to derive stochastic bounds for a larger class of networks which satisfy less restrictive symmetry assumptions.
Edit intent: clarity
Original:  We first show that these performance metrics can be evaluated with a complexity that is polynomial in the system size when we allow for some controlled asymmetry, in the sense that the network contains a fixed number of parts wherein all queues are `exchangeable ' . This in turn allows us to derive stochastic bounds for a larger class of networks which satisfy less restrictive symmetry assumptions.
Revised:  We first show that these performance metrics can be evaluated with a complexity that is polynomial in the system size when we allow for some controlled asymmetry, in the sense that the network contains a fixed number of parts , so that queues are exchangeable within each part and asymmetric across different parts . This in turn allows us to derive stochastic bounds for a larger class of networks which satisfy less restrictive symmetry assumptions.
Edit intent: meaning-changed
Original:  We first show that these performance metrics can be evaluated with a complexity that is polynomial in the system size when we allow for some controlled asymmetry, in the sense that the network contains a fixed number of parts wherein all queues are `exchangeable ' . This in turn allows us to derive stochastic bounds for a larger class of networks which satisfy less restrictive symmetry assumptions.
Revised:  We first show that these performance metrics can be evaluated with a complexity that is polynomial in the system size when we allow for some controlled asymmetry, in the sense that the network contains a fixed number of parts wherein all queues are `exchangeable ' . This in turn allows us to derive stochastic bounds for a larger class of systems which satisfy less restrictive symmetry assumptions.
Edit intent: fluency
Original: We propose a credit risk approach in which financial institutions , modelled as a portfolio of risky assets characterized by a probability of default and a correlation matrix, are the nodes of a network whose links are credit exposures that would be partially lost in case of neighbours' default .
Revised: The interconnectedness of financial institutions affects instability and credit crises. To quantify systemic risk we introduce here the PD model, a dynamic model that combines credit risk techniques with a contagion mechanism on the network of exposures among banks. A potential loss distribution is obtained through a multi-period Monte Carlo simulation that considers the probability of default and a correlation matrix, are the nodes of a network whose links are credit exposures that would be partially lost in case of neighbours' default .
Edit intent: meaning-changed
Original: We propose a credit risk approach in which financial institutions , modelled as a portfolio of risky assets characterized by a probability of default and a correlation matrix, are the nodes of a network whose links are credit exposures that would be partially lost in case of neighbours' default .
Revised: We propose a credit risk approach in which financial institutions , modelled as a portfolio of risky assets characterized by a probability of default (PD) of the banks and their tendency of defaulting in the same time interval. A contagion process increases the PD of banks exposed toward distressed counterparties .
Edit intent: meaning-changed
Original:  The systemic risk of the network is described in terms of the loss distribution over time obtained with a multi-period Montecarlo simulation process, during which the nodes can default, triggering a change in the probability of default in their neighbourhood as a contagion mechanism.
Revised:  The systemic risk is measured by statistics of the loss distribution over time obtained with a multi-period Montecarlo simulation process, during which the nodes can default, triggering a change in the probability of default in their neighbourhood as a contagion mechanism.
Edit intent: clarity
Original:  The systemic risk of the network is described in terms of the loss distribution over time obtained with a multi-period Montecarlo simulation process, during which the nodes can default, triggering a change in the probability of default in their neighbourhood as a contagion mechanism. In particular, we have considered the expected loss and introduced new measures of network stress called PDImpact and PDRank . They are expressed in monetary terms as the already known DebtRank and can be used to assess the importance of a node in the network. The model exhibits two regimes of 'weak' and 'strong' contagion, the latter characterized by the depletion of the loss distribution at intermediate losses in favour of fatter tails.
Revised:  The systemic risk of the network is described in terms of the loss distribution , while the contribution of each node is quantified by the new measures PDRank and PDImpact. We illustrate how the model works on the network of the latter characterized by the depletion of the loss distribution at intermediate losses in favour of fatter tails.
Edit intent: clarity
Original:  The model exhibits two regimes of 'weak' and 'strong' contagion, the latter characterized by the depletion of the loss distribution at intermediate losses in favour of fatter tails. Also, in systems with strong contagion , low average correlation between nodes corresponds to larger losses.
Revised:  The model exhibits two regimes of 'weak' and 'strong' contagion, the European Global Systemically Important Banks. For a certain range of the banks' capital and of their assets volatility, our results reveal the emergence of a strong contagion regime where lower default correlation between banks corresponds to higher losses.
Edit intent: meaning-changed
Original:  This seems at odds with the diversification benefit obtained in standard credit risk models . Results suggest that the credit exposure network of the European global systemically important banks is in a weak contagion regime, but strong contagion could be approached in periods characterized by extreme volatility or in cases where the financial institutions are not adequately capitalized .
Revised:  This is the opposite of the diversification benefits postulated by standard credit risk models . Results suggest that the credit exposure network of the European global systemically important banks is in a weak contagion regime, but strong contagion could be approached in periods characterized by extreme volatility or in cases where the financial institutions are not adequately capitalized .
Edit intent: clarity
Original:  This seems at odds with the diversification benefit obtained in standard credit risk models . Results suggest that the credit exposure network of the European global systemically important banks is in a weak contagion regime, but strong contagion could be approached in periods characterized by extreme volatility or in cases where the financial institutions are not adequately capitalized .
Revised:  This seems at odds with the diversification benefit obtained in standard credit risk models used by banks and regulators who could therefore underestimate the capital needed to overcome a period of crisis, thereby contributing to the financial system instability .
Edit intent: coherence
Original: We consider the robust exponential utility maximization problem in discrete time: An investor maximizes the worst case expected exponential utility with respect to a family of non-dominated probabilistic models of her endowment by dynamically investing in a financial market  .
Revised: We consider the robust exponential utility maximization problem in discrete time: An investor maximizes the worst case expected exponential utility with respect to a family of nondominated probabilistic models of her endowment by dynamically investing in a financial market .
Edit intent: clarity