wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Based on that, we draw a parallel between the folding process and the dynamics for a network of coupled oscillators described by the Kuramoto model  .
Revised:  Based on that, we draw a parallel between the folding process and the dynamics for a network of coupled oscillators described by the Kuramoto model . The pattern of synchronized cluster formation, growing and assembling helps to solve the Levinthal's paradox .
Edit intent: meaning-changed
Original:  We also study the time evolution of the predictability of price changes on the sample of 20 biggest companies on Warsaw's market and investigate the relationships inside this group, as well as the time evolution of the predictability of those price changes .
Revised:  We also study the time evolution of the predictability of price changes on the sample of 20 biggest companies on Warsaw's market and investigate the relationships inside this group, as well as the stock's sector .
Edit intent: clarity
Original: In the aftermath of the interbank market collapse of 2007-08, the traditional idea that systemic risk is primarily the risk of cascading bank defaults has evolved into the view that it involves both cascading bank defaults as well as funding liquidity shocks, and that both types of shocks impair the functioning of the remaining undefaulted banks .
Revised: In the aftermath of the interbank market collapse of 2007-08, the scope of systemic risk research has broadened to encompass a wide range of channels, notably asset correlations, default contagion, illiquidity contagion, and asset firesales .
Edit intent: meaning-changed
Original:   Our paper introduces a deliberately simplified model which integrates insolvency and illiquidity in financial networks and that can provide answers to the question of how illiquidity or default of one bank can influence the overall level of liquidity stress and default in the network.
Revised:  The main goal of the double cascade model we introduce is to integrate these two facets. In a default cascade, insolvency of a given bank will create a shock to the asset side of the balance sheet of each of its creditor banks. Under some circumstances, such "downstream" shocks can cause further insolvencies that may build up to create a global insolvency cascade. On the other hand, in a stress cascade, illiquidity that hits a given bank will create a shock to the liability side of the balance sheet of each of its debtor banks. Such "upstream" shocks can cause further illiquidity stresses that may build up to create a global illiquidity cascade. Our paper introduces a deliberately simplified model which integrates insolvency and illiquidity in financial networks and that can provide answers to the question of how illiquidity or default of one bank can influence the overall level of liquidity stress and default in the network.
Edit intent: meaning-changed
Original:   Our paper introduces a deliberately simplified model which integrates insolvency and illiquidity in financial networks and that can provide answers to the question of how illiquidity or default of one bank can influence the overall level of liquidity stress and default in the network.
Revised:  Our paper introduces a deliberately simplified network model of insolvency and illiquidity in financial networks and that can provide answers to the question of how illiquidity or default of one bank can influence the overall level of liquidity stress and default in the network.
Edit intent: clarity
Original:   Our paper introduces a deliberately simplified model which integrates insolvency and illiquidity in financial networks and that can provide answers to the question of how illiquidity or default of one bank can influence the overall level of liquidity stress and default in the network.
Revised:  Our paper introduces a deliberately simplified model which integrates insolvency and illiquidity that can quantify how illiquidity or default of one bank can influence the overall level of liquidity stress and default in the network.
Edit intent: clarity
Original:   Our paper introduces a deliberately simplified model which integrates insolvency and illiquidity in financial networks and that can provide answers to the question of how illiquidity or default of one bank can influence the overall level of liquidity stress and default in the network.
Revised:  Our paper introduces a deliberately simplified model which integrates insolvency and illiquidity in financial networks and that can provide answers to the question of how illiquidity or default of one bank influences the overall level of liquidity stress and default in the network.
Edit intent: fluency
Original:  First, this paper proposes a stylized model of individual bank balance sheets that builds in regulatory constraints. Secondly, three different possible states of a bank, namely the normal state, the stressed state and the insolvent state, are identified with conditions on the bank's balance sheet. Thirdly, the paper models the behavioural response of a bank when it finds itself in the stressed or insolvent states. Importantly, a stressed bank seeks to protect itself from the default of its counterparties, but creates stress in the network by forcing its debtor banks to raise cash. Versions of these proposed models can be solved by large-network asymptotic cascade formulas.
Revised:  Under an assumption we call "locally tree-like independence", we derive large-network asymptotic cascade formulas.
Edit intent: meaning-changed
Original: The scope of financial systemic risk research encompasses a wide range of  channels and effects, including asset correlation shocks, default contagion, illiquidity contagion, and asset firesales. For example, insolvency of a given bank will create a shock to the asset side of the balance sheet of each of its creditor banks and under some circumstances, such "downstream" shocks can cause further insolvencies that may build up to create what is called an insolvency or default cascade. On the other hand, funding illiquidity that hits a given bank will create a shock to the liability side of the balance sheet of each of its debtor banks. Under some circumstances, such "upstream" shocks can cause illiquidity in further banks that may build up to create an illiquidity cascade.
Revised: The scope of financial systemic risk research encompasses a wide range of channels and effects, including asset correlation shocks, default contagion, illiquidity contagion, and asset fire sales.
Edit intent: meaning-changed
Original:  The experiments clearly support the main conclusion that  in the absence of  fire sales, the average eventual level of defaults in a financial network is negatively related to the strength of banks' liquidity stress response and the eventual level of stress in the network.
Revised:  The experiments clearly support the main conclusion that when banks respond to liquidity stress by hoarding liquidity, then in the absence of fire sales, the average eventual level of defaults in a financial network is negatively related to the strength of banks' liquidity stress response and the eventual level of stress in the network.
Edit intent: meaning-changed
Original:  The experiments clearly support the main conclusion that  in the absence of  fire sales, the average eventual level of defaults in a financial network is negatively related to the strength of banks' liquidity stress response and the eventual level of stress in the network.
Revised:  The experiments clearly support the main conclusion that in the absence of asset fire sales, the average eventual level of defaults in a financial network is negatively related to the strength of banks' liquidity stress response and the eventual level of stress in the network.
Edit intent: meaning-changed
Original:  The experiments clearly support the main conclusion that  in the absence of  fire sales, the average eventual level of defaults in a financial network is negatively related to the strength of banks' liquidity stress response and the eventual level of stress in the network.
Revised:  The experiments clearly support the main conclusion that in the absence of fire sales, the level of defaults in a financial network is negatively related to the strength of banks' liquidity stress response and the eventual level of stress in the network.
Edit intent: clarity
Original:  The experiments clearly support the main conclusion that  in the absence of  fire sales, the average eventual level of defaults in a financial network is negatively related to the strength of banks' liquidity stress response and the eventual level of stress in the network.
Revised:  The experiments clearly support the main conclusion that in the absence of fire sales, the average eventual level of defaults in a financial network is negatively related to the strength of bank liquidity hoarding and the eventual level of stress in the network.
Edit intent: clarity
Original:  This quantity or the hydration thermodynamics of a small molecule analog of the side-chain or some combination of such estimates, have anchored the interpretation of many of the seminal experiments on protein stability and folding and in the genesis of the current views on dominant interactions stabilizing proteins .
Revised:  This quantity or the hydration thermodynamics of a small molecule analog of the side-chain or some combination of such estimates, have anchored the interpretation of seminal experiments on protein stability and folding and in the genesis of the current views on dominant interactions stabilizing proteins .
Edit intent: clarity
Original:  This quantity or the hydration thermodynamics of a small molecule analog of the side-chain or some combination of such estimates, have anchored the interpretation of many of the seminal experiments on protein stability and folding and in the genesis of the current views on dominant interactions stabilizing proteins .
Revised:  This quantity or the hydration thermodynamics of a small molecule analog of the side-chain or some combination of such estimates, have anchored the interpretation of many of the seminal experiments on protein stability and folding .
Edit intent: coherence
Original:  The hydration thermodynamics of g-butane or the conditional hydration thermodynamics of isoleucine from the GGIGG pentapeptide proves unsatisfactory in predicting the properties of either IGGGG or the isoleucine-substituted helical deca-peptides .
Revised:  The excess enthalpy and entropy of hydration of g-butane or the conditional hydration thermodynamics of isoleucine from the GGIGG pentapeptide proves unsatisfactory in predicting the properties of either IGGGG or the isoleucine-substituted helical deca-peptides .
Edit intent: meaning-changed
Original:  The hydration thermodynamics of g-butane or the conditional hydration thermodynamics of isoleucine from the GGIGG pentapeptide proves unsatisfactory in predicting the properties of either IGGGG or the isoleucine-substituted helical deca-peptides .
Revised:  The hydration thermodynamics of g-butane model are also more negative than the corresponding conditional quantities for the side-chain. The conditional solvation of isoleucine in GGIGG also proves unsatisfactory in predicting the properties of either IGGGG or the isoleucine-substituted helical deca-peptides .
Edit intent: meaning-changed
Original:  The hydration thermodynamics of g-butane or the conditional hydration thermodynamics of isoleucine from the GGIGG pentapeptide proves unsatisfactory in predicting the properties of either IGGGG or the isoleucine-substituted helical deca-peptides .
Revised:  The hydration thermodynamics of g-butane or the conditional hydration thermodynamics of isoleucine from the GGIGG pentapeptide proves unsatisfactory in describing the conditional solvation of isoleucine in the helical peptides .
Edit intent: clarity
Original: Among network modeling tasks, identifying the rewiring of network structure is particularly instrumental in revealing and pinpointing the molecular cause of a disease. Effective incorporation of biological prior knowledge into network learning algorithms can leverage domain knowledge and make data driven inference more robust and biologically relevant. We formulate the inference of condition specific network structures that incorporates relevant prior knowledge as a convex optimization problem, and develop an efficient learning algorithm to jointly infer the biological networks as well as their changes.
Revised: Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, the modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. We formulated the inference of condition specific network structures that incorporates relevant prior knowledge as a convex optimization problem, and develop an efficient learning algorithm to jointly infer the biological networks as well as their changes.
Edit intent: coherence
Original:  We formulate the inference of condition specific network structures that incorporates relevant prior knowledge as a convex optimization problem, and develop an efficient learning algorithm to jointly infer the biological networks as well as their changes.
Revised:  We formulate the inference of differential dependency networks that incorporates both conditional data and prior knowledge as a convex optimization problem, and develop an efficient learning algorithm to jointly infer the biological networks as well as their changes.
Edit intent: meaning-changed
Original:  We formulate the inference of condition specific network structures that incorporates relevant prior knowledge as a convex optimization problem, and develop an efficient learning algorithm to jointly infer the biological networks as well as their changes. We test the proposed method on simulation data sets and demonstrate the effectiveness of this method .
Revised:  We formulate the inference of condition specific network structures that incorporates relevant prior knowledge as a convex optimization problem, and develop an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to random knowledge and based on which, developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets .
Edit intent: meaning-changed
Original: Gene regulatory networks are commonly used for modeling biological processesand revealing underlying molecular mechanisms. The reconstruction of gene regulatory networks from observational data is a challengingtask , especially considering the large number of players (e.g. genes) involved and the small number of biological replicates available for analysis.
Revised: Gene regulatory networks are commonly used to study biological processes. The inference of gene covariation networks from observational data is a challengingtask , especially considering the large number of players (e.g. genes) involved and the small number of biological replicates available for analysis.
Edit intent: meaning-changed
Original: Regulators clearly believe that derivativescan never be risk free. Regulators have risk preferences and by imposing costly actions on banks they have made derivatives markets incomplete. These actions have idiosyncratic effects, for example the stress period for Market Risk capital is determined at the bank level, not at desk level. Idiosyncratic effects mean that no single measure makes assets and derivatives martingales for all market participants.
Revised: Regulations impose idiosyncratic capital and funding costs for holding derivatives. Capital requirements are costly because derivatives desks are risky businesses; funding is costly in part because regulations increase the minimum funding tenor. Idiosyncratic costs mean no single measure makes assets and derivatives martingales for all market participants.
Edit intent: meaning-changed
Original:  Market participants have idiosyncratic, multiple, risk-neutral measures but the market does not. Practically, we show that derivatives desks leak PnL (profit-and-loss) even with idealized markets providing credit protection contracts and unlimited liquidity facilities (i.e. repos with zero haircuts). This PnL leak means that derivatives desks are inherently risky as they must rely on competitive advantages to price in the costs of their risks. This strictly positive risk level means that Regulatory-required capital must also have strictly positive costs. Hence Regulatory-compliant derivatives markets are incomplete. If we relax our assumptions by permitting haircuts on repos the situation is qualitatively worse because new Regulatory-driven costs (liquidity buffers) enter the picture. These additional funding costs must be met by desks further stressing their business models. One consequence of Regulatory-driven incomplete-market pricing is that the FVA debate is resolved in favor of both sides: academics on principles (pay for risk); and practitioners on practicalities (desks do pay). As a second consequence we identify appropriate exit prices .
Revised:  This has implications for exit prices and mark-to-market .
Edit intent: meaning-changed
Original:  It is shown that, under some mild integrability conditions, a process can be approximated in mean square sense and in other strong metrics by adapted binomial processes, i.e., by processes with fixed size binary increments at sampling points  .
Revised:  It is shown that, under some mild integrability conditions, a process can be approximated in mean square sense and in other strong metrics by binomial processes, i.e., by processes with fixed size binary increments at sampling points .
Edit intent: clarity
Original:  It is shown that, under some mild integrability conditions, a process can be approximated in mean square sense and in other strong metrics by adapted binomial processes, i.e., by processes with fixed size binary increments at sampling points  .
Revised:  It is shown that, under some mild integrability conditions, a process can be approximated in mean square sense and in other strong metrics by adapted binomial processes, i.e., by processes with fixed size binary increments at sampling points . Moreover, this approximation can be causal, i.e., at every time it requires only past historical values of the underlying process .
Edit intent: meaning-changed
Original:  It provides an explicit algorithm of how to rewire the corresponding complexes  . Using the concept of genus induction, we construct RNA-RNA interaction complexes of fixed topological genus g uniformly in linear time  .
Revised:  It provides an explicit algorithm of how to rewire the corresponding complexes and an unambiguous decomposition grammar . Using the concept of genus induction, we construct RNA-RNA interaction complexes of fixed topological genus g uniformly in linear time .
Edit intent: meaning-changed
Original:  It provides an explicit algorithm of how to rewire the corresponding complexes  . Using the concept of genus induction, we construct RNA-RNA interaction complexes of fixed topological genus g uniformly in linear time  .
Revised:  It provides an explicit algorithm of how to rewire the corresponding complexes . Using the concept of genus induction, we construct bicellular maps of fixed topological genus g uniformly in linear time .
Edit intent: meaning-changed
Original:  It provides an explicit algorithm of how to rewire the corresponding complexes  . Using the concept of genus induction, we construct RNA-RNA interaction complexes of fixed topological genus g uniformly in linear time  .
Revised:  It provides an explicit algorithm of how to rewire the corresponding complexes . Using the concept of genus induction, we construct RNA-RNA interaction complexes of fixed topological genus g uniformly in linear time . We present various statistics on these topological RNA complexes and compare our findings with biological complexes. Furthermore we show how to construct loop-energy based complexes using our decomposition grammar .
Edit intent: meaning-changed
Original:  To describe volatility  two of the nonlinear time series are combined into  TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto- Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.
Revised:  To describe volatility , two of the nonlinear time series are combined into TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto- Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.
Edit intent: fluency
Original:  To describe volatility  two of the nonlinear time series are combined into  TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto- Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.
Revised:  To describe volatility two of the non-linear time series are combined into TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto- Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.
Edit intent: fluency
Original:  To describe volatility  two of the nonlinear time series are combined into  TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto- Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.
Revised:  To describe volatility two of the nonlinear time series are combined into form TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto- Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.
Edit intent: fluency
Original:  To describe volatility  two of the nonlinear time series are combined into  TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto- Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.
Revised:  To describe volatility two of the nonlinear time series are combined into TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto-Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.
Edit intent: fluency
Original:  The method incorporates in a very natural manner amino acid similarity in the calculation of covariance, and accounts for low number of observations by a regularization parameter that depends on the effective number of sequences in the alignment. Most importantly, inversion of the sample covariance matrix allows the computation of partial correlations between pairs of residues, thereby removing the effect of spurious transitive correlations.
Revised:  The basic assumption is that spatially proximal residues in a protein coevolve to maintain the physicochemical complementarity of the amino acids involved in the contact. Our regularized inversion of the sample covariance matrix allows the computation of partial correlations between pairs of residues, thereby removing the effect of spurious transitive correlations.
Edit intent: clarity
Original:   When tested on a set of protein families from PFAM , we found the RLS algorithm to have superior performance compared to PSICOV, a state-of-the-art method for contact prediction  .
Revised:  The method also accounts for low number of observations by means of a regularization parameter that depends on the effective number of sequences in the alignment. When tested on a set of protein families from PFAM , we found the RLS algorithm to have superior performance compared to PSICOV, a state-of-the-art method for contact prediction .
Edit intent: meaning-changed