wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The model also demonstrates both the A to B and the B to A transitions. The number of degrees of freedom is reduced to six grains per nucleotide. We show that this is the minimal number sufficient for this purpose. The key features of the model are (1) simulation of sugarrepuckering between C2'-endo and C3'-endo by the use of one nonharmonic potential and one three-particle potential, (2 ) explicit representation of ions in solution around the DNA, (3) implicit solventapproach and (4 ) sequence dependence.
Revised:  The model also demonstrates both the A to B and the A-form during interactions with biological molecules. We propose a CG representation of the ribose conformational flexibility. We substantiate the choice of the CG sites (6 per nucleotide) needed for the "sugar" GC DNA model, and obtain the potentials of the CG interactions between the sites by the "bottom-up" approach using the all-atom AMBER force field. We show that the representation of the ribose flexibility requires one non-harmonic and one three-particle potential, (2 ) explicit representation of ions in solution around the DNA, (3) implicit solventapproach and (4 ) sequence dependence.
Edit intent: clarity
Original:  The key features of the model are (1) simulation of sugarrepuckering between C2'-endo and C3'-endo by the use of one nonharmonic potential and one three-particle potential, (2 ) explicit representation of ions in solution around the DNA, (3) implicit solventapproach and (4 ) sequence dependence.
Revised:  The key features of the model are (1) simulation of sugarrepuckering between C2'-endo and C3'-endo by the use of one nonharmonic potential and one three-particle potential, the forms of both the potentials being different from the ones generally used. The model also includes (i ) explicit representation of ions in solution around the DNA, (3) implicit solventapproach and (4 ) sequence dependence.
Edit intent: meaning-changed
Original:  The key features of the model are (1) simulation of sugarrepuckering between C2'-endo and C3'-endo by the use of one nonharmonic potential and one three-particle potential, (2 ) explicit representation of ions in solution around the DNA, (3) implicit solventapproach and (4 ) sequence dependence.
Revised:  The key features of the model are (1) simulation of sugarrepuckering between C2'-endo and C3'-endo by the use of one nonharmonic potential and one three-particle potential, (2 ) explicit representation of ions (in an implicit solvent) and (ii ) sequence dependence.
Edit intent: clarity
Original:  We obtain parameters of the model from the all atom AMBER force field. The model can be used to study large local deformations of long DNA molecules (for example, in binding with proteins) . Small modification of the model can provide the possibility of modeling base pairs openings in melting, transcription and replication. And one can also simulate the interactions of the DNA molecule with different types of ions in different kinds of solutions .
Revised:  With these features, the sugar CG DNA model reproduces (with the same parameters) both the B- and A- stable forms under corresponding conditions and demonstrates both the A to B and the B to A phase transitions .
Edit intent: meaning-changed
Original:  Finally, we analyze the relationship between surplus-invariant acceptance sets and loss-based and excess-invariant risk measures, which have been recently studied by Cont, Deguest, and He , and by Staum  .
Revised:  Finally, we emphasize the advantages of dealing with surplus-invariant acceptance sets and loss-based and excess-invariant risk measures, which have been recently studied by Cont, Deguest, and He , and by Staum .
Edit intent: clarity
Original:  Finally, we analyze the relationship between surplus-invariant acceptance sets and loss-based and excess-invariant risk measures, which have been recently studied by Cont, Deguest, and He , and by Staum  .
Revised:  Finally, we analyze the relationship between surplus-invariant acceptance sets as the primary object rather than directly with risk measures, such as loss-based and excess-invariant risk measures, which have been recently studied by Cont, Deguest, and He , and by Staum .
Edit intent: meaning-changed
Original:  Finally, we analyze the relationship between surplus-invariant acceptance sets and loss-based and excess-invariant risk measures, which have been recently studied by Cont, Deguest, and He , and by Staum  .
Revised:  Finally, we analyze the relationship between surplus-invariant acceptance sets and loss-based and excess-invariant risk measures, which have been recently studied by Cont, Deguest, and He (2013) and by Staum .
Edit intent: meaning-changed
Original:  Finally, we analyze the relationship between surplus-invariant acceptance sets and loss-based and excess-invariant risk measures, which have been recently studied by Cont, Deguest, and He , and by Staum  .
Revised:  Finally, we analyze the relationship between surplus-invariant acceptance sets and loss-based and excess-invariant risk measures, which have been recently studied by Cont, Deguest, and He , and by Staum (2013), respectively .
Edit intent: meaning-changed
Original:  In order to demonstrate a strength of the optimality of the bin-width we compare the  \delta-spectrum for various bin-widths . Implications for the multifractal \delta-spectrum as a function of Renyi 's q parameter are also discussed and graphically represented .
Revised:  In order to demonstrate a strength of the optimality of the bin-width we compare the \delta-spectrum for various bin-widths and show the robustness of the method, especially for large values of q. For such values, other methods in use, e.g., those based on moment estimation, tend to fail for heavy-tailed data or data with long correlations. Connection between the \delta-spectrum as a function of Renyi 's q parameter are also discussed and graphically represented .
Edit intent: meaning-changed
Original: Network theory is today a central topic in computational systems biology as a framework to understand and reconstruct relations among biological components. For example, constructing networks from a gene expression dataset provides a set of possible hypotheses explaining connections among genes, vital knowledge to advancing our understanding of URLanisms as systems. Here we briefly survey aspects at the intersection of information theory and network biology.
Revised: We introduce concepts and tools at the intersection of information theory and network biology.
Edit intent: clarity
Original:  We show that Shannon's information entropy, Kolmogorov complexity and algorithmic probability quantify different aspects of  biological networks at the interplay of local and global pattern detection  .
Revised:  We show that Shannon's information entropy, Kolmogorov complexity and algorithmic probability quantify different aspects of biological networks at the interplay of local and global pattern detection , including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs .
Edit intent: meaning-changed
Original:  We provide approximations to the algorithmic probability and Kolmogorov complexity of motifs connected to the asymptotic topological propertiesof networks .
Revised:  We provide approximations to the algorithmic probability and Kolmogorov complexity for different types of graphs characterizing their variant and invariant properties. We introduce formal definitions of complexity for both labelled and unlabelled graphs and prove that the Kolmogorov complexity of a labelled graph is a good approximation of the Kolmogorov complexity of the unlabelled graph .
Edit intent: meaning-changed
Original:  We show that Shannon's information entropy, Kolmogorov complexity and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local (e.g. graph motifs) and global pattern detection, including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs  .
Revised:  We show that Shannon's information entropy, compressibility and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local (e.g. graph motifs) and global pattern detection, including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs .
Edit intent: clarity
Original:  We show that Shannon's information entropy, Kolmogorov complexity and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local (e.g. graph motifs) and global pattern detection, including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs  .
Revised:  We show that Shannon's information entropy, Kolmogorov complexity and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local and global pattern detection, including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs .
Edit intent: clarity
Original:  We show that Shannon's information entropy, Kolmogorov complexity and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local (e.g. graph motifs) and global pattern detection, including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs  .
Revised:  We show that Shannon's information entropy, Kolmogorov complexity and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local (e.g. graph motifs) and global pattern detection, including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs , and the recovery of topological properties from numerical kinetic properties simulating gene expression arrays .
Edit intent: meaning-changed
Original:  We show that Shannon's information entropy, compressibility and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local and global pattern detection  , including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression arrays .
Revised:  We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different aspects of synthetic and biological networks at the intersection of local and global pattern detection , including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression arrays .
Edit intent: meaning-changed
Original:  We show that Shannon's information entropy, compressibility and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local and global pattern detection  , including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression arrays .
Revised:  We show that Shannon's information entropy, compressibility and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local and global pattern detection . This includes, for example , including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression arrays .
Edit intent: meaning-changed
Original:  We show that Shannon's information entropy, compressibility and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local and global pattern detection  , including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression arrays .
Revised:  We show that Shannon's information entropy, compressibility and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local and global pattern detection , the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression arrays .
Edit intent: clarity
Original:  We show that Shannon's information entropy, compressibility and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local and global pattern detection  , including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression arrays .
Revised:  We show that Shannon's information entropy, compressibility and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local and global pattern detection , including, the detection of the connectivity phase transition leading to the emergence of giant components in Erdos-Renyi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression arrays .
Edit intent: fluency
Original:  We show that Shannon's information entropy, compressibility and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local and global pattern detection  , including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression arrays .
Revised:  We show that Shannon's information entropy, compressibility and algorithmic probability quantify different aspects of synthetic and biological networks at the intersection of local and global pattern detection , including, the detection of the connectivity phase transition leading to the emergence of giant components in Erd\"os-R\'enyi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data .
Edit intent: clarity
Original:  We introduce formal definitions of complexity for both labelled and unlabelled graphs and prove that the Kolmogorov complexity of a labelled graph is a good approximation of the Kolmogorov complexity of the unlabelled graph  .
Revised:  We introduce formal definitions of complexity for both labelled and unlabelled graphs and prove that the Kolmogorov complexity of a labelled graph is a good approximation of the Kolmogorov complexity of the unlabelled graph and thus a robust definition of graph complexity .
Edit intent: meaning-changed
Original:  We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different  aspects of synthetic and biological networks at the intersection of local and global pattern detection. This includes, for example, the detection of the connectivity phase transition leading to the emergence of giant components in Erdos-Renyi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data.
Revised:  We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different aspects of synthetic and biological data. We show examples such as the emergence of giant components in Erdos-Renyi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data.
Edit intent: clarity
Original:  We introduce formal definitions of complexity for both labelled and unlabelled graphs and prove that the Kolmogorov complexity of a labelled graph is a good approximation of the Kolmogorov complexity of the unlabelled graph and thus a robust definition of graph complexity.
Revised:  We introduce formal definitions of complexity for both labeled and unlabeled graphs and prove that the Kolmogorov complexity of a labelled graph is a good approximation of the Kolmogorov complexity of the unlabelled graph and thus a robust definition of graph complexity.
Edit intent: fluency
Original:  We introduce formal definitions of complexity for both labelled and unlabelled graphs and prove that the Kolmogorov complexity of a labelled graph is a good approximation of the Kolmogorov complexity of the unlabelled graph and thus a robust definition of graph complexity.
Revised:  We introduce formal definitions of complexity for both labelled and unlabelled graphs and prove that the Kolmogorov complexity of a labeled graph is a good approximation of the Kolmogorov complexity of the unlabelled graph and thus a robust definition of graph complexity.
Edit intent: fluency
Original:  We introduce formal definitions of complexity for both labelled and unlabelled graphs and prove that the Kolmogorov complexity of a labelled graph is a good approximation of the Kolmogorov complexity of the unlabelled graph and thus a robust definition of graph complexity.
Revised:  We introduce formal definitions of complexity for both labelled and unlabelled graphs and prove that the Kolmogorov complexity of a labelled graph is a good approximation of its unlabeled Kolmogorov complexity and thus a robust definition of graph complexity.
Edit intent: clarity
Original:  The proposed model is based on a standard jump diffusion process for price and volatility augmented by a bivariate Hawkes process for the two jump components. The latter process specifies a joint dynamic structure for the price and volatility jump intensities, with the intensity of a volatility jump also directly affected by a jump in the price. The impact of certain aspects of the model on the higher-order conditional moments for returns is investigated. In particular, the differential effects of the jump intensities and the random process for latent volatility itself, are measured and documented.
Revised:  The proposed model is based on a standard jump diffusion process for price and volatility of an asset are modelled using a joint Hawkes process in conjunction with a bivariate jump diffusion.
Edit intent: clarity
Original:  A state space representation of the model is constructed using both financial returnsand non-parametric measures of integrated volatility and price jumps as the observable quantities. Bayesian inference , based on a Markov chain Monte Carlo algorithm , is used to obtain a posterior distribution for the relevant model parameters and latent variables, and to analyze various hypotheses about the dynamics in, and the relationship between, the jump intensities .
Revised:  A state space representation of the model is constructed using both financial returnsand non-parametric measures of integrated volatility and price jumps , to the specified model components; with Bayesian inference conducted using a Markov chain Monte Carlo algorithm , is used to obtain a posterior distribution for the relevant model parameters and latent variables, and to analyze various hypotheses about the dynamics in, and the relationship between, the jump intensities .
Edit intent: clarity
Original:  Bayesian inference , based on a Markov chain Monte Carlo algorithm , is used to obtain a posterior distribution for the relevant model parameters and latent variables, and to analyze various hypotheses about the dynamics in, and the relationship between, the jump intensities .
Revised:  Bayesian inference , based on a Markov chain Monte Carlo algorithm . The calculation of marginal likelihoods for the proposed and related models is discussed .
Edit intent: clarity
Original:  An extensive empirical investigation using data based on the S&P500 market index over a period ending in early-2013 is conducted. Substantial empirical support for dynamic jump intensities is documented, with predictive accuracy enhanced by the inclusion of this type of specification.
Revised:  An extensive empirical investigation using data based on the S&P500 market index , with substantial support for dynamic jump intensities is documented, with predictive accuracy enhanced by the inclusion of this type of specification.
Edit intent: clarity
Original:  An extensive empirical investigation is undertaken using  the S&P500 market index  , with substantial support for dynamic jump intensities - including in terms of predictive accuracy - documented.
Revised:  An extensive empirical investigation is undertaken using the S&P500 market index over the 1996 to 2014 period , with substantial support for dynamic jump intensities - including in terms of predictive accuracy - documented.
Edit intent: meaning-changed
Original:  Such a structurenaturally arises from the close interrelation between the Shareholding Network  and the Board of Directors network . Inorder to capture the simultaneous effects on both networks on Corporate Governance , we propose to model the Corporate Governance multiple network structure via tensor analysis.
Revised:  Such a structurenaturally arises from the close interrelation between the Shareholding Network and the Board of Directors network (BD). In order to capture the simultaneous effects on both networks on Corporate Governance , we propose to model the Corporate Governance multiple network structure via tensor analysis.
Edit intent: clarity
Original:  The estimates given in the paper show that the potential difference across the membrane of the unexcited axon (resting potential) can be explained by the difference in surface densities of the bound charges on the inner and outer surfaces of the membrane .
Revised:  It is shown that the ions of the surface charge are in a relatively deep (as compared to kBT) potential wells, which are localized near the dipole heads of phospholipid membrane. It makes impossible for ions to slip along the membrane surface. Simple experiments for verifying the correctness of the considered model are proposed. A developed approach can be used for estimations of the surface charges on the inner and outer surfaces of the membrane .
Edit intent: meaning-changed
Original:  The estimates given in the paper show that the potential difference across the membrane of the unexcited axon (resting potential) can be explained by the difference in surface densities of the bound charges on the inner and outer surfaces of the membrane .
Revised:  The estimates given in the paper show that the potential difference across the membrane of the unexcited axon (resting potential) can be explained by the difference in surface densities of the bound charges on the outer and inner membrane of the cell .
Edit intent: clarity
Original:  The main result of the paper is that the only tail risk measure that satisfies both a set of economic axioms proposed by Schmeidler (1989, Econometrica) and the statistical property of elicitability (i.e. there exists an objective function such that minimizing the expected objective function yields the risk measure;
Revised:  The main result is that the only tail risk measure that satisfies both a set of economic axioms proposed by Schmeidler (1989, Econometrica) and the statistical property of elicitability (i.e. there exists an objective function such that minimizing the expected objective function yields the risk measure;
Edit intent: clarity