wanyu/IteraTeR_full_sent huggingface dataset:
Original: By Gyongy's theorem, a local and stochastic volatility  model is calibrated to the market prices of all  call options with positive maturities and strikes if its local volatility function is equal to the ratio of the Dupire local volatility function over the root conditional mean square of the stochastic volatility factor given the spot value.
Revised: By Gyongy's theorem, a local and stochastic volatility model is calibrated to the market prices of all European call options with positive maturities and strikes if its local volatility function is equal to the ratio of the Dupire local volatility function over the root conditional mean square of the stochastic volatility factor given the spot value.
Edit intent: meaning-changed
Original:  We then extend these results to the special case of the LSV model called Regime Switching Local Volatility , where the stochastic volatility factor is a jump process taking finitely many values and with jump intensities depending on the spot level.
Revised:  We then extend these results to the special case of the LSV model called regime switching local volatility , where the stochastic volatility factor is a jump process taking finitely many values and with jump intensities depending on the spot level.
Edit intent: fluency
Original:  The performance results for a wide range of tensor contractions suggest that GETT has the potential of becoming the method of choice: While GETT exhibits excellent performance across the board, its effectiveness for bandwidth-bound tensor contractions is especially impressive, outperforming existing approaches by up to 12.3 \times.
Revised:  The performance results for a wide range of tensor contractions suggest that GETT has the potential of becoming the method of choice: While GETT exhibits excellent performance across the board, its effectiveness for bandwidth-bound tensor contractions is especially impressive, outperforming existing approaches by up to 12.4 \times.
Edit intent: meaning-changed
Original:  More precisely, GETT achieves speedups of up to 1.42 \times over an equivalent-sized GEMM for bandwidth-bound tensor contractions while attaining up to 91.
Revised:  More precisely, GETT achieves speedups of up to 1.41 \times over an equivalent-sized GEMM for bandwidth-bound tensor contractions while attaining up to 91.
Edit intent: meaning-changed
Original:  Instead, we implement TC using the much more flexible BLIS framework, which allows for reshaping of the tensor to be fused with internal partitioning and packing operations, requiring no explicit reshaping operations or additional workspace.
Revised:  Instead, we implement TC using the flexible BLIS framework, which allows for reshaping of the tensor to be fused with internal partitioning and packing operations, requiring no explicit reshaping operations or additional workspace.
Edit intent: clarity
Original:  Instead, we implement TC using the flexible BLIS framework, which allows for reshaping of the tensor to be fused with internal partitioning and packing operations, requiring no explicit reshaping operations or additional workspace.
Revised:  Instead, we implement TC using the flexible BLIS framework, which allows for transposition (reshaping) of the tensor to be fused with internal partitioning and packing operations, requiring no explicit reshaping operations or additional workspace.
Edit intent: meaning-changed
Original:  Instead, we implement TC using the flexible BLIS framework, which allows for reshaping of the tensor to be fused with internal partitioning and packing operations, requiring no explicit reshaping operations or additional workspace.
Revised:  Instead, we implement TC using the flexible BLIS framework, which allows for reshaping of the tensor to be fused with internal partitioning and packing operations, requiring no explicit transposition operations or additional workspace.
Edit intent: clarity
Original:  The exchanging of identical structural elements  typical of domain-swapped proteins make them suitable candidates to validate this possibility . Here we analyze 110 non redundant domain-swapped dimers filtered from the 3Dswap and Proswap databases, by keeping only structures not affected by the presence of holes along the main backbone chain.
Revised:  The exchanging of identical structural elements typical of domain-swapped proteins make them suitable candidates to validate the possibility that mutual entanglement between chains may play a similar role for protein complexes. We suggest that such entanglement is captured by the linking number. This represents, for two closed curves, the number of times that each curve winds around the other. We show that closing the curves is not necessary, as a novel parameter G', termed Gaussian entanglement, is strongly correlated with the linking number. Based on 110 non redundant domain-swapped dimers filtered from the 3Dswap and Proswap databases, by keeping only structures not affected by the presence of holes along the main backbone chain.
Edit intent: meaning-changed
Original:  Here we analyze 110 non redundant domain-swapped dimers filtered from the 3Dswap and Proswap databases, by keeping only structures not affected by the presence of holes along the main backbone chain. The linking number G' determined by Gauss integrals on the C_\alpha backbones is shown to be a solid and efficient tool for quantifying the mutual entanglement, also due to its strong correlation with the topological linking averaged over many closures of the chains. Our analysis evidences a quite high fraction of chains with a significant linking , that is with |G'| > 1. We report that Nature promotes configurations with G'<0 and surprisingly, it seems to suppress linking of long proteins.
Revised:  Here we analyze 110 non redundant domain-swapped dimers, our analysis evidences a quite high fraction of chains with a significant linking , that is with |G'| > 1. We report that Nature promotes configurations with G'<0 and surprisingly, it seems to suppress linking of long proteins.
Edit intent: coherence
Original:  Our analysis evidences a quite high fraction of chains with a significant linking , that is with |G'| > 1. We report that Nature promotes configurations with G'<0 and surprisingly, it seems to suppress linking of long proteins.
Revised:  Our analysis evidences a high fraction of chains with a significant linking , that is with |G'| > 1. We report that Nature promotes configurations with G'<0 and surprisingly, it seems to suppress linking of long proteins.
Edit intent: clarity
Original:  Our analysis evidences a quite high fraction of chains with a significant linking , that is with |G'| > 1. We report that Nature promotes configurations with G'<0 and surprisingly, it seems to suppress linking of long proteins.
Revised:  Our analysis evidences a quite high fraction of chains with a significant intertwining , that is with |G'| > 1. We report that Nature promotes configurations with G'<0 and surprisingly, it seems to suppress linking of long proteins.
Edit intent: clarity
Original:  Our analysis evidences a quite high fraction of chains with a significant linking , that is with |G'| > 1. We report that Nature promotes configurations with G'<0 and surprisingly, it seems to suppress linking of long proteins.
Revised:  Our analysis evidences a quite high fraction of chains with a significant linking , that is with |G'| > 1. We report that Nature promotes configurations with negative mutual entanglement and surprisingly, it seems to suppress linking of long proteins.
Edit intent: meaning-changed
Original:  Our analysis evidences a quite high fraction of chains with a significant linking , that is with |G'| > 1. We report that Nature promotes configurations with G'<0 and surprisingly, it seems to suppress linking of long proteins. While proteins composed of about 100 residues can be well linked in the swapped dimer form, this is not observed for much longer chains. Upon dissociationof a few dimers via numerical simulations, we observe an exponential decay of the linking number with time, providing an additional useful characterization of the entanglement within swapped dimers. Our results provide a novel and robust topology-based classification of protein-swapped dimers together with some preliminary evidence of its impact on their physical and biological properties.
Revised:  Our analysis evidences a quite high fraction of chains with a significant linking , that is with |G'| > 1. We report that Nature promotes configurations with G'<0 and surprisingly, it seems to suppress intertwining in long protein dimers. Supported by numerical simulations of dimer dissociation, our results provide a novel and robust topology-based classification of protein-swapped dimers together with some preliminary evidence of its impact on their physical and biological properties.
Edit intent: coherence
Original:  We obtain the arbitrage-free price of the American swaption and the optimal exercise strategies in terms of swap rates for both fixed-rate payer and receiver swaps  .
Revised:  We obtain the arbitrage-free price of the American swaption and the optimal exercise strategies in terms of swap rates for both fixed-rate payer and receiver swaps . Finally, we show that Bermudan swaptions can be efficiently priced as well .
Edit intent: meaning-changed
Original: This paper analyzes multi-period mortgage risk at loan and pool levels using an unprecedented dataset of  over 120 million prime and subprime mortgages originated across the United States between 1995 and 2014, which includes the individual characteristics of each loan, monthly updates on loan performance over the life of a loan, and a number of time-varying economic variables at the zip code level.
Revised: We develop a deep learning model of multi-period mortgage risk at loan and pool levels using an unprecedented dataset of over 120 million prime and subprime mortgages originated across the United States between 1995 and 2014, which includes the individual characteristics of each loan, monthly updates on loan performance over the life of a loan, and a number of time-varying economic variables at the zip code level.
Edit intent: clarity
Original: This paper analyzes multi-period mortgage risk at loan and pool levels using an unprecedented dataset of  over 120 million prime and subprime mortgages originated across the United States between 1995 and 2014, which includes the individual characteristics of each loan, monthly updates on loan performance over the life of a loan, and a number of time-varying economic variables at the zip code level.
Revised: This paper analyzes multi-period mortgage risk and use it to analyze an unprecedented dataset of over 120 million prime and subprime mortgages originated across the United States between 1995 and 2014, which includes the individual characteristics of each loan, monthly updates on loan performance over the life of a loan, and a number of time-varying economic variables at the zip code level.
Edit intent: meaning-changed
Original: This paper analyzes multi-period mortgage risk at loan and pool levels using an unprecedented dataset of  over 120 million prime and subprime mortgages originated across the United States between 1995 and 2014, which includes the individual characteristics of each loan, monthly updates on loan performance over the life of a loan, and a number of time-varying economic variables at the zip code level.
Revised: This paper analyzes multi-period mortgage risk at loan and pool levels using an unprecedented dataset of origination and monthly performance records for over 120 million prime and subprime mortgages originated across the United States between 1995 and 2014, which includes the individual characteristics of each loan, monthly updates on loan performance over the life of a loan, and a number of time-varying economic variables at the zip code level.
Edit intent: meaning-changed
Original: This paper analyzes multi-period mortgage risk at loan and pool levels using an unprecedented dataset of  over 120 million prime and subprime mortgages originated across the United States between 1995 and 2014, which includes the individual characteristics of each loan, monthly updates on loan performance over the life of a loan, and a number of time-varying economic variables at the zip code level.
Revised: This paper analyzes multi-period mortgage risk at loan and pool levels using an unprecedented dataset of over 120 million mortgages originated across the United States between 1995 and 2014, which includes the individual characteristics of each loan, monthly updates on loan performance over the life of a loan, and a number of time-varying economic variables at the zip code level.
Edit intent: clarity
Original: This paper analyzes multi-period mortgage risk at loan and pool levels using an unprecedented dataset of  over 120 million prime and subprime mortgages originated across the United States between 1995 and 2014, which includes the individual characteristics of each loan, monthly updates on loan performance over the life of a loan, and a number of time-varying economic variables at the zip code level.
Revised: This paper analyzes multi-period mortgage risk at loan and pool levels using an unprecedented dataset of over 120 million prime and subprime mortgages originated across the US between 1995 and 2014, which includes the individual characteristics of each loan, monthly updates on loan performance over the life of a loan, and a number of time-varying economic variables at the zip code level.
Edit intent: fluency
Original: This paper analyzes multi-period mortgage risk at loan and pool levels using an unprecedented dataset of  over 120 million prime and subprime mortgages originated across the United States between 1995 and 2014, which includes the individual characteristics of each loan, monthly updates on loan performance over the life of a loan, and a number of time-varying economic variables at the zip code level. We develop, estimate, and test dynamic machine learning models for mortgage prepayment, delinquency, and foreclosure which capture loan-to-loan correlation due to geographic proximity and exposure to common risk factors. The basic building block is a deep neural network which addresses the nonlinear relationship between the explanatory variables and loan performance.
Revised: This paper analyzes multi-period mortgage risk at loan and pool levels using an unprecedented dataset of over 120 million prime and subprime mortgages originated across the United States between 1995 and 2014. Our estimators of term structures of conditional probabilities of prepayment, foreclosure and various states of delinquency incorporate the dynamics of a large number of loan-specific as well as macroeconomic variables down to the zip-code level. The estimators uncover the highly nonlinear nature of the relationship between the explanatory variables and loan performance.
Edit intent: clarity
Original:  The basic building block is a deep neural network which addresses the nonlinear relationship between the explanatory variables and loan performance. Our likelihood estimators, which are based on 3.5 billion borrower-month observations, indicate that mortgage risk is strongly influenced by local economic factors such as zip-code level foreclosure rates. The out-of-sample predictive performance of our deep learning model is a significant improvement over linear models such as logistic regression. Model parameters are estimated using GPU parallel computing due to the computational challenges associated with the large amount of data. The deep learning model's superior accuracy compared to linear models directly translates into improved performance for investors. Portfolios constructed with the deep learning model have lower prepayment and delinquency rates than portfolios chosen with a logistic regression .
Revised:  The basic building block is a deep neural network which addresses the nonlinear relationship between the variables and borrower behavior, especially prepayment. They also highlight the effects of local economic conditions on borrower behavior. State unemployment has the greatest explanatory power among all variables, offering strong evidence of the tight connection between housing finance markets and the macroeconomy. The sensitivity of a borrower to changes in unemployment strongly depends upon current unemployment. It also significantly varies across the entire borrower population, which highlights the interaction of unemployment and many other variables. These findings have important implications for mortgage-backed security investors, rating agencies, and housing finance policymakers .
Edit intent: meaning-changed
Original: In cellular biology, reaching a target before being degraded or trapped is ubiquitous. An interesting example is given by the virus journey inside the cell cytoplasm: in order to replicate, most viruses have to reach the nucleus before being trapped or degraded.
Revised: A certain class of viruses replicates inside a cell if they can enter the nucleus through one of many small target pores, before being permanently trapped or degraded.
Edit intent: clarity
Original:  We present here a general approach to estimate the probability and the conditional mean first passage time for such a viral particle to attain safely the nucleus, covered with many (around two thousands ) small absorbing pores.
Revised:  We adopt for viral motion a switching stochastic process model and we estimate here the probability and the conditional mean first passage time for such a viral particle to attain safely the nucleus, covered with many (around two thousands ) small absorbing pores.
Edit intent: meaning-changed
Original:  We present here a general approach to estimate the probability and the conditional mean first passage time for such a viral particle to attain safely the nucleus, covered with many (around two thousands ) small absorbing pores. Due to this large number of small holes, which defines the limiting scale , any Brownian simulationsare very unstable. Our new asymptotic formulas precisely account for this phenomena and allow to quantify the cytoplasmic stage of viral infection .
Revised:  We present here a general approach to estimate the probability and the conditional mean first passage time for such a viral particle to attain alive the nucleus. The cell nucleus is covered with thousands of small absorbing nuclear pores and the minimum distance between them defines the smallest spatial scale that limits the efficiency of stochastic simulations. Using the Neuman-Green's function method to solve the steady-state Fokker-Planck equation, we derive asymptotic formula for the probability and mean arrival time to a small window for various pores' distributions, that agree with stochastic simulations. These formulas reveal how key geometrical parameters defines the cytoplasmic stage of viral infection .
Edit intent: meaning-changed
Original:  In the present paper we propose an algorithm to obtain statistically-validated  projections of bipartite networks . The criterion adopted to quantify the similarity of nodes rests upon the similarity of their neighborhoods : in order for any two nodes to be linked, a significantly-large number of neighbors must be shared.
Revised:  In the present paper we propose an algorithm to obtain statistically-validated projections of bipartite networks , which implements a simple rule : in order for any two nodes to be linked, a significantly-large number of neighbors must be shared.
Edit intent: meaning-changed
Original:  The output of our algorithm thus consists of a matrix of  p-values, from which a validated projection can be straightforwardly obtained, upon running a multiple hypothesis testing and linking only the nodes which pass it.
Revised:  The output of our algorithm thus consists of a matrix of link-specific p-values, from which a validated projection can be straightforwardly obtained, upon running a multiple hypothesis testing and linking only the nodes which pass it.
Edit intent: meaning-changed
Original:  The output of our algorithm thus consists of a matrix of  p-values, from which a validated projection can be straightforwardly obtained, upon running a multiple hypothesis testing and linking only the nodes which pass it. We apply our algorithm to social and economic bipartite networks: when projecting the network of countries and exported goods on the countries layer, our method is able to cluster countries with similar a industrialisation; we also analysed a social network  of users and positively-rated movieson the films layer: in this caseour approach divides movies in clusters of similar genres or audience .
Revised:  The output of our algorithm thus consists of a matrix of p-values, from which a validated projection can be straightforwardly obtained, upon running a multiple hypothesis test and retaining only the statistically significant links. Finally, to test our method we analyze a social network of users and positively-rated movieson the films layer: in this caseour approach divides movies in clusters of similar genres or audience .
Edit intent: clarity
Original:  we also analysed a social network  of users and positively-rated movieson the films layer: in this caseour approach divides movies in clusters of similar genres or audience .
Revised:  we also analysed a social network (i.e. the MovieLens dataset, a bipartite network of users and positively-rated movieson the films layer: in this caseour approach divides movies in clusters of similar genres or audience .
Edit intent: meaning-changed
Original:  we also analysed a social network  of users and positively-rated movieson the films layer: in this caseour approach divides movies in clusters of similar genres or audience .
Revised:  we also analysed a social network of users and rated movies) and an economic network (i.e. the countries-products World Trade Web representation): while, in the first case, projecting MovieLens on the films layer allows clusters of movies belonging to similar genres to be detected, in the second case, projecting the World Trade Web on the countries layer reveals a modular structure of similarly-industrialized clusters of nations .
Edit intent: meaning-changed
Original:  One of the major problems encountered when dealing with bipartite networks is obtaining a (monopartite) projection over the layer of interest which preserves as much as possible the information encoded into the original bipartite structure  .
Revised:  One of the most important issues encountered when modeling bipartite networks is obtaining a (monopartite) projection over the layer of interest which preserves as much as possible the information encoded into the original bipartite structure .
Edit intent: clarity
Original:  One of the major problems encountered when dealing with bipartite networks is obtaining a (monopartite) projection over the layer of interest which preserves as much as possible the information encoded into the original bipartite structure  .
Revised:  One of the major problems encountered when dealing with bipartite networks is devising a way to obtain a (monopartite) projection over the layer of interest which preserves as much as possible the information encoded into the original bipartite structure .
Edit intent: clarity
Original:  One of the major problems encountered when dealing with bipartite networks is obtaining a (monopartite) projection over the layer of interest which preserves as much as possible the information encoded into the original bipartite structure  .
Revised:  One of the major problems encountered when dealing with bipartite networks is obtaining a (monopartite) projection on the layer of interest which preserves as much as possible the information encoded into the original bipartite structure .
Edit intent: fluency