wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We propose a LOw Rank and sparsE Covariance estimator (LOREC) to exploit this general structure in the high-dimensional setting. Analysis of this estimator shows that it recovers exactly the rank and support of the two componentsrespectively . Convergence rates under various  norms are also presented.
Revised:  This paper introduces a penalized approximation framework to recover such model structures from large covariance matrix estimation. We propose an estimator based on minimizing a non-likelihood loss with separable non-smooth penalty functions. This estimator is shown to recover exactly the rank and support of the two componentsrespectively . Convergence rates under various norms are also presented.
Edit intent: clarity
Original:  The estimatoris computed efficiently using convex optimization. We propose an iterative algorithm , based on Nesterov's method, to solve the optimization criterion. The algorithm is shown to produce a solution within O(1/t^2) of the optimal, after any finite t iterations.
Revised:  To compute this estimator, we further develop a first-order iterative algorithm to solve a convex optimization problem that contains separa- ble non-smooth functions, and the algorithm is shown to produce a solution within O(1/t^2) of the optimal, after any finite t iterations.
Edit intent: meaning-changed
Original:  This extension may be useful to understand how solvency capital re- quirement computed for a given financial institution may be affected by the presence of additional risks .
Revised:  This extension may be useful to understand how solvency capital re- quirement computed for a given financial institution may be affected by the presence of risks that cannot be diversify away. This is typically the case for a network of highly interconnected financial institutions in a macro-prudential regulatory system.
Edit intent: meaning-changed
Original:  We also generalize the bivariate Conditional-Tail-Expectation (CTE), previously introduced by Di Bernardino et al. (2011), in a multivariate set- ting and we study its behavior.
Revised:  We also generalize the bivariate Conditional-Tail-Expectation (CTE), previously introduced by Di Bern- ardino et al. (2011), in a multivariate set- ting and we study its behavior.
Edit intent: fluency
Original:  We also generalize the bivariate Conditional-Tail-Expectation (CTE), previously introduced by Di Bernardino et al. (2011), in a multivariate set- ting and we study its behavior.
Revised:  We also generalize the bivariate Conditional-Tail-Expectation (CTE), previously introduced by Di Bernardino et al. (2011), in a multivariate setting and we study its behavior.
Edit intent: fluency
Original: In this paper, we introduce a multivariate extension of the classical univariate Value-at-Risk (VaR)  . This extension may be useful to understand how solvency capital requirement is affected by the presence of risks that cannot be diversify away.
Revised: In this paper, we introduce two alternative extensions of the classical univariate Value-at-Risk (VaR) . This extension may be useful to understand how solvency capital requirement is affected by the presence of risks that cannot be diversify away.
Edit intent: meaning-changed
Original: In this paper, we introduce a multivariate extension of the classical univariate Value-at-Risk (VaR)  . This extension may be useful to understand how solvency capital requirement is affected by the presence of risks that cannot be diversify away.
Revised: In this paper, we introduce a multivariate extension of the classical univariate Value-at-Risk (VaR) in a multivariate setting. The two proposed multivariate VaR are vector-valued measures with the same dimension as the underlying risk portfolio. The lower-orthant VaR is constructed from level sets of multivariate distribution functions whereas the upper-orthant VaR is constructed from level sets of multivariate survival functions . This extension may be useful to understand how solvency capital requirement is affected by the presence of risks that cannot be diversify away.
Edit intent: meaning-changed
Original: In this paper, we introduce a multivariate extension of the classical univariate Value-at-Risk (VaR)  . This extension may be useful to understand how solvency capital requirement is affected by the presence of risks that cannot be diversify away. This is typically the case for a network of highly interconnected financial institutions in a macro-prudential regulatory system. We also generalize the bivariate Conditional-Tail-Expectation (CTE), previously introduced by Di Bern- ardino et al. (2011), in a multivariate setting and we study its behavior.
Revised: In this paper, we introduce a multivariate extension of the classical univariate Value-at-Risk (VaR) . 
Edit intent: coherence
Original:  By exploring a wide range of realistic parameters we and that, when the energy profile for ET along the chain is relatively at , just a small coherent contribution can provide a robust and significant increase in ET rate (above the semi-classical diffusive-hopping rate), even at physiologically-relevant temperatures.
Revised:  By exploring a wide range of realistic parameters we find that, when the energy profile for ET along the chain is relatively at , just a small coherent contribution can provide a robust and significant increase in ET rate (above the semi-classical diffusive-hopping rate), even at physiologically-relevant temperatures.
Edit intent: coherence
Original:  By exploring a wide range of realistic parameters we and that, when the energy profile for ET along the chain is relatively at , just a small coherent contribution can provide a robust and significant increase in ET rate (above the semi-classical diffusive-hopping rate), even at physiologically-relevant temperatures.
Revised:  By exploring a wide range of realistic parameters we and that, when the energy profile for ET along the chain is relatively flat , just a small coherent contribution can provide a robust and significant increase in ET rate (above the semi-classical diffusive-hopping rate), even at physiologically-relevant temperatures.
Edit intent: meaning-changed
Original: We investigate two methods for reducing estimation error in portfolio optimization with Conditional Value-at-Risk (CVaR ). The first method is nonparametric : penalize portfolios with large variances in mean and CVaR estimations.
Revised: We introduce performance-based regularization (PBR), a new approach to addressing estimation risk in data-driven optimization, to mean-CVaR portfolio optimization. We assume the available log-return data is iid, and detail the approach for two cases: nonparametric and parametric (the log-return distribution belongs in the elliptical family ). The first method is nonparametric : penalize portfolios with large variances in mean and CVaR estimations.
Edit intent: meaning-changed
Original: We investigate two methods for reducing estimation error in portfolio optimization with Conditional Value-at-Risk (CVaR ). The first method is nonparametric : penalize portfolios with large variances in mean and CVaR estimations.
Revised: We investigate two methods for reducing estimation error in portfolio optimization with Conditional Value-at-Risk (CVaR ). The nonparametric PBR method penalizes portfolios with large variances in mean and CVaR estimations.
Edit intent: meaning-changed
Original: We investigate two methods for reducing estimation error in portfolio optimization with Conditional Value-at-Risk (CVaR ). The first method is nonparametric : penalize portfolios with large variances in mean and CVaR estimations.
Revised: We investigate two methods for reducing estimation error in portfolio optimization with Conditional Value-at-Risk (CVaR ). The first method is nonparametric : penalize portfolios with large variability in mean and CVaR estimations.
Edit intent: fluency
Original:  The penalized problem is solvable by a quadratically-constrained quadratic program, and can be interpreted as a chance-constrained program. We show the original and penalized solutions follow the Central Limit Theorem with computable covariance by extending M-estimation results from statistics. The second method is parametric : solve the empirical Markowitz problem instead if the log-return distribution is in the elliptical family (which includes Gaussian and t distributions), as then the population frontiers of the Markowitz and mean-CVaR problems are equivalent . Numerical simulations show both methods improve upon the empirical mean-CVaR solution under an elliptical model, with the Markowitz solution dominating.
Revised:  The parametric PBR method solves the empirical Markowitz problem instead if the log-return distribution is in the elliptical family (which includes Gaussian and t distributions), as then the population frontiers of the Markowitz and mean-CVaR problems are equivalent . Numerical simulations show both methods improve upon the empirical mean-CVaR solution under an elliptical model, with the Markowitz solution dominating.
Edit intent: coherence
Original:  The second method is parametric : solve the empirical Markowitz problem instead if the log-return distribution is in the elliptical family (which includes Gaussian and t distributions), as then the population frontiers of the Markowitz and mean-CVaR problems are equivalent . Numerical simulations show both methods improve upon the empirical mean-CVaR solution under an elliptical model, with the Markowitz solution dominating.
Revised:  The second method is parametric : solve the empirical Markowitz problem instead of the empirical mean-CVaR problem, as the solutions of the Markowitz and mean-CVaR problems are equivalent . Numerical simulations show both methods improve upon the empirical mean-CVaR solution under an elliptical model, with the Markowitz solution dominating.
Edit intent: clarity
Original:  The second method is parametric : solve the empirical Markowitz problem instead if the log-return distribution is in the elliptical family (which includes Gaussian and t distributions), as then the population frontiers of the Markowitz and mean-CVaR problems are equivalent . Numerical simulations show both methods improve upon the empirical mean-CVaR solution under an elliptical model, with the Markowitz solution dominating.
Revised:  The second method is parametric : solve the empirical Markowitz problem instead if the log-return distribution is in the elliptical family (which includes Gaussian and t distributions), as then the population frontiers of the Markowitz and mean-CVaR problems are equivalent when the log-return distribution is elliptical. We derive the asymptotic behavior of the nonparametric PBR solution, which leads to insight into the effect of penalization, and justification of the parametric PBR method. We also show via simulations that the PBR methods produce efficient frontiers that are, on average, closer to the population efficient frontier than the empirical approach to the mean-CVaR solution under an elliptical model, with the Markowitz solution dominating.
Edit intent: meaning-changed
Original:  The second method is parametric : solve the empirical Markowitz problem instead if the log-return distribution is in the elliptical family (which includes Gaussian and t distributions), as then the population frontiers of the Markowitz and mean-CVaR problems are equivalent . Numerical simulations show both methods improve upon the empirical mean-CVaR solution under an elliptical model, with the Markowitz solution dominating. The penalized solution dominates under a non-elliptical model with heavy one-sided loss .
Revised:  The second method is parametric : solve the empirical Markowitz problem instead if the log-return distribution is in the elliptical family (which includes Gaussian and t distributions), as then the population frontiers of the Markowitz and mean-CVaR problems are equivalent . Numerical simulations show both methods improve upon the empirical mean-CVaR problem, with less variability .
Edit intent: meaning-changed
Original:  In particular, we are interested in density expansions of the projection (X_T^1,...,X_T^l), at time T>0, with l \leq d. Global conditions are found which replace the well-known "not-in-cutlocus" condition known from heat-kernel asymptotics ; cf. G. Ben Arous (1988).
Revised:  In particular, we are interested in density expansions of the projection (X_T^1,...,X_T^l), at time T>0, with l \leq d. Global conditions are found which replace the well-known "not-in-cutlocus" condition known from heat-kernel asymptotics .
Edit intent: coherence
Original: The inverse first passage time problem asks whether, for a Brownian motion B and a nonnegative random variable \zeta, there exists a time-varying barrier b such that P\{B_s>b(s), \, 0 %DIFDELCMD < \le %%% s%DIFDELCMD < \le %%%  t\}=P\{\zeta>t\}.
Revised: The inverse first passage time problem asks whether, for a Brownian motion B and a nonnegative random variable \zeta, there exists a time-varying barrier b such that P\{B_s>b(s), 0 %DIFDELCMD < \le %%% s%DIFDELCMD < \le %%% t\}=P\{\zeta>t\}.
Edit intent: fluency
Original: The inverse first passage time problem asks whether, for a Brownian motion B and a nonnegative random variable \zeta, there exists a time-varying barrier b such that P\{B_s>b(s), \, 0 %DIFDELCMD < \le %%% s%DIFDELCMD < \le %%%  t\}=P\{\zeta>t\}.
Revised: The inverse first passage time problem asks whether, for a Brownian motion B and a nonnegative random variable \zeta, there exists a time-varying barrier b such that P\{B_s>b(s), \, 0 %DIFDELCMD < \le %%% %DIFDELCMD < \le %%% t\}=P\{\zeta>t\}.
Edit intent: meaning-changed
Original: The inverse first passage time problem asks whether, for a Brownian motion B and a nonnegative random variable \zeta, there exists a time-varying barrier b such that P\{B_s>b(s), \, 0 %DIFDELCMD < \le %%% s%DIFDELCMD < \le %%%  t\}=P\{\zeta>t\}.
Revised: The inverse first passage time problem asks whether, for a Brownian motion B and a nonnegative random variable \zeta, there exists a time-varying barrier b such that P\{B_s>b(s), \, 0 %DIFDELCMD < \le %%% s%DIFDELCMD < \le %%% \leq s\leq t\}=P\{\zeta>t\}.
Edit intent: meaning-changed
Original:  We study a "smoothed" version of this problem and ask whether there is a "barrier" b such that \mathbb{E[\exp(-\lambda \int_0^t \psi(B_s - b(s)) \, ds)] } [\exp(-\lambda\int_0^t\psi(B_s-b(s))\,ds)]} =P\{\zeta >t\}, where \lambda is a killing rate parameter  and \psi:R %DIFDELCMD < \to [%%% 0,1 is a non-increasing \to[0,1]  function.
Revised:  We study a "smoothed" version of this problem and ask whether there is a "barrier" b such that [\exp(-\lambda \int_0^t \psi(B_s - b(s)) \, ds)] } [\exp(-\lambda\int_0^t\psi(B_s-b(s))\,ds)]} =P\{\zeta >t\}, where \lambda is a killing rate parameter and \psi:R %DIFDELCMD < \to [%%% 0,1 is a non-increasing \to[0,1] function.
Edit intent: coherence
Original:  We study a "smoothed" version of this problem and ask whether there is a "barrier" b such that \mathbb{E[\exp(-\lambda \int_0^t \psi(B_s - b(s)) \, ds)] } [\exp(-\lambda\int_0^t\psi(B_s-b(s))\,ds)]} =P\{\zeta >t\}, where \lambda is a killing rate parameter  and \psi:R %DIFDELCMD < \to [%%% 0,1 is a non-increasing \to[0,1]  function.
Revised:  We study a "smoothed" version of this problem and ask whether there is a "barrier" b such that \mathbb{E[\exp(-\lambda \int_0^t \psi(B_s - b(s)) \, ds)] } \mathbb{E[\exp(-\lambda\int_0^t\psi(B_s-b(s))\,ds)]} =P\{\zeta >t\}, where \lambda is a killing rate parameter and \psi:R %DIFDELCMD < \to [%%% 0,1 is a non-increasing \to[0,1] function.
Edit intent: fluency
Original:  We study a "smoothed" version of this problem and ask whether there is a "barrier" b such that \mathbb{E[\exp(-\lambda \int_0^t \psi(B_s - b(s)) \, ds)] } [\exp(-\lambda\int_0^t\psi(B_s-b(s))\,ds)]} =P\{\zeta >t\}, where \lambda is a killing rate parameter  and \psi:R %DIFDELCMD < \to [%%% 0,1 is a non-increasing \to[0,1]  function.
Revised:  We study a "smoothed" version of this problem and ask whether there is a "barrier" b such that \mathbb{E[\exp(-\lambda \int_0^t \psi(B_s - b(s)) \, ds)] } [\exp(-\lambda\int_0^t\psi(B_s-b(s))\,ds)]} =P\{\zeta >t\}, where \lambda is a killing rate parameter , and \psi:R %DIFDELCMD < \to [%%% 0,1 is a non-increasing \to[0,1] function.
Edit intent: fluency
Original:  We study a "smoothed" version of this problem and ask whether there is a "barrier" b such that \mathbb{E[\exp(-\lambda \int_0^t \psi(B_s - b(s)) \, ds)] } [\exp(-\lambda\int_0^t\psi(B_s-b(s))\,ds)]} =P\{\zeta >t\}, where \lambda is a killing rate parameter  and \psi:R %DIFDELCMD < \to [%%% 0,1 is a non-increasing \to[0,1]  function.
Revised:  We study a "smoothed" version of this problem and ask whether there is a "barrier" b such that \mathbb{E[\exp(-\lambda \int_0^t \psi(B_s - b(s)) \, ds)] } [\exp(-\lambda\int_0^t\psi(B_s-b(s))\,ds)]} =P\{\zeta >t\}, where \lambda is a killing rate parameter and \psi:R %DIFDELCMD < \to [%%% is a non-increasing \to[0,1] function.
Edit intent: fluency
Original:  We study a "smoothed" version of this problem and ask whether there is a "barrier" b such that \mathbb{E[\exp(-\lambda \int_0^t \psi(B_s - b(s)) \, ds)] } [\exp(-\lambda\int_0^t\psi(B_s-b(s))\,ds)]} =P\{\zeta >t\}, where \lambda is a killing rate parameter  and \psi:R %DIFDELCMD < \to [%%% 0,1 is a non-increasing \to[0,1]  function.
Revised:  We study a "smoothed" version of this problem and ask whether there is a "barrier" b such that \mathbb{E[\exp(-\lambda \int_0^t \psi(B_s - b(s)) \, ds)] } [\exp(-\lambda\int_0^t\psi(B_s-b(s))\,ds)]} =P\{\zeta >t\}, where \lambda is a killing rate parameter and \psi:R %DIFDELCMD < \to [%%% 0,1 \to[0,1] function.
Edit intent: clarity
Original:  We study a "smoothed" version of this problem and ask whether there is a "barrier" b such that \mathbb{E[\exp(-\lambda \int_0^t \psi(B_s - b(s)) \, ds)] } [\exp(-\lambda\int_0^t\psi(B_s-b(s))\,ds)]} =P\{\zeta >t\}, where \lambda is a killing rate parameter  and \psi:R %DIFDELCMD < \to [%%% 0,1 is a non-increasing \to[0,1]  function.
Revised:  We study a "smoothed" version of this problem and ask whether there is a "barrier" b such that \mathbb{E[\exp(-\lambda \int_0^t \psi(B_s - b(s)) \, ds)] } [\exp(-\lambda\int_0^t\psi(B_s-b(s))\,ds)]} =P\{\zeta >t\}, where \lambda is a killing rate parameter and \psi:R %DIFDELCMD < \to [%%% 0,1 is a non-increasing \to[0,1] is a nonincreasing function.
Edit intent: fluency
Original:  We prove that if \psi is suitably smooth, the function t\mapsto P\{\zeta>t\} is twice continuously differentiable, and the condition 0<- \frac{d \log \mathbb{P\{\zeta > t\}}{dt} } \{\zeta>t\}}{dt}} <\lambda holds for the hazard rate of \zeta, then there exists a unique continuously differentiable function b solving the smoothed problem.
Revised:  We prove that if \psi is suitably smooth, the function t\mapsto P\{\zeta>t\} is twice continuously differentiable, and the condition 0<- \{\zeta > t\}}{dt} } \{\zeta>t\}}{dt}} <\lambda holds for the hazard rate of \zeta, then there exists a unique continuously differentiable function b solving the smoothed problem.
Edit intent: meaning-changed
Original:  We prove that if \psi is suitably smooth, the function t\mapsto P\{\zeta>t\} is twice continuously differentiable, and the condition 0<- \frac{d \log \mathbb{P\{\zeta > t\}}{dt} } \{\zeta>t\}}{dt}} <\lambda holds for the hazard rate of \zeta, then there exists a unique continuously differentiable function b solving the smoothed problem.
Revised:  We prove that if \psi is suitably smooth, the function t\mapsto P\{\zeta>t\} is twice continuously differentiable, and the condition 0<- \frac{d \log \mathbb{P\{\zeta > t\}}{dt} } \frac{d\log\mathbb{P\{\zeta>t\}}{dt}} <\lambda holds for the hazard rate of \zeta, then there exists a unique continuously differentiable function b solving the smoothed problem.
Edit intent: fluency
Original:  Here we introduce a user-friendly implementation of our previous network module determination method, ModuLand , as a plug-in of the widely used Cytoscape program. We show the utility of this approach a.) to identify an extensively overlapping modular structure ;
Revised:  Here we introduce a user-friendly implementation of our previous network module determination method, ModuLand , as a plug-in provides Cytoscape users an algorithm determining a.) to identify an extensively overlapping modular structure ;
Edit intent: clarity
Original:  c.) to identify key nodes of high community centrality, modular overlap or bridgeness in protein structure, protein-protein interaction and metabolic networks. Availability and implementation: The ModuLand Cytoscape plug-in was written in C++, has a  JAVA-based graphical interface  , can be installed as a single plug-in and can run on Windows, Linux, or Mac OS.
Revised:  c.) module cores predicting the function of the whole module; and d.) key nodes bridging two or multiple modules in complex networks. The plug-in was written in C++, has a JAVA-based graphical interface , can be installed as a single plug-in and can run on Windows, Linux, or Mac OS.
Edit intent: meaning-changed
Original:  Availability and implementation: The ModuLand Cytoscape plug-in was written in C++, has a  JAVA-based graphical interface  , can be installed as a single plug-in and can run on Windows, Linux, or Mac OS.
Revised:  Availability and implementation: The ModuLand Cytoscape plug-in was written in C++, has a detailed JAVA-based graphical interface , can be installed as a single plug-in and can run on Windows, Linux, or Mac OS.
Edit intent: meaning-changed
Original:  Availability and implementation: The ModuLand Cytoscape plug-in was written in C++, has a  JAVA-based graphical interface  , can be installed as a single plug-in and can run on Windows, Linux, or Mac OS.
Revised:  Availability and implementation: The ModuLand Cytoscape plug-in was written in C++, has a JAVA-based graphical interface with various colouring options , can be installed as a single plug-in and can run on Windows, Linux, or Mac OS.
Edit intent: meaning-changed
Original:  Availability and implementation: The ModuLand Cytoscape plug-in was written in C++, has a  JAVA-based graphical interface  , can be installed as a single plug-in and can run on Windows, Linux, or Mac OS.
Revised:  Availability and implementation: The ModuLand Cytoscape plug-in was written in C++, has a JAVA-based graphical interface , can be installed as a single file and can run on Windows, Linux, or Mac OS.
Edit intent: meaning-changed
Original: Summary: The ModuLand plug-in provides Cytoscape users an algorithm determining a.) extensively overlapping network modules ; b. ) several hierarchical layers of modules, where meta-nodes of the higher hierarchical layer represent modules of the lower layer ;
Revised: Summary: The ModuLand plug-in provides Cytoscape users an algorithm determining a.) extensively overlapping network modules . Moreover, it identifies several hierarchical layers of modules, where meta-nodes of the higher hierarchical layer represent modules of the lower layer ;
Edit intent: coherence
Original:  The plug-in was written in C++, has a detailed JAVA-based graphical interface with various colouring options , can be installed as a single file and can run on Windows, Linux, or Mac OS.
Revised:  The plug-in was written in C++, has a detailed JAVA-based graphical interface with various colouring options . The ModuLand tool can run on Windows, Linux, or Mac OS.
Edit intent: meaning-changed
Original: This paper considers exponential utility indifference pricing for a multidimensional non-traded assets model and provides two approximations for the utility indifference price : a linear approximation by Picard iteration and a semigroup approximation by splitting techniques .
Revised: This paper considers exponential utility indifference pricing for a multidimensional non-traded assets model subject to inter-temporal default risk, and provides a semigroup approximation for the utility indifference price : a linear approximation by Picard iteration and a semigroup approximation by splitting techniques .
Edit intent: meaning-changed