wanyu/IteraTeR_full_sent huggingface dataset:
Original: Using the elements from the theory of quadratic backward stochastic differential equations with unbounded terminal data , we solve an exponential utility maximization problem with unbounded random endowments under portfolio constraints.
Revised: We solve an exponential utility maximization problem with unbounded payoffs and portfolio constraints, via the theory of quadratic backward stochastic differential equations with unbounded terminal data , we solve an exponential utility maximization problem with unbounded random endowments under portfolio constraints.
Edit intent: meaning-changed
Original: Using the elements from the theory of quadratic backward stochastic differential equations with unbounded terminal data , we solve an exponential utility maximization problem with unbounded random endowments under portfolio constraints. Our results thus generalize the previous results of Hu et al. (2005) [Ann.
Revised: Using the elements from the theory of quadratic backward stochastic differential equations with unbounded terminal data . This generalizes the previous work of Hu et al. (2005) [Ann.
Edit intent: clarity
Original:  Appl. Probab., 15, 1691--1712] from bounded to unbounded case. As an application , we study utility indifference valuation of financial derivatives with unbounded payoffs, obtaining a new convex dual representation of the prices and their asymptotics for the risk aversion parameter  .
Revised:  Appl. Probab., 15, 1691--1712] from the bounded to an unbounded framework. Furthermore , we study utility indifference valuation of financial derivatives with unbounded payoffs, obtaining a new convex dual representation of the prices and their asymptotics for the risk aversion parameter .
Edit intent: clarity
Original:  As an application , we study utility indifference valuation of financial derivatives with unbounded payoffs, obtaining a new convex dual representation of the prices and their asymptotics for the risk aversion parameter  .
Revised:  As an application , we study utility indifference valuation of financial derivatives with unbounded payoffs, and derive a novel convex dual representation of the prices and their asymptotics for the risk aversion parameter .
Edit intent: clarity
Original:  As an application , we study utility indifference valuation of financial derivatives with unbounded payoffs, obtaining a new convex dual representation of the prices and their asymptotics for the risk aversion parameter  .
Revised:  As an application , we study utility indifference valuation of financial derivatives with unbounded payoffs, obtaining a new convex dual representation of the prices . In particular, we obtain new asymptotic behavior as the risk aversion parameter .
Edit intent: meaning-changed
Original:  As an application , we study utility indifference valuation of financial derivatives with unbounded payoffs, obtaining a new convex dual representation of the prices and their asymptotics for the risk aversion parameter  .
Revised:  As an application , we study utility indifference valuation of financial derivatives with unbounded payoffs, obtaining a new convex dual representation of the prices and their asymptotics for the risk aversion parameter tends to either zero or infinity .
Edit intent: meaning-changed
Original:  The model faithfully reproduces both the binding free energy profiles and the probability densities of the perturbation energy as a function of the alchemical progress parameter \lambda. . The model offers a rationalization for the characteristic shape of the free energy profiles.
Revised:  The model faithfully reproduces both the binding free energy profiles and the probability densities of the perturbation energy as a function of the alchemical progress parameter \lambda. The model offers a rationalization for the characteristic shape of the free energy profiles.
Edit intent: fluency
Original: This paper proves a conjecture from LO17%DIFDELCMD < ]%%% : A nearly flat, acutely triangulated convex cap C  has an edge-unfolding to a non-overlapping polygon in the plane.
Revised:  LO17%DIFDELCMD < ]%%% : A nearly flat, acutely triangulated convex cap C has an edge-unfolding to a non-overlapping polygon in the plane.
Edit intent: coherence
Original: This paper proves a conjecture from LO17%DIFDELCMD < ]%%% : A nearly flat, acutely triangulated convex cap C  has an edge-unfolding to a non-overlapping polygon in the plane.
Revised: This paper proves a conjecture from %DIFDELCMD < ]%%% : A nearly flat, acutely triangulated convex cap C has an edge-unfolding to a non-overlapping polygon in the plane.
Edit intent: meaning-changed
Original: This paper proves a conjecture from LO17%DIFDELCMD < ]%%% : A nearly flat, acutely triangulated convex cap C  has an edge-unfolding to a non-overlapping polygon in the plane.
Revised: This paper proves a conjecture from LO17%DIFDELCMD < ]%%% The main result of this paper is a proof that a nearly flat, acutely triangulated convex cap C has an edge-unfolding to a non-overlapping polygon in the plane.
Edit intent: meaning-changed
Original: This paper proves a conjecture from LO17%DIFDELCMD < ]%%% : A nearly flat, acutely triangulated convex cap C  has an edge-unfolding to a non-overlapping polygon in the plane.
Revised: This paper proves a conjecture from LO17%DIFDELCMD < ]%%% : A nearly flat, acutely triangulated convex cap C in R^3 has an edge-unfolding to a non-overlapping polygon in the plane.
Edit intent: meaning-changed
Original:   "Nearly flat" means that every  face normal forms a sufficiently small angle  with the z-axis . Although the result is not surprising, the proof relies on some recently developed concepts , angle-monotone and radially monotone curves  .
Revised:  "Nearly flat" means that every face normal forms a sufficiently small angle with the z-axis orthogonal to the halfspace bounding plane. The size of Phi depends on the acuteness gap alpha: if every triangle angle is at most pi/2-alpha, then Phi ~= 0.36 sqrt(alpha) suffices; e.g., for alpha ~= 3deg, Phi = 5deg. Even if C is closed to a polyhedron by adding the convex polygonal base under C, this polyhedron can be edge-unfolded without overlap. The proof employs the recent concepts of angle-monotone and radially monotone curves .
Edit intent: meaning-changed
Original:  Although the result is not surprising, the proof relies on some recently developed concepts , angle-monotone and radially monotone curves  .
Revised:  Although the result is not surprising, the proof relies on some recently developed concepts , angle-monotone and radially monotone curves . The proof is constructive, leading to a polynomial-time algorithm for finding the edge-cuts, at worst O(n^2); a version has been implemented .
Edit intent: meaning-changed
Original:  We apply this algorithm to work efficiently with a discrete variant of the hyperbolic random graph model . This model is gaining popularity in the analysis of scale-free networks, which are ubiquitous in many fields, from social network analysis to biology.
Revised:  We apply this algorithm to work efficiently with a discrete variant of the hyperbolic random graph model (HRG) has proven useful in the analysis of scale-free networks, which are ubiquitous in many fields, from social network analysis to biology.
Edit intent: coherence
Original:  We present experimental results conducted on real world networks  .
Revised:  However, working with this model is algorithmically and conceptually challenging because of the nature of the distances in the hyperbolic plane. In this paper we study the algorithmic properties of regularly generated triangulations in the hyperbolic plane. We propose a discrete variant of the HRG model where nodes are mapped to the vertices of such a triangulation; our algorithms allow us to work with this model in a simple yet efficient way. We present experimental results conducted on real world networks .
Edit intent: meaning-changed
Original:  Finally we provide a simple correction mechanism to the small sample bias of the "non-paramteric" estimator based on the distance between the mode and the mean of its asymptotic distribution for the case of heavy tailed data generating process.
Revised:  Finally we provide a simple correction mechanism to the small sample bias of the non-parametric estimator based on the distance between the mode and the mean of its asymptotic distribution for the case of heavy tailed data generating process.
Edit intent: fluency
Original:  We study different approaches to the estimation of the Gini index in presence of a heavy tailed data generating process, that is, one with Paretan tails and/or in the stable distribution class with finite mean but non-finite variance ( with tail index \alpha\in(1,2)).
Revised:  We study different approaches to the estimation of the Gini index in presence of a fat-tailed data generator process, i.e. one in the stable distribution class with finite mean but non-finite variance ( with tail index \alpha\in(1,2)).
Edit intent: coherence
Original:  We study different approaches to the estimation of the Gini index in presence of a heavy tailed data generating process, that is, one with Paretan tails and/or in the stable distribution class with finite mean but non-finite variance ( with tail index \alpha\in(1,2)).
Revised:  We study different approaches to the estimation of the Gini index in presence of a heavy tailed data generating process, that is, one with Paretan tails and/or in the stable distribution class with finite mean but infinite variance (i.e. with tail index \alpha\in(1,2)).
Edit intent: clarity
Original:  While the Gini index is a measurement of fat tailedness, little attention has been brought to a significant downward bias in conventional applications, one that increases with lower values of \alpha. First, we show how the "non-parametric" estimator of the Gini index undergoes a phase transition in the symmetry structure of its asymptotic distribution  as the data distribution shifts from the domain of attraction of a light tail distribution to the domain of attraction of a fat tailed, infinite variance one.
Revised:  We show that, in such a case, the Gini coefficient can not be reliably estimated using conventional nonparametric methods, because of a downward bias that emerges in case of fat tails. We start by discussing how the nonparametric estimator of the Gini index undergoes a phase transition in the symmetry structure of its asymptotic distribution as the data distribution shifts from the domain of attraction of a light tail distribution to the domain of attraction of a fat tailed, infinite variance one.
Edit intent: clarity
Original:  First, we show how the "non-parametric" estimator of the Gini index undergoes a phase transition in the symmetry structure of its asymptotic distribution  as the data distribution shifts from the domain of attraction of a light tail distribution to the domain of attraction of a fat tailed, infinite variance one.
Revised:  First, we show how the "non-parametric" estimator of the Gini index undergoes a phase transition in the symmetry structure of its asymptotic distribution , as the data distribution shifts from the domain of attraction of a light tail distribution to the domain of attraction of a fat tailed, infinite variance one.
Edit intent: fluency
Original:  First, we show how the "non-parametric" estimator of the Gini index undergoes a phase transition in the symmetry structure of its asymptotic distribution  as the data distribution shifts from the domain of attraction of a light tail distribution to the domain of attraction of a fat tailed, infinite variance one. Second, we show how the maximum likelihood estimator outperforms the non-parametric requiring a much smaller sample size to reach efficiency.
Revised:  First, we show how the "non-parametric" estimator of the Gini index undergoes a phase transition in the symmetry structure of its asymptotic distribution as the data distribution shifts from the domain of attraction of a light-tailed distribution to that of a fat-tailed one, especially in the case of infinite variance. We show how the maximum likelihood estimator outperforms the non-parametric requiring a much smaller sample size to reach efficiency.
Edit intent: clarity
Original:  Finally  we provide a simple correction mechanism to the small sample bias of the non-parametric estimator based on the distance between the mode and the mean of its asymptotic distribution for the case of heavy tailed data generating process .
Revised:  Finally , for fat-tailed data, we provide a simple correction mechanism to the small sample bias of the non-parametric estimator based on the distance between the mode and the mean of its asymptotic distribution for the case of heavy tailed data generating process .
Edit intent: meaning-changed
Original:  Finally  we provide a simple correction mechanism to the small sample bias of the non-parametric estimator based on the distance between the mode and the mean of its asymptotic distribution for the case of heavy tailed data generating process .
Revised:  Finally we provide a simple correction mechanism to the small sample bias of the nonparametric estimator based on the distance between the mode and the mean of its asymptotic distribution for the case of heavy tailed data generating process .
Edit intent: coherence
Original: We study different approaches to the estimation of the Gini index in presence of a fat-tailed data generator process, i.e. one in the stable distribution class with finite mean but infinite variance (i.e. with tail index \alpha\in(1,2)).
Revised: We study the problems related to the estimation of the Gini index in presence of a fat-tailed data generator process, i.e. one in the stable distribution class with finite mean but infinite variance (i.e. with tail index \alpha\in(1,2)).
Edit intent: clarity
Original: We study different approaches to the estimation of the Gini index in presence of a fat-tailed data generator process, i.e. one in the stable distribution class with finite mean but infinite variance (i.e. with tail index \alpha\in(1,2)).
Revised: We study different approaches to the estimation of the Gini index in presence of a fat-tailed data generating process, i.e. one in the stable distribution class with finite mean but infinite variance (i.e. with tail index \alpha\in(1,2)).
Edit intent: fluency
Original:  We show that, in such a case, the Gini coefficient can not be reliably estimated using conventional nonparametric methods, because of a downward bias that emerges in case of fat tails.
Revised:  We show that, in such a case, the Gini coefficient cannot be reliably estimated using conventional nonparametric methods, because of a downward bias that emerges in case of fat tails.
Edit intent: fluency
Original:   We start by discussing how the nonparametric estimator of the Gini index undergoes a phase transition in the symmetry structure of its asymptotic distribution, as the data distribution shifts from the domain of attraction of a light-tailed distribution to that of a fat-tailed one, especially in the case of infinite variance.
Revised:  This has important implications for the ongoing discussion about economic inequality. We start by discussing how the nonparametric estimator of the Gini index undergoes a phase transition in the symmetry structure of its asymptotic distribution, as the data distribution shifts from the domain of attraction of a light-tailed distribution to that of a fat-tailed one, especially in the case of infinite variance.
Edit intent: meaning-changed
Original: The efficient representation and management of simplicial and cell complexes is an active research topic in several fields, including geometric modeling, computer graphics, scientific visualization, and geographic data processing. In this paper, we propose the Stellar tree, a topological data structure for performing efficient topological queries on simplicial and non-simplicial complexes .
Revised: The efficient representation and management of simplicial and cell complexes . A Stellar decomposition of a complex is a collection of regions indexing the complex's vertices and cells such that each region has sufficient information to locally reconstruct the star of its vertices, i.e., the cells incident in the region's vertices. Stellar decompositions are general in that they can compactly represent and efficiently traverse arbitrary complexes with a manifold or non-manifold domain They are scalable to complexes in high dimension and of large size, and they enable users to easily construct tailored application-dependent data structures using a fraction of the memory required by the corresponding topological data structure on the global complex. As a concrete realization of this model for spatially embedded complexes, we introduce the Stellar tree, a topological data structure for performing efficient topological queries on simplicial and non-simplicial complexes .
Edit intent: meaning-changed
Original:  In this paper, we propose the Stellar tree, a topological data structure for performing efficient topological queries on simplicial and non-simplicial complexes .
Revised:  In this paper, we propose the Stellar tree, which combines a nested spatial tree with a simple tuning parameter to control the number of vertices in a region. Stellar trees exploit the complex's spatial locality by reordering vertex and cell indices according to the spatial decomposition and by compressing sequential ranges of indices. Stellar trees are competitive with state-of-the-art topological data structures for manifold simplicial complexes and offer significant improvements for cell complexes and non-simplicial complexes .
Edit intent: meaning-changed
Original:  In this paper, we propose the Stellar tree, a topological data structure for performing efficient topological queries on simplicial and non-simplicial complexes . We prove that a Stellar tree provides a scalable, compact and flexible data structure to represent these complexes, using a fraction of the memory required by a corresponding topological data structure on the global complex .
Revised:  In this paper, we propose the Stellar tree, a topological data structure for performing efficient topological queries on simplicial and non-manifold simplicial complexes. As a proxy for larger applications, we describe how Stellar trees can be used to generate existing state-of-the-art topological data structures. In addition to faster generation times, the reduced memory requirements of a Stellar tree enable generating these data structures over large and high-dimensional complexes even on machines with limited resources .
Edit intent: meaning-changed
Original: In this paper, we introduce a generalized asymmetric fronts propagation model based on the geodesic dis- tances with applications to image segmentation. The key ingredient of the fronts propagation and the computation of the geodesic distances is the construction of the metrics.
Revised: In this paper, we introduce a generalized asymmetric fronts propagation model based on the geodesic distances and Eikonal partial differential equations. One of the key ingredients for the computation of the geodesic distances is the construction of the metrics.
Edit intent: meaning-changed
Original:  The key ingredient of the fronts propagation and the computation of the geodesic distances is the construction of the metrics. We make use of the Finsler metrics with Randers form, by which the asymmetric and anisotropic measures of the geodesic curve length are taken into account to avoid the shortcuts and leaking problems.
Revised:  The key ingredient of the fronts propagation and the computation of the geodesic distances is the geodesic metrics, which can govern the action of the geodesic distance propagation. We consider a Finsler metric with the Randers form, by which the asymmetric and anisotropic measures of the geodesic curve length are taken into account to avoid the shortcuts and leaking problems.
Edit intent: clarity
Original:  We make use of the Finsler metrics with Randers form, by which the asymmetric and anisotropic measures of the geodesic curve length are taken into account to avoid the shortcuts and leaking problems.
Revised:  We make use of the Finsler metrics with Randers form, through which the asymmetry and anisotropy enhancements can be taken into account to avoid the shortcuts and leaking problems.
Edit intent: clarity
Original:  We make use of the Finsler metrics with Randers form, by which the asymmetric and anisotropic measures of the geodesic curve length are taken into account to avoid the shortcuts and leaking problems. This allows the fronts of the geodesic distance map to benefit from the asymmetric information derived from the image edges .
Revised:  We make use of the Finsler metrics with Randers form, by which the asymmetric and anisotropic measures of the geodesic curve length are taken into account to prevent the fronts leaking problem during the fronts propagation. These enhancements can be derived from the image edges .
Edit intent: clarity
Original: We offer a novel way of thinking about the modelling of the time-varying distributions of financial asset returns. Borrowing ideas from symbolic data analysis, we consider data representations beyond scalars and vectors.
Revised: Motivated by the need for effectively summarising, modelling, and forecasting the distributional characteristics of intra-daily returns, as well as the recent work on forecasting histogram-valued time-series in the area of symbolic data analysis, we consider data representations beyond scalars and vectors.
Edit intent: meaning-changed
Original:  Borrowing ideas from symbolic data analysis, we consider data representations beyond scalars and vectors. Specifically, we consider a quantile function as an observation, and develop a new class of dynamic models for quantile-function-valued (QF-valued) time series.
Revised:  Borrowing ideas from symbolic data analysis, we develop a time-series model for forecasting quantile-function-valued (QF-valued) time series.
Edit intent: clarity
Original:  Specifically, we consider a quantile function as an observation, and develop a new class of dynamic models for quantile-function-valued (QF-valued) time series. In order to make statistical inferences and account for parameter uncertainty , we propose a method whereby a likelihood function can be constructed for QF-valued data, and develop an adaptive MCMC sampling algorithm for simulating from the posterior distribution.
Revised:  Specifically, we consider a quantile function as an observation, and develop a new class of dynamic models for quantile-function-valued (QF-valued) daily summaries for intra-daily returns. We call this model the dynamic quantile function (DQF) model. Instead of a histogram, we propose to use a g-and-h quantile function to summarise the distribution of intra-daily returns. We work with a Bayesian formulation of the DQF model in order to make statistical inferences and account for parameter uncertainty , we propose a method whereby a likelihood function can be constructed for QF-valued data, and develop an adaptive MCMC sampling algorithm for simulating from the posterior distribution.
Edit intent: meaning-changed
Original:  In order to make statistical inferences and account for parameter uncertainty , we propose a method whereby a likelihood function can be constructed for QF-valued data, and develop an adaptive MCMC sampling algorithm for simulating from the posterior distribution.
Revised:  In order to make statistical inference while accounting for parameter uncertainty , we propose a method whereby a likelihood function can be constructed for QF-valued data, and develop an adaptive MCMC sampling algorithm for simulating from the posterior distribution.
Edit intent: clarity