wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Complex relationships between scale, morphology, and frequency lead to optimized forward swimming speeds for a particular bell composition, stemming from intricate vortex wake topology, interactions, and fluid mixing. Lastly, we offer an open source computational jellyfish locomotion model to the science community that can be used as a starting place for future numerical experimentation.
Revised:  Thorough 2D parameter subspace explorations illustrated optimal parameter combinations in which give rise to enhanced swimming performance. All performance metrics indicated a higher sensitivity to bell actuation frequency than fluid scale or contraction phase kinematics, via Sobol sensitivity analysis, on a high performance parameter subspace. Moreover, Pareto-like fronts were identified in the overall performance space involving the cost of transport and forward swimming speed. Patterns emerged within these performance spaces when highlighting different parameter regions, which complemented the global sensitivity results. Lastly, an open source computational jellyfish locomotion model to the science community that can be used as a starting place for future numerical experimentation.
Edit intent: meaning-changed
Original:  The package includes five major elements: tools for preparing the initial states and environmental conditions for a set of samples, automatic generation of dynamical equations based on a dictionary of modeling assumptions, random parameter sampling with tunable levels of metabolic and taxonomic structure, parallel integration of the dynamical equations, and support for metacommunity dynamics with migration between samples.
Revised:  The Community Simulator includes five major elements: tools for preparing the initial states and environmental conditions for a set of samples, automatic generation of dynamical equations based on a dictionary of modeling assumptions, random parameter sampling with tunable levels of metabolic and taxonomic structure, parallel integration of the dynamical equations, and support for metacommunity dynamics with migration between samples.
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Original: Graph classification is a difficult problem that has drawn a lot of attention from the machine learning community over the past few years. This is mainly due to the fact that, contrarily to Euclidean vectors, the inherent complexity of graph structures can be quite hard to encode and handle for traditional classifiers. Even though kernels have been proposed in the literature, the increase in the dataset sizes has greatly limited the use of kernel methods since computation and storage of kernel matrices has become impracticable. In this article, we propose to use extended persistence diagrams to efficiently encode graph structure. More precisely, we show that using the so-called heat kernel signatures for the computation of these extended persistence diagrams allows one to quickly and efficiently summarize the graph structure. Then, we build on the recent development of neural networks for point clouds to define an architecture for (extended) persistence diagrams which is modular and easy-to-use. Finally, we demonstrate the usefulness of our approach by validating our architecture on several graph datasets, on which the obtained results are comparable to the state-of-the-art for graph classification .
Revised: Persistence diagrams, a key descriptor from Topological Data Analysis, encode and summarize all sorts of topological features and have already proved pivotal in many different applications of data science. But persistence diagrams are weakly structured and therefore constitute a difficult input for most Machine Learning techniques. To address this concern several vectorization methods have been put forward that embed persistence diagrams into either finite-dimensional Euclidean spaces or implicit Hilbert spaces with kernels. But finite-dimensional embeddings are prone to miss a lot of information about persistence diagrams, while kernel methods require the full computation of the kernel matrix. We introduce PersLay: a simple, highly modular layer of learning architecture for persistence diagrams that allows to exploit the full capacities of neural networks on topological information from any dataset. This layer encompasses most of the vectorization methods of the literature. We illustrate its strengths on challenging classification problems on dynamical systems orbit or real-life graph data, with results improving or comparable to the state-of-the-art for graph classification .
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Original:  Finally, we demonstrate the usefulness of our approach by validating our architecture on several graph datasets, on which the obtained results are comparable to the state-of-the-art for graph classification .
Revised:  Finally, we demonstrate the usefulness of our approach by validating our architecture on several graph datasets, on which the obtained results are comparable to the state-of-the-art . In order to exploit topological information from graph data, we show how graph structures can be encoded in the so-called extended persistence diagrams computed with the heat kernel signatures of the graphs .
Edit intent: meaning-changed
Original:  But finite-dimensional embeddings are prone to miss a lot of information about persistence diagrams , while kernel methods require the full computation of the kernel matrix. We introduce PersLay: a simple, highly modular layer of learning architecture for persistence diagrams that allows to exploit the full capacities of neural networks on topological information from any dataset. This layer encompasses most of the vectorization methods of the literature.
Revised:  In this work, we focus on persistence diagrams built on top of graphs. Relying on extended persistence theory and the so-called heat kernel signature, we show how graphs can be encoded by (extended) persistence diagrams in a provably stable way. We then propose a general and versatile framework for learning vectorizations of persistence diagrams, which encompasses most of the vectorization methods of the literature.
Edit intent: meaning-changed
Original:  We illustrate its strengths on challenging classification problems on dynamical systems orbit or real-life graph data, with results improving or comparable to the state-of-the-art. In order to exploit topological information from graph data, we show how graph structures can be encoded in the so-called extended persistence diagrams computed with the heat kernel signatures of the graphs .
Revised:  We illustrate its strengths on challenging classification problems on dynamical systems orbit or real-life graph datasets .
Edit intent: coherence
Original: This study examines whether the market efficiencies of major cryptocurrencies (e.g., Bitcoin , Ethereum, and Ripple) change over time based on the adaptive market hypothesis (AMH) of Lo (2004) .
Revised: This study examines whether the market efficiencies of major cryptocurrencies (e.g., Bitcoin , Ethereum, and Ripple) change over time based on Lo's (2004) adaptive market hypothesis (AMH) of Lo (2004) .
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Original: This study examines whether the market efficiencies of major cryptocurrencies (e.g., Bitcoin , Ethereum, and Ripple) change over time based on the adaptive market hypothesis (AMH) of Lo (2004) .
Revised: This study examines whether the market efficiencies of major cryptocurrencies (e.g., Bitcoin , Ethereum, and Ripple) change over time based on the adaptive market hypothesis (AMH) .
Edit intent: clarity
Original:  In particular, we measure the degree of market efficiency using Ito et al.'s (2014, 2016, 2017) generalized least squares-based time-varying model  .
Revised:  In particular, we measure the degree of market efficiency using Ito et al.'s (2014, 2016, 2017) generalized least squares-based time-varying model that does not depend on sample size, unlike previous studies that used conventional methods .
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Original:  The empirical results show that (1) the degree of market efficiency varies with time in cryptocurrency markets, (2) the market efficiency level of Bitcoin is higher than that of the other markets over most periods, and (3) the market efficiency of cryptocurrencies has evolved .
Revised:  The empirical results show that (1) the degree of market efficiency varies with time in the markets, (2) the market efficiency level of Bitcoin is higher than that of the other markets over most periods, and (3) the market efficiency of cryptocurrencies has evolved .
Edit intent: clarity
Original:  The empirical results show that (1) the degree of market efficiency varies with time in cryptocurrency markets, (2) the market efficiency level of Bitcoin is higher than that of the other markets over most periods, and (3) the market efficiency of cryptocurrencies has evolved .
Revised:  The empirical results show that (1) the degree of market efficiency varies with time in cryptocurrency markets, (2) the degree of market efficiency varies with time, (2) Bitcoin's market efficiency level is higher than that of the other markets over most periods, and (3) the market efficiency of cryptocurrencies has evolved .
Edit intent: clarity
Original:  The empirical results show that (1) the degree of market efficiency varies with time in cryptocurrency markets, (2) the market efficiency level of Bitcoin is higher than that of the other markets over most periods, and (3) the market efficiency of cryptocurrencies has evolved .
Revised:  The empirical results show that (1) the degree of market efficiency varies with time in cryptocurrency markets, (2) the market efficiency level of Bitcoin is higher than that of Ethereum over most periods, and (3) the market efficiency of cryptocurrencies has evolved .
Edit intent: meaning-changed
Original:  The empirical results show that (1) the degree of market efficiency varies with time in cryptocurrency markets, (2) the market efficiency level of Bitcoin is higher than that of the other markets over most periods, and (3) the market efficiency of cryptocurrencies has evolved .
Revised:  The empirical results show that (1) the degree of market efficiency varies with time in cryptocurrency markets, (2) the market efficiency level of Bitcoin is higher than that of the other markets over most periods, and (3) a market with high market liquidity has been evolving .
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Original: In 2015, Guth proved that for any set of k-dimensional  varieties in R^d and for any positive integer D, there exists a polynomial of degree at most D whose zero-set divides R^d into open connected "cells, " so that only a small fraction of the given varieties intersect each cell . Guth's result generalized an earlier result of Guth and Katz for points.
Revised: In 2015, Guth proved that for any set of k-dimensional bounded complexity varieties in R^d and for any positive integer D, there exists a polynomial of degree at most D whose zero-set divides R^d into open connected "cells, " so that only a small fraction of the given varieties intersect each cell . Guth's result generalized an earlier result of Guth and Katz for points.
Edit intent: meaning-changed
Original: In 2015, Guth proved that for any set of k-dimensional  varieties in R^d and for any positive integer D, there exists a polynomial of degree at most D whose zero-set divides R^d into open connected "cells, " so that only a small fraction of the given varieties intersect each cell . Guth's result generalized an earlier result of Guth and Katz for points.
Revised: In 2015, Guth proved that for any set of k-dimensional varieties in R^d and for any positive integer D, there exists a polynomial of degree at most D whose zero set divides R^d into open connected "cells, " so that only a small fraction of the given varieties intersect each cell . Guth's result generalized an earlier result of Guth and Katz for points.
Edit intent: fluency
Original: In 2015, Guth proved that for any set of k-dimensional  varieties in R^d and for any positive integer D, there exists a polynomial of degree at most D whose zero-set divides R^d into open connected "cells, " so that only a small fraction of the given varieties intersect each cell . Guth's result generalized an earlier result of Guth and Katz for points.
Revised: In 2015, Guth proved that for any set of k-dimensional varieties in R^d and for any positive integer D, there exists a polynomial of degree at most D whose zero-set divides R^d into open connected sets, so that only a small fraction of the given varieties intersect each cell . Guth's result generalized an earlier result of Guth and Katz for points.
Edit intent: clarity
Original: In 2015, Guth proved that for any set of k-dimensional  varieties in R^d and for any positive integer D, there exists a polynomial of degree at most D whose zero-set divides R^d into open connected "cells, " so that only a small fraction of the given varieties intersect each cell . Guth's result generalized an earlier result of Guth and Katz for points.
Revised: In 2015, Guth proved that for any set of k-dimensional varieties in R^d and for any positive integer D, there exists a polynomial of degree at most D whose zero-set divides R^d into open connected "cells, " so that only a small fraction of the given varieties intersect each of these sets . Guth's result generalized an earlier result of Guth and Katz for points.
Edit intent: clarity
Original:  Given a set of n input  curves and a positive integer D, we efficiently construct a decomposition of space into O(D^3\log^3{D}) open cells, each of which meets O(n/D^2) curves from the input.
Revised:  Given a set of n input algebraic curves and a positive integer D, we efficiently construct a decomposition of space into O(D^3\log^3{D}) open cells, each of which meets O(n/D^2) curves from the input.
Edit intent: meaning-changed
Original:  Given a set of n input  curves and a positive integer D, we efficiently construct a decomposition of space into O(D^3\log^3{D}) open cells, each of which meets O(n/D^2) curves from the input.
Revised:  Given a set of n input curves and a positive integer D, we efficiently construct a decomposition of space into O(D^3\log^3{D}) open "cells," each of which meets O(n/D^2) curves from the input.
Edit intent: clarity
Original:  As an application, we revisit the problem of eliminating depth cycles among non-vertical lines in 3-space, recently studied by Aronov and Sharir (2018), and show an algorithm that cuts n such lines into O(n^{3/2+ \varepsilon }) pieces that are depth-cycle free, for any \varepsilon > 0.
Revised:  As an application, we revisit the problem of eliminating depth cycles among non-vertical lines in 3-space, recently studied by Aronov and Sharir (2018), and show an algorithm that cuts n such lines into O(n^{3/2+ \epsilon }) pieces that are depth-cycle free, for any \varepsilon > 0.
Edit intent: fluency
Original:  As an application, we revisit the problem of eliminating depth cycles among non-vertical lines in 3-space, recently studied by Aronov and Sharir (2018), and show an algorithm that cuts n such lines into O(n^{3/2+ \varepsilon }) pieces that are depth-cycle free, for any \varepsilon > 0.
Revised:  As an application, we revisit the problem of eliminating depth cycles among non-vertical lines in 3-space, recently studied by Aronov and Sharir (2018), and show an algorithm that cuts n such lines into O(n^{3/2+ \varepsilon }) pieces that are depth-cycle free, for any \epsilon > 0.
Edit intent: clarity
Original:  To address this challenge, we present an approach for constructing customizable, cross-machine performance models for GPU kernels, including a mechanism to automatically and symbolically gather performance-relevant kernel operation counts, a tool for formulating mathematical models using these counts, and a customizable parameterized collection of benchmark kernels used to fit models to GPUs in a black-box fashion.
Revised:  To address this challenge, we present an approach for constructing customizable, cross-machine performance models for GPU kernels, including a mechanism to automatically and symbolically gather performance-relevant kernel operation counts, a tool for formulating mathematical models using these counts, and a customizable parameterized collection of benchmark kernels used to calibrate models to GPUs in a black-box fashion.
Edit intent: clarity
Original:  each example models execution times for multiple variants of a particular computation: two matrix multiplication variants, four Discontinuous Galerkin (DG) differentiation operation variants, and two 2-D first-order finite difference computation variants.
Revised:  each example models execution times for multiple variants of a particular computation: two matrix multiplication variants, four Discontinuous Galerkin (DG) differentiation operation variants, and two 2-D five-point finite difference stencil variants.
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Original:  In this paper, we develop a gender specific and age dependent classification method to diagnose the Parkinson's disease using the handwriting based measurements.The gender specific and age dependent classifier was observed significantly outperforming the generalized classifier.
Revised:  In this paper, we develop a gender specific and age dependent classification method to diagnose the Parkinson's disease using the online handwriting recorded from individuals with Parkinson's(n=37;m/f-19/18;age-69.3+-10.9years) and healthy controls(n=38;m/f-20/18;age-62.4+-11.3 years).The sex specific and age dependent classifier was observed significantly outperforming the generalized classifier.
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Original:  An improved accuracy of 83.75\%(SD = 1.63) with the female specific classifier, and 79.55\%(SD=1.58) with the old age dependent classifier was observed in comparison to 75.76\%(SD=1.17) accuracy with the generalized classifier.
Revised:  An improved accuracy of 83.75\%(SD + 1.63) with the female specific classifier, and 79.55\%(SD=1.58) with the old age dependent classifier was observed in comparison to 75.76\%(SD=1.17) accuracy with the generalized classifier.
Edit intent: meaning-changed
Original:  An improved accuracy of 83.75\%(SD = 1.63) with the female specific classifier, and 79.55\%(SD=1.58) with the old age dependent classifier was observed in comparison to 75.76\%(SD=1.17) accuracy with the generalized classifier.
Revised:  An improved accuracy of 83.75\%(SD = 1.63) with female specific classifier, and 79.55\%(SD=1.58) with the old age dependent classifier was observed in comparison to 75.76\%(SD=1.17) accuracy with the generalized classifier.
Edit intent: fluency
Original:  An improved accuracy of 83.75\%(SD = 1.63) with the female specific classifier, and 79.55\%(SD=1.58) with the old age dependent classifier was observed in comparison to 75.76\%(SD=1.17) accuracy with the generalized classifier.
Revised:  An improved accuracy of 83.75\%(SD = 1.63) with the female specific classifier, and 79.55\%(SD=1.58) with old age dependent classifier was observed in comparison to 75.76\%(SD=1.17) accuracy with the generalized classifier.
Edit intent: fluency
Original:  This paper aims to determine some necessary and sufficient mathematical conditions for an appropriate metapopulations network partitioning (spatial cut) such that populations remain linearly stable in the partitioninginduced daughter subnetworks. For that, metapopulations stability analysed and role of the Fiedler value of a metapopulations induced graph Laplacian described first .
Revised:  In this work, we determine if there exist(s) set(s) of dispersal connections removal of which causes partitioning(s) of the metapopulations network into dynamically stable sub-networks .
Edit intent: clarity
Original:  Our study suggests, sufficiently connected metapopulations network (having the Fiedler value greater than or equal to some threshold level) along with the constrained internal patch dynamicsalways results in stable metapopulations dynamics around its co-existential equilibrium solution .
Revised:  Our study finds that there exists a lower bound threshold Fiedler value which guarantees the dynamical stability of the network dynamics. Necessary and sufficient mathematical conditions for finding partitions that result in sub-networks with the desired threshold Fiedler values have been derived and illustrated with examples. Although posed and discussed in the ecological context, it may be pointed out that such partitioning problems exist across any spatially discrete but connected dynamical systems with reaction-diffusion. Non-ecological examples are power distribution grids, intra-cellular reaction pathway networks and high density nano-fluidic lab-on-chip applications .
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Original:  For two-player social dilemmas (namely, the Prisoner's Dilemma, Snowdrift, Stag Hunt, and Harmony), we characterize (stable) equilibrium points and analytically calculate the probability of having a certain number of equilibria  .
Revised:  For two-player social dilemmas (namely, the Prisoner's Dilemma, Snowdrift, Stag Hunt, and Harmony), we characterize (stable) equilibrium points and analytically calculate the probability of having a certain number of equilibria when the payoff entries are uniformly distributed .
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Original:  The problem originates due to the nonsynchronous nature of intraday financial data whereas to estimate the Copula, we need synchronous observations. We show that this problem may lead to serious underestimation of the Copula parameter .
Revised:  The problem originates due to the nonsynchronous nature of intraday financial data . We propose a consistent estimator of the correlation coefficient in case of Elliptical copula and show that the plug-in copula estimator is uniformly convergent. For non-elliptical copulas, we capture the dependence through Kendall's Tau. We demonstrate underestimation of the Copula parameter .
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Original:  We show that this problem may lead to serious underestimation of the Copula parameter . We propose a modification to obtain a consistent estimatorin case of Elliptical Copula or to reduce the bias significantly in case of general copulas .
Revised:  We show that this problem may lead to serious underestimation of the copula parameter and use a quadratic model to propose an improved estimator. In simulations, the proposed estimator reduces the bias significantly for a general class of copulas. We apply the proposed methods to real data of several stock prices .
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Original: This paper studies an optimal portfolio problem for a DC pension plan considering both interest rate risk and longevity risk . In the accumulation phase, plan members pay constant contributions continuously into the pension fund. We assume that the evolution of mortality rate of all the plan members can be described by the same stochastic process and a representative member is chosen to study the problem . At retirement time, the pension fund is used to purchase a lifetime annuity and a minimum guarantee is required by the representative member. To hedge the longevity risk , we introduce a mortality-linked security, i.e. a longevity bond , into the financial market .
Revised: Pension schemes all over the world are under increasing pressure to efficiently hedge the longevity risk posed by ageing populations. In this work, we study an optimal investment problem for a defined contribution pension scheme which decides to hedge the longevity risk , we introduce a mortality-linked security, i.e. a longevity bond , into the financial market .
Edit intent: clarity