wanyu/IteraTeR_v2 huggingface dataset:
Original:  Therefore, we consider a two-scale homogenisation approach and model the membrane as a Kirchhoff-Love shell on the macroscale and as Euler-Bernoulli rods on the microscale.
Revised:  Therefore, we use a two-scale homogenisation approach and model the membrane as a Kirchhoff-Love shell on the macroscale and as Euler-Bernoulli rods on the microscale.
Edit intent: coherence
Original:  Adversarial attacks that confuse SoftHebb are also confusing to the human eye.
Revised:  Notably, adversarial attacks that confuse SoftHebb are also confusing to the human eye.
Edit intent: coherence
Original:  We are interested in characterizing the long-time behavior of the dynamic model -- i.e., whether edge weights evolve to have stable signs (positive or negative) and structural balance (the multiplication of weights on any triangle is non-negative)  Our main theoretical result solves the above dynamic system with time-evolving opinions V(t)=[ v_1 (t),\cdots, v_n (t)] and social tie weights W(t)=[w_{ij}(t)]_{n\times n}.
Revised:  We are interested in characterizing the long-time behavior of the dynamic model -- i.e., whether edge weights evolve to have stable signs (positive or negative) and structural balance (the multiplication of weights on any triangle is non-negative) . Our main theoretical result solves the above dynamic system with time-evolving opinions V(t)=[ v_1 (t),\cdots, v_n (t)] and social tie weights W(t)=[w_{ij}(t)]_{n\times n}.
Edit intent: fluency
Original:  We are interested in characterizing the long-time behavior of the dynamic model -- i.e., whether edge weights evolve to have stable signs (positive or negative) and structural balance (the multiplication of weights on any triangle is non-negative)  Our main theoretical result solves the above dynamic system with time-evolving opinions V(t)=[ v_1 (t),\cdots, v_n (t)] and social tie weights W(t)=[w_{ij}(t)]_{n\times n}.
Revised:  We are interested in characterizing the long-time behavior of the dynamic model -- i.e., whether edge weights evolve to have stable signs (positive or negative) and structural balance (the multiplication of weights on any triangle is non-negative) Our main theoretical result solves the above dynamic system with time-evolving opinions V(t)=[ v1 (t),\cdots, v_n (t)] and social tie weights W(t)=[w_{ij}(t)]_{n\times n}.
Edit intent: fluency
Original:  We are interested in characterizing the long-time behavior of the dynamic model -- i.e., whether edge weights evolve to have stable signs (positive or negative) and structural balance (the multiplication of weights on any triangle is non-negative)  Our main theoretical result solves the above dynamic system with time-evolving opinions V(t)=[ v_1 (t),\cdots, v_n (t)] and social tie weights W(t)=[w_{ij}(t)]_{n\times n}.
Revised:  We are interested in characterizing the long-time behavior of the dynamic model -- i.e., whether edge weights evolve to have stable signs (positive or negative) and structural balance (the multiplication of weights on any triangle is non-negative) Our main theoretical result solves the above dynamic system with time-evolving opinions V(t)=[ v_1 (t),\cdots, vn (t)] and social tie weights W(t)=[w_{ij}(t)]_{n\times n}.
Edit intent: fluency
Original:  The first one is that both sign stability and structural balance (for any triangle with individual i,j,k, w_{ij}w_{jk}w_{ki} \geq 0 \geq0 ) occur.
Revised:  The first one is that both sign stability and structural balance (for any triangle with individual i,j,k, w_{ij}w_{jk}w_{ki} \geq0 ) occur.
Edit intent: fluency
Original:  However, for  multi-armed bandit (MAB) with nontrivial switching cost, even if the switching cost is a constant, the classic paper by Banks and Sundaram BS94 showed that no index strategy can be optimal.
Revised:  However, for multi-armed bandit with nontrivial switching cost, even if the switching cost is a constant, the classic paper by Banks and Sundaram BS94 showed that no index strategy can be optimal.
Edit intent: clarity
Original:  To the best of our knowledge, this  is the first index strategy that achieves a constant approximation factor for a general  MAB variant with switching costs.
Revised:  To the best of our knowledge, this index strategy is the first index strategy that achieves a constant approximation factor for a general MAB variant with switching costs.
Edit intent: fluency
Original:  To the best of our knowledge, this  is the first index strategy that achieves a constant approximation factor for a general  MAB variant with switching costs.
Revised:  To the best of our knowledge, this is the first strategy that achieves a constant approximation factor for a general MAB variant with switching costs.
Edit intent: clarity
Original: Background: The deployment of various networks (e.g., Internet of Things (IoT) and mobile networks) and databases (e.g., nutrition tables and food compositional databases)  in the food system generates massive information silos due to the well-known data harmonization problem  .
Revised:  The deployment of various networks (e.g., Internet of Things (IoT) and mobile networks) and databases (e.g., nutrition tables and food compositional databases) in the food system generates massive information silos due to the well-known data harmonization problem .
Edit intent: coherence
Original: Background: The deployment of various networks (e.g., Internet of Things (IoT) and mobile networks) and databases (e.g., nutrition tables and food compositional databases)  in the food system generates massive information silos due to the well-known data harmonization problem  .
Revised: Background: The deployment of various networks (e.g., Internet of Things (IoT) and mobile networks) , databases (e.g., nutrition tables and food compositional databases) in the food system generates massive information silos due to the well-known data harmonization problem .
Edit intent: fluency
Original:  The food knowledge graph provides a unified and standardized conceptual terminology and their relationships in a structured form  and thus can transform these information silos across the whole food system to a more reusable globally digitally connected Internet of Food , enabling every stage of the food system from farm-to-fork.
Revised:  The food knowledge graph provides a unified and standardized conceptual terminology and their relationships in a structured form , and thus can transform these information silos across the whole food system to a more reusable globally digitally connected Internet of Food , enabling every stage of the food system from farm-to-fork.
Edit intent: fluency
Original:  The food knowledge graph provides a unified and standardized conceptual terminology and their relationships in a structured form  and thus can transform these information silos across the whole food system to a more reusable globally digitally connected Internet of Food , enabling every stage of the food system from farm-to-fork.
Revised:  The food knowledge graph provides a unified and standardized conceptual terminology and their relationships in a structured form and thus can transform food information silos to a more reusable globally digitally connected Internet of Food , enabling every stage of the food system from farm-to-fork.
Edit intent: clarity
Original:  Scope and approach: We review the evolution of food knowledge organization , from food classification, food ontology to food knowledge graphs.
Revised:  Scope and approach: We review the evolution of food knowledge organization mainly from food classification, food ontology to food knowledge graphs.
Edit intent: coherence
Original:  Future directions for food knowledge graphs cover several fields such as multimodal food knowledge graphs and food intelligence .
Revised:  We also discuss future directions in this field, such as food knowledge graphs and food intelligence .
Edit intent: clarity
Original: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in  machine learning, signal processing, and information sciences  . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and  upper bound this divergence.
Revised: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in statistics, machine learning, signal processing, and information sciences . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and upper bound this divergence.
Edit intent: clarity
Original: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in  machine learning, signal processing, and information sciences  . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and  upper bound this divergence.
Revised: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in machine learning, signal processing, and information sciences in general . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and upper bound this divergence.
Edit intent: clarity
Original: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in  machine learning, signal processing, and information sciences  . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and  upper bound this divergence.
Revised: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in machine learning, signal processing, and information sciences . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and /or upper bound this divergence.
Edit intent: coherence
Original:  In particular, we consider Polynomial Exponential Densities, and design a goodness-of-fit criterion to measure the dissimilarity between a GMM and a PED which is a generalization of the Hyv\"arinen divergence.
Revised:  In particular, we consider Exponential-Polynomial Densities, and design a goodness-of-fit criterion to measure the dissimilarity between a GMM and a PED which is a generalization of the Hyv\"arinen divergence.
Edit intent: clarity
Original: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in statistics, machine learning, signal processing, and information sciencesin general . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and /or upper bound this divergence.
Revised: The Jeffreys divergence is a renown symmetrization of the oriented Kullback-Leibler divergence which is often used in statistics, machine learning, signal processing, and information sciencesin general . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and /or upper bound this divergence.
Edit intent: fluency
Original: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in statistics, machine learning, signal processing, and information sciencesin general . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and /or upper bound this divergence.
Revised: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence broadly used in information sciences . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and /or upper bound this divergence.
Edit intent: clarity
Original: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in statistics, machine learning, signal processing, and information sciencesin general . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and /or upper bound this divergence.
Revised: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in statistics, machine learning, signal processing, and information sciencesin general . Since the Jeffreys divergence between Gaussian mixture models is not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and /or upper bound this divergence.
Edit intent: clarity
Original: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in statistics, machine learning, signal processing, and information sciencesin general . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and /or upper bound this divergence.
Revised: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in statistics, machine learning, signal processing, and information sciencesin general . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, various techniques with pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and /or upper bound this divergence.
Edit intent: fluency
Original: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in statistics, machine learning, signal processing, and information sciencesin general . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and /or upper bound this divergence.
Revised: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in statistics, machine learning, signal processing, and information sciencesin general . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either estimate, approximate, or (iii) lower and /or upper bound this divergence.
Edit intent: fluency
Original: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in statistics, machine learning, signal processing, and information sciencesin general . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or (iii) lower and /or upper bound this divergence.
Revised: The Jeffreys divergence is a renown symmetrization of the statistical Kullback-Leibler divergence which is often used in statistics, machine learning, signal processing, and information sciencesin general . Since the Jeffreys divergence between the ubiquitous Gaussian Mixture Models are not available in closed-form, many techniques with various pros and cons have been proposed in the literature to either (i) estimate, (ii) approximate, or lower and upper bound this divergence.
Edit intent: fluency
Original:  In this work , we propose a simple yet fast heuristic to approximate the Jeffreys divergence between two univariate GMMs of arbitrary number of components.
Revised:  In this paper , we propose a simple yet fast heuristic to approximate the Jeffreys divergence between two univariate GMMs of arbitrary number of components.
Edit intent: clarity
Original:  The heuristic relies on converting GMMs into pairs of dually parameterized probability densities belonging to exponential families .
Revised:  Our heuristic relies on converting GMMs into pairs of dually parameterized probability densities belonging to exponential families .
Edit intent: fluency
Original:  The heuristic relies on converting GMMs into pairs of dually parameterized probability densities belonging to exponential families .
Revised:  The heuristic relies on converting the mixtures into pairs of dually parameterized probability densities belonging to exponential families .
Edit intent: clarity
Original:  The heuristic relies on converting GMMs into pairs of dually parameterized probability densities belonging to exponential families .
Revised:  The heuristic relies on converting GMMs into pairs of dually parameterized probability densities belonging to an exponential family .
Edit intent: fluency
Original:  In particular, we consider Exponential-Polynomial Densities , and design a  goodness-of-fit criterion to measure the dissimilarity between a GMM and a EPD which is a generalization of the Hyv\"arinen divergence .
Revised:  In particular, we consider the versatile polynomial exponential family densities , and design a goodness-of-fit criterion to measure the dissimilarity between a GMM and a EPD which is a generalization of the Hyv\"arinen divergence .
Edit intent: clarity
Original:  In particular, we consider Exponential-Polynomial Densities , and design a  goodness-of-fit criterion to measure the dissimilarity between a GMM and a EPD which is a generalization of the Hyv\"arinen divergence .
Revised:  In particular, we consider Exponential-Polynomial Densities , and design a goodness-of-fit divergence is a generalization of the Hyv\"arinen divergence .
Edit intent: clarity
Original:  This criterion allows one to select the orders of the EPDs to approximate the GMMs . We demonstrate experimentally that the computational time of our heuristic improves over the stochastic Monte Carlo estimation baseline by several orders of magnitude  while approximating reasonably well the Jeffreys divergence, specially when the univariate mixtures have a  small number of modes  .
Revised:  This criterion allows one to select the orders of the EPDs to approximate the mixtures . We demonstrate experimentally that the computational time of our heuristic improves over the stochastic Monte Carlo estimation baseline by several orders of magnitude while approximating reasonably well the Jeffreys divergence, specially when the univariate mixtures have a small number of modes .
Edit intent: clarity
Original:  This criterion allows one to select the orders of the EPDs to approximate the GMMs . We demonstrate experimentally that the computational time of our heuristic improves over the stochastic Monte Carlo estimation baseline by several orders of magnitude  while approximating reasonably well the Jeffreys divergence, specially when the univariate mixtures have a  small number of modes  .
Revised:  This criterion allows one to select the orders of the EPDs to approximate the GMMs . We demonstrate experimentally that our heuristic to approximate the Jeffreys divergence improves by several orders of magnitude while approximating reasonably well the Jeffreys divergence, specially when the univariate mixtures have a small number of modes .
Edit intent: clarity
Original:  This criterion allows one to select the orders of the EPDs to approximate the GMMs . We demonstrate experimentally that the computational time of our heuristic improves over the stochastic Monte Carlo estimation baseline by several orders of magnitude  while approximating reasonably well the Jeffreys divergence, specially when the univariate mixtures have a  small number of modes  .
Revised:  This criterion allows one to select the orders of the EPDs to approximate the GMMs . We demonstrate experimentally that the computational time of our heuristic improves over the stochastic Monte Carlo estimation baseline by several orders of magnitude while approximating reasonably well the Jeffreys divergence, specially when the mixtures have a small number of modes .
Edit intent: clarity
Original:  This criterion allows one to select the orders of the EPDs to approximate the GMMs . We demonstrate experimentally that the computational time of our heuristic improves over the stochastic Monte Carlo estimation baseline by several orders of magnitude  while approximating reasonably well the Jeffreys divergence, specially when the univariate mixtures have a  small number of modes  .
Revised:  This criterion allows one to select the orders of the EPDs to approximate the GMMs . We demonstrate experimentally that the computational time of our heuristic improves over the stochastic Monte Carlo estimation baseline by several orders of magnitude while approximating reasonably well the Jeffreys divergence, specially when the univariate mixtures have a very small number of modes .
Edit intent: clarity
Original:  One of the most intractable problems is how to represent text instances of arbitrary shapes.
Revised:  One of the hardest problems in this task is how to represent text instances of arbitrary shapes.
Edit intent: clarity
Original:  Although many state-of-the-art methods have been proposed to model irregular texts in a flexible manner, most of them lose simplicity and robustness.
Revised:  Although many methods have been proposed to model irregular texts in a flexible manner, most of them lose simplicity and robustness.
Edit intent: clarity
Original:  Specifically, we utilize the centripetal shifts to implement the pixel aggregation, which guide the external text pixels to the internal text kernels.
Revised:  Specifically, we utilize the centripetal shifts to implement pixel aggregation, which guide the external text pixels to the internal text kernels.
Edit intent: fluency
Original:  Specifically, we utilize the centripetal shifts to implement the pixel aggregation, which guide the external text pixels to the internal text kernels.
Revised:  Specifically, we utilize the centripetal shifts to implement the pixel aggregation, guiding the external text pixels to the internal text kernels.
Edit intent: coherence
Original:  The relaxation operation is integrated into the dense regression for centripetal shifts, allowing the correct prediction in a range , not a specific value.
Revised:  The relaxation operation is integrated into the dense regression for centripetal shifts, allowing the correct prediction in a range instead of a specific value.
Edit intent: fluency