wanyu/IteraTeR_v2 huggingface dataset:
Original: We consider the classic motion planning problem defined over a roadmap in which a vehicle seeks to find an optimal path to a given destination from a given starting location in presence of an attacker who can launch attacks on the vehicle over any edge of the roadmap.
Revised: We consider the classic motion planning problem defined over a roadmap in which a vehicle seeks to find an optimal path from a source to a destination in presence of an attacker who can launch attacks on the vehicle over any edge of the roadmap.
Edit intent: clarity
Original:  We model this problem using the framework of a  zero-sum dynamic game  with a stopping state being played simultaneously by the two players .
Revised:  We model this problem using the framework of a zero-sum dynamic game with a stopping state played between an attacker and defender .
Edit intent: clarity
Original:  We characterize the Nash equilibria of this game and provide closed form expressions for the case of two actions per player.
Revised:  We characterize the Nash equiliria of an edge-game and provide closed form expressions for the case of two actions per player.
Edit intent: clarity
Original:  We characterize the Nash equilibria of this game and provide closed form expressions for the case of two actions per player.
Revised:  We characterize the Nash equilibria of this game and provide closed form expressions for two actions per player.
Edit intent: clarity
Original:  We further provide an analytic lower bound on the value of the game and characterize conditions under which it grows sub-linearly with the number of stages.
Revised:  We further provide an analytic and approximate expression on the value of the game and characterize conditions under which it grows sub-linearly with the number of stages.
Edit intent: clarity
Original:  We further provide an analytic lower bound on the value of the game and characterize conditions under which it grows sub-linearly with the number of stages.
Revised:  We further provide an analytic lower bound on the value of an edge-game and characterize conditions under which it grows sub-linearly with the number of stages.
Edit intent: clarity
Original:  We then study the sensitivity of the Nash equilibrium to  (i) the cost of using the countermeasure, (ii) the cost of motion and (iii) the benefit of disabling the attack.
Revised:  We study the sensitivity of the Nash equilibrium to (i) the cost of using the countermeasure, (ii) the cost of motion and (iii) the benefit of disabling the attack.
Edit intent: coherence
Original:  We then study the sensitivity of the Nash equilibrium to  (i) the cost of using the countermeasure, (ii) the cost of motion and (iii) the benefit of disabling the attack.
Revised:  We then study the sensitivity of Nash equilibrium to (i) the cost of using the countermeasure, (ii) the cost of motion and (iii) the benefit of disabling the attack.
Edit intent: fluency
Original:  We then study the sensitivity of the Nash equilibrium to  (i) the cost of using the countermeasure, (ii) the cost of motion and (iii) the benefit of disabling the attack.
Revised:  We then study the sensitivity of the Nash equilibrium to the (i) the cost of using the countermeasure, (ii) the cost of motion and (iii) the benefit of disabling the attack.
Edit intent: fluency
Original:  We then study the sensitivity of the Nash equilibrium to  (i) the cost of using the countermeasure, (ii) the cost of motion and (iii) the benefit of disabling the attack.
Revised:  We then study the sensitivity of the Nash equilibrium to (i) cost of using the countermeasure, (ii) the cost of motion and (iii) the benefit of disabling the attack.
Edit intent: fluency
Original:  We then study the sensitivity of the Nash equilibrium to  (i) the cost of using the countermeasure, (ii) the cost of motion and (iii) the benefit of disabling the attack.
Revised:  We then study the sensitivity of the Nash equilibrium to (i) the cost of using the countermeasure, (ii) cost of motion and (iii) the benefit of disabling the attack.
Edit intent: fluency
Original:  We then study the sensitivity of the Nash equilibrium to  (i) the cost of using the countermeasure, (ii) the cost of motion and (iii) the benefit of disabling the attack.
Revised:  We then study the sensitivity of the Nash equilibrium to (i) the cost of using the countermeasure, (ii) the cost of motion and (iii) benefit of disabling the attack.
Edit intent: fluency
Original:  We provide simple and explicit formulas for Jacobian matrices for the semi-discrete systems of ODEs produced by entropy stable discretizations.
Revised:  We provide simple and efficient formulas for Jacobian matrices for the semi-discrete systems of ODEs produced by entropy stable discretizations.
Edit intent: clarity
Original: In this article, we consider the  path-following method based on the perturbed Newton flow with the new trust-region time-stepping scheme for the standard linear programming problem.
Revised: In this article, we consider the path-following method and the trust-region time-stepping scheme for the standard linear programming problem.
Edit intent: coherence
Original: In this article, we consider the  path-following method based on the perturbed Newton flow with the new trust-region time-stepping scheme for the standard linear programming problem.
Revised: In this article, we consider the path-following method based on the perturbed Newton flow with the new trust-region strategy for the standard linear programming problem.
Edit intent: clarity
Original:  For the problem with deficient rank matrix and the noise right-hand-side vector , we also give the pre-processing method based on the singular value decomposition .
Revised:  For the deficient rank problem with the small noisy data , we also give the pre-processing method based on the singular value decomposition .
Edit intent: clarity
Original:  Then, we analyze the global convergence of the new method when the initial point is strictly primal-dual feasible.
Revised:  Then, we prove the global convergence of the new method when the initial point is strictly primal-dual feasible.
Edit intent: clarity
Original:  Ferris \mbox{ Gonzaga1992,FMW2007 the built-in subroutine linprog.m of the MATLAB environment , which was implemented by S. Mehrotra and Y. Zhang \mbox{ MATLAB,Mehrotra1992,Zhang1998\hspace{0pt} ).
Revised:  Ferris \mbox{ Gonzaga1992,FMW2007 the built-in subroutine linprog.m of the MATLAB environment \hspace{0pt} ).
Edit intent: coherence
Original:  }  Numerical results show that the new method is more robust than those methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Revised:  } . Numerical results show that the new method is more robust than those methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Edit intent: fluency
Original:  }  Numerical results show that the new method is more robust than those methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Revised:  } Numerical results show that the new method is more robust than the other two methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Edit intent: clarity
Original:  For the deficient rank problem with the small noisy data, we also give the pre-processing method based on the QR decomposition with column pivoting.
Revised:  For the rank-deficient problem with the small noisy data, we also give the pre-processing method based on the QR decomposition with column pivoting.
Edit intent: fluency
Original:  For the deficient rank problem with the small noisy data, we also give the pre-processing method based on the QR decomposition with column pivoting.
Revised:  For the deficient rank problem with the small noisy data, we also give the preprocessing method based on the QR decomposition with column pivoting.
Edit intent: fluency
Original:  Numerical results show that the new method is more robust than the other two methods for the deficient rank problem with the small noise data.
Revised:  Numerical results show that the new method is more robust than the other two methods for the rank-deficient problem with the small noise data.
Edit intent: fluency
Original: Transcribing spoken audio samples into  International Phonetic Alphabet (IPA) has long been reserved for experts.
Revised: Transcribing spoken audio samples into the International Phonetic Alphabet (IPA) has long been reserved for experts.
Edit intent: fluency
Original:  In this study, we instead examined the use of an Artificial Neural Network (ANN) model to automatically extract the IPA  pronunciation of a word based on its audio pronunciation, hence its name Generating IPA Pronunciation From Audio (GIPFA).
Revised:  In this study, we examine the use of an Artificial Neural Network (ANN) model to automatically extract the IPA pronunciation of a word based on its audio pronunciation, hence its name Generating IPA Pronunciation From Audio (GIPFA).
Edit intent: coherence
Original:  Interestingly, by studying inference errors, the model made it possible to highlight possible errors in the dataset as well as identifying the closest phonemes in French.
Revised:  Interestingly, by studying inference errors, the model made it possible to highlight possible errors in the dataset as well as to identify the closest phonemes in French.
Edit intent: fluency
Original: The balls-into-bins model randomly allocates n sequential balls into n bins , as follows: each ball selects a set D of\le \le =\{B_1,B_2,\ldots,B_{s_t}\}, and each edge B\inH^{(t)} contains at least a logarithmic number of bins.
Revised: The balls-into-bins randomly allocates n sequential balls into n bins , as follows: each ball selects a set D of\le \le =\{B_1,B_2,\ldots,B_{s_t}\}, and each edge B\inH^{(t)} contains at least a logarithmic number of bins.
Edit intent: clarity
Original: The balls-into-bins model randomly allocates n sequential balls into n bins , as follows: each ball selects a set D of\le \le =\{B_1,B_2,\ldots,B_{s_t}\}, and each edge B\inH^{(t)} contains at least a logarithmic number of bins.
Revised: The balls-into-bins model which randomly allocates m balls into n bins , as follows: each ball selects a set D of\le \le =\{B_1,B_2,\ldots,B_{s_t}\}, and each edge B\inH^{(t)} contains at least a logarithmic number of bins.
Edit intent: clarity
Original: The balls-into-bins model randomly allocates n sequential balls into n bins , as follows: each ball selects a set D of\le \le =\{B_1,B_2,\ldots,B_{s_t}\}, and each edge B\inH^{(t)} contains at least a logarithmic number of bins.
Revised: The balls-into-bins model randomly allocates n sequential balls into n bins , as follows: each ball selects a set D of\le t\le =\{B_1,B_2,\ldots,B_{s_t}\}, and each edge B\inH^{(t)} contains at least a logarithmic number of bins.
Edit intent: clarity
Original:  Themaximum load is the,\ldots, H^{(m)} satisfy a } \emph{  maximum number of balls in any bin.
Revised:  The is the,\ldots, H^{(m)} satisfy a } \emph{ maximum number of balls in any bin.
Edit intent: clarity
Original:  Themaximum load is the,\ldots, H^{(m)} satisfy a } \emph{  maximum number of balls in any bin.
Revised:  Themaximum load is the,\ldots, H^{(m)} satisfy a } \emph{ maximum number of balls 
Edit intent: clarity
Original: %DIFDELCMD < }%%% \ showed that provided ties are broken randomly, after n balls have been placed the \emph{maximum load}, is  \log_d\log n +O(1), with high probability.
Revised: %DIFDELCMD < }%%% \ showed that provided ties are broken randomly, after n balls have been placed the \emph{maximum load}, is at most \log_d\log n +O(1), with high probability.
Edit intent: clarity
Original:  We consider this popular paradigm in a dynamic environment where the bins are structured as a\emph{dynamic hypergraph . A dynamic hypergraph is a sequence of hypergraphs, say \mathcal{H}^{(t)}, arriving over discrete times t= 1 ,2,\ldots, such that the vertex set of \mathcal{H^{(t)}'s is the set of n bins, but (hyper)edges may change over time.
Revised:  \emph{dynamic hypergraph . A dynamic hypergraph is a sequence of hypergraphs, say \mathcal{H}^{(t)}, arriving over discrete times t= 1 ,2,\ldots, such that the vertex set of \mathcal{H^{(t)}'s is the set of n bins, but (hyper)edges may change over time.
Edit intent: clarity
Original:  We consider this popular paradigm in a dynamic environment where the bins are structured as a\emph{dynamic hypergraph . A dynamic hypergraph is a sequence of hypergraphs, say \mathcal{H}^{(t)}, arriving over discrete times t= 1 ,2,\ldots, such that the vertex set of \mathcal{H^{(t)}'s is the set of n bins, but (hyper)edges may change over time.
Revised:  We consider this popular paradigm in a dynamic environment where the bins are structured as a\emph{ . A dynamic hypergraph is a sequence of hypergraphs, say \mathcal{H}^{(t)}, arriving over discrete times t= 1 ,2,\ldots, such that the vertex set of \mathcal{H^{(t)}'s is the set of n bins, but (hyper)edges may change over time.
Edit intent: clarity
Original:  We consider this popular paradigm in a dynamic environment where the bins are structured as a\emph{dynamic hypergraph . A dynamic hypergraph is a sequence of hypergraphs, say \mathcal{H}^{(t)}, arriving over discrete times t= 1 ,2,\ldots, such that the vertex set of \mathcal{H^{(t)}'s is the set of n bins, but (hyper)edges may change over time.
Revised:  We consider this popular paradigm in a dynamic environment where the bins are structured as a\emph{dynamic hypergraph . A dynamic hypergraph is a sequence of hypergraphs, say \mathcal{H}^{(t)}, 1 ,2,\ldots, such that the vertex set of \mathcal{H^{(t)}'s is the set of n bins, but (hyper)edges may change over time.
Edit intent: clarity
Original:  The model can also be seen as an adversarial model where an adversary decides the structure of } ^{(t)}, 1}\le \le  the possible sets of d bins available to each ball .
Revised:  The model can also be seen as an adversarial model where an adversary decides the structure of } ^{(t)}, 1}\le t\le the possible sets of d bins available to each ball .
Edit intent: clarity
Original:   As a result  , training classifiers with seven classical centralities leads to a nearly maximum average precision function (0.995)  .
Revised:  As a result , training classifiers with seven classical indicators leads to a nearly maximum average precision function (0.995) .
Edit intent: clarity
Original: Recent generative models for 2D images achieve impressive visual results, but clearly lack the ability to perform 3D reasoning.
Revised: While recent generative models for 2D images achieve impressive visual results, but clearly lack the ability to perform 3D reasoning.
Edit intent: coherence
Original: Recent generative models for 2D images achieve impressive visual results, but clearly lack the ability to perform 3D reasoning.
Revised: Recent generative models for 2D images achieve impressive visual results, they clearly lack the ability to perform 3D reasoning.
Edit intent: coherence
Original:  In this work, we leverage recent advances in differentiable rendering to design a framework that can generate triangle meshes and associated high-resolution texture maps, using only 2D supervision from single-view natural images.
Revised:  In this work, we bridge this gap by leveraging recent advances in differentiable rendering to design a framework that can generate triangle meshes and associated high-resolution texture maps, using only 2D supervision from single-view natural images.
Edit intent: clarity
Original:  In this work, we leverage recent advances in differentiable rendering to design a framework that can generate triangle meshes and associated high-resolution texture maps, using only 2D supervision from single-view natural images.
Revised:  In this work, we leverage recent advances in differentiable rendering . We design a framework that can generate triangle meshes and associated high-resolution texture maps, using only 2D supervision from single-view natural images.
Edit intent: fluency
Original:  We demonstrate the efficacy of our method on Pascal3D+ Cars and the CUBbirds dataset , both in an unconditional setting and in settings where the model is conditioned on class labels, attributes, and text.
Revised:  We demonstrate the efficacy of our method on Pascal3D+ Cars and CUB , both in an unconditional setting and in settings where the model is conditioned on class labels, attributes, and text.
Edit intent: clarity
Original:  We consider two common strategies for defending: designing robust SVM algorithms and data sanitization.
Revised:  We study two commonly used strategies for defending: designing robust SVM algorithms and data sanitization.
Edit intent: clarity
Original:  Though several robust SVM algorithms have been proposed before, most of them either are in lack of adversarial-resilience  or rely on strong assumptions about the data distribution or the attacker's behavior.
Revised:  Though several robust SVM algorithms have been proposed before, most of them either are in lack of adversarial-resilience , or rely on strong assumptions about the data distribution or the attacker's behavior.
Edit intent: fluency
Original:  Moreover, the research on the complexities is still quite limited nowadays .
Revised:  Moreover, the research on their complexities is still quite limited nowadays .
Edit intent: fluency
Original:  Moreover, the research on the complexities is still quite limited nowadays .
Revised:  Moreover, the research on the complexities is still quite limited .
Edit intent: coherence
Original: The geodesic distance-based dual-front curve evolution model is a powerful and efficient solution to the active contours and image segmentation issues.
Revised: The geodesic distance-based dual-front active contour models are known as a powerful and efficient solution to the active contours and image segmentation issues.
Edit intent: clarity
Original:  In its basic formulation , the dual-front model regards the meeting interfaces of two adjacent Voronoi regions as the evolving curves in the course of curve evolution.
Revised:  In its basic formulation , the dual-front models, the evolving contours can be considered as the interfaces of adjacent Voronoi regions as the evolving curves in the course of curve evolution.
Edit intent: clarity
Original:  Moreover, the proposed dual-front model can be applied for image segmentation in conjunction with various region-based homogeneity terms , whereas the original model only makes use of the piecewise constant case .
Revised:  Moreover, the proposed dual-front model can be applied for image segmentation in conjunction with various region-based homogeneity terms .
Edit intent: coherence
Original:  (ii) show  LRGA aligns with this 2-FWL formulation ;
Revised:  (ii) show LRGA aligns with 2-FWL formulation ;
Edit intent: clarity
Original:  The latter means the generalization error can be made arbitrarily small when training LRGA to learn the 2-FWL algorithm.
Edit intent: coherence
Original:  In fact, this ability is thought to bootstrap language skills to the learning of image patterns which might be a key to a more animal-like type of intelligence.
Revised:  In fact, this ability is thought to bootstrap language skills and learning of image patterns which might be a key to a more animal-like type of intelligence.
Edit intent: clarity
Original:  In this work, we  evaluate and compare three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, and value decomposition) in a diverse range of fully-cooperative multi-agent learning tasks.
Revised:  In this work, we consistently evaluate and compare three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, and value decomposition) in a diverse range of fully-cooperative multi-agent learning tasks.
Edit intent: clarity
Original:  In this work, we  evaluate and compare three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, and value decomposition) in a diverse range of fully-cooperative multi-agent learning tasks.
Revised:  In this work, we evaluate and compare three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, value decomposition) in a diverse range of fully-cooperative multi-agent learning tasks.
Edit intent: fluency
Original:  In this work, we  evaluate and compare three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, and value decomposition) in a diverse range of fully-cooperative multi-agent learning tasks.
Revised:  In this work, we evaluate and compare three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, and value decomposition) in a diverse range of cooperative multi-agent learning tasks.
Edit intent: clarity
Original:  Our experiments can serve as a reference for the expected performance of algorithms across different learning tasks .
Revised:  Our experiments serve as a reference for the expected performance of algorithms across different learning tasks .
Edit intent: clarity
Original:  In this work, we consistently evaluate and compare three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, value decomposition) in a diverse range of cooperative multi-agent learning tasks.
Revised:  In this work, we provide a systematic evaluation and comparison of three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, value decomposition) in a diverse range of cooperative multi-agent learning tasks.
Edit intent: clarity
Original:  This representation naturally does not rely on voxel-by-voxel correspondence and is robust towards noise.
Revised:  This representation naturally does not rely on voxel-by-voxel correspondence and is robust to noise.
Edit intent: clarity
Original:  Algorithmic influence maximization techniques have been proposed to aid with the choice of influencers (or peer leaders) in such interventions.
Revised:  Algorithmic influence maximization techniques have been proposed to aid with the choice of "peer leaders" or "influencers" in such interventions.
Edit intent: fluency
Original:  Traditional algorithms for influence maximization have not been designed with social interventions in mind.
Revised:  Yet, traditional algorithms for influence maximization have not been designed with social interventions in mind.
Edit intent: coherence
Original:  Traditional algorithms for influence maximization have not been designed with social interventions in mind.
Revised:  Traditional algorithms for influence maximization have not been designed with these interventions in mind.
Edit intent: clarity
Original:  Existing techniques  require committing to a single domain-specific fairness measure.
Revised:  Existing techniques come with two major drawbacks. First, they require committing to a single domain-specific fairness measure.
Edit intent: coherence
Original:  We generalize the theory regarding these principles and show under what circumstances these principles can be satisfied by a welfare function.
Revised:  We generalize the theory regarding these principles and show under what circumstances our proposed principles can be satisfied by a welfare function.
Edit intent: clarity
Original:  We show that the resulting optimization problem is monotone and submodular and can be solved  with optimality guarantees.
Revised:  We show that the resulting optimization problem is monotone and submodular and can be solved efficiently with optimality guarantees.
Edit intent: clarity