wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This is in contrast to other recently proposed methods for solving conditional moment problems based on adversarial machine learning, which do not incorporate optimal weighting, do not establish asymptotic normality, and are not semiparametrically efficient  .
Revised:  This is in contrast to other recently proposed methods for solving conditional moment problems based on adversarial machine learning, which do not incorporate optimal weighting, do not establish asymptotic normality, and are not semiparametrically efficient . In addition, we provide corresponding inference algorithms based on the same kind of variational reformulations, both for kernel- and neural net-based varieties. Finally, we demonstrate the strong performance of our proposed estimation and inference algorithms in a detailed series of synthetic experiments .
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Original:  In this paper, we first systematically compare these two methods  . Extensive experiment results demonstrate that dense representation method significantly outperforms the sparse representation, but costs more time and storage occupation.
Revised:  In this paper, we first systematically compare these two methods from four aspects: (1) effectiveness; (2) index stoarge; (3) search time cost; (4) human evaluation . Extensive experiment results demonstrate that dense representation method significantly outperforms the sparse representation, but costs more time and storage occupation.
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Original:  In order to overcome these fatal weaknesses of the dense representation method, we also propose an ultra-fast, low-storage, and highly effective Deep Semantic Hashing Coarse-grained selection method, called DSHC model.
Revised:  In order to overcome these fatal weaknesses of dense representation method, we also propose an ultra-fast, low-storage, and highly effective Deep Semantic Hashing Coarse-grained selection method, called DSHC model.
Edit intent: fluency
Original:  In order to overcome these fatal weaknesses of the dense representation method, we also propose an ultra-fast, low-storage, and highly effective Deep Semantic Hashing Coarse-grained selection method, called DSHC model.
Revised:  In order to overcome these fatal weaknesses of the dense representation method, we propose an ultra-fast, low-storage, and highly effective Deep Semantic Hashing Coarse-grained selection method, called DSHC model.
Edit intent: coherence
Original:  Specifically, in our proposed DSHC model, a hashing optimizing module that consists of two auto-encoder models is stacked on a well trained dense representation model, and three loss functions are designed to optimize it.
Revised:  Specifically, in our proposed DSHC model, a hashing optimizing module that consists of two autoencoder models is stacked on a well trained dense representation model, and three loss functions are designed to optimize it.
Edit intent: fluency
Original:  Specifically, in our proposed DSHC model, a hashing optimizing module that consists of two auto-encoder models is stacked on a well trained dense representation model, and three loss functions are designed to optimize it.
Revised:  Specifically, in our proposed DSHC model, a hashing optimizing module that consists of two auto-encoder models is stacked on a trained dense representation model, and three loss functions are designed to optimize it.
Edit intent: clarity
Original:  We experimentally validate and benchmark our method against  standard grasp-sampling methods on 790 grasps in simulation and 20 grasps on a real Franka Emika Panda.
Revised:  We experimentally validate and benchmark our method against a standard grasp-sampling methods on 790 grasps in simulation and 20 grasps on a real Franka Emika Panda.
Edit intent: fluency
Original:  We experimentally validate and benchmark our method against  standard grasp-sampling methods on 790 grasps in simulation and 20 grasps on a real Franka Emika Panda.
Revised:  We experimentally validate and benchmark our method against standard grasp-sampling method on 790 grasps in simulation and 20 grasps on a real Franka Emika Panda.
Edit intent: fluency
Original:  Remarkably, our approach is up to 20-30 times faster than the baseline, a significant improvement that opens the door to feedback-based grasp re-planning and task informative grasping. 
Revised:  Remarkably, our approach is up to 20-30 times faster than the baseline, a significant improvement that opens the door to feedback-based grasp re-planning and task informative grasping. Code is available at URL
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Original:  We provide an algorithm that does not need such an exhaustive search, and show that the best possible bounds for the amplitude can be obtained propagating complex pairs only from the convex hull  . Because the convex hull is always tightly inscribed in the respective rigorous bounding box resulting from interval arithmetic, we conclude that the obtained bounds are guaranteed to yield the true bounds .
Revised:  We provide an algorithm that does not need such an exhaustive search, and show that the best possible bounds can be obtained propagating complex pairs only from the convex hull . Because the convex hull is always tightly inscribed in the respective rigorous bounding box resulting from interval arithmetic, we conclude that the obtained bounds are guaranteed to yield the true bounds .
Edit intent: clarity
Original:  We provide an algorithm that does not need such an exhaustive search, and show that the best possible bounds for the amplitude can be obtained propagating complex pairs only from the convex hull  . Because the convex hull is always tightly inscribed in the respective rigorous bounding box resulting from interval arithmetic, we conclude that the obtained bounds are guaranteed to yield the true bounds .
Revised:  We provide an algorithm that does not need such an exhaustive search, and show that the best possible bounds for the amplitude can be obtained propagating complex pairs only from the convex hull of endpoints at each term of the Fourier series . Because the convex hull is always tightly inscribed in the respective rigorous bounding box resulting from interval arithmetic, we conclude that the obtained bounds are guaranteed to yield the true bounds .
Edit intent: meaning-changed
Original:  We provide an algorithm that does not need such an exhaustive search, and show that the best possible bounds for the amplitude can be obtained propagating complex pairs only from the convex hull  . Because the convex hull is always tightly inscribed in the respective rigorous bounding box resulting from interval arithmetic, we conclude that the obtained bounds are guaranteed to yield the true bounds .
Revised:  We provide an algorithm that does not need such an exhaustive search, and show that the best possible bounds for the amplitude can be obtained propagating complex pairs only from the convex hull . Because the convex hull is always tightly inscribed in the respective rigorous bounding box resulting from interval arithmetic, we conclude that the obtained bounds are guaranteed to enclose the true values .
Edit intent: clarity
Original:  We first define a metric to quantify the connectivity of a system with uncertain robot positions. This metric is used to design our trajectory planner based on a distributed alternating direction method of multipliers (ADMM) framework.
Revised:  Next, the algebraic connectivity of the weighted undirected graph is maintained above a specified lower limit using a trajectory planner based on a distributed alternating direction method of multipliers (ADMM) framework.
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Original: In this research work, an explicit Runge-Kutta-Fehlberg  time integration with a fourth-order compact finite difference scheme in space  is employed for solving the regime-switching pricing model.
Revised: In this research work, an explicit Runge-Kutta-Fehlberg (RKF) time integration with a fourth-order compact finite difference scheme in space is employed for solving the regime-switching pricing model.
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Original: In this research work, an explicit Runge-Kutta-Fehlberg  time integration with a fourth-order compact finite difference scheme in space  is employed for solving the regime-switching pricing model.
Revised: In this research work, an explicit Runge-Kutta-Fehlberg time integration with a fourth-order compact finite difference scheme in space and a high order analytical approximation of the optimal exercise boundary is employed for solving the regime-switching pricing model.
Edit intent: meaning-changed
Original:  As such, it enables us to employ fourth-order spatial discretization and an adaptive time integration with Dirichlet boundary conditions for obtaining the numerical solution of the asset option, option Greeks, and the optimal exercise boundary for each regime .
Revised:  As such, it enables us to employ fourth-order spatial discretization and an adaptive time integration with Dirichlet boundary conditions for obtaining the numerical solution of the asset option, and the option Greeks for each regime .
Edit intent: clarity
Original:  As such, it enables us to employ fourth-order spatial discretization and an adaptive time integration with Dirichlet boundary conditions for obtaining the numerical solution of the asset option, option Greeks, and the optimal exercise boundary for each regime . In the set of equations, Hermite interpolation with Newton basis is used to estimate the coupled assets options and option Greeks .
Revised:  As such, it enables us to employ fourth-order spatial discretization and an adaptive time integration with Dirichlet boundary conditions for obtaining the numerical solution of the asset option, option Greeks, and the optimal exercise boundary for each regime using fourth-order spatial discretization and adaptive time integration. In particular, the coupled assets options and option Greeks .
Edit intent: coherence
Original:  In the set of equations, Hermite interpolation with Newton basis is used to estimate the coupled assets options and option Greeks . A numerical experiment is carried out with two- and four-regimes examples and results are compared with the existing methods.
Revised:  In the set of equations, Hermite interpolation with Newton basis is used to estimate the coupled assets options and option Greeks are estimated using Hermite interpolation with Newton basis. Finally, a numerical experiment is carried out with two- and four-regimes examples and results are compared with the existing methods.
Edit intent: coherence
Original:  Results were obtained by means of Monte Carlo simulations on a square lattice and subsequently confirmed by a  comparison of different strategies' payoff under pair approximation , leading to a phase diagram of the possible states.
Revised:  Results were obtained by means of Monte Carlo simulations on a square lattice and subsequently confirmed by a pairwise comparison of different strategies' payoff under pair approximation , leading to a phase diagram of the possible states.
Edit intent: clarity
Original:  Based on meta-paths, it considers two sources of information for an object x: (1) Meta-path-based neighbors of x are retrieved and ranked, and the top-k neighbors are retained. (2) The meta-path instances of x to its selected neighbors are used to derive meta-path-based contexts. ConCH utilizes the above information to co-derive object embeddings and context embeddings via graph convolution.
Revised:  ConCH co-derives object embeddings and context embeddings via graph convolution.
Edit intent: clarity
Original:  This paper introduces Voronoi Progressive Widening (VPW), a general technique to modify tree search algorithms to effectively handle continuous or hybrid action spaces , and proposes and evaluates three continuous space solvers: VOSS, VOWSS, and VOMCPOW. VOSS and VOWSSare theoretical tools based on sparse sampling and Voronoi optimistic optimization designed to justify VPW-based  online solvers .
Revised:  This paper introduces Voronoi Progressive Widening (VPW), a generalization of Voronoi optimistic optimization (VOO) and action progressive widening to partially observable Markov decision processes (POMDPs). Tree search algorithms can use VPW to effectively handle continuous or hybrid action spaces , and proposes and evaluates three continuous space solvers: VOSS, VOWSS, and VOMCPOW. VOSS and VOWSSare theoretical tools based on sparse sampling and Voronoi optimistic optimization designed to justify VPW-based online solvers .
Edit intent: meaning-changed
Original:  This paper introduces Voronoi Progressive Widening (VPW), a general technique to modify tree search algorithms to effectively handle continuous or hybrid action spaces , and proposes and evaluates three continuous space solvers: VOSS, VOWSS, and VOMCPOW. VOSS and VOWSSare theoretical tools based on sparse sampling and Voronoi optimistic optimization designed to justify VPW-based  online solvers .
Revised:  This paper introduces Voronoi Progressive Widening (VPW), a general technique to modify tree search algorithms to effectively handle continuous or hybrid action spaces by efficiently balancing local and global action searching. This paper proposes two VPW-based online solvers .
Edit intent: meaning-changed
Original:  This paper introduces Voronoi Progressive Widening (VPW), a general technique to modify tree search algorithms to effectively handle continuous or hybrid action spaces , and proposes and evaluates three continuous space solvers: VOSS, VOWSS, and VOMCPOW. VOSS and VOWSSare theoretical tools based on sparse sampling and Voronoi optimistic optimization designed to justify VPW-based  online solvers .
Revised:  This paper introduces Voronoi Progressive Widening (VPW), a general technique to modify tree search algorithms to effectively handle continuous or hybrid action spaces , and proposes and evaluates three continuous space solvers: VOSS, VOWSS, and VOMCPOW. VOSS and VOWSSare theoretical tools based on sparse sampling and Voronoi optimistic optimization designed to justify VPW-based algorithms and analyzes them from theoretical and simulation perspectives. Voronoi Optimistic Weighted Sparse Sampling (VOWSS) is a theoretical tool that justifies VPW-based online solvers .
Edit intent: meaning-changed
Original:  This paper introduces Voronoi Progressive Widening (VPW), a general technique to modify tree search algorithms to effectively handle continuous or hybrid action spaces , and proposes and evaluates three continuous space solvers: VOSS, VOWSS, and VOMCPOW. VOSS and VOWSSare theoretical tools based on sparse sampling and Voronoi optimistic optimization designed to justify VPW-based  online solvers . While previous algorithms have enjoyed convergence guarantees for problems with continuous state and observation spaces, VOWSS is the first  with global convergence guarantees for problems that additionally have continuous action spaces.
Revised:  This paper introduces Voronoi Progressive Widening (VPW), a general technique to modify tree search algorithms to effectively handle continuous or hybrid action spaces , and proposes and evaluates three continuous space solvers: VOSS, VOWSS, and VOMCPOW. VOSS and VOWSSare theoretical tools based on sparse sampling and Voronoi optimistic optimization designed to justify VPW-based online solvers , and it is the first with global convergence guarantees for problems that additionally have continuous action spaces.
Edit intent: coherence
Original:  While previous algorithms have enjoyed convergence guarantees for problems with continuous state and observation spaces, VOWSS is the first  with global convergence guarantees for problems that additionally have continuous action spaces. VOMCPOW is a versatile and efficient VPW-based algorithm that consistently outperforms POMCPOW and BOMCP in several simulation experiments.
Revised:  While previous algorithms have enjoyed convergence guarantees for problems with continuous state and observation spaces, VOWSS is the first with global convergence guarantees for continuous state, action, and observation POMDPs. Voronoi Optimistic Monte Carlo Planning with Observation Weighting (VOMCPOW) is a versatile and efficient VPW-based algorithm that consistently outperforms POMCPOW and BOMCP in several simulation experiments.
Edit intent: meaning-changed
Original:  Both T and G have the same node set, and for every node pair s,t, the minimum st-cut in T is also an exact minimum st-cut in G. We give the first subcubic-time algorithm that constructs such a tree for an unweighted graph G . Its time complexity is O(n^{ 2.75 }), for n=|V(G)|;
Revised:  Both T and G have the same node set, and for every node pair s,t, the minimum (s,t)-cut in T is also an exact minimum st-cut in G. We give the first subcubic-time algorithm that constructs such a tree for an unweighted graph G . Its time complexity is O(n^{ 2.75 }), for n=|V(G)|;
Edit intent: clarity
Original:  Both T and G have the same node set, and for every node pair s,t, the minimum st-cut in T is also an exact minimum st-cut in G. We give the first subcubic-time algorithm that constructs such a tree for an unweighted graph G . Its time complexity is O(n^{ 2.75 }), for n=|V(G)|;
Revised:  Both T and G have the same node set, and for every node pair s,t, the minimum st-cut in T is also an exact minimum (s,t)-cut in G. We give the first subcubic-time algorithm that constructs such a tree for an unweighted graph G . Its time complexity is O(n^{ 2.75 }), for n=|V(G)|;
Edit intent: clarity
Original:  Both T and G have the same node set, and for every node pair s,t, the minimum st-cut in T is also an exact minimum st-cut in G. We give the first subcubic-time algorithm that constructs such a tree for an unweighted graph G . Its time complexity is O(n^{ 2.75 }), for n=|V(G)|;
Revised:  Both T and G have the same node set, and for every node pair s,t, the minimum st-cut in T is also an exact minimum st-cut in G. We give the first subcubic-time algorithm that constructs such a tree for a simple graph G (unweighted with no parallel edges) . Its time complexity is O(n^{ 2.75 }), for n=|V(G)|;
Edit intent: meaning-changed
Original:  Both T and G have the same node set, and for every node pair s,t, the minimum st-cut in T is also an exact minimum st-cut in G. We give the first subcubic-time algorithm that constructs such a tree for an unweighted graph G . Its time complexity is O(n^{ 2.75 }), for n=|V(G)|;
Revised:  Both T and G have the same node set, and for every node pair s,t, the minimum st-cut in T is also an exact minimum st-cut in G. We give the first subcubic-time algorithm that constructs such a tree for an unweighted graph G . Its time complexity is O(n^{ 2.5 }), for n=|V(G)|;
Edit intent: meaning-changed
Original:  the new algorithm can be viewed as a fine-grained reduction to O(n) Max-Flow computations on n-node graphs . Thus, under the assumption that Max-Flow can be computed in almost-linear time, our time bound improves further to n^{2.5+o(1) .
Revised:  the new algorithm can be viewed as a fine-grained reduction to O(n) Max-Flow computations on n-node graphs .
Edit intent: coherence
Original:  I allow for data that may be discrete or continuous, and low-, high-, or infinite-dimensional. I impose the additional structure of the reproducing kernel Hilbert space (RKHS), a popular non-parametric setting in machine learning. I prove uniform consistency and provide finite sample rates of convergence.
Revised:  I allow for data that may be discrete or continuous, and low, high, or infinite dimensional. I prove uniform consistency and provide finite sample rates of convergence.
Edit intent: coherence
Original: In the multi-robot collaboration domain, training with Reinforcement Learning (RL) can become intractable, and performance starts to deteriorate drastically as the number of robots increases. In this work , we proposed a distributed multi-robot learning architecture with a scalable communication structure capable of learning a robust communication policy for time-varying communication topology.
Revised: We consider the problem of classifying a map using a team of communicating robots. It is assumed that all robots have localized visual sensing capabilities and can exchange their information with neighboring robots. Using a graph decomposition technique , we proposed a distributed multi-robot learning architecture with a scalable communication structure capable of learning a robust communication policy for time-varying communication topology.
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Original:  In this work , we proposed a distributed multi-robot learning architecture with a scalable communication structure capable of learning a robust communication policy for time-varying communication topology. We construct the communication structure with Long-Short Term Memory (LSTM) cells and star graphs, in which the computational complexity of the proposed learning algorithm scales linearly with the number of robots and suitable for application with a large number of robots.
Revised:  In this work , we proposed an offline learning structure that makes every robot capable of communicating with and fusing information from its neighbors to plan its next move towards the most informative parts of the environment for map classification purposes. The main idea is to decompose a given undirected graph into a union of directed star graphs and train robots w.r.t a bounded number of star graphs. This will significantly reduce the computational cost of offline training and makes learning scalable (independent of the number of robots and suitable for application with a large number of robots.
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Original:  We construct the communication structure with Long-Short Term Memory (LSTM) cells and star graphs, in which the computational complexity of the proposed learning algorithm scales linearly with the number of robots and suitable for application with a large number of robots. The proposed methodology is validated with a map classification problem in the simulated environment. It is shown that the proposed architecture achieves a comparable classification accuracy with the centralized methods, maintains high performance with various numbers of robots without additional training cost, and robust to hacking and loss of the robots in the network .
Revised:  We construct the communication structure with Long-Short Term Memory (LSTM) cells and star graphs, in which the computational complexity of the proposed learning algorithm scales linearly with the number of robots ). Our approach is particularly useful for fast map classification in large environments using a large number of communicating robots. We validate the usefulness of our proposed methodology through extensive simulations .
Edit intent: coherence
Original:  To account for environmental and sensing uncertainty, we extend  Linear Temporal Logic (LTL) by including sensor-based predicates allowing us to incorporate uncertainty and probabilistic satisfaction requirements directly into the task specification.
Revised:  To account for environmental and sensing uncertainty, we extend a fragment of Linear Temporal Logic (LTL) by including sensor-based predicates allowing us to incorporate uncertainty and probabilistic satisfaction requirements directly into the task specification.
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Original:  To account for environmental and sensing uncertainty, we extend  Linear Temporal Logic (LTL) by including sensor-based predicates allowing us to incorporate uncertainty and probabilistic satisfaction requirements directly into the task specification.
Revised:  To account for environmental and sensing uncertainty, we extend Linear Temporal Logic (LTL) , called co-safe LTL, by including perception-based atomic predicates allowing us to incorporate uncertainty and probabilistic satisfaction requirements directly into the task specification.
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