wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our approach is validated on the YCB-Video dataset which is widely adopted in the Computer Vision and Robotics community, demonstrating that we can achieve and even surpass performance of the state-of-the-art, with a significant reduction ({\sim}6\times) of the training time.
Revised:  Our approach is validated on the YCB-Video dataset which is widely adopted in the computer vision and robotics community, demonstrating that we can achieve and even surpass performance of the state-of-the-art, with a significant reduction ({\sim}6\times) of the training time.
Edit intent: fluency
Original:  In this paper, we present a model that aggregates and combines evidence from multiple passages to generate question-answer pairs . Particularly, our model reads a large number of passages to find as many interpretations as possible.
Revised:  In this paper, we present a model that aggregates and combines evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs . Particularly, our model reads a large number of passages to find as many interpretations as possible.
Edit intent: meaning-changed
Original:  In addition, we propose a novel round-trip prediction approach to  generate additional interpretations that our model fails to find in the first pass, and then verify and filter out the incorrect question-answer pairs to arrive at the final disambiguated output.
Revised:  In addition, we propose a novel round-trip prediction approach to iteratively generate additional interpretations that our model fails to find in the first pass, and then verify and filter out the incorrect question-answer pairs to arrive at the final disambiguated output.
Edit intent: clarity
Original:  On the recently introduced AmbigQA open-domain question answering dataset, our model, named Refuel, achieves a new state-of-the-art , outperforming the previous best model by a large margin. We also conduct comprehensive analyses to validate the effectiveness of our proposed round-trip prediction .
Revised:  On the recently introduced AmbigQA open-domain question answering dataset, our model, named Refuel, achieves a new state-of-the-art performance on the AmbigQA dataset, and shows competitive performance on NQ-Open and TriviaQA. The proposed round-trip prediction .
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Original:  Based on experiments on the publicly available LibriSpeechMix dataset, we show that HEAT can achieve better accuracy compared with PIT, and the SURT model with 120 milliseconds algorithmic latency constraint compares favorably with the offline sequence-to-sequence based baseline model in terms of accuracy.
Revised:  Based on experiments on the publicly available LibriSpeechMix dataset, we show that HEAT can achieve better accuracy compared with PIT, and the SURT model with 150 milliseconds algorithmic latency constraint compares favorably with the offline sequence-to-sequence based baseline model in terms of accuracy.
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Original:  Experiments demonstrate that our model is able to generalize well in previously unseen problem instances, and  it achieves a 47 \% improvement over the benchmark success rate, even in very large-scale instances that are 100x larger than the training instances.
Revised:  Experiments demonstrate that our model is able to generalize well in previously unseen problem instances, and that it achieves a 47 \% improvement over the benchmark success rate, even in very large-scale instances that are 100x larger than the training instances.
Edit intent: fluency
Original: Traditional symbolic reasoning engines, while attractive for their precision and explicability, have a few major drawbacks: the use of brittle inference procedures that rely on exact matching /unification of logical terms, an inability to deal with uncertainty, and the need for a precompiled rule-base of knowledge (the "knowledge acquisition" problem).
Revised: Traditional symbolic reasoning engines, while attractive for their precision and explicability, have a few major drawbacks: the use of brittle inference procedures that rely on exact matching (unification) of logical terms, an inability to deal with uncertainty, and the need for a precompiled rule-base of knowledge (the "knowledge acquisition" problem).
Edit intent: fluency
Original:  These issues are particularly severe for the Natural Language Understanding (NLU) task, where we often use implicit background knowledge to understand and reason about text, resort to imperfect/ fuzzy alignment of concepts and relations during reasoning, and constantly deal with ambiguity in representations.
Revised:  These issues are particularly severe for the Natural Language Understanding (NLU) task, where we often use implicit background knowledge to understand and reason about text, resort to fuzzy alignment of concepts and relations during reasoning, and constantly deal with ambiguity in representations.
Edit intent: clarity
Original: For an unweighted graph on kterminals , Kratsch and Wahlstr\"om constructed a vertex sparsifier  with O(k^3) vertices via the theory of representative families on matroids.
Revised: Let G be a graph and S, T \subseteq V(G) be (possibly overlapping) sets of terminals, |S|=|T|=k. We are interested in computing a vertex sparsifier for terminal cuts in G, i.e., a graph H on a smallest possible number of vertices, where S \cup T \subseteq V(H) and such that for every A \subseteq S and B \subseteq T the size of a minimum (A,B)-vertex cut is the same in G as in H. We assume that our graphs are unweighted and that terminals may be part of the min-cut. In previous work , Kratsch and Wahlstr\"om constructed a vertex sparsifier with O(k^3) vertices via the theory of representative families on matroids.
Edit intent: meaning-changed
Original: For an unweighted graph on kterminals , Kratsch and Wahlstr\"om constructed a vertex sparsifier  with O(k^3) vertices via the theory of representative families on matroids.
Revised: For an unweighted graph on kterminals , Kratsch and Wahlstr\"om (FOCS 2012/JACM 2020) used connections to matroid theory to show that a vertex sparsifier with O(k^3) vertices via the theory of representative families on matroids.
Edit intent: meaning-changed
Original: For an unweighted graph on kterminals , Kratsch and Wahlstr\"om constructed a vertex sparsifier  with O(k^3) vertices via the theory of representative families on matroids.
Revised: For an unweighted graph on kterminals , Kratsch and Wahlstr\"om constructed a vertex sparsifier H with O(k^3) vertices via the theory of representative families on matroids.
Edit intent: meaning-changed
Original: For an unweighted graph on kterminals , Kratsch and Wahlstr\"om constructed a vertex sparsifier  with O(k^3) vertices via the theory of representative families on matroids. Since their breakthrough result in 2012, no improvement upon the O(k^3) bound has been found .
Revised: For an unweighted graph on kterminals , Kratsch and Wahlstr\"om constructed a vertex sparsifier with O(k^3) vertices can be computed in randomized polynomial time, even for arbitrary digraphs G. However, since then, no improvements on the size O(k^3) bound has been found .
Edit intent: meaning-changed
Original:  In this paper, we interpret Kratsch and Wahlstr\"om's result through the lens of Bollob\'as's Two-Families Theorem from extremal combinatorics .
Revised:  In this paper, we interpret Kratsch and Wahlstr\"om's result through the lens of Bollob\'as's Two-Families Theorem in extremal combinatorics and introduce the use of total orderings into Kratsch and Wahlstr\"om's methods .
Edit intent: meaning-changed
Original:  This new perspective allows us to close the gap for directed acyclic graphs and obtain a tight bound of \Theta(k^2) . Central to our approach is the concept of skew-symmetry from extremal combinatorics, and we derive a similar theory for the representation of skew-symmetric families that may have future applications).
Revised:  This new perspective allows us to construct a sparsifier H of \Theta(k^2) . Central to our approach is the concept of skew-symmetry from extremal combinatorics, and we derive a similar theory for the representation of skew-symmetric families that may have future applications).
Edit intent: meaning-changed
Original:  This new perspective allows us to close the gap for directed acyclic graphs and obtain a tight bound of \Theta(k^2) . Central to our approach is the concept of skew-symmetry from extremal combinatorics, and we derive a similar theory for the representation of skew-symmetric families that may have future applications).
Revised:  This new perspective allows us to close the gap for directed acyclic graphs and obtain a tight bound of \Theta(k^2) vertices for the case that G is a DAG. We also show how to compute H in time near-linear in the size of G, improving on the previous O(n^{\omega+1).
Edit intent: meaning-changed
Original:  In particular, within the framework of fast auxiliary space preconditioning (FASP), we develop optimal multilevel preconditioners for the Laplace-Beltrami type equation discretized by surface Lagrange, nonconforming linear, and discontinuous Galerkin elements.
Revised:  In particular, within the framework of fast auxiliary space preconditioning (FASP), we develop efficient and user-friendly multilevel preconditioners for the Laplace-Beltrami type equation discretized by surface Lagrange, nonconforming linear, and discontinuous Galerkin elements.
Edit intent: clarity
Original:  In particular, within the framework of fast auxiliary space preconditioning (FASP), we develop optimal multilevel preconditioners for the Laplace-Beltrami type equation discretized by surface Lagrange, nonconforming linear, and discontinuous Galerkin elements.
Revised:  In particular, within the framework of fast auxiliary space preconditioning (FASP), we develop optimal multilevel preconditioners for the Laplace--Beltrami type equation discretized by surface Lagrange, nonconforming linear, and discontinuous Galerkin elements.
Edit intent: fluency
Original:  In particular, within the framework of fast auxiliary space preconditioning (FASP), we develop optimal multilevel preconditioners for the Laplace-Beltrami type equation discretized by surface Lagrange, nonconforming linear, and discontinuous Galerkin elements.
Revised:  In particular, within the framework of fast auxiliary space preconditioning (FASP), we develop optimal multilevel preconditioners for the Laplace-Beltrami type equation discretized by Lagrange, nonconforming linear, and discontinuous Galerkin elements.
Edit intent: clarity
Original:  The proposed process for detecting non-overlapping ellipses outperformed two established edge chaining/following methods, proposed by Fornaciari and Patraucean, in images.
Revised:  The proposed process for detecting non-overlapping ellipses outperformed two established edge chaining/following methods, proposed by Fornaciari , Patraucean, and Pangiotakis, in images.
Edit intent: meaning-changed
Original:  The proposed process for detecting non-overlapping ellipses outperformed three established methods, proposed by Fornaciari, Patraucean, and Pangiotakis, in images .
Revised:  The proposed process for detecting non-overlapping ellipses achieved F-measure of 99.3\% on real images, compared to F-measures of 42.4\%, 65.6\%, and 59.2\%, obtained using the methods of Fornaciari, Patraucean, and Pangiotakis, in images .
Edit intent: meaning-changed
Original: We explore the possibility of combining Markov chain Monte Carlo (MCMC) with the rare event sampling approach known as ``splitting and killing. " First, we prove there is a unique splitting method providing asymptotically consistent estimates when combined with MCMC.
Revised: We explore the possibility of combining Markov chain Monte Carlo (MCMC) estimates of rare event probabilities, and we make three contributions. First, we prove there is a unique splitting method providing asymptotically consistent estimates when combined with MCMC.
Edit intent: meaning-changed
Original: In typical online matching problems, the goal is to maximize the number of matches. This paper studies online bipartite matching from the perspective of group-level fairness, and the goal is to balance the number of matches made across different groups of online nodes. We assume that an online node's group belongings are revealed upon arrival, and measure performance based on the group with the smallest fraction of its nodes matched at the end. We distinguish between two different objectives: long-run fairness, where the algorithm is audited for its fairness over many realizations from the same instance;
Revised: We consider the allocation of limited resources to heterogeneous customers who arrive in an online fashion. We would like to allocate the resources "fairly", so that no group of customers is marginalized in terms of their overall service rate. We study whether this is possible to do so in an online fashion, and if so, what a good online allocation policy is. We model this problem using online bipartite matching under stationary arrivals, a fundamental model in the literature typically studied under the objective of maximizing the total number of customers served. We instead study the objective of maximizing the minimum service rate across all groups, and propose two notions of fairness: long-run fairness, where the algorithm is audited for its fairness over many realizations from the same instance;
Edit intent: meaning-changed
Original:  We distinguish between two different objectives: long-run fairness, where the algorithm is audited for its fairness over many realizations from the same instance; and short-run fairness, where the algorithm is audited for its fairness on a single realization.
Revised:  We distinguish between two different objectives: long-run and short-run fairness, where the algorithm is audited for its fairness on a single realization.
Edit intent: clarity
Original:  and short-run fairness, where the algorithm is audited for its fairness on a single realization. We focus on the known-IID model of online arrivals and analyze, under both objectives, the competitive ratio for two classes of algorithms: non-rejecting algorithms, which must match an online node as long as a neighbor is available; and general online algorithms , which are allowed to reject online nodes to improve fairness .
Revised:  and short-run . For these fairness objectives, we analyze how competitive online algorithms can be, in comparison to offline algorithms which know the sequence of demands in advance .
Edit intent: clarity
Original:  For long-run fairness, we analyze two online algorithms (sampling and pooling ) which establish asymptotic optimality across many different regimes (no specialized supplies, no rare demand types, or imbalanced supply/demand).
Revised:  For long-run fairness, we propose two online heuristics (Sampling and Pooling ) which establish asymptotic optimality across many different regimes (no specialized supplies, no rare demand types, or imbalanced supply/demand).
Edit intent: clarity
Original:  For long-run fairness, we analyze two online algorithms (sampling and pooling ) which establish asymptotic optimality across many different regimes (no specialized supplies, no rare demand types, or imbalanced supply/demand).
Revised:  For long-run fairness, we analyze two online algorithms (sampling and pooling ) which establish asymptotic optimality in different regimes (no specialized supplies, no rare demand types, or imbalanced supply/demand).
Edit intent: clarity
Original:  we also derive a probabilistic rejection algorithm which is asymptotically optimal as total demandincreases .
Revised:  we also derive a probabilistic rejection algorithm which is asymptotically optimal in the total demand. Depending on the overall scarcity of resources, either our Sampling or Pooling heuristics could be desirable. The most difficult situation for online allocation occurs when the total supply is just enough to serve the total demand. We simulate our algorithms on a public ride-hailing dataset, which both demonstrates the efficacy of our heuristics and validates our managerial insights .
Edit intent: meaning-changed
Original:  The main goal is to support the fight against negative infodemic by (1) contributing a diverse set of curated relevant datasets (2) offering relevant areas to study using the datasets (3) demonstrating how relevant datasets, strategies and state-of-the-art IT tools can be leveraged in managing the pandemic.
Revised:  Consequently, the main goal is to support the fight against negative infodemic by (1) contributing a diverse set of curated relevant datasets (2) offering relevant areas to study using the datasets (3) demonstrating how relevant datasets, strategies and state-of-the-art IT tools can be leveraged in managing the pandemic.
Edit intent: coherence
Original:  The main goal is to support the fight against negative infodemic by (1) contributing a diverse set of curated relevant datasets (2) offering relevant areas to study using the datasets (3) demonstrating how relevant datasets, strategies and state-of-the-art IT tools can be leveraged in managing the pandemic.
Revised:  The main goal is to support the fight against negative infodemic by (1) contributing a diverse set of curated relevant datasets (2) recommending relevant areas to study using the datasets (3) demonstrating how relevant datasets, strategies and state-of-the-art IT tools can be leveraged in managing the pandemic.
Edit intent: clarity
Original:  The main goal is to support the fight against negative infodemic by (1) contributing a diverse set of curated relevant datasets (2) offering relevant areas to study using the datasets (3) demonstrating how relevant datasets, strategies and state-of-the-art IT tools can be leveraged in managing the pandemic.
Revised:  The main goal is to support the fight against negative infodemic by (1) contributing a diverse set of curated relevant datasets (2) offering relevant areas to study using the datasets (3) discussion on how relevant datasets, strategies and state-of-the-art IT tools can be leveraged in managing the pandemic.
Edit intent: clarity
Original:  The new stability condition and the derived MPCS overcome the difficulties arising in the existing terminal weight based MPC framework, including the need of searching a suitable terminal weight , possible poor performance caused by an inappropriate terminal weight and not being horizon dependent .
Revised:  The new stability condition and the derived MPCS overcome the difficulties arising in the existing terminal weight based MPC framework, including the need of searching a suitable terminal weight and possible poor performance caused by an inappropriate terminal weight and not being horizon dependent .
Edit intent: fluency
Original:  The new stability condition and the derived MPCS overcome the difficulties arising in the existing terminal weight based MPC framework, including the need of searching a suitable terminal weight , possible poor performance caused by an inappropriate terminal weight and not being horizon dependent .
Revised:  The new stability condition and the derived MPCS overcome the difficulties arising in the existing terminal weight based MPC framework, including the need of searching a suitable terminal weight , possible poor performance caused by an inappropriate terminal weight .
Edit intent: coherence
Original:  Given a visual observation of the scene and the gripper, AdaGrasp infers the possible grasping poses and their grasp scores by computing the cross convolution between the shape encodings of the input gripper and scene.
Revised:  Given a visual observation of the scene and the gripper, AdaGrasp infers the possible grasp poses and their grasp scores by computing the cross convolution between the shape encodings of the input gripper and scene.
Edit intent: fluency
Original:  Given a visual observation of the scene and the gripper, AdaGrasp infers the possible grasping poses and their grasp scores by computing the cross convolution between the shape encodings of the input gripper and scene.
Revised:  Given a visual observation of the scene and the gripper, AdaGrasp infers the possible grasping poses and their grasp scores by computing the cross convolution between the shape encodings of the gripper and scene.
Edit intent: clarity
Original:  Extensive experiments demonstrate that our proposed method achieves a significant improvement in sample efficiency on various complex continuous robotic control problems in MuJoCo and outperforms the advanced model-based / model-free RL methods  .
Revised:  Extensive experiments demonstrate that our proposed method achieves a significant improvement in sample efficiency on various complex continuous robotic control problems in MuJoCo and outperforms the advanced model-based / model-free RL methods . The source code is available .
Edit intent: meaning-changed
Original:  Here we address how SOs emerge locally and can recruit large parts of the brain . A whole-brain model that produces bistable states and adaptation-induced SOs is fit to fMRI and EEG datausing a multi-objective evolutionary algorithm .
Revised:  Here we address how SOs emerge locally and can recruit large parts of the brain using a whole-brain model that produces bistable states and adaptation-induced SOs is fit to fMRI and EEG datausing a multi-objective evolutionary algorithm .
Edit intent: coherence
Original: Motion planning is a fundamental problem in robotics and machine perception.Sampling-based planners find accurate solutions by exhaustively exploring the space, but are inefficient and tend to produce jerky motions. Optimization and learning-based planners are more efficient and produce smooth trajectories. However, a significant hurdle that these approaches face is constructing a differentiable cost function that simultaneously minimizes path length and avoids collisions. These two objectives are conflicting by nature -- path length is continuous and well-behaved, but collisions are discrete non-differentiable events. Reconciling these terms has been a significant challenge in optimization-based motion planning. The main contribution of this paper is a novel cost function that guarantees collision-free shortest paths are found at its minimum.
Revised: We demonstrate that challenging shortest path problems can be solved via direct spline regression from a neural network, trained in an unsupervised manner (i.e. without requiring ground truth optimal paths for training). To achieve this, we derive a geometry-dependent optimal cost function whose minima guarantees collision-free shortest paths are found at its minimum.
Edit intent: meaning-changed
Original:  The main contribution of this paper is a novel cost function that guarantees collision-free shortest paths are found at its minimum. We show that our approach works seamlessly with RGBD input and predicts high-quality paths in 2D, 3D, and 6 DoF robotic manipulator settings. Our method also reduces training and inference time compared to existing approaches, in some cases by orders of magnitude .
Revised:  The main contribution of this paper is a novel cost function that guarantees collision-free solutions. Our method beats state-of-the-art supervised learning baselines for shortest path planning, with a much more scalable training pipeline, and a significant speedup in inference time .
Edit intent: clarity
Original:  However, most of the existing deep learning-based methods either assume strong physics prior, or depend on specific initial and boundary conditions, or require data in dense regular grid making them inapt for modeling unknown PDEs from sparsely-observed data.
Revised:  However, most of the existing deep learning methods for modeling physical dynamics either focus on solving known PDEs or require data in dense regular grid making them inapt for modeling unknown PDEs from sparsely-observed data.
Edit intent: clarity