wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To address this bottleneck, we prove a formal equivalence between the well-studied Dominance Product problem and the problem of computing the WMG. We prove a similar connection between the so called Dominating Pairs problem and the problem of verifying whether a given candidate is a possible winner .
Revised:  To address this bottleneck, we prove a formal equivalence between the well-studied Dominance Product problem and the problem of computing the WMG. We prove a similar connection between the so called Dominating Pairs problem and the problem of finding a winner in the Schulze method .
Edit intent: meaning-changed
Original: The collaboration of unmanned aerial vehicles (UAVs), also known as aerial swarm, has become a popular research topic for its practicality and flexibility in plenty of scenarios. However, one of the most fundamental components for autonomous aerial swarm systems in GPS-denied areas, the robust decentralized relative state estimation, remains to be an extremely challenging research topic.
Revised: The decentralized state estimation is one of the most fundamental components for autonomous aerial swarm systems in GPS-denied areas, the robust decentralized relative state estimation, remains to be an extremely challenging research topic.
Edit intent: coherence
Original:  In order to address this research niche, the Omni-swarm, an aerial swarm system with decentralized Omni-directional visual-inertial-UWB state estimation , which features robustness, accuracy, and global consistency, is proposed in this paper.
Revised:  To address this research niche, the Omni-swarm, an aerial swarm system with decentralized Omni-directional visual-inertial-UWB state estimation , which features robustness, accuracy, and global consistency, is proposed in this paper.
Edit intent: coherence
Original:  In order to address this research niche, the Omni-swarm, an aerial swarm system with decentralized Omni-directional visual-inertial-UWB state estimation , which features robustness, accuracy, and global consistency, is proposed in this paper.
Revised:  In order to address this research niche, the Omni-swarm, a decentralized omnidirectional visual-inertial-UWB state estimation , which features robustness, accuracy, and global consistency, is proposed in this paper.
Edit intent: clarity
Original:  In order to address this research niche, the Omni-swarm, an aerial swarm system with decentralized Omni-directional visual-inertial-UWB state estimation , which features robustness, accuracy, and global consistency, is proposed in this paper.
Revised:  In order to address this research niche, the Omni-swarm, an aerial swarm system with decentralized Omni-directional visual-inertial-UWB state estimation system for the aerial swarm is proposed in this paper.
Edit intent: clarity
Original:  We introduce a map-based localization method using deep learning tools to perform relative localization and re-localization within the aerial swarm while achieving the fast initializationand maintaining the global consistencyof state estimation. Furthermore, to overcome the sensors' visibility issues with the limited field of view (FoV), which severely affect the performance of the state estimation, Omni-directional sensors, including fisheye cameras and ultra-wideband (UWB) sensors, are adopted.
Revised:  In order to solve the issues of observability, complicated initialization, insufficient accuracy and lack of global consistency, we introduce an omnidirectional perception system as the front-end of the Omni-swarm, consisting of omnidirectional sensors, which includes stereo fisheye cameras and ultra-wideband (UWB) sensors, are adopted.
Edit intent: clarity
Original:  Furthermore, to overcome the sensors' visibility issues with the limited field of view (FoV), which severely affect the performance of the state estimation, Omni-directional sensors, including fisheye cameras and ultra-wideband (UWB) sensors, are adopted. The state estimation module, together with the planning and the control modules, is integrated on the aerial system with Omni-directional sensors to attain the Omni-swarm, and extensive experiments are performed to verify the validity and examine the performance of the proposed framework  .
Revised:  Furthermore, to overcome the sensors' visibility issues with the limited field of view (FoV), which severely affect the performance of the state estimation, Omni-directional sensors, including fisheye cameras and ultra-wideband (UWB) sensors, and extensive experiments are performed to verify the validity and examine the performance of the proposed framework .
Edit intent: coherence
Original:  The state estimation module, together with the planning and the control modules, is integrated on the aerial system with Omni-directional sensors to attain the Omni-swarm, and extensive experiments are performed to verify the validity and examine the performance of the proposed framework  .
Revised:  The state estimation module, together with the planning and the control modules, is integrated on the aerial system with Omni-directional sensors to attain the Omni-swarm, and algorithms, which includes fisheye visual inertial odometry (VIO), multi-drone map-based localization and visual object detector. A graph-based optimization and forward propagation working as the back-end of the .
Edit intent: meaning-changed
Original:  The state estimation module, together with the planning and the control modules, is integrated on the aerial system with Omni-directional sensors to attain the Omni-swarm, and extensive experiments are performed to verify the validity and examine the performance of the proposed framework  .
Revised:  The state estimation module, together with the planning and the control modules, is integrated on the aerial system with Omni-directional sensors to attain the Omni-swarm, and extensive experiments are performed to verify the validity and examine the performance of the proposed frameworkOmni-swarm .
Edit intent: fluency
Original:  The state estimation module, together with the planning and the control modules, is integrated on the aerial system with Omni-directional sensors to attain the Omni-swarm, and extensive experiments are performed to verify the validity and examine the performance of the proposed framework  .
Revised:  The state estimation module, together with the planning and the control modules, is integrated on the aerial system with Omni-directional sensors to attain the Omni-swarm, and extensive experiments are performed to verify the validity and examine the performance of the proposed framework to fuse the measurements from the front-end .
Edit intent: meaning-changed
Original:  According to the experiment result, the proposed framework can achieve centimeter-level relative state estimation accuracy while ensuring global consistency  .
Revised:  According to the experiment result, the proposed framework can achieve centimeter-level relative state estimation accuracy while ensuring global consistency . Moreover, supported by the Omni-swarm, inter-drone collision avoidance can be accomplished in a whole decentralized scheme without any external device, demonstrating the potential of Omni-swarm to be the foundation of autonomous aerial swarm flights in different scenarios .
Edit intent: meaning-changed
Original:  Specifically, we first propose a Global-guided Correlation Estimation (GCE) to generate feature correlation maps of local features and global features, which help to localize the high-and low-correlation regions for identifying the same person.
Revised:  Specifically, we first propose a Global-guided Correlation Estimation (GCE) to generate feature correlation maps of local features and global features, which help to localize the high- and low-correlation regions for identifying the same person.
Edit intent: fluency
Original:  Experimental results on the BraTS 2019 dataset show that TransBTS outperforms state-of-the-art  methods for brain tumor segmentation on 3D MRI scans.
Revised:  Extensive experimental results on both BraTS 2019 dataset show that TransBTS outperforms state-of-the-art methods for brain tumor segmentation on 3D MRI scans.
Edit intent: meaning-changed
Original:  Experimental results on the BraTS 2019 dataset show that TransBTS outperforms state-of-the-art  methods for brain tumor segmentation on 3D MRI scans.
Revised:  Experimental results on the BraTS 2019 and 2020 datasets show that TransBTS outperforms state-of-the-art methods for brain tumor segmentation on 3D MRI scans.
Edit intent: meaning-changed
Original:  Experimental results on the BraTS 2019 dataset show that TransBTS outperforms state-of-the-art  methods for brain tumor segmentation on 3D MRI scans.
Revised:  Experimental results on the BraTS 2019 dataset show that TransBTS achieves comparable or higher results than previous state-of-the-art methods for brain tumor segmentation on 3D MRI scans.
Edit intent: meaning-changed
Original:  Experimental results on the BraTS 2019 dataset show that TransBTS outperforms state-of-the-art  methods for brain tumor segmentation on 3D MRI scans.
Revised:  Experimental results on the BraTS 2019 dataset show that TransBTS outperforms state-of-the-art 3D methods for brain tumor segmentation on 3D MRI scans.
Edit intent: meaning-changed
Original:  While the policy update is done with the inferred, potentially dense reward function, the original sparse reward is used to provide a self-supervisory signal for the reward update by serving as an ordering over the observed trajectories.
Revised:  While the policy update is performed with the inferred, potentially dense reward function, the original sparse reward is used to provide a self-supervisory signal for the reward update by serving as an ordering over the observed trajectories.
Edit intent: clarity
Original:  Experimental results on several sparse-reward environments demonstrate that the proposed algorithm is not only significantly more sample efficient than the state-of-the-art baseline, but also achieves a similar sample efficiency to MDPs that use hand-designed dense reward functions.
Revised:  Experimental results on several sparse-reward environments demonstrate that the proposed algorithm is not only significantly more sample efficient than the state-of-the-art reinforcement learning baseline but also achieves a similar sample efficiency to MDPs that use hand-designed dense reward functions.
Edit intent: meaning-changed
Original:  Experimental results on several sparse-reward environments demonstrate that the proposed algorithm is not only significantly more sample efficient than the state-of-the-art baseline, but also achieves a similar sample efficiency to MDPs that use hand-designed dense reward functions.
Revised:  Experimental results on several sparse-reward environments demonstrate that the proposed algorithm is not only significantly more sample efficient than the state-of-the-art baseline, but also achieves a similar sample efficiency to a baseline that uses hand-designed dense reward functions.
Edit intent: clarity
Original:  The proposed framework alternates between updating a policy and inferring a reward function. While the policy update is performed with the inferred, potentially dense reward function, the original sparse reward is used to provide a self-supervisory signal for the reward update by serving as an ordering over the observed trajectories .
Revised:  The proposed framework alternates between classification-based reward inference and policy update steps -- the original sparse reward is used to provide a self-supervisory signal for the reward update by serving as an ordering over the observed trajectories .
Edit intent: clarity
Original:  While the policy update is performed with the inferred, potentially dense reward function, the original sparse reward is used to provide a self-supervisory signal for the reward update by serving as an ordering over the observed trajectories . The proposed framework is based on the theory that altering the reward function does not affect the optimal policy of the original MDP as long as certain relations between the altered and the original reward are maintained.
Revised:  While the policy update is performed with the inferred, potentially dense reward function, the original sparse reward is used to provide a self-supervisory signal for reward inference by ranking trajectories that the agent observes, while the policy update is performed with the newly inferred, typically dense reward function. We introduce theory that shows that, under certain conditions, this alteration of the reward function does not affect the optimal policy of the original MDP as long as certain relations between the altered and the original reward are maintained.
Edit intent: meaning-changed
Original:  The proposed framework is based on the theory that altering the reward function does not affect the optimal policy of the original MDP as long as certain relations between the altered and the original reward are maintained. We name the proposed framework ClAssification-based Reward Shaping (CaReS), since the altered reward is learned in a self-supervised manner using classifier-based reward inference . Experimental results on several sparse-reward environments demonstrate that  the proposed algorithm is not only significantly more sample efficient than the state-of-the-art reinforcement learning baseline but also achieves a similar sample efficiency to a baseline that uses hand-designed dense reward functions  .
Revised:  The proposed framework is based on the theory that altering the reward function does not affect the optimal policy of the original MDP , while potentially increasing learning speed significantly . Experimental results on several sparse-reward environments demonstrate that the proposed algorithm is not only significantly more sample efficient than the state-of-the-art reinforcement learning baseline but also achieves a similar sample efficiency to a baseline that uses hand-designed dense reward functions .
Edit intent: clarity
Original:  We name the proposed framework ClAssification-based Reward Shaping (CaReS), since the altered reward is learned in a self-supervised manner using classifier-based reward inference . Experimental results on several sparse-reward environments demonstrate that  the proposed algorithm is not only significantly more sample efficient than the state-of-the-art reinforcement learning baseline but also achieves a similar sample efficiency to a baseline that uses hand-designed dense reward functions  .
Revised:  We name the proposed framework ClAssification-based Reward Shaping (CaReS), since the altered reward is learned in a self-supervised manner using classifier-based reward inference . Experimental results on several sparse-reward environments demonstrate that , across multiple domains, the proposed algorithm is not only significantly more sample efficient than the state-of-the-art reinforcement learning baseline but also achieves a similar sample efficiency to a baseline that uses hand-designed dense reward functions .
Edit intent: clarity
Original:  We name the proposed framework ClAssification-based Reward Shaping (CaReS), since the altered reward is learned in a self-supervised manner using classifier-based reward inference . Experimental results on several sparse-reward environments demonstrate that  the proposed algorithm is not only significantly more sample efficient than the state-of-the-art reinforcement learning baseline but also achieves a similar sample efficiency to a baseline that uses hand-designed dense reward functions  .
Revised:  We name the proposed framework ClAssification-based Reward Shaping (CaReS), since the altered reward is learned in a self-supervised manner using classifier-based reward inference . Experimental results on several sparse-reward environments demonstrate that the proposed algorithm is not only significantly more sample efficient than a standard RL baseline using sparse rewards, but, at times, also achieves similar sample efficiency to a baseline that uses hand-designed dense reward functions .
Edit intent: clarity
Original:  We name the proposed framework ClAssification-based Reward Shaping (CaReS), since the altered reward is learned in a self-supervised manner using classifier-based reward inference . Experimental results on several sparse-reward environments demonstrate that  the proposed algorithm is not only significantly more sample efficient than the state-of-the-art reinforcement learning baseline but also achieves a similar sample efficiency to a baseline that uses hand-designed dense reward functions  .
Revised:  We name the proposed framework ClAssification-based Reward Shaping (CaReS), since the altered reward is learned in a self-supervised manner using classifier-based reward inference . Experimental results on several sparse-reward environments demonstrate that the proposed algorithm is not only significantly more sample efficient than the state-of-the-art reinforcement learning baseline but also achieves a similar sample efficiency compared to when hand-designed dense reward functions .
Edit intent: clarity
Original:  We name the proposed framework ClAssification-based Reward Shaping (CaReS), since the altered reward is learned in a self-supervised manner using classifier-based reward inference . Experimental results on several sparse-reward environments demonstrate that  the proposed algorithm is not only significantly more sample efficient than the state-of-the-art reinforcement learning baseline but also achieves a similar sample efficiency to a baseline that uses hand-designed dense reward functions  .
Revised:  We name the proposed framework ClAssification-based Reward Shaping (CaReS), since the altered reward is learned in a self-supervised manner using classifier-based reward inference . Experimental results on several sparse-reward environments demonstrate that the proposed algorithm is not only significantly more sample efficient than the state-of-the-art reinforcement learning baseline but also achieves a similar sample efficiency to a baseline that uses hand-designed dense reward functions are used .
Edit intent: clarity
Original:  Our results include the following: For the pairwise spanner problem, in which the pairs of vertices to be spanned arrive online, we present an efficient randomized O(n^{4/5})-competitive algorithm for graphs with general lengths, where n is the number of vertices of the given graph. For graphs with uniform lengths, we give an efficient randomized O(n^{2/3+\epsilon})-competitive algorithm, and an efficient deterministic O(k^{1/2+\epsilon})-competitive algorithm, where k is the number of terminal pairs.
Revised:  Our results include the following: For the pairwise spanner problem, in which the pairs of vertices to be spanned arrive online, we present an efficient randomized O(n^{4/5})-competitive algorithm for graphs with general lengths, where n is the number of vertices . With uniform lengths, we give an efficient randomized O(n^{2/3+\epsilon})-competitive algorithm, and an efficient deterministic O(k^{1/2+\epsilon})-competitive algorithm, where k is the number of terminal pairs.
Edit intent: clarity
Original:    For the directed Steiner forest problem with uniform costs, in which the pairs of vertices to be connected arrive online, we present an efficient randomized O(n^{2/3 + \epsilon})-competitive algorithm.
Revised:  In the offline setting, the current best approximation ratio with uniform lengths is For the directed Steiner forest problem with uniform costs, in which the pairs of vertices to be connected arrive online, we present an efficient randomized O(n^{2/3 + \epsilon})-competitive algorithm.
Edit intent: meaning-changed
Original:    For the directed Steiner forest problem with uniform costs, in which the pairs of vertices to be connected arrive online, we present an efficient randomized O(n^{2/3 + \epsilon})-competitive algorithm.
Revised:  (n^{3/5 + \epsilon For the directed Steiner forest problem with uniform costs, in which the pairs of vertices to be connected arrive online, we present an efficient randomized O(n^{2/3 + \epsilon})-competitive algorithm.
Edit intent: meaning-changed
Original:  In the offline setting , the current best approximation ratio for pairwise spanners with uniform lengths and directed Steiner forests with uniform costs are both O(n^{ 3 / 5 + \epsilon}), due to Chlamtac, Dinitz, Kortsarz, and Laekhanukit (TALG 2020).
Revised:  In the offline version , the current best approximation ratio for pairwise spanners with uniform lengths and directed Steiner forests with uniform costs are both O(n^{ 3 / 5 + \epsilon}), due to Chlamtac, Dinitz, Kortsarz, and Laekhanukit (TALG 2020).
Edit intent: clarity
Original:  In the offline setting , the current best approximation ratio for pairwise spanners with uniform lengths and directed Steiner forests with uniform costs are both O(n^{ 3 / 5 + \epsilon}), due to Chlamtac, Dinitz, Kortsarz, and Laekhanukit (TALG 2020).
Revised:  In the offline setting , the current best approximation ratio with uniform costs is O(n^{ 3 / 5 + \epsilon}), due to Chlamtac, Dinitz, Kortsarz, and Laekhanukit (TALG 2020).
Edit intent: coherence
Original:  In the offline setting , the current best approximation ratio for pairwise spanners with uniform lengths and directed Steiner forests with uniform costs are both O(n^{ 3 / 5 + \epsilon}), due to Chlamtac, Dinitz, Kortsarz, and Laekhanukit (TALG 2020).
Revised:  In the offline setting , the current best approximation ratio for pairwise spanners with uniform lengths and directed Steiner forests with uniform costs are both O(n^{ 26 / 5 + \epsilon}), due to Chlamtac, Dinitz, Kortsarz, and Laekhanukit (TALG 2020).
Edit intent: meaning-changed
Original:  In the offline setting , the current best approximation ratio for pairwise spanners with uniform lengths and directed Steiner forests with uniform costs are both O(n^{ 3 / 5 + \epsilon}), due to Chlamtac, Dinitz, Kortsarz, and Laekhanukit (TALG 2020).
Revised:  In the offline setting , the current best approximation ratio for pairwise spanners with uniform lengths and directed Steiner forests with uniform costs are both O(n^{ 3 / 45 + \epsilon}), due to Chlamtac, Dinitz, Kortsarz, and Laekhanukit (TALG 2020).
Edit intent: meaning-changed
Original:  In the offline setting , the current best approximation ratio for pairwise spanners with uniform lengths and directed Steiner forests with uniform costs are both O(n^{ 3 / 5 + \epsilon}), due to Chlamtac, Dinitz, Kortsarz, and Laekhanukit (TALG 2020). We observe that a small modification of the online covering framework by Buchbinder and Naor implies a polynomial-time primal-dual approach with separation oracles, which a priori might perform exponentially many calls to the oracle .
Revised:  In the offline setting , the current best approximation ratio for pairwise spanners with uniform lengths and directed Steiner forests with uniform costs are both O(n^{ 3 / 5 + \epsilon}), due to Abboud and Bodwin (SODA 2018). A small modification of the online covering framework by Buchbinder and Naor implies a polynomial-time primal-dual approach with separation oracles, which a priori might perform exponentially many calls to the oracle .
Edit intent: meaning-changed
Original:  We observe that a small modification of the online covering framework by Buchbinder and Naor implies a polynomial-time primal-dual approach with separation oracles, which a priori might perform exponentially many calls to the oracle .
Revised:  We observe that a small modification of the online covering framework by Buchbinder and Naor implies a polynomial-time primal-dual approach with separation oracles, which a priori might perform exponentially many calls .
Edit intent: clarity
Original:  However, when relaxing the linear assumption with neural networks (e.g., Cox-MLP \mbox{%DIFAUXCMD katzman2018deepsurv,kvamme2019time , we lose the guaranteed coverage.
Revised:  However, when relaxing the linear assumption with neural networks (e.g., Cox-MLP (Katzman et al., 2018; Kvamme et al., 2019)) , we lose the guaranteed coverage.
Edit intent: clarity