wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Namely, if the input model supports efficient \ell_2-norm importance sampling, and given A \in C^{m\times n} with minimum singular value \sigma and  b \in C^m as input , we can output a description of an x  such that \|x -
Revised:  Namely, if the input model supports efficient \ell_2-norm importance sampling, and given A \in C^{m\times n} with minimum singular value \sigma and b \in C^m , we can output a description of an x such that \|x -
Edit intent: clarity
Original:  Namely, if the input model supports efficient \ell_2-norm importance sampling, and given A \in C^{m\times n} with minimum singular value \sigma and  b \in C^m as input , we can output a description of an x  such that \|x -
Revised:  Namely, if the input model supports efficient \ell_2-norm importance sampling, and given A \in C^{m\times n} with minimum singular value \sigma and b \in C^m as input , we can output a description of an x \in \mathbb{C such that \|x -
Edit intent: meaning-changed
Original:  A^+b\| \leq \varepsilon\|A^+b\| in \mathcal{O} %DIFDELCMD < \left(%%% \frac{\|A\|_{\mathrm{F}^6\|A\|^2}{\sigma^8\varepsilon^4}}%DIFDELCMD < \right) %%% \Big(}^6\|A\|^2}{\sigma^8\varepsilon^4}}\Big) time, improving on previous "quantum-inspired" algorithms in this line of research by a factor of \|A\|^{14}{\sigma^{14}\varepsilon^2} [Chia et al., STOC'20].
Revised:  A^+b\| \leq \varepsilon\|A^+b\| in \mathcal{O} %DIFDELCMD < \left(%%% }^6\|A\|^2}{\sigma^8\varepsilon^4}}%DIFDELCMD < \right) %%% \Big(}^6\|A\|^2}{\sigma^8\varepsilon^4}}\Big) time, improving on previous "quantum-inspired" algorithms in this line of research by a factor of \|A\|^{14}{\sigma^{14}\varepsilon^2} [Chia et al., STOC'20].
Edit intent: meaning-changed
Original:  A^+b\| \leq \varepsilon\|A^+b\| in \mathcal{O} %DIFDELCMD < \left(%%% \frac{\|A\|_{\mathrm{F}^6\|A\|^2}{\sigma^8\varepsilon^4}}%DIFDELCMD < \right) %%% \Big(}^6\|A\|^2}{\sigma^8\varepsilon^4}}\Big) time, improving on previous "quantum-inspired" algorithms in this line of research by a factor of \|A\|^{14}{\sigma^{14}\varepsilon^2} [Chia et al., STOC'20].
Revised:  A^+b\| \leq \varepsilon\|A^+b\| in \mathcal{O} %DIFDELCMD < \left(%%% \frac{\|A\|_{\mathrm{F}^6\|A\|^2}{\sigma^8\varepsilon^4}}%DIFDELCMD < \right) %%% \Big(\frac{\|A\|_{\mathrm{F}^6\|A\|^2}{\sigma^8\varepsilon^4}}\Big) time, improving on previous "quantum-inspired" algorithms in this line of research by a factor of \|A\|^{14}{\sigma^{14}\varepsilon^2} [Chia et al., STOC'20].
Edit intent: coherence
Original:  Our qualitative analysis illustrates cases where only the multimodal systems are able to translate correctly from English into gender-marked languages, as well as deal with differences in word order  such as adjective-noun placement between English and French.
Revised:  Our qualitative analysis illustrates cases where only the multimodal systems are able to translate correctly from English into gender-marked languages, as well as deal with differences in word order , such as adjective-noun placement between English and French.
Edit intent: fluency
Original:  In 2020, to reduce the domain gap between synthetic training and real test RGB images, the participants were provided 350K photorealistic trainining images generated by BlenderProc4BOP, a ~ new open-source and light-weight physically-based renderer (PBR) and procedural data generator.
Revised:  In 2020, to reduce the domain gap between synthetic training and real test RGB images, the participants were provided 350K photorealistic training images generated by BlenderProc4BOP, a ~ new open-source and light-weight physically-based renderer (PBR) and procedural data generator.
Edit intent: fluency
Original:  In 2020, to reduce the domain gap between synthetic training and real test RGB images, the participants were provided 350K photorealistic trainining images generated by BlenderProc4BOP, a ~ new open-source and light-weight physically-based renderer (PBR) and procedural data generator.
Revised:  In 2020, to reduce the domain gap between synthetic training and real test RGB images, the participants were provided 350K photorealistic trainining images generated by BlenderProc4BOP, a new open-source and light-weight physically-based renderer (PBR) and procedural data generator.
Edit intent: fluency
Original:  Although the top-performing methods rely on RGB-D image channels, strong results were achieved when only RGB channels were used at both training and test time -- out of 26 evaluated methods, the third method was trained on RGB channels of PBR and real images, while the fifth was trained on PBR images only.
Revised:  Although the top-performing methods rely on RGB-D image channels, strong results were achieved when only RGB channels were used at both training and test time - out of the 26 evaluated methods, the third method was trained on RGB channels of PBR and real images, while the fifth was trained on PBR images only.
Edit intent: fluency
Original:  Although the top-performing methods rely on RGB-D image channels, strong results were achieved when only RGB channels were used at both training and test time -- out of 26 evaluated methods, the third method was trained on RGB channels of PBR and real images, while the fifth was trained on PBR images only.
Revised:  Although the top-performing methods rely on RGB-D image channels, strong results were achieved when only RGB channels were used at both training and test time -- out of 26 evaluated methods, the third method was trained on RGB channels of PBR and real images, while the fifth on RGB channels of PBR images only.
Edit intent: fluency
Original:  We achieve 8.3 x reduction in run-time memory footprints and 3.5 x speed up (Galaxy N10+) such that our proposed compression strategy enables efficient implementation for on-device NMT.
Revised:  We achieve 8.3 \times reduction in run-time memory footprints and 3.5 x speed up (Galaxy N10+) such that our proposed compression strategy enables efficient implementation for on-device NMT.
Edit intent: meaning-changed
Original:  We achieve 8.3 x reduction in run-time memory footprints and 3.5 x speed up (Galaxy N10+) such that our proposed compression strategy enables efficient implementation for on-device NMT.
Revised:  We achieve 8.3 x reduction in run-time memory footprints and 3.5 \times speed up (Galaxy N10+) such that our proposed compression strategy enables efficient implementation for on-device NMT.
Edit intent: meaning-changed
Original: Leveraging line features to improve location accuracy of point-based visual-inertial SLAM (VINS) is gaining importance as they provide additional constraint of scene structureregularity, however, real-time performance  has not been focused .
Revised: Leveraging line features to improve localization accuracy of point-based visual-inertial SLAM (VINS) is gaining importance as they provide additional constraint of scene structureregularity, however, real-time performance has not been focused .
Edit intent: clarity
Original: Leveraging line features to improve location accuracy of point-based visual-inertial SLAM (VINS) is gaining importance as they provide additional constraint of scene structureregularity, however, real-time performance  has not been focused .
Revised: Leveraging line features to improve location accuracy of point-based visual-inertial SLAM (VINS) is gaining interest as they provide additional constraint of scene structureregularity, however, real-time performance has not been focused .
Edit intent: clarity
Original: Leveraging line features to improve location accuracy of point-based visual-inertial SLAM (VINS) is gaining importance as they provide additional constraint of scene structureregularity, however, real-time performance  has not been focused .
Revised: Leveraging line features to improve location accuracy of point-based visual-inertial SLAM (VINS) is gaining importance as they provide additional constraints on scene structure. However, real-time performance has not been focused .
Edit intent: fluency
Original: Leveraging line features to improve location accuracy of point-based visual-inertial SLAM (VINS) is gaining importance as they provide additional constraint of scene structureregularity, however, real-time performance  has not been focused .
Revised: Leveraging line features to improve location accuracy of point-based visual-inertial SLAM (VINS) is gaining importance as they provide additional constraint of scene structureregularity, however, real-time performance when incorporating line features in VINS has not been focused .
Edit intent: meaning-changed
Original: Leveraging line features to improve location accuracy of point-based visual-inertial SLAM (VINS) is gaining importance as they provide additional constraint of scene structureregularity, however, real-time performance  has not been focused .
Revised: Leveraging line features to improve location accuracy of point-based visual-inertial SLAM (VINS) is gaining importance as they provide additional constraint of scene structureregularity, however, real-time performance has not been addressed .
Edit intent: clarity
Original:  This paper presents PL-VINS, a real-time optimization-based monocular VINS method with point and line  , developed based on  state-of-the-art point-based VINS-Mono vins.
Revised:  This paper presents PL-VINS, a real-time optimization-based monocular VINS method with point and line features , developed based on state-of-the-art point-based VINS-Mono vins.
Edit intent: meaning-changed
Original:  This paper presents PL-VINS, a real-time optimization-based monocular VINS method with point and line  , developed based on  state-of-the-art point-based VINS-Mono vins.
Revised:  This paper presents PL-VINS, a real-time optimization-based monocular VINS method with point and line , developed based on the state-of-the-art point-based VINS-Mono vins.
Edit intent: fluency
Original:  Observe that current works use  LSD lsd algorithm to extract lines, however, the LSD is designed for scene shape representation instead of specific pose estimation problem, which becomes the bottleneck for the real-time performance due to its expensive cost.
Revised:  We observe that current works use LSD lsd algorithm to extract lines, however, the LSD is designed for scene shape representation instead of specific pose estimation problem, which becomes the bottleneck for the real-time performance due to its expensive cost.
Edit intent: coherence
Original:  Observe that current works use  LSD lsd algorithm to extract lines, however, the LSD is designed for scene shape representation instead of specific pose estimation problem, which becomes the bottleneck for the real-time performance due to its expensive cost.
Revised:  Observe that current works use the LSD lsd algorithm to extract lines, however, the LSD is designed for scene shape representation instead of specific pose estimation problem, which becomes the bottleneck for the real-time performance due to its expensive cost.
Edit intent: fluency
Original:  Observe that current works use  LSD lsd algorithm to extract lines, however, the LSD is designed for scene shape representation instead of specific pose estimation problem, which becomes the bottleneck for the real-time performance due to its expensive cost.
Revised:  Observe that current works use LSD lsd algorithm to extract line features; however, LSD is designed for scene shape representation instead of specific pose estimation problem, which becomes the bottleneck for the real-time performance due to its expensive cost.
Edit intent: clarity
Original:  Observe that current works use  LSD lsd algorithm to extract lines, however, the LSD is designed for scene shape representation instead of specific pose estimation problem, which becomes the bottleneck for the real-time performance due to its expensive cost.
Revised:  Observe that current works use LSD lsd algorithm to extract lines, however, the LSD is designed for scene shape representation instead of the pose estimation problem, which becomes the bottleneck for the real-time performance due to its expensive cost.
Edit intent: clarity
Original:  Observe that current works use  LSD lsd algorithm to extract lines, however, the LSD is designed for scene shape representation instead of specific pose estimation problem, which becomes the bottleneck for the real-time performance due to its expensive cost.
Revised:  Observe that current works use LSD lsd algorithm to extract lines, however, the LSD is designed for scene shape representation instead of specific pose estimation problem, which becomes the bottleneck for the real-time performance due to its high computational cost.
Edit intent: clarity
Original:  Further, by representing a line landmark with Pl\"{u}cker coordinate, the line reprojection residual is modeled as midpoint-to-line distance then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinate . Experiments in public EuRoc benchmark dataset show the location error of our method is down 12-16\\%DIF < compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization.
Revised:  Further, by representing space lines with the Pl\"{u}cker coordinate, the line reprojection residual is modeled as midpoint-to-line distance then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinate . Experiments in public EuRoc benchmark dataset show the location error of our method is down 12-16\\%DIF < compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization.
Edit intent: clarity
Original:  Further, by representing a line landmark with Pl\"{u}cker coordinate, the line reprojection residual is modeled as midpoint-to-line distance then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinate . Experiments in public EuRoc benchmark dataset show the location error of our method is down 12-16\\%DIF < compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization.
Revised:  Further, by representing a line landmark with Pl\"{u}cker coordinates, the residual error in line estimation is modeled in terms of the point-to-line distance, which is then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinate . Experiments in public EuRoc benchmark dataset show the location error of our method is down 12-16\\%DIF < compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization.
Edit intent: clarity
Original:  Further, by representing a line landmark with Pl\"{u}cker coordinate, the line reprojection residual is modeled as midpoint-to-line distance then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinate . Experiments in public EuRoc benchmark dataset show the location error of our method is down 12-16\\%DIF < compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization.
Revised:  Further, by representing a line landmark with Pl\"{u}cker coordinate, the line reprojection residual is modeled as midpoint-to-line distance then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinates . Experiments in public EuRoc benchmark dataset show the location error of our method is down 12-16\\%DIF < compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization.
Edit intent: fluency
Original:  Further, by representing a line landmark with Pl\"{u}cker coordinate, the line reprojection residual is modeled as midpoint-to-line distance then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinate . Experiments in public EuRoc benchmark dataset show the location error of our method is down 12-16\\%DIF < compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization.
Revised:  Further, by representing a line landmark with Pl\"{u}cker coordinate, the line reprojection residual is modeled as midpoint-to-line distance then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinate . Experiments in a public benchmark dataset show the location error of our method is down 12-16\\%DIF < compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization.
Edit intent: clarity
Original:  Further, by representing a line landmark with Pl\"{u}cker coordinate, the line reprojection residual is modeled as midpoint-to-line distance then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinate . Experiments in public EuRoc benchmark dataset show the location error of our method is down 12-16\\%DIF < compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization.
Revised:  Further, by representing a line landmark with Pl\"{u}cker coordinate, the line reprojection residual is modeled as midpoint-to-line distance then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinate . Experiments in public EuRoc benchmark dataset show that the localization error of our method is down 12-16\\%DIF < compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization.
Edit intent: fluency
Original:  Further, by representing a line landmark with Pl\"{u}cker coordinate, the line reprojection residual is modeled as midpoint-to-line distance then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinate . Experiments in public EuRoc benchmark dataset show the location error of our method is down 12-16\\%DIF < compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization.
Revised:  Further, by representing a line landmark with Pl\"{u}cker coordinate, the line reprojection residual is modeled as midpoint-to-line distance then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinate . Experiments in public EuRoc benchmark dataset show the location error of our method is 12-16\\%DIF < compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization.
Edit intent: clarity
Original: Open-domain multi-hop question answering (QA) requires to retrieve multiple supporting documents, some of which have little lexical overlap with the question and can only be located by iterative document retrieval. However , multi-step document retrieval often incurs more relevant but non-supporting documents, which dampens the downstream noise-sensitive reader module for answer extraction.
Revised: Open-domain multi-hop question answering (QA) are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered. Also , multi-step document retrieval often incurs more relevant but non-supporting documents, which dampens the downstream noise-sensitive reader module for answer extraction.
Edit intent: meaning-changed
Original:  To address this challenge , we propose Dynamic Document Reranking (DDR) to iteratively retrieve, rerank and filter documents, and adaptively determine when to stop the retrieval process.
Revised:  To address this challenge , we propose a unified QA framework to answer any-hop open-domain questions, which iteratively retrieves, reranks and filters documents, and adaptively determine when to stop the retrieval process.
Edit intent: meaning-changed
Original:  DDR employs an entity-linked document graph for multi-document interaction, which boosts up the retrieval performance. Experiments on HotpotQA full wiki setting show that our method achieves more than 7 points higher reranking performance over the previous best retrieval model, and also achieves state-of-the-art question answering performance on the official leaderboard .
Revised:  DDR employs an entity-linked document graph for multi-document interaction as the core of our iterative reranking framework. Our method consistently achieves performance comparable to or better than the state-of-the-art question answering performance on the official leaderboard .
Edit intent: clarity
Original:  Experiments on HotpotQA full wiki setting show that our method achieves more than 7 points higher reranking performance over the previous best retrieval model, and also achieves state-of-the-art question answering performance on the official leaderboard .
Revised:  Experiments on HotpotQA full wiki setting show that our method achieves more than 7 points higher reranking performance over the previous best retrieval model, and also achieves state-of-the-art on both single-hop and multi-hop open-domain QA datasets, including Natural Questions Open, SQuAD Open, and HotpotQA .
Edit intent: meaning-changed
Original:  However, a majority of planning and control algorithms reason about system dynamics rather than the predicted agent tracklets  that are commonly output by trajectory forecasting methods, which can hinder their integration.
Revised:  However, a majority of planning and control algorithms reason about system dynamics rather than the predicted agent tracklets (i.e., ordered sets of waypoints) that are commonly output by trajectory forecasting methods, which can hinder their integration.
Edit intent: meaning-changed
Original:  Conventional debiasing research mainly studies from the view of data representation, e.g. balancing data distribution or learning unbiased models and representations, ignoring the mechanism that how humans accomplish this task. Inspired by the role of the prefrontal cortex (PFC) in hierarchical reasoning , we analyze this problem from a novel cognition perspective: learning a hierarchical cognitive structure of the highly-biased relationships and navigating that hierarchy to locate the classes , making the tail classes receive more attention in a coarse-to-fine mode.
Revised:  Conventional debiasing research mainly studies from the view of data representation, e.g. balancing data distribution or learning unbiased models and representations, ignoring the correlations among the biased classes. In this work , we analyze this problem from a novel cognition perspective: learning a hierarchical cognitive structure of the highly-biased relationships and navigating that hierarchy to locate the classes , making the tail classes receive more attention in a coarse-to-fine mode.
Edit intent: clarity
Original:  Inspired by the role of the prefrontal cortex (PFC) in hierarchical reasoning , we analyze this problem from a novel cognition perspective: learning a hierarchical cognitive structure of the highly-biased relationships and navigating that hierarchy to locate the classes , making the tail classes receive more attention in a coarse-to-fine mode.
Revised:  Inspired by the role of the prefrontal cortex (PFC) in hierarchical reasoning , we analyze this problem from a novel cognition perspective: automatically building a hierarchical cognitive structure of the highly-biased relationships and navigating that hierarchy to locate the classes , making the tail classes receive more attention in a coarse-to-fine mode.
Edit intent: meaning-changed
Original:  Inspired by the role of the prefrontal cortex (PFC) in hierarchical reasoning , we analyze this problem from a novel cognition perspective: learning a hierarchical cognitive structure of the highly-biased relationships and navigating that hierarchy to locate the classes , making the tail classes receive more attention in a coarse-to-fine mode.
Revised:  Inspired by the role of the prefrontal cortex (PFC) in hierarchical reasoning , we analyze this problem from a novel cognition perspective: learning a hierarchical cognitive structure from the biased predictions and navigating that hierarchy to locate the classes , making the tail classes receive more attention in a coarse-to-fine mode.
Edit intent: clarity