wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This gives the first approximation algorithm for dD KS for d > 3. Furthermore, we provide a more sophisticated harmonic-based algorithm, which we call HGaP_k, that is T_{\infty}^{d-1}(1+\epsilon) -asymptotic-approximate for dD BP for the rotational case .
Revised:  This gives the first approximation algorithm for dD KS for d > 3. Furthermore, we provide a more sophisticated harmonic-based algorithm, which we call HGaP_k, having AAR T_{\infty}^{d-1}(1+\epsilon) -asymptotic-approximate for dD BP for the rotational case .
Edit intent: meaning-changed
Original:  This gives the first approximation algorithm for dD KS for d > 3. Furthermore, we provide a more sophisticated harmonic-based algorithm, which we call HGaP_k, that is T_{\infty}^{d-1}(1+\epsilon) -asymptotic-approximate for dD BP for the rotational case .
Revised:  This gives the first approximation algorithm for dD KS for d > 3. Furthermore, we provide a more sophisticated harmonic-based algorithm, which we call HGaP_k, that is T_{\infty}^{d-1}(1+\epsilon) .
Edit intent: coherence
Original:  Here we are given n sets of dD cuboidal items and we have to choose exactly one (resp. at most one for the knapsack variant) item from each set and then pack the chosen items.
Revised:  Here we are given n sets of d-dimensional cuboidal items and we have to choose exactly one (resp. at most one for the knapsack variant) item from each set and then pack the chosen items.
Edit intent: fluency
Original:  We propose a representation called "closed symmetry loop" ' (csl) from these insights, where the angle of relevant vectors is multiplied by the symmetry order and then generalize it to 6-DOF.
Revised:  We propose a representation called "closed symmetry loop" (csl) from these insights, where the angle of relevant vectors is multiplied by the symmetry order and then generalize it to 6-DOF.
Edit intent: fluency
Original:  The localization in indoor environment is challenging for that there may be many unreliable features on dynamic or repetitive objects. Such kind of reliability cannot be well learned by existing Convolutional Neural Network (CNN) based feature extractors. We propose a novel network, RaP-Net, which explicitly addresses feature invariability with a region-wise predictor, and combines it with a point-wise predictor to select reliable keypoints in an image .
Revised:  The localization in indoor environment is challenging for that there may be many unreliable features on dynamic objects or repetitive regions will disturb robust feature matching and thus, challenge indoor localization greatly. To conquer such an issue, we propose a novel network, RaP-Net, which explicitly addresses feature invariability with a region-wise predictor, and combines it with a point-wise predictor to select reliable keypoints in an image .
Edit intent: meaning-changed
Original:  We propose a novel network, RaP-Net, which explicitly addresses feature invariability with a region-wise predictor, and combines it with a point-wise predictor to select reliable keypoints in an image .
Revised:  We propose a novel network, RaP-Net, which explicitly addresses feature invariability with a region-wise predictor, and combines it with a point-wise reliability, and then extract features by considering both of them .
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Original:  Experimental results show that the proposed RaP-Net trained with the OpenLORIS-Location dataset significantly outperforms existing CNN-based keypoint extraction algorithms for indoor localization.
Revised:  Experimental results show that the proposed RaP-Net trained with the OpenLORIS-Location dataset achieves an excellent performance in the feature matching task and significantly outperforms state-of-the-arts feature algorithms in indoor localization.
Edit intent: meaning-changed
Original:  In this work, we propose to avoid manual annotation and to learn video question answering (VideoQA) from millions of readily-available narrated videos. We propose to automatically generate question-answer pairs from transcribed video narrations leveraging a state-of-the-art text transformer pipeline and obtain a new large-scale VideoQA training dataset .
Revised:  In this work, we propose to avoid manual annotation and to learn video question answering making use of automatic cross-modal supervision. We leverage a question generation transformer trained on text data and use it to generate question-answer pairs from transcribed video narrations leveraging a state-of-the-art text transformer pipeline and obtain a new large-scale VideoQA training dataset .
Edit intent: meaning-changed
Original:  We propose to automatically generate question-answer pairs from transcribed video narrations leveraging a state-of-the-art text transformer pipeline and obtain a new large-scale VideoQA training dataset .
Revised:  We propose to automatically generate question-answer pairs from transcribed video narrations . Given narrated videos, we then automatically generate the HowToVQA69M dataset with 69M video-question-answer triplets .
Edit intent: meaning-changed
Original:  To handle the open vocabulary of diverse answers in this dataset, we propose a training procedure based on a contrastive loss between a video-question multi-modal transformer and an answer embedding. We evaluate our model on the zero-shot VideoQA task and show excellent results, in particular for rare answers.
Revised:  To handle the open vocabulary of diverse answers in this dataset, we propose a training procedure based on a contrastive loss between a video-question multi-modal transformer and an answer transformer. We introduce the zero-shot VideoQA task and show excellent results, in particular for rare answers.
Edit intent: clarity
Original:  Furthermore, we demonstrate that finetuning our model on target datasets significantly outperforms the state of the art on MSRVTT-QA, MSVD-QA and ActivityNet-QA  .
Revised:  Furthermore, we demonstrate that finetuning our model on target datasets significantly outperforms the state of the art on MSRVTT-QA, MSVD-QA , ActivityNet-QA .
Edit intent: fluency
Original:  Furthermore, we demonstrate that finetuning our model on target datasets significantly outperforms the state of the art on MSRVTT-QA, MSVD-QA and ActivityNet-QA  .
Revised:  Furthermore, we demonstrate that finetuning our model on target datasets significantly outperforms the state of the art on MSRVTT-QA, MSVD-QA and ActivityNet-QA and How2QA .
Edit intent: meaning-changed
Original:  However, existing tools for querying, analyzing , and exploring data in cloud warehouses are either limited in facilitating iterative ad-hoc analysis or difficult to use for the largest segment of enterprise users, who have limited time, interest, or skills for writing queries .
Revised:  However, existing tools for analyzing data in CDWs are either limited in facilitating iterative ad-hoc analysis or difficult to use for the largest segment of enterprise users, who have limited time, interest, or skills for writing queries .
Edit intent: clarity
Original:  However, existing tools for querying, analyzing , and exploring data in cloud warehouses are either limited in facilitating iterative ad-hoc analysis or difficult to use for the largest segment of enterprise users, who have limited time, interest, or skills for writing queries .
Revised:  However, existing tools for querying, analyzing , and exploring data in cloud warehouses are either limited in ad-hoc analysis or difficult to use for the largest segment of enterprise users, who have limited time, interest, or skills for writing queries .
Edit intent: clarity
Original:  However, existing tools for querying, analyzing , and exploring data in cloud warehouses are either limited in facilitating iterative ad-hoc analysis or difficult to use for the largest segment of enterprise users, who have limited time, interest, or skills for writing queries .
Revised:  However, existing tools for querying, analyzing , and exploring data in cloud warehouses are either limited in facilitating iterative ad-hoc transformations or difficult to use for the largest segment of enterprise users, who have limited time, interest, or skills for writing queries .
Edit intent: clarity
Original:  However, existing tools for querying, analyzing , and exploring data in cloud warehouses are either limited in facilitating iterative ad-hoc analysis or difficult to use for the largest segment of enterprise users, who have limited time, interest, or skills for writing queries .
Revised:  However, existing tools for querying, analyzing , and exploring data in cloud warehouses are either limited in facilitating iterative ad-hoc analysis or difficult to use for business users, the largest user segment in enterprises .
Edit intent: clarity
Original:  Sigma Worksheet enables users to perform powerful interactive ad-hoc visual data analysis without writing queries. To thisend, it combines the notion of a multidimensional data cube with an expressive, spreadsheet-like query construction interface , in order to ease the generation of otherwise complex queries.
Revised:  Sigma Worksheet enables users to perform powerful interactive ad-hoc visual analysis of data in CDWs at scale. For this, Sigma Worksheet provides an accessible spreadsheet-like query construction interface , in order to ease the generation of otherwise complex queries.
Edit intent: meaning-changed
Original:  To thisend, it combines the notion of a multidimensional data cube with an expressive, spreadsheet-like query construction interface , in order to ease the generation of otherwise complex queries. The Worksheet interface borrows successful features from electronic spreadsheets to support the large segment of users familiar with that environment. We demonstrate the usefulness of Sigma Worksheet through two in-depth use casesand report on user feedback that we elicit through a 100-person survey followed by a semi-structured interview study with 70 participants.
Revised:  To thisend, it combines the notion of a multidimensional data cube with an expressive, spreadsheet-like interface for data analysis through direct manipulation. Sigma Worksheet dynamically constructs matching SQL queries from user interactions on this familiar interface, building on the versatility and expressivity of SQL. Sigma Worksheet executes constructed queries directly on CDWs, leveraging the superior characteristics of the new generation CDWs, including scalability. To evaluate Sigma Worksheet, we first demonstrate its expressivity through two real life use cases, cohort analysis and sessionization. We then measure the performance of the Worksheet generated queries with a set of experiments using the TPC-H benchmark. Results show the performance of our compiled SQL queries is comparable to that of the reference queries of the benchmark. Finally, to assess the usefulness of Sigma Worksheet in deployment, we elicit feedback through two in-depth use casesand report on user feedback that we elicit through a 100-person survey followed by a semi-structured interview study with 70 participants.
Edit intent: meaning-changed
Original:  We find that Sigma Worksheet provides an easier to use and learn interface for accessing, transforming, exploring, and analyzing data in cloud data warehouses, improving the visual data analysis and exploration experience for both expert and non-expert enterprise users.
Revised:  We find that Sigma Worksheet is easier to use and learn interface for accessing, transforming, exploring, and analyzing data in cloud data warehouses, improving the visual data analysis and exploration experience for both expert and non-expert enterprise users.
Edit intent: clarity
Original:  However, existing ZSID systems suffer from two limitations: 1) They are not good at modeling the relationship between seen and unseen intents , when the label names are given in the form of raw phrases or sentences . 2) They cannot effectively recognize unseen intents under the generalized intent detection (GZSID) setting.
Revised:  However, existing ZSID systems suffer from two limitations: 1) They are not good at modeling the relationship between seen and unseen intents . 2) They cannot effectively recognize unseen intents under the generalized intent detection (GZSID) setting.
Edit intent: coherence
Original:  To address this problem, we propose a class-transductive framework that utilizes unseen class labels to learn Disentangled Intent Representations ( DIR ). Specifically, we allow the model to predict unseen intents in the trainingstage , with the corresponding label names serving as input utterances.
Revised:  To address this problem, we propose a novel framework that utilizes unseen class labels to learn Disentangled Intent Representations ( DIR ). Specifically, we allow the model to predict unseen intents in the trainingstage , with the corresponding label names serving as input utterances.
Edit intent: clarity
Original:  To address this problem, we propose a class-transductive framework that utilizes unseen class labels to learn Disentangled Intent Representations ( DIR ). Specifically, we allow the model to predict unseen intents in the trainingstage , with the corresponding label names serving as input utterances.
Revised:  To address this problem, we propose a class-transductive framework that utilizes unseen class labels to learn Class-Transductive Intent Representations ( DIR ). Specifically, we allow the model to predict unseen intents in the trainingstage , with the corresponding label names serving as input utterances.
Edit intent: clarity
Original:  To address this problem, we propose a class-transductive framework that utilizes unseen class labels to learn Disentangled Intent Representations ( DIR ). Specifically, we allow the model to predict unseen intents in the trainingstage , with the corresponding label names serving as input utterances.
Revised:  To address this problem, we propose a class-transductive framework that utilizes unseen class labels to learn Disentangled Intent Representations ( CTIR ). Specifically, we allow the model to predict unseen intents in the trainingstage , with the corresponding label names serving as input utterances.
Edit intent: meaning-changed
Original:  To address this problem, we propose a class-transductive framework that utilizes unseen class labels to learn Disentangled Intent Representations ( DIR ). Specifically, we allow the model to predict unseen intents in the trainingstage , with the corresponding label names serving as input utterances.
Revised:  To address this problem, we propose a class-transductive framework that utilizes unseen class labels to learn Disentangled Intent Representations ( DIR ). Specifically, we allow the model to predict unseen intents during training , with the corresponding label names serving as input utterances.
Edit intent: clarity
Original:  Under this framework , we introduce a multi-task learning objective, which encourages the model to learn the distinctions among intents, and a similarity scorer, which estimates the connections among intents more accurately based on the learned intent representations.
Revised:  On this basis , we introduce a multi-task learning objective, which encourages the model to learn the distinctions among intents, and a similarity scorer, which estimates the connections among intents more accurately based on the learned intent representations.
Edit intent: clarity
Original:  Under this framework , we introduce a multi-task learning objective, which encourages the model to learn the distinctions among intents, and a similarity scorer, which estimates the connections among intents more accurately based on the learned intent representations. Since the purpose of DIR is to provide better intent representations, it can be easily integrated with existing ZSID and GZSID methods.
Revised:  Under this framework , we introduce a multi-task learning objective, which encourages the model to learn the distinctions among intents, and a similarity scorer, which estimates the connections among intents more accurately . CTIR is easy to implement and can be integrated with existing ZSID and GZSID methods.
Edit intent: coherence
Original:  Considering the idea and theory of the "split-step" parabolic approximation, a discrete PE model using the Chebyshev spectral method (CSM) is derived, and the code is developed.
Revised:  Considering the idea and theory of the wide-angle rational approximation, a discrete PE model using the Chebyshev spectral method (CSM) is derived, and the code is developed.
Edit intent: meaning-changed
Original:  We use the problems of two ideal fluid waveguides as examples, i.e., one with a constant sound speed in shallow water and one with a Munk sound speed profile in the deep ocean.
Revised:  This method is currently suitable only for range-independent waveguides. Taking three ideal fluid waveguides as examples, i.e., one with a constant sound speed in shallow water and one with a Munk sound speed profile in the deep ocean.
Edit intent: meaning-changed
Original:  We use the problems of two ideal fluid waveguides as examples, i.e., one with a constant sound speed in shallow water and one with a Munk sound speed profile in the deep ocean. The correctness of the  discrete PE model using the CSM to solve a simple underwater acoustic propagation problem is verified.
Revised:  We use the problems of two ideal fluid waveguides as examples, the discrete PE model using the CSM to solve a simple underwater acoustic propagation problem is verified.
Edit intent: clarity
Original:  The test results show that compared with the finite difference discrete PE model, the proposed method in this paper has a higher accuracy in the calculation of underwater acoustic propagation in a simple marine environment and requires fewer discrete grid points.
Revised:  The test results show that compared with the finite difference discrete PE model, the proposed method in this paper can achieve higher accuracy in the calculation of underwater acoustic propagation in a simple marine environment and requires fewer discrete grid points.
Edit intent: clarity
Original:  In particular  deep neural networks have recently gained significant attention . In thiswork we present ATOM3D, a collection of both novel and existing  datasets spanning several key classes of biomolecules , to systematically assess such learning methods.
Revised:  In particular deep neural networks have recently gained significant attention , but their widespread adoption in the biomolecular domain has been limited by a lack of either systematic performance benchmarks or a unified toolkit for interacting with molecular data. To address this, we present ATOM3D, a collection of both novel and existing datasets spanning several key classes of biomolecules , to systematically assess such learning methods.
Edit intent: meaning-changed
Original:  In thiswork we present ATOM3D, a collection of both novel and existing  datasets spanning several key classes of biomolecules , to systematically assess such learning methods. We develop three-dimensional molecular learning networks for each of these tasks , finding that they consistently improve performance relative to  one- and two-dimensional methods .
Revised:  In thiswork we present ATOM3D, a collection of both novel and existing datasets spanning several key classes of biomolecules . We implement several classes of three-dimensional molecular learning networks for each of these tasks , finding that they consistently improve performance relative to one- and two-dimensional methods .
Edit intent: clarity
Original:  The specific choice of architecture proves to be critical for performance, with three-dimensional convolutional networks excelling at tasks involving complex geometries, while graph networks perform well on systems requiring detailed positional information . Furthermore, equivariant networks show significant promise.
Revised:  The specific choice of architecture proves to be critical for performance, with three-dimensional convolutional networks excelling at tasks involving complex geometries, while graph networks perform well on systems requiring detailed positional information , and the more recently developed equivariant networks showing significant promise.
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Original:  Our results indicate  many molecular problems stand to gain from three-dimensional molecular learning . All code and datasets can be accessed via URL .
Revised:  Our results indicate many molecular problems stand to gain from three-dimensional molecular learning , and that there is potential for improvement on many tasks which remain underexplored. To lower the barrier to entry and facilitate further developments in the field, we also provide a comprehensive suite of tools for dataset processing, model training, and evaluation in our open-source atom3d Python package. All datasets are available for download from URL .
Edit intent: meaning-changed
Original:  If the ratio \frac L \delta is fixed, where L is the total length of P and \delta is the minimum distance between any two points on P, other than the intersection points, a 2 (1+\epsilon)-approximation algorithm exists for disks, and a (1+\epsilon)-approximation algorithm exists for convex polygons  , with time complexities O(\log (L/\delta){\epsilon}n^3) and O( \frac{\log (L/\delta){\epsilon}} {\epsilon^{5/2}}} n^3\log n), respectively.
Revised:  Similarly, the concept of c-packedness can be defined for any scaling of a given shape. Assuming L is the total length of P and \delta is the minimum distance between any two points on P, other than the intersection points, a 2 (1+\epsilon)-approximation algorithm exists for disks, and a (1+\epsilon)-approximation algorithm exists for convex polygons , with time complexities O(\log (L/\delta){\epsilon}n^3) and O( \frac{\log (L/\delta){\epsilon}} {\epsilon^{5/2}}} n^3\log n), respectively.
Edit intent: meaning-changed
Original:  If the ratio \frac L \delta is fixed, where L is the total length of P and \delta is the minimum distance between any two points on P, other than the intersection points, a 2 (1+\epsilon)-approximation algorithm exists for disks, and a (1+\epsilon)-approximation algorithm exists for convex polygons  , with time complexities O(\log (L/\delta){\epsilon}n^3) and O( \frac{\log (L/\delta){\epsilon}} {\epsilon^{5/2}}} n^3\log n), respectively.
Revised:  If the ratio \frac L \delta is fixed, where L is the total length of P and \delta is the minimum distance between any pair of points on two non-intersecting edges of P, (1+\epsilon)-approximation algorithm exists for disks, and a (1+\epsilon)-approximation algorithm exists for convex polygons , with time complexities O(\log (L/\delta){\epsilon}n^3) and O( \frac{\log (L/\delta){\epsilon}} {\epsilon^{5/2}}} n^3\log n), respectively.
Edit intent: clarity
Original:  If the ratio \frac L \delta is fixed, where L is the total length of P and \delta is the minimum distance between any two points on P, other than the intersection points, a 2 (1+\epsilon)-approximation algorithm exists for disks, and a (1+\epsilon)-approximation algorithm exists for convex polygons  , with time complexities O(\log (L/\delta){\epsilon}n^3) and O( \frac{\log (L/\delta){\epsilon}} {\epsilon^{5/2}}} n^3\log n), respectively.
Revised:  If the ratio \frac L \delta is fixed, where L is the total length of P and \delta is the minimum distance between any two points on P, other than the intersection points, a 2 (1+\epsilon)-approximation algorithms exist for disks, and a (1+\epsilon)-approximation algorithm exists for convex polygons , with time complexities O(\log (L/\delta){\epsilon}n^3) and O( \frac{\log (L/\delta){\epsilon}} {\epsilon^{5/2}}} n^3\log n), respectively.
Edit intent: fluency