wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We then present a dynamic model of the growth of directed citation networks and show that such systematic disparities in citations can arise from individual preferences to cite authors in the same group (homophily) or the other group (heterophily) , highly cited or active authors (preferential attachment), as well as the size of the group and how frequently new authors join.
Revised:  We then present a dynamic model of the growth of directed citation networks and show that citations disparities arise from individual preferences to cite authors in the same group (homophily) or the other group (heterophily) , highly cited or active authors (preferential attachment), as well as the size of the group and how frequently new authors join.
Edit intent: clarity
Original:  We then present a dynamic model of the growth of directed citation networks and show that such systematic disparities in citations can arise from individual preferences to cite authors in the same group (homophily) or the other group (heterophily) , highly cited or active authors (preferential attachment), as well as the size of the group and how frequently new authors join.
Revised:  We then present a dynamic model of the growth of directed citation networks and show that such systematic disparities in citations can arise from individual preferences to cite authors from the same group (homophily) or the other group (heterophily) , highly cited or active authors (preferential attachment), as well as the size of the group and how frequently new authors join.
Edit intent: fluency
Original:  We then present a dynamic model of the growth of directed citation networks and show that such systematic disparities in citations can arise from individual preferences to cite authors in the same group (homophily) or the other group (heterophily) , highly cited or active authors (preferential attachment), as well as the size of the group and how frequently new authors join.
Revised:  We then present a dynamic model of the growth of directed citation networks and show that such systematic disparities in citations can arise from individual preferences to cite authors in the same group (homophily) , highly cited or active authors (preferential attachment), as well as the size of the group and how frequently new authors join.
Edit intent: coherence
Original:  Empirical performance evaluations, with both synthetic and real-world datasets, establish the  advantage of the tensor CUR approximations over other state-of-the-art low multilinear rank tensor approximations.
Revised:  Empirical performance evaluations, with both synthetic and real-world datasets, establish the speed advantage of the tensor CUR approximations over other state-of-the-art low multilinear rank tensor approximations.
Edit intent: meaning-changed
Original:  Compared with the non-local method, the proposed RCCA-3D module reduces the number of parameters and FLOPs by 25\% and 11 \% for video context modeling.
Revised:  Compared with the non-local method, the proposed RCCA-3D module reduces the number of parameters and FLOPs by 25\% and 30 \% for video context modeling.
Edit intent: meaning-changed
Original:  We evaluate the performance of RCCA-3D with two latest action recognition networks on three datasets and make a thorough analysis of the architecture, obtaining the best way to factorize and fuse the relation maps.
Revised:  We evaluate the performance of RCCA-3D with two latest action recognition networks on three datasets and make a thorough analysis of the architecture, obtaining the optimal way to factorize and fuse the relation maps.
Edit intent: clarity
Original:  In this paper, we show that the most elementary index, namely the common neighbor (CN) index, is dominated by the leading eigenvector of the adjacency matrix of the target network  .
Revised:  In this paper, we show that the most elementary index, namely the common neighbor (CN) index, is dominated by the leading eigenvector of the adjacency matrix of the target network , with each eigenvector's contribution being proportional to the square of the corresponding eigenvalue. As in many real networks, there is a huge gap between the largest eigenvalue and the second largest eigenvalue, the CN index is thus dominated by the leading eigenvector and much useful information contained in other eigenvectors may be overlooked .
Edit intent: meaning-changed
Original:  We introduce a new environment, where agents Learn-to-Race (L2R) in simulated Formula-E style racing, using multimodal information--from virtual cameras to a comprehensive array of inertial measurement sensors.
Revised:  We introduce a new environment, where agents Learn-to-Race (L2R) in simulated competition-style racing, using multimodal information--from virtual cameras to a comprehensive array of inertial measurement sensors.
Edit intent: clarity
Original:  To achieve this, we use Stein's method for Markov chains BN19, RR19 to show that a ``good'' coupling for a local Markov chain yields strong bounds on the spectral independence of the distribution in the sense of ALO20.
Revised:  To achieve this, we use Stein's method for Markov chains BN19, RR19 to show that a "good" coupling for a local Markov chain yields strong bounds on the spectral independence of the distribution in the sense of ALO20.
Edit intent: fluency
Original:  The second and third layers actively re-plan and execute the path, exploiting the continuously updated map  . Moreover, they allow the robot to maximize its visibility of 3D visual features in the environment .
Revised:  The second and third layers actively re-plan and execute the path, exploiting the continuously updated map and local features information . Moreover, they allow the robot to maximize its visibility of 3D visual features in the environment .
Edit intent: meaning-changed
Original:  The second and third layers actively re-plan and execute the path, exploiting the continuously updated map  . Moreover, they allow the robot to maximize its visibility of 3D visual features in the environment .
Revised:  The second and third layers actively re-plan and execute the path, exploiting the continuously updated map . Moreover, we introduce two utility formulations to account for the obstacle presence in the field of view and the robot's location .
Edit intent: meaning-changed
Original:  Through rigorous simulations, real robot experiments and comparisons with the state-of-the-art methods, we demonstrate that our approach achieves similar coverage and lesser overall map entropy  while keeping the traversed distance up to 36\% less than the other methods  . Code and implementation details are provided as open-source .
Revised:  Through rigorous simulations, real robot experiments and comparisons with the state-of-the-art methods, we demonstrate that our approach achieves similar coverage results with lesser overall map entropy while keeping the traversed distance up to 36\% less than the other methods . Code and implementation details are provided as open-source .
Edit intent: clarity
Original:  Through rigorous simulations, real robot experiments and comparisons with the state-of-the-art methods, we demonstrate that our approach achieves similar coverage and lesser overall map entropy  while keeping the traversed distance up to 36\% less than the other methods  . Code and implementation details are provided as open-source .
Revised:  Through rigorous simulations, real robot experiments and comparisons with the state-of-the-art methods, we demonstrate that our approach achieves similar coverage and lesser overall map entropy . This is obtained while keeping the traversed distance up to 36\% less than the other methods . Code and implementation details are provided as open-source .
Edit intent: coherence
Original:  Through rigorous simulations, real robot experiments and comparisons with the state-of-the-art methods, we demonstrate that our approach achieves similar coverage and lesser overall map entropy  while keeping the traversed distance up to 36\% less than the other methods  . Code and implementation details are provided as open-source .
Revised:  Through rigorous simulations, real robot experiments and comparisons with the state-of-the-art methods, we demonstrate that our approach achieves similar coverage and lesser overall map entropy while keeping the traversed distance up to 39\% shorter than the other methods . Code and implementation details are provided as open-source .
Edit intent: meaning-changed
Original:  Through rigorous simulations, real robot experiments and comparisons with the state-of-the-art methods, we demonstrate that our approach achieves similar coverage and lesser overall map entropy  while keeping the traversed distance up to 36\% less than the other methods  . Code and implementation details are provided as open-source .
Revised:  Through rigorous simulations, real robot experiments and comparisons with the state-of-the-art methods, we demonstrate that our approach achieves similar coverage and lesser overall map entropy while keeping the traversed distance up to 36\% less than the other methods and without increasing the wheels' total rotation amount . Code and implementation details are provided as open-source .
Edit intent: meaning-changed
Original:  Through rigorous simulations, real robot experiments and comparisons with the state-of-the-art methods, we demonstrate that our approach achieves similar coverage and lesser overall map entropy  while keeping the traversed distance up to 36\% less than the other methods  . Code and implementation details are provided as open-source .
Revised:  Through rigorous simulations, real robot experiments and comparisons with the state-of-the-art methods, we demonstrate that our approach achieves similar coverage and lesser overall map entropy while keeping the traversed distance up to 36\% less than the other methods . Code and implementation details are provided as opensource .
Edit intent: fluency
Original:  The effectiveness of DeepOPF-V is validated by case studies of several IEEE test systems . Compared with existing approaches , DeepOPF-V achieves a state-of-art computation speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.
Revised:  The effectiveness of DeepOPF-V is validated by simulations on IEEE 118/300-bus systems and a 2000-bus test system . Compared with existing approaches , DeepOPF-V achieves a state-of-art computation speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.
Edit intent: meaning-changed
Original:  The effectiveness of DeepOPF-V is validated by case studies of several IEEE test systems . Compared with existing approaches , DeepOPF-V achieves a state-of-art computation speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.
Revised:  The effectiveness of DeepOPF-V is validated by case studies of several IEEE test systems . Compared with existing studies , DeepOPF-V achieves a state-of-art computation speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.
Edit intent: clarity
Original:  The effectiveness of DeepOPF-V is validated by case studies of several IEEE test systems . Compared with existing approaches , DeepOPF-V achieves a state-of-art computation speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.
Revised:  The effectiveness of DeepOPF-V is validated by case studies of several IEEE test systems . Compared with existing approaches , DeepOPF-V achieves decent computation speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.
Edit intent: clarity
Original:  The effectiveness of DeepOPF-V is validated by case studies of several IEEE test systems . Compared with existing approaches , DeepOPF-V achieves a state-of-art computation speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.
Revised:  The effectiveness of DeepOPF-V is validated by case studies of several IEEE test systems . Compared with existing approaches , DeepOPF-V achieves a state-of-art computation speedup up to four orders of magnitude and has better performance in preserving the feasibility of the solution.
Edit intent: meaning-changed
Original:  The effectiveness of DeepOPF-V is validated by case studies of several IEEE test systems . Compared with existing approaches , DeepOPF-V achieves a state-of-art computation speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.
Revised:  The effectiveness of DeepOPF-V is validated by case studies of several IEEE test systems . Compared with existing approaches , DeepOPF-V achieves a state-of-art computation speedup up to three orders of magnitude and comparable performance in optimality gap and preserving the feasibility of the solution.
Edit intent: meaning-changed
Original:  Fast-mode imbalances of the order of the slower dynamics can be negated by employing a blended numerical model with seamless access to the compressible and the soundproof pseudo-incompressible dynamics.
Revised:  Acoustic fast-mode imbalances of the order of the slower dynamics can be negated by employing a blended numerical model with seamless access to the compressible and the soundproof pseudo-incompressible dynamics.
Edit intent: clarity
Original:  Here, the blended modelling strategy by Benacchio et al.  (2014) is upgraded in an advanced numerical framework and extended with a Bayesian local ensemble data assimilation method.
Revised:  Here, the blended modelling strategy by Benacchio et al. , MWR, vol. 142 (2014) is upgraded in an advanced numerical framework and extended with a Bayesian local ensemble data assimilation method.
Edit intent: meaning-changed
Original:  Travelling vortex and bubble convection experiments show that the imbalance arising from assimilation of the momentum fields can be eliminated by using this blended model, thereby achieving balanced analysis fields .
Revised:  Idealised two-dimensional travelling vortex and buoyancy-driven bubble convection experiments show that the imbalance arising from assimilation of the momentum fields can be eliminated by using this blended model, thereby achieving balanced analysis fields .
Edit intent: meaning-changed
Original:  Travelling vortex and bubble convection experiments show that the imbalance arising from assimilation of the momentum fields can be eliminated by using this blended model, thereby achieving balanced analysis fields . The leftover imbalance in the thermodynamics can be quantified by scale analysis .
Revised:  Travelling vortex and bubble convection experiments show that the imbalance arising from assimilation of the momentum fields can be eliminated by using this blended model, thereby achieving balanced analysis fields .
Edit intent: coherence
Original: Purpose: This study aims to provide a hybrid approach for patent claim classificationwith Sentence-BERT (SBERT) and K Nearest Neighbours (KNN) and explicitly focuses on the patent claims.
Revised: This study provides an efficient approach for using text data to calculate patent-to-patent (p2p) technological similarity, and presents a hybrid framework for leveraging the resulting p2p similarity for applications such as semantic search and automated patent classification. We create embeddings using Sentence-BERT (SBERT) and K Nearest Neighbours (KNN) and explicitly focuses on the patent claims.
Edit intent: meaning-changed
Original:  Patent classification is a multi-label classification task in which the number of labels can be greater than 640 at the subclass level. The proposed framework predicts individual input patent class and subclass based on finding top k semantic similarity patents. Design/Methodology/Approach: The study uses transformer models based on Augmented SBERT and RoBERTa.
Revised:  To further increase the patent embedding quality, we use transformer models based on Augmented SBERT and RoBERTa.
Edit intent: clarity
Original:  Design/Methodology/Approach: The study uses transformer models based on Augmented SBERT and RoBERTa. We use a different approach to predict patent classification by finding top k similar patent claims and using the KNNalgorithm to predict patent class or subclass.
Revised:  Design/Methodology/Approach: The study uses transformer models based on SBERT and RoBERT, and apply the augmented approach for fine-tuning SBERT by in-domain supervised patent claims data. We leverage SBERTs efficiency in creating embedding distance measures to map p2p similarity in large sets of patent data. We deploy our framework for classification with a simple Nearest Neighbors (KNN) model that predicts Cooperative Patent Classification (CPC) of a patent based on the CPC assignment of the K patents with the highest p2p similarity. We thereby validate that p2p similarity captures their technological features in terms of CPC overlap, and at the same demonstrate the usefulness of this approach for automatic patent classification by finding top k similar patent claims and using the KNNalgorithm to predict patent class or subclass.
Edit intent: meaning-changed
Original:  We use a different approach to predict patent classification by finding top k similar patent claims and using the KNNalgorithm to predict patent class or subclass. Besides, in this study, we just focus on patent claims, and in the future study, we add other appropriate parts of patent documents. Findings: The findings suggest the relevance of hybrid models to predict multi-label classification based on text data.
Revised:  We use a different approach to predict patent classification based on text data.
Edit intent: coherence
Original:  In this study, we predict the class and subclass of the patent based on semantic claims similarity. The end-user interpretability of the results is one of the essential positive points of the model.
Revised:  In this study, we predict the class and subclass of the out-of-sample model validation, we are able to perform a multi-label prediction of all assigned CPC classes on the results is one of the essential positive points of the model.
Edit intent: meaning-changed
Original:  The end-user interpretability of the results is one of the essential positive points of the model. Originality/Value: The main contribution of the study included: 1) Using the Augmented approach for fine-tuning SBERT by in-domain supervised patent claims data. 2) Improving results based on a hybrid model for patent classification. The best result of F1-score at the subclass level was > 69\%) Proposing the practical model with high interpretability of results .
Revised:  The end-user interpretability of the subclass (640) level on 163,269 patents with an accuracy of 54\% and F1 score > 69\%) Proposing the practical model with high interpretability of results .
Edit intent: meaning-changed
Original:  The best result of F1-score at the subclass level was > 69\%) Proposing the practical model with high interpretability of results .
Revised:  The best result of F1-score at the subclass level was > 63\%, which suggests that our model outperforms the current state-of-the-art in text-based multi-label and multi-class patent classification by a margin of > 18\% F1 score. We furthermore discuss the applicability of the presented framework for semantic IP search, patent landscaping, and technology intelligence. We finally point towards a future research agenda for leveraging multi-source patent embeddings, their appropriateness across applications, as well as to improve and validate patent embeddings by creating domain-expert curated Semantic Textual Similarity (STS) benchmark datasets .
Edit intent: meaning-changed
Original: Low-order finite-element discretizations are well-known to provide effective preconditioners for the linear systems that arise from higher-order discretizations of the Poissonequation .
Revised: Low-order finite-element discretizations are well-known to provide effective preconditioners for the linear systems that arise from higher-order discretizations of some PDEs, such as Poisson's equation, is to leverage effective preconditioners for their low-order analogs .
Edit intent: meaning-changed
Original:  Our proposed approach performs on par with state-of-the-art approaches on the limited TuSimple benchmark, and sets a new state-of-the-art on the challenging CULane  dataset.
Revised:  Our proposed approach performs on par with state-of-the-art approaches on the limited TuSimple benchmark, and sets a new state-of-the-art on the challenging CULane dataset and the recently introduced Unsupervised LLAMAS dataset.
Edit intent: meaning-changed
Original: During the onset of the COVID-19 pandemic, conflicting incentives caused most stakeholders to dislike corporate social responsibility (CSR) -- measured by firms' charitable donations -- since it would further burden firms' already strained finances.
Revised: During the onset of the COVID-19 pandemic, conflicting incentives caused most shareholders to adverse corporate social responsibility (CSR) -- measured by firms' charitable donations -- since it would further burden firms' already strained finances.
Edit intent: clarity
Original:  Image gains do not pass-through to institutional shareholders, who instead preferred to donate themselves rather than having the firms in their portfolios donate. Our results cast doubts on large corporations' willingness to enforce CSR measures for firms in their portfolio .
Revised:  Image gains do not pass-through to institutional shareholders, who instead preferred to donate themselves rather than having the firms they invested in donate. Taken together, our results cast doubts on large corporations' willingness to enforce CSR measures for firms in their portfolio .
Edit intent: clarity
Original:  This paper explores the destabilising feedback loops in predator-prey, mutualistic and competitive systems and shows how matrix structure plays a significant role in their stability. A method is then described in which a variable acting on the self-regulating terms of the community matrix enables one to determine the size of the key feedback that leads to a system's tipping point  .
Revised:  This paper explores the destabilising feedback loops in predator-prey, mutualistic and competitive systems in terms of sets of the matrix elements to explore how sign structure affects how the elements contribute to instability. An analytic method is then described in which a variable acting on the self-regulating terms of the community matrix enables one to determine the size of the key feedback that leads to a system's tipping point .
Edit intent: meaning-changed
Original:  A method is then described in which a variable acting on the self-regulating terms of the community matrix enables one to determine the size of the key feedback that leads to a system's tipping point  .
Revised:  A method is then described in which a variable acting on the self-regulating terms of the community matrix enables one , depending on the field of the maximal eigenvalue, to either determine the size of the key feedback that leads to a system's tipping point .
Edit intent: meaning-changed
Original:  A method is then described in which a variable acting on the self-regulating terms of the community matrix enables one to determine the size of the key feedback that leads to a system's tipping point  .
Revised:  A method is then described in which a variable acting on the self-regulating terms of the community matrix enables one to determine the size of the key feedback that leads to a system's tipping point , or narrows down the search space .
Edit intent: meaning-changed