wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Theoretical analysis shows that this unique contrastive reweighting scheme helps in the representation space to both align the positive samples with the query and reduce the mutual information between the negative sample and query .
Revised:  Theoretical analysis reveals the connection between CACR and CL from the perspectives of both positive attraction and negative repulsion and shows the benefits in both efficiency and robustness brought by separately contrasting within the sampled positive and negative pairs .
Edit intent: meaning-changed
Original:  Extensive large-scale experiments on standard vision tasks show that CCT not only consistently outperforms existing  methods on benchmark datasets in contrastive representation learning but also provides interpretable contrastive weights and latent representations.
Revised:  Extensive large-scale experiments on standard vision tasks show that CACR not only consistently outperforms existing methods on benchmark datasets in contrastive representation learning but also provides interpretable contrastive weights and latent representations.
Edit intent: meaning-changed
Original:  Extensive large-scale experiments on standard vision tasks show that CCT not only consistently outperforms existing  methods on benchmark datasets in contrastive representation learning but also provides interpretable contrastive weights and latent representations.
Revised:  Extensive large-scale experiments on standard vision tasks show that CCT not only consistently outperforms existing CL methods on benchmark datasets in contrastive representation learning but also provides interpretable contrastive weights and latent representations.
Edit intent: meaning-changed
Original:  Extensive large-scale experiments on standard vision tasks show that CCT not only consistently outperforms existing  methods on benchmark datasets in contrastive representation learning but also provides interpretable contrastive weights and latent representations.
Revised:  Extensive large-scale experiments on standard vision tasks show that CCT not only consistently outperforms existing methods on benchmark datasets in representation learning, but also provides interpretable contrastive weights and latent representations.
Edit intent: fluency
Original:  Extensive large-scale experiments on standard vision tasks show that CCT not only consistently outperforms existing  methods on benchmark datasets in contrastive representation learning but also provides interpretable contrastive weights and latent representations. PyTorch code will be provided .
Revised:  Extensive large-scale experiments on standard vision tasks show that CCT not only consistently outperforms existing methods on benchmark datasets in contrastive representation learning but also provides interpretable contrastive weights , demonstrating the efficacy of the proposed doubly contrastive strategy .
Edit intent: meaning-changed
Original:  We firstly ] take a closer look at the mappings of various existing mapped WENO schemes and devise a general formula for them.
Revised:  We firstly ], the order-preserving (OP) criterion was originally introduced and carefully used to devise a new mapped WENO scheme that performs satisfactorily in long-run simulations, and hence it was indicated that the OP criterion plays a critical role in the maintenance of low-dissipation and robustness for the mapped WENO schemes. Thus, in our present work, we firstly define the family of the mapped WENO schemes, whose mappings meet the OP criterion, as OP-Mapped WENO. Next, we attentively take a closer look at the mappings of various existing mapped WENO schemes and devise a general formula for them.
Edit intent: meaning-changed
Original:  It helps us to extend the order-preserving (OP ) criterion , originally defined and carefully examined in 18%DIFDELCMD < ]%%% , into the design of the mappings.
Revised:  It helps us to extend the order-preserving (OP ) criterion , originally defined and carefully examined in 18%DIFDELCMD < ]%%% OP criterion into the design of the mappings.
Edit intent: clarity
Original:  It helps us to extend the order-preserving (OP ) criterion , originally defined and carefully examined in 18%DIFDELCMD < ]%%% , into the design of the mappings.Next , we propose the  implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes  where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Revised:  It helps us to extend the order-preserving (OP ) criterion , originally defined and carefully examined in 18%DIFDELCMD < ]%%% , into the design of the improved mappings. Then , we propose the implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Edit intent: clarity
Original: Next , we propose the  implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes  where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Revised: Next , we propose the generalized implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Edit intent: meaning-changed
Original: Next , we propose the  implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes  where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Revised: Next , we propose the implementation of obtaining a group of OP-Mapped WENO schemes, named MOP-WENO-X as they are developed from the existing mapped WENO-X schemes where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Edit intent: meaning-changed
Original: Next , we propose the  implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes  where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Revised: Next , we propose the implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes , where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Edit intent: fluency
Original: Next , we propose the  implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes  where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Revised: Next , we propose the implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes where the notation "X" is used to identify the version of the existing mapped WENO scheme 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Edit intent: coherence
Original: Next , we propose the  implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes  where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Revised: Next , we propose the implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M %DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Edit intent: meaning-changed
Original: Next , we propose the  implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes  where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Revised: Next , we propose the implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Edit intent: coherence
Original: Next , we propose the  implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes  where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Revised: Next , we propose the implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 %DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Edit intent: meaning-changed
Original: Next , we propose the  implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes  where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Revised: Next , we propose the implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% 15%DIFDELCMD < ]%%% , et al.
Edit intent: coherence
Original: Next , we propose the  implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes  where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al.
Revised: Next , we propose the implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 %DIFDELCMD < ]%%% , et al.
Edit intent: meaning-changed
Original: Next , we propose the  implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes  where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% , et al. Then we build the resultant mapped WENO schemes and denote them as MOP-WENO-X .
Revised: Next , we propose the implementation of obtaining the new mappings satisfying the OP criterion from those of the existing mapped WENO-X schemes where the notation "X" is used to identify the version of the existing mapped WENO scheme , e. g., X = M 11%DIFDELCMD < ]%%% , PM6 3%DIFDELCMD < ]%%% , or PPM5 15%DIFDELCMD < ]%%% . Finally, extensive numerical experiments and comparisons with competing schemes are conducted to demonstrate the enhanced performances of the MOP-WENO-X .
Edit intent: meaning-changed
Original:  Then we build the resultant mapped WENO schemes and denote them as MOP-WENO-X . The numerical solutions of the one-dimensional linear advection equation with different initial conditions and some standard numerical experiments of two-dimensional Euler system, computed by the MOP-WENO-X schemes , are compared with the ones generated by their corresponding WENO-X schemes and the WENO-JS scheme. To summarize, the MOP-WENO-X schemes gain definite advatages in terms of attaining high resolutions and meanwhile avoiding spurious oscillations near discontinuities for long output time simulations of the one-dimensional linear advection problems, as well as significantly reducing the post-shock oscillations in the simulations of the two-dimensional steady problems with strong shock waves .
Revised:  Then we build the resultant mapped WENO schemes and denote them as MOP-WENO-X schemes gain definite advatages in terms of attaining high resolutions and meanwhile avoiding spurious oscillations near discontinuities for long output time simulations of the one-dimensional linear advection problems, as well as significantly reducing the post-shock oscillations in the simulations of the two-dimensional steady problems with strong shock waves .
Edit intent: coherence
Original:  To summarize, the MOP-WENO-X schemes gain definite advatages in terms of attaining high resolutions and meanwhile avoiding spurious oscillations near discontinuities for long output time simulations of the one-dimensional linear advection problems, as well as significantly reducing the post-shock oscillations in the simulations of the two-dimensional steady problems with strong shock waves .
Revised:  To summarize, the MOP-WENO-X schemes .
Edit intent: coherence
Original: We investigate a class of multi-object rearrangement problems in which a robotic manipulator, capable of carrying an item and making item swaps, is tasked to sort items stored in lattices  in a time-optimal manner.
Revised: We propose and study a class of multi-object rearrangement problems in which a robotic manipulator, capable of carrying an item and making item swaps, is tasked to sort items stored in lattices in a time-optimal manner.
Edit intent: clarity
Original: We investigate a class of multi-object rearrangement problems in which a robotic manipulator, capable of carrying an item and making item swaps, is tasked to sort items stored in lattices  in a time-optimal manner.
Revised: We investigate a class of rearrangement problems under a novel pick-n-swap prehensile manipulation model, in which a robotic manipulator, capable of carrying an item and making item swaps, is tasked to sort items stored in lattices in a time-optimal manner.
Edit intent: meaning-changed
Original: We investigate a class of multi-object rearrangement problems in which a robotic manipulator, capable of carrying an item and making item swaps, is tasked to sort items stored in lattices  in a time-optimal manner.
Revised: We investigate a class of multi-object rearrangement problems in which a robotic manipulator, capable of carrying an item and making item swaps, is tasked to sort items stored in lattices of variable dimensions in a time-optimal manner.
Edit intent: meaning-changed
Original:  We systematically analyze the intrinsic optimality structure, which is fairly rich and intriguing, under different levels of item distinguishability (fully labeled  or partially labeled  ) and different lattice dimensions.
Revised:  We systematically analyze the intrinsic optimality structure, which is fairly rich and intriguing, under different levels of item distinguishability (fully labeled , where each item has a unique label, or partially labeled ) and different lattice dimensions.
Edit intent: meaning-changed
Original:  We systematically analyze the intrinsic optimality structure, which is fairly rich and intriguing, under different levels of item distinguishability (fully labeled  or partially labeled  ) and different lattice dimensions.
Revised:  We systematically analyze the intrinsic optimality structure, which is fairly rich and intriguing, under different levels of item distinguishability (fully labeled or partially labeled , where multiple items may be of the same type ) and different lattice dimensions.
Edit intent: meaning-changed
Original:  Focusing on the most practical setting of one and two dimensions, we develop efficient ( low polynomial time ) algorithms that optimally perform rearrangements on 1D lattices under both fully and partially labeled settings.
Revised:  Focusing on the most practical setting of one and two dimensions, we develop efficient ( low polynomial time cycle-following based algorithms that optimally perform rearrangements on 1D lattices under both fully and partially labeled settings.
Edit intent: meaning-changed
Original:  Focusing on the most practical setting of one and two dimensions, we develop efficient ( low polynomial time ) algorithms that optimally perform rearrangements on 1D lattices under both fully and partially labeled settings.
Revised:  Focusing on the most practical setting of one and two dimensions, we develop efficient ( low polynomial time ) algorithms that optimally perform rearrangements on 1D lattices under both fully- and partially-labeled settings.
Edit intent: fluency
Original:  Despite their NP-hardness, we are able to again develop efficient algorithms for 2D fully and partially labeled settingsthat are asymptotically optimal , in expectation, assuming that the initial configuration is randomly selected . Simulation studies confirm the effectiveness of our algorithms in comparison to natural greedy best-first algorithms.
Revised:  Despite their NP-hardness, we prove that efficient cycle-following based algorithms remain asymptotically optimal for 2D fully and partially labeled settingsthat are asymptotically optimal , in expectation, assuming that the initial configuration is randomly selected . Simulation studies confirm the effectiveness of our algorithms in comparison to natural greedy best-first algorithms.
Edit intent: clarity
Original:  Despite their NP-hardness, we are able to again develop efficient algorithms for 2D fully and partially labeled settingsthat are asymptotically optimal , in expectation, assuming that the initial configuration is randomly selected . Simulation studies confirm the effectiveness of our algorithms in comparison to natural greedy best-first algorithms.
Revised:  Despite their NP-hardness, we are able to again develop efficient algorithms for 2D fully- and partially-labeled settings , in expectation, assuming that the initial configuration is randomly selected . Simulation studies confirm the effectiveness of our algorithms in comparison to natural greedy best-first algorithms.
Edit intent: clarity
Original:  Despite their NP-hardness, we are able to again develop efficient algorithms for 2D fully and partially labeled settingsthat are asymptotically optimal , in expectation, assuming that the initial configuration is randomly selected . Simulation studies confirm the effectiveness of our algorithms in comparison to natural greedy best-first algorithms.
Revised:  Despite their NP-hardness, we are able to again develop efficient algorithms for 2D fully and partially labeled settingsthat are asymptotically optimal , in expectation, using the interesting fact that random permutations induce only a small number of cycles. We further improve these algorithms to provide 1.x-optimality when the number of items is small . Simulation studies confirm the effectiveness of our algorithms in comparison to natural greedy best-first algorithms.
Edit intent: meaning-changed
Original:  Despite their NP-hardness, we are able to again develop efficient algorithms for 2D fully and partially labeled settingsthat are asymptotically optimal , in expectation, assuming that the initial configuration is randomly selected . Simulation studies confirm the effectiveness of our algorithms in comparison to natural greedy best-first algorithms.
Revised:  Despite their NP-hardness, we are able to again develop efficient algorithms for 2D fully and partially labeled settingsthat are asymptotically optimal , in expectation, assuming that the initial configuration is randomly selected . Simulation studies corroborate the effectiveness of our algorithms in comparison to natural greedy best-first algorithms.
Edit intent: fluency
Original:  Despite their NP-hardness, we are able to again develop efficient algorithms for 2D fully and partially labeled settingsthat are asymptotically optimal , in expectation, assuming that the initial configuration is randomly selected . Simulation studies confirm the effectiveness of our algorithms in comparison to natural greedy best-first algorithms.
Revised:  Despite their NP-hardness, we are able to again develop efficient algorithms for 2D fully and partially labeled settingsthat are asymptotically optimal , in expectation, assuming that the initial configuration is randomly selected . Simulation studies confirm the effectiveness of our algorithms.
Edit intent: meaning-changed
Original: Inspired by the conclusion that human choose the visual cortex regions which corresponding to the real size of the object to analyze the features of the object, when realizing the objects in the real world . This paper presents a framework -- SizeNetwhich based on both the real sizes and the features of objects , to solve objects recognition problems.
Revised: Inspired by the conclusion that human choose the visual cortex regions which corresponding to the real size of the object to analyze the features of the object, when realizing the objects in the real world , this paper presents a framework -- SizeNetwhich based on both the real sizes and the features of objects , to solve objects recognition problems.
Edit intent: fluency
Original:  SizeNet was used for the objects recognition experiments on the homemade Rsize dataset, and compared with State-of-the-art Methods AlexNet, VGG-16, Inception V3, Resnet-18  DenseNet-121.
Revised:  SizeNet was used for object recognition experiments on the homemade Rsize dataset, and compared with State-of-the-art Methods AlexNet, VGG-16, Inception V3, Resnet-18 DenseNet-121.
Edit intent: fluency
Original:  SizeNet was used for the objects recognition experiments on the homemade Rsize dataset, and compared with State-of-the-art Methods AlexNet, VGG-16, Inception V3, Resnet-18  DenseNet-121.
Revised:  SizeNet was used for the objects recognition experiments on the homemade Rsize dataset, and was compared with the state-of-the-art methods AlexNet, VGG-16, Inception V3, Resnet-18 DenseNet-121.
Edit intent: fluency
Original:  SizeNet was used for the objects recognition experiments on the homemade Rsize dataset, and compared with State-of-the-art Methods AlexNet, VGG-16, Inception V3, Resnet-18  DenseNet-121.
Revised:  SizeNet was used for the objects recognition experiments on the homemade Rsize dataset, and compared with State-of-the-art Methods AlexNet, VGG-16, Inception V3, Resnet-18 , and DenseNet-121.
Edit intent: coherence
Original:  SizeNet can solve the two problems that correctly recognize the objects whose features are highly similar but the real sizes are obviously different from each other, and correctly distinguish the target object from the interference objects whose real sizes are obviously different from the target object.
Revised:  SizeNet can solve the two problems of correctly recognizing objects with highly similar features but real sizes that are obviously different from each other, and correctly distinguish the target object from the interference objects whose real sizes are obviously different from the target object.
Edit intent: clarity