wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We introduce TANGO, a novel neural model  for predicting task-specific tool interactions . TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator . TANGO encodes the world state comprising of objects and symbolic relationships between them  using a graph neural network.
Revised:  We introduce TANGO, a novel neural model for predicting task-specific tool interactions . TANGO is trained using demonstrations from human teachers instructing a virtual robot in a physics simulator . TANGO encodes the world state comprising of objects and symbolic relationships between them using a graph neural network.
Edit intent: clarity
Original:  We introduce TANGO, a novel neural model  for predicting task-specific tool interactions . TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator . TANGO encodes the world state comprising of objects and symbolic relationships between them  using a graph neural network.
Revised:  We introduce TANGO, a novel neural model for predicting task-specific tool interactions . TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot . TANGO encodes the world state comprising of objects and symbolic relationships between them using a graph neural network.
Edit intent: clarity
Original:  We introduce TANGO, a novel neural model  for predicting task-specific tool interactions . TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator . TANGO encodes the world state comprising of objects and symbolic relationships between them  using a graph neural network.
Revised:  We introduce TANGO, a novel neural model for predicting task-specific tool interactions . TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator . TANGO encodes the world state , comprising objects and symbolic relationships between them using a graph neural network.
Edit intent: fluency
Original:  We introduce TANGO, a novel neural model  for predicting task-specific tool interactions . TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator . TANGO encodes the world state comprising of objects and symbolic relationships between them  using a graph neural network.
Revised:  We introduce TANGO, a novel neural model for predicting task-specific tool interactions . TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator . TANGO encodes the world state comprising of objects and symbolic relationships between them , using a graph neural network.
Edit intent: fluency
Original: In 18%DIFDELCMD < ]%%% , the authors have designed a new finite-volume mapped WENO scheme by introducing a new criterion which is defined as order-preserving (OP) in the design of the mapping.
Revised:  18%DIFDELCMD < ]%%% , the authors have designed a new finite-volume mapped WENO scheme by introducing a new criterion which is defined as order-preserving (OP) in the design of the mapping.
Edit intent: coherence
Original: In 18%DIFDELCMD < ]%%% , the authors have designed a new finite-volume mapped WENO scheme by introducing a new criterion which is defined as order-preserving (OP) in the design of the mapping.
Revised: In %DIFDELCMD < ]%%% , the authors have designed a new finite-volume mapped WENO scheme by introducing a new criterion which is defined as order-preserving (OP) in the design of the mapping.
Edit intent: coherence
Original: In 18%DIFDELCMD < ]%%% , the authors have designed a new finite-volume mapped WENO scheme by introducing a new criterion which is defined as order-preserving (OP) in the design of the mapping. With this new criterion, a new mapping function was devised and the relevant mapped WENO scheme enjoyed the advantage that has the capacity to attain high resolutions and avoid spurious oscillations near discontinuities when solving the one-dimensional linear advection problems, expecially for long output times.
Revised: In 18%DIFDELCMD < ]%%% A serious and ubiquitous issue in existing mapped WENO schemes is that most of them can hardly preserve high resolutions and avoid spurious oscillations near discontinuities when solving the one-dimensional linear advection problems, expecially for long output times.
Edit intent: clarity
Original:  With this new criterion, a new mapping function was devised and the relevant mapped WENO scheme enjoyed the advantage that has the capacity to attain high resolutions and avoid spurious oscillations near discontinuities when solving the one-dimensional linear advection problems, expecially for long output times.
Revised:  With this new criterion, a new mapping function was devised and the relevant mapped WENO scheme enjoyed the advantage that has the capacity to attain high resolutions and in the meantime prevent spurious oscillations on solving hyperbolic conservation laws with long output times.
Edit intent: clarity
Original:  Moreover, this new scheme, dubbed as MOP-WENO-ACMk, can significantly reduce the post-shock oscillations in the simulations of the two-dimensional steady problems with strong shock waves. In this article  , we  take a closer look at  various existing mapped WENO schemes in references to obtain a general formula for their mapping functions that allows the extension of the OP criterion to all existing mapped WENO schemes .
Revised:  Our goal in this article is to address this widely concerned problem , we take a closer look at various existing mapped WENO schemes in references to obtain a general formula for their mapping functions that allows the extension of the OP criterion to all existing mapped WENO schemes .
Edit intent: coherence
Original:  In this article  , we  take a closer look at  various existing mapped WENO schemes in references to obtain a general formula for their mapping functions that allows the extension of the OP criterion to all existing mapped WENO schemes .
Revised:  In this article 3,4 , we take a closer look at various existing mapped WENO schemes in references to obtain a general formula for their mapping functions that allows the extension of the OP criterion to all existing mapped WENO schemes .
Edit intent: meaning-changed
Original:  In this article  , we  take a closer look at  various existing mapped WENO schemes in references to obtain a general formula for their mapping functions that allows the extension of the OP criterion to all existing mapped WENO schemes .
Revised:  In this article , 15,29,16,18 take a closer look at various existing mapped WENO schemes in references to obtain a general formula for their mapping functions that allows the extension of the OP criterion to all existing mapped WENO schemes .
Edit intent: meaning-changed
Original:  In this article  , we  take a closer look at  various existing mapped WENO schemes in references to obtain a general formula for their mapping functions that allows the extension of the OP criterion to all existing mapped WENO schemes .
Revised:  In this article , we .We firstly take a closer look at various existing mapped WENO schemes in references to obtain a general formula for their mapping functions that allows the extension of the OP criterion to all existing mapped WENO schemes .
Edit intent: coherence
Original:  In this article  , we  take a closer look at  various existing mapped WENO schemes in references to obtain a general formula for their mapping functions that allows the extension of the OP criterion to all existing mapped WENO schemes .
Revised:  In this article , we take a closer look at the mappings of various existing mapped WENO schemes in references to obtain a general formula for their mapping functions that allows the extension of the OP criterion to all existing mapped WENO schemes .
Edit intent: meaning-changed
Original:  In this article  , we  take a closer look at  various existing mapped WENO schemes in references to obtain a general formula for their mapping functions that allows the extension of the OP criterion to all existing mapped WENO schemes .
Revised:  In this article , we take a closer look at various existing mapped WENO schemes and devise a general formula for their mapping functions that allows the extension of the OP criterion to all existing mapped WENO schemes .
Edit intent: clarity
Original:  In this article  , we  take a closer look at  various existing mapped WENO schemes in references to obtain a general formula for their mapping functions that allows the extension of the OP criterion to all existing mapped WENO schemes . The improved mapped WENO scheme considerring the OP criterion based on the existing mapped WENO-X schemeis named ] ] ] ] MOP-WENO-X.
Revised:  In this article , we take a closer look at various existing mapped WENO schemes in references to obtain a general formula for them. It helps us to extend the order-preserving (OP) criterion, originally defined and carefully examined in ] ] ] ] MOP-WENO-X.
Edit intent: coherence
Original:  The improved mapped WENO scheme considerring the OP criterion based on the existing mapped WENO-X schemeis named ] ] ] ] MOP-WENO-X.
Revised:  The improved mapped WENO scheme considerring the OP criterion based on the existing mapped WENO-X schemeis named ], 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 ] ] ] MOP-WENO-X.
Edit intent: meaning-changed
Original:  The improved mapped WENO scheme considerring the OP criterion based on the existing mapped WENO-X schemeis named ] ] ] ] MOP-WENO-X.
Revised:  The improved mapped WENO scheme considerring the OP criterion based on the existing mapped WENO-X schemeis named ] ] ], or PPM5 ] MOP-WENO-X.
Edit intent: meaning-changed
Original:  The improved mapped WENO scheme considerring the OP criterion based on the existing mapped WENO-X schemeis named ] ] ] ] MOP-WENO-X.
Revised:  The improved mapped WENO scheme considerring the OP criterion based on the existing mapped WENO-X schemeis named ] ] ] ], et al. Then we build the resultant mapped WENO schemes and denote them as MOP-WENO-X.
Edit intent: meaning-changed
Original:  Numerical solutions of the one-dimensional linear advection equation with different initial conditions  computed by the MOP-WENO-X schemes  are compared with the ones generated by the corresponding WENO-X schemes and the WENO-JS scheme.
Revised:  The numerical solutions of the one-dimensional linear advection equation with different initial conditions computed by the MOP-WENO-X schemes are compared with the ones generated by the corresponding WENO-X schemes and the WENO-JS scheme.
Edit intent: fluency
Original:  Numerical solutions of the one-dimensional linear advection equation with different initial conditions  computed by the MOP-WENO-X schemes  are compared with the ones generated by the corresponding WENO-X schemes and the WENO-JS scheme.
Revised:  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 the corresponding WENO-X schemes and the WENO-JS scheme.
Edit intent: meaning-changed
Original:  Numerical solutions of the one-dimensional linear advection equation with different initial conditions  computed by the MOP-WENO-X schemes  are compared with the ones generated by the corresponding WENO-X schemes and the WENO-JS scheme.
Revised:  Numerical solutions of the one-dimensional linear advection equation with different initial conditions computed by the MOP-WENO-X schemes , are compared with the ones generated by the corresponding WENO-X schemes and the WENO-JS scheme.
Edit intent: fluency
Original:  Numerical solutions of the one-dimensional linear advection equation with different initial conditions  computed by the MOP-WENO-X schemes  are compared with the ones generated by the corresponding WENO-X schemes and the WENO-JS scheme.
Revised:  Numerical solutions of the one-dimensional linear advection equation with different initial conditions computed by the MOP-WENO-X schemes are compared with the ones generated by their corresponding WENO-X schemes and the WENO-JS scheme.
Edit intent: clarity
Original:  To summarize, the MOP-WENO-X schemes also enjoy the advantages of the MOP-WENO-ACMk scheme proposed in 18 .
Revised:  To summarize, the MOP-WENO-X schemes also enjoy the advantages of the MOP-WENO-ACMk scheme proposed in 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: meaning-changed
Original:  The proposed algorithm and implementation allows three contributions: (1) To the best of our knowledge, we derive the first OTG algorithm with non-zero target acceleration, resulting in a complete defined target state . (2) This is the first open-source prototype of time-optimal OTG with limited jerk  . (3) Ruckig allows for directional velocity and acceleration limits, enabling robots to better use their dynamical resources.
Revised:  The proposed algorithm and implementation allows three contributions: (1) To the best of our knowledge, we derive the first time-optimal OTG algorithm for arbitrary, multi-dimensional target states, in particular including non-zero target acceleration, resulting in a complete defined target state . (2) This is the first open-source prototype of time-optimal OTG with limited jerk . (3) Ruckig allows for directional velocity and acceleration limits, enabling robots to better use their dynamical resources.
Edit intent: meaning-changed
Original:  The proposed algorithm and implementation allows three contributions: (1) To the best of our knowledge, we derive the first OTG algorithm with non-zero target acceleration, resulting in a complete defined target state . (2) This is the first open-source prototype of time-optimal OTG with limited jerk  . (3) Ruckig allows for directional velocity and acceleration limits, enabling robots to better use their dynamical resources.
Revised:  The proposed algorithm and implementation allows three contributions: (1) To the best of our knowledge, we derive the first OTG algorithm with non-zero target acceleration . (2) This is the first open-source prototype of time-optimal OTG with limited jerk . (3) Ruckig allows for directional velocity and acceleration limits, enabling robots to better use their dynamical resources.
Edit intent: coherence
Original:  The proposed algorithm and implementation allows three contributions: (1) To the best of our knowledge, we derive the first OTG algorithm with non-zero target acceleration, resulting in a complete defined target state . (2) This is the first open-source prototype of time-optimal OTG with limited jerk  . (3) Ruckig allows for directional velocity and acceleration limits, enabling robots to better use their dynamical resources.
Revised:  The proposed algorithm and implementation allows three contributions: (1) To the best of our knowledge, we derive the first OTG algorithm with non-zero target acceleration, resulting in a complete defined target state . (2) This is the first open-source prototype of time-optimal OTG with limited jerk and complete time synchronization for multiple DoFs . (3) Ruckig allows for directional velocity and acceleration limits, enabling robots to better use their dynamical resources.
Edit intent: meaning-changed
Original:  Our results raise important questions for future work about how, and to what extent, pre-trained language models capture knowledge about abstract semantic relations %DIFDELCMD < \footnote{%%% Source code and data to reproduce our experimental results are available in the following repository: URL
Revised:  Our results raise important questions for future work about how, and to what extent, pre-trained language models capture knowledge about abstract semantic relations %DIFDELCMD < \footnote{%%% .
Edit intent: coherence
Original: We show how an Euler tour for a tree on n vertices with maximum degree d can be stored in 2 n + o (n) bits such that queries take O (\log n) time and updates take O (d \log^{1 + \epsilon time.
Revised: We show how a collection of Euler-tour trees for a forest on n vertices with maximum degree d can be stored in 2 n + o (n) bits such that queries take O (\log n) time and updates take O (d \log^{1 + \epsilon time.
Edit intent: clarity
Original: We show how an Euler tour for a tree on n vertices with maximum degree d can be stored in 2 n + o (n) bits such that queries take O (\log n) time and updates take O (d \log^{1 + \epsilon time.
Revised: We show how an Euler tour for a tree on n vertices can be stored in 2 n + o (n) bits such that queries take O (\log n) time and updates take O (d \log^{1 + \epsilon time.
Edit intent: clarity
Original: We show how an Euler tour for a tree on n vertices with maximum degree d can be stored in 2 n + o (n) bits such that queries take O (\log n) time and updates take O (d \log^{1 + \epsilon time.
Revised: We show how an Euler tour for a tree on n vertices with maximum degree d can be stored in 2 n + o (n) bits such that simple queries take constant time, more complex queries take logarithmic time and updates take O (d \log^{1 + \epsilon time.
Edit intent: meaning-changed
Original: We show how an Euler tour for a tree on n vertices with maximum degree d can be stored in 2 n + o (n) bits such that queries take O (\log n) time and updates take O (d \log^{1 + \epsilon time.
Revised: We show how an Euler tour for a tree on n vertices with maximum degree d can be stored in 2 n + o (n) bits such that queries take O (\log n) time and updates take polylogarithmic amortized time.
Edit intent: meaning-changed
Original:  We achieve an FID of 2.97 on ImageNet 128 \times 128 \times128 , 4.59 on ImageNet 256 \times 256 \times256 , and 7.72 on ImageNet 512 \times 512 \times512 , and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution.
Revised:  We achieve an FID of 2.97 on ImageNet 128 \times128 , 4.59 on ImageNet 256 \times 256 \times256 , and 7.72 on ImageNet 512 \times 512 \times512 , and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution.
Edit intent: fluency
Original:  We achieve an FID of 2.97 on ImageNet 128 \times 128 \times128 , 4.59 on ImageNet 256 \times 256 \times256 , and 7.72 on ImageNet 512 \times 512 \times512 , and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution.
Revised:  We achieve an FID of 2.97 on ImageNet 128 \times 128 \times128 , 4.59 on ImageNet 256 \times256 , and 7.72 on ImageNet 512 \times 512 \times512 , and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution.
Edit intent: fluency
Original:  We achieve an FID of 2.97 on ImageNet 128 \times 128 \times128 , 4.59 on ImageNet 256 \times 256 \times256 , and 7.72 on ImageNet 512 \times 512 \times512 , and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution.
Revised:  We achieve an FID of 2.97 on ImageNet 128 \times 128 \times128 , 4.59 on ImageNet 256 \times 256 \times256 , and 7.72 on ImageNet 512 \times512 , and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution.
Edit intent: fluency
Original:  Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to \times256  3.85 on ImageNet 512\times512.
Revised:  Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256\times256 3.85 on ImageNet 512\times512.
Edit intent: meaning-changed
Original:  However, noisy edges and nodes in the graph may make the clusteringresults worse. In this paper , we propose a novel dual graph embedding network (DGEN) to improve the robustness of the graph clustering to the noisy nodes and edges.
Revised:  However, noisy edges and nodes in the graph that are not conducive to graph clustering, which we call spurious edges. This paper is the first attempt to employ graph pooling technique for node clustering and we propose a novel dual graph embedding network (DGEN) to improve the robustness of the graph clustering to the noisy nodes and edges.
Edit intent: meaning-changed
Original:  In this paper , we propose a novel dual graph embedding network (DGEN) to improve the robustness of the graph clustering to the noisy nodes and edges. DGEN is designed as a two-step graph encoder connected by a graph pooling layer , which learns the graph embedding of the selected nodes.
Revised:  In this paper , we propose a novel dual graph embedding network (DGEN) , which is designed as a two-step graph encoder connected by a graph pooling layer , which learns the graph embedding of the selected nodes.
Edit intent: coherence
Original:  DGEN is designed as a two-step graph encoder connected by a graph pooling layer , which learns the graph embedding of the selected nodes. Based on the assumption that a node and its nearest neighbors should belong to the same cluster, we devise the neighbor cluster pooling (NCPool)  to select the most informative subset of vertices based on the clustering assignments of nodes and their nearest neighbor .
Revised:  DGEN is designed as a two-step graph encoder connected by a graph pooling layer , which learns the graph embedding . In our model, it is assumed that if a node and its nearest neighbors should belong to the same cluster, we devise the neighbor cluster pooling (NCPool) to select the most informative subset of vertices based on the clustering assignments of nodes and their nearest neighbor .
Edit intent: clarity
Original:  Based on the assumption that a node and its nearest neighbors should belong to the same cluster, we devise the neighbor cluster pooling (NCPool)  to select the most informative subset of vertices based on the clustering assignments of nodes and their nearest neighbor .
Revised:  Based on the assumption that a node and its nearest neighboring node are close to the same cluster, we devise the neighbor cluster pooling (NCPool) to select the most informative subset of vertices based on the clustering assignments of nodes and their nearest neighbor .
Edit intent: clarity
Original:  Based on the assumption that a node and its nearest neighbors should belong to the same cluster, we devise the neighbor cluster pooling (NCPool)  to select the most informative subset of vertices based on the clustering assignments of nodes and their nearest neighbor .
Revised:  Based on the assumption that a node and its nearest neighbors should belong to the same clustering center, this node is an informative node and this edge can be considered as a cluster-friendly edge. Based on this assumption, the neighbor cluster pooling (NCPool) to select the most informative subset of vertices based on the clustering assignments of nodes and their nearest neighbor .
Edit intent: meaning-changed
Original:  Based on the assumption that a node and its nearest neighbors should belong to the same cluster, we devise the neighbor cluster pooling (NCPool)  to select the most informative subset of vertices based on the clustering assignments of nodes and their nearest neighbor .
Revised:  Based on the assumption that a node and its nearest neighbors should belong to the same cluster, we devise the neighbor cluster pooling (NCPool) is devised to select the most informative subset of vertices based on the clustering assignments of nodes and their nearest neighbor .
Edit intent: clarity