wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, we present the design and implementation of FiberStars, a visual analysis tool for tractography data that allows the interactive and scalable visualization of brain fiber clusters in 2D and 3D  . With FiberStars, researchers can analyze and compare multiple subjects in large collections of brain fibers  .
Revised:  In this paper, we present the design and implementation of FiberStars, a visual analysis tool for tractography data that allows the interactive and scalable visualization of brain fiber clusters in 2D and 3D anatomy with compact 2D visualizations . With FiberStars, researchers can analyze and compare multiple subjects in large collections of brain fibers .
Edit intent: meaning-changed
Original:  In this paper, we present the design and implementation of FiberStars, a visual analysis tool for tractography data that allows the interactive and scalable visualization of brain fiber clusters in 2D and 3D  . With FiberStars, researchers can analyze and compare multiple subjects in large collections of brain fibers  .
Revised:  In this paper, we present the design and implementation of FiberStars, a visual analysis tool for tractography data that allows the interactive and scalable visualization of brain fiber clusters in 2D and 3D . With FiberStars, researchers can analyze and compare multiple subjects in large collections of brain fibers using different views .
Edit intent: meaning-changed
Original:  However, existing GNNs often meet complex graph structures with scarce labeled nodes and suffer from the limitations of non-robustness, over-smoothing, and overfitting. To address these issues , we propose a simple yet effective GNN framework---Graph Random Neural Network (Grand).
Revised:  However, existing GNNs often meet complex graph structures with scarce labeled nodes and suffer from the limitations of non-robustness, over-smoothing, non-robustness, and weak-generalization when labeled nodes are scarce. In this paper , we propose a simple yet effective GNN framework---Graph Random Neural Network (Grand).
Edit intent: meaning-changed
Original:  To address these issues , we propose a simple yet effective GNN framework---Graph Random Neural Network (Grand). Different from the deterministic propagation in existing GNNs, Grand adopts a random propagation strategy to enhance model robustness.
Revised:  To address these issues , we propose a simple yet effective framework---GRAPH RANDOM NEURAL NETWORKS (GRAND)---to address these issues. In GRAND, we first design a random propagation strategy to enhance model robustness.
Edit intent: clarity
Original:  Different from the deterministic propagation in existing GNNs, Grand adopts a random propagation strategy to enhance model robustness. This strategy also naturally enables Grand to decouple the propagation from feature transformation, reducing the risks of over-smoothing and overfitting. Moreover, random propagation acts as an efficient method for graph data augmentation.
Revised:  Different from the deterministic propagation in existing GNNs, Grand adopts a random propagation strategy to perform graph data augmentation.
Edit intent: clarity
Original:  Based on this, we propose the consistency regularization for Grand by leveraging the distributional consistency of unlabeled nodes in multiple augmentations, improving the generalization capacity of the model . Extensive experiments on graph benchmark datasets suggest that Grand significantly outperforms state-of-the-art GNN baselines on semi-supervised graph learning tasks .
Revised:  Then we leverage consistency regularization to optimize the prediction consistency of unlabeled nodes in multiple augmentations, improving the generalization capacity of the model . Extensive experiments on graph benchmark datasets suggest that Grand significantly outperforms state-of-the-art GNN baselines on semi-supervised graph learning tasks .
Edit intent: clarity
Original:  Based on this, we propose the consistency regularization for Grand by leveraging the distributional consistency of unlabeled nodes in multiple augmentations, improving the generalization capacity of the model . Extensive experiments on graph benchmark datasets suggest that Grand significantly outperforms state-of-the-art GNN baselines on semi-supervised graph learning tasks .
Revised:  Based on this, we propose the consistency regularization for Grand by leveraging the distributional consistency of unlabeled nodes across different data augmentations . Extensive experiments on graph benchmark datasets suggest that Grand significantly outperforms state-of-the-art GNN baselines on semi-supervised graph learning tasks .
Edit intent: clarity
Original:  Based on this, we propose the consistency regularization for Grand by leveraging the distributional consistency of unlabeled nodes in multiple augmentations, improving the generalization capacity of the model . Extensive experiments on graph benchmark datasets suggest that Grand significantly outperforms state-of-the-art GNN baselines on semi-supervised graph learning tasks .
Revised:  Based on this, we propose the consistency regularization for Grand by leveraging the distributional consistency of unlabeled nodes in multiple augmentations, improving the generalization capacity of the model . Extensive experiments on graph benchmark datasets suggest that GRAND significantly outperforms state-of-the-art GNN baselines on semi-supervised graph learning tasks .
Edit intent: clarity
Original:  Based on this, we propose the consistency regularization for Grand by leveraging the distributional consistency of unlabeled nodes in multiple augmentations, improving the generalization capacity of the model . Extensive experiments on graph benchmark datasets suggest that Grand significantly outperforms state-of-the-art GNN baselines on semi-supervised graph learning tasks .
Revised:  Based on this, we propose the consistency regularization for Grand by leveraging the distributional consistency of unlabeled nodes in multiple augmentations, improving the generalization capacity of the model . Extensive experiments on graph benchmark datasets suggest that Grand significantly outperforms state-of-the-art GNN baselines on semi-supervised node classification .
Edit intent: clarity
Original:  We propose CLaSS (Controlled Latent attribute Space Sampling) - a novel and efficient computational method for attribute-controlled generation of molecules, which leverages guidance from classifiers trained on an informative latent space of molecules modeled using a deep generative autoencoder.
Revised:  We propose CLaSS (Controlled Latent attribute Space Sampling) - an efficient computational method for attribute-controlled generation of molecules, which leverages guidance from classifiers trained on an informative latent space of molecules modeled using a deep generative autoencoder.
Edit intent: clarity
Original:  Synthesis and wet lab testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including the hard-to-treat multidrug-resistant  K. pneumoniae, as well as low in vitro and in vivo toxicity  .
Revised:  Synthesis and testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including the hard-to-treat multidrug-resistant K. pneumoniae, as well as low in vitro and in vivo toxicity .
Edit intent: clarity
Original:  Synthesis and wet lab testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including the hard-to-treat multidrug-resistant  K. pneumoniae, as well as low in vitro and in vivo toxicity  .
Revised:  Synthesis and wet lab testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including one multidrug-resistant K. pneumoniae, as well as low in vitro and in vivo toxicity .
Edit intent: clarity
Original:  Synthesis and wet lab testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including the hard-to-treat multidrug-resistant  K. pneumoniae, as well as low in vitro and in vivo toxicity  .
Revised:  Synthesis and wet lab testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including the hard-to-treat multidrug-resistant and one antibiotic-resistant K. pneumoniae, as well as low in vitro and in vivo toxicity .
Edit intent: meaning-changed
Original:  Synthesis and wet lab testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including the hard-to-treat multidrug-resistant  K. pneumoniae, as well as low in vitro and in vivo toxicity  .
Revised:  Synthesis and wet lab testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including the hard-to-treat multidrug-resistant K. pneumoniae, via membrane pore formation. Both antimicrobials exhibit low in vitro and in vivo toxicity .
Edit intent: meaning-changed
Original:  Synthesis and wet lab testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including the hard-to-treat multidrug-resistant  K. pneumoniae, as well as low in vitro and in vivo toxicity  .
Revised:  Synthesis and wet lab testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including the hard-to-treat multidrug-resistant K. pneumoniae, as well as low in vitro and in vivo toxicity and mitigate the onset of drug resistance .
Edit intent: meaning-changed
Original: We work on dynamic problems with collected data\subsetR^p based on collected sample points } \{x_i\} that distributed on a manifold \mathcal{M\subsetR^p} ^n \subset M that probe the unknown manifold M} . Through the diffusion map, we first learn the reaction coordinates \{y_i\} \subset \mathcal{N^n\subset N corresponding to \{x_i\}_{i=1}^n, } where N is a manifold isometrically embedded into an Euclidean space \mathbb{R^\ell for }  and isometrically embedded in \mathbb{R}^\ell with } \ell \ll p.
Revised: We study the Langevin dynamics of a physical system with manifold structure \mathcal{M\subsetR^p based on collected sample points } \{x_i\} that distributed on a manifold \mathcal{M\subsetR^p} ^n \subset M that probe the unknown manifold M} . Through the diffusion map, we first learn the reaction coordinates \{y_i\} \subset \mathcal{N^n\subset N corresponding to \{x_i\}_{i=1}^n, } where N is a manifold isometrically embedded into an Euclidean space \mathbb{R^\ell for } and isometrically embedded in \mathbb{R}^\ell with } \ell \ll p.
Edit intent: meaning-changed
Original: We work on dynamic problems with collected data\subsetR^p based on collected sample points } \{x_i\} that distributed on a manifold \mathcal{M\subsetR^p} ^n \subset M that probe the unknown manifold M} . Through the diffusion map, we first learn the reaction coordinates \{y_i\} \subset \mathcal{N^n\subset N corresponding to \{x_i\}_{i=1}^n, } where N is a manifold isometrically embedded into an Euclidean space \mathbb{R^\ell for }  and isometrically embedded in \mathbb{R}^\ell with } \ell \ll p.
Revised: We work on dynamic problems with collected data\subsetR^p based on collected sample points } \{x_i\} \subsetR^p} ^n \subset M that probe the unknown manifold M} . Through the diffusion map, we first learn the reaction coordinates \{y_i\} \subset \mathcal{N^n\subset N corresponding to \{x_i\}_{i=1}^n, } where N is a manifold isometrically embedded into an Euclidean space \mathbb{R^\ell for } and isometrically embedded in \mathbb{R}^\ell with } \ell \ll p.
Edit intent: clarity
Original: We work on dynamic problems with collected data\subsetR^p based on collected sample points } \{x_i\} that distributed on a manifold \mathcal{M\subsetR^p} ^n \subset M that probe the unknown manifold M} . Through the diffusion map, we first learn the reaction coordinates \{y_i\} \subset \mathcal{N^n\subset N corresponding to \{x_i\}_{i=1}^n, } where N is a manifold isometrically embedded into an Euclidean space \mathbb{R^\ell for }  and isometrically embedded in \mathbb{R}^\ell with } \ell \ll p.
Revised: We work on dynamic problems with collected data\subsetR^p based on collected sample points } \{x_i\} that distributed on a manifold \mathcal{M\subsetR^p} _{i=1^n \subset M that probe the unknown manifold M} . Through the diffusion map, we first learn the reaction coordinates \{y_i\} \subset \mathcal{N^n\subset N corresponding to \{x_i\}_{i=1}^n, } where N is a manifold isometrically embedded into an Euclidean space \mathbb{R^\ell for } and isometrically embedded in \mathbb{R}^\ell with } \ell \ll p.
Edit intent: clarity
Original: We work on dynamic problems with collected data\subsetR^p based on collected sample points } \{x_i\} that distributed on a manifold \mathcal{M\subsetR^p} ^n \subset M that probe the unknown manifold M} . Through the diffusion map, we first learn the reaction coordinates \{y_i\} \subset \mathcal{N^n\subset N corresponding to \{x_i\}_{i=1}^n, } where N is a manifold isometrically embedded into an Euclidean space \mathbb{R^\ell for }  and isometrically embedded in \mathbb{R}^\ell with } \ell \ll p.
Revised: We work on dynamic problems with collected data\subsetR^p based on collected sample points } \{x_i\} that distributed on a manifold \mathcal{M\subsetR^p} ^n \subset M that probe the unknown manifold M} . Through the diffusion map, we first learn the reaction coordinates \{y_i\} _{i=1^n\subset N corresponding to \{x_i\}_{i=1}^n, } where N is a manifold isometrically embedded into an Euclidean space \mathbb{R^\ell for } and isometrically embedded in \mathbb{R}^\ell with } \ell \ll p.
Edit intent: meaning-changed
Original: We work on dynamic problems with collected data\subsetR^p based on collected sample points } \{x_i\} that distributed on a manifold \mathcal{M\subsetR^p} ^n \subset M that probe the unknown manifold M} . Through the diffusion map, we first learn the reaction coordinates \{y_i\} \subset \mathcal{N^n\subset N corresponding to \{x_i\}_{i=1}^n, } where N is a manifold isometrically embedded into an Euclidean space \mathbb{R^\ell for }  and isometrically embedded in \mathbb{R}^\ell with } \ell \ll p.
Revised: We work on dynamic problems with collected data\subsetR^p based on collected sample points } \{x_i\} that distributed on a manifold \mathcal{M\subsetR^p} ^n \subset M that probe the unknown manifold M} . Through the diffusion map, we first learn the reaction coordinates \{y_i\} \subset \mathcal{N^n\subset N corresponding to \{x_i\}_{i=1}^n, } where N is a manifold ^\ell for } and isometrically embedded in \mathbb{R}^\ell with } \ell \ll p.
Edit intent: clarity
Original: We work on dynamic problems with collected data\subsetR^p based on collected sample points } \{x_i\} that distributed on a manifold \mathcal{M\subsetR^p} ^n \subset M that probe the unknown manifold M} . Through the diffusion map, we first learn the reaction coordinates \{y_i\} \subset \mathcal{N^n\subset N corresponding to \{x_i\}_{i=1}^n, } where N is a manifold isometrically embedded into an Euclidean space \mathbb{R^\ell for }  and isometrically embedded in \mathbb{R}^\ell with } \ell \ll p.
Revised: We work on dynamic problems with collected data\subsetR^p based on collected sample points } \{x_i\} that distributed on a manifold \mathcal{M\subsetR^p} ^n \subset M that probe the unknown manifold M} . Through the diffusion map, we first learn the reaction coordinates \{y_i\} \subset \mathcal{N^n\subset N corresponding to \{x_i\}_{i=1}^n, } where N is a manifold isometrically embedded into an Euclidean space \mathbb{R^\ell for } diffeomorphic to \mathcal{M and isometrically embedded in \mathbb{R}^\ell with } \ell \ll p.
Edit intent: meaning-changed
Original:  The reaction coordinates enable us to obtain an efficient approximation for the dynamics described by a Fokker-Planck equation on the manifold \mathcal{N} . By using the reaction coordinates , we propose an implementable, unconditionally stable, data-driven upwind scheme which automatically incorporates the manifold structure of \mathcal{N}.
Revised:  The induced Langevin dynamics on \mathcal{N approximation for the dynamics described by a Fokker-Planck equation on the manifold \mathcal{N} . By using the reaction coordinates , we propose an implementable, unconditionally stable, data-driven upwind scheme which automatically incorporates the manifold structure of \mathcal{N}.
Edit intent: meaning-changed
Original:  The reaction coordinates enable us to obtain an efficient approximation for the dynamics described by a Fokker-Planck equation on the manifold \mathcal{N} . By using the reaction coordinates , we propose an implementable, unconditionally stable, data-driven upwind scheme which automatically incorporates the manifold structure of \mathcal{N}.
Revised:  The reaction coordinates enable us to obtain an efficient approximation for the Langevin dynamics on \mathcal{N Fokker-Planck equation on the manifold \mathcal{N} . By using the reaction coordinates , we propose an implementable, unconditionally stable, data-driven upwind scheme which automatically incorporates the manifold structure of \mathcal{N}.
Edit intent: meaning-changed
Original:  The reaction coordinates enable us to obtain an efficient approximation for the dynamics described by a Fokker-Planck equation on the manifold \mathcal{N} . By using the reaction coordinates , we propose an implementable, unconditionally stable, data-driven upwind scheme which automatically incorporates the manifold structure of \mathcal{N}.
Revised:  The reaction coordinates enable us to obtain an efficient approximation for the dynamics described by a Fokker-Planck equation on the manifold \mathcal{N} in terms of the reaction coordinates , we propose an implementable, unconditionally stable, data-driven upwind scheme which automatically incorporates the manifold structure of \mathcal{N}.
Edit intent: meaning-changed
Original:  The reaction coordinates enable us to obtain an efficient approximation for the dynamics described by a Fokker-Planck equation on the manifold \mathcal{N} . By using the reaction coordinates , we propose an implementable, unconditionally stable, data-driven upwind scheme which automatically incorporates the manifold structure of \mathcal{N}.
Revised:  The reaction coordinates enable us to obtain an efficient approximation for the dynamics described by a Fokker-Planck equation on the manifold \mathcal{N} . By using the reaction coordinates \mathsf{y propose an implementable, unconditionally stable, data-driven upwind scheme which automatically incorporates the manifold structure of \mathcal{N}.
Edit intent: meaning-changed
Original:  The reaction coordinates enable us to obtain an efficient approximation for the dynamics described by a Fokker-Planck equation on the manifold \mathcal{N} . By using the reaction coordinates , we propose an implementable, unconditionally stable, data-driven upwind scheme which automatically incorporates the manifold structure of \mathcal{N}.
Revised:  The reaction coordinates enable us to obtain an efficient approximation for the dynamics described by a Fokker-Planck equation on the manifold \mathcal{N} . By using the reaction coordinates , we propose an implementable, unconditionally stable, data-driven finite volume scheme for this Fokker-Planck equation, which automatically incorporates the manifold structure of \mathcal{N}.
Edit intent: meaning-changed
Original:  The proposed upwind scheme leads to a Markov chain with transition probability_i\}_{i=1}^n with an approximated transition probability and jump rate } between the nearest neighbor points.
Revised:  The proposed upwind scheme leads to a Markov chain on \{\mathsf{y_i\}_{i=1}^n with an approximated transition probability and jump rate } between the nearest neighbor points.
Edit intent: meaning-changed
Original:  Finally, we develop an algorithm to pullback the trajectory to the original high dimensional space as a generative data for the new physical system}  .
Revised:  Finally, we develop an algorithm to pullback the trajectory to the original high dimensional space as a generative data for the new physical system} After an unconditionally stable explicit time discretization, the data-driven finite volume scheme gives an approximated Markov process for the Langevin dynamics on \mathcal{N .
Edit intent: meaning-changed
Original:  However, the study regarding Heterogeneous Information Network (HIN) is still limited, because the existing HIN-oriented GCN methods  suffer from two deficiencies: (1) they cannot flexibly exploit all possible meta-paths , and some even require the user to specify useful ones ;
Revised:  However, regarding Heterogeneous Information Network (HIN) is still limited, because the existing HIN-oriented GCN methods suffer from two deficiencies: (1) they cannot flexibly exploit all possible meta-paths , and some even require the user to specify useful ones ;
Edit intent: clarity
Original:  However, the study regarding Heterogeneous Information Network (HIN) is still limited, because the existing HIN-oriented GCN methods  suffer from two deficiencies: (1) they cannot flexibly exploit all possible meta-paths , and some even require the user to specify useful ones ;
Revised:  However, the study regarding Heterogeneous Information Network (HIN) , existing HIN-oriented GCN methods suffer from two deficiencies: (1) they cannot flexibly exploit all possible meta-paths , and some even require the user to specify useful ones ;
Edit intent: coherence
Original:  However, the study regarding Heterogeneous Information Network (HIN) is still limited, because the existing HIN-oriented GCN methods  suffer from two deficiencies: (1) they cannot flexibly exploit all possible meta-paths , and some even require the user to specify useful ones ;
Revised:  However, the study regarding Heterogeneous Information Network (HIN) is still limited, because the existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot flexibly exploit all possible meta-paths , and some even require the user to specify useful ones ;
Edit intent: fluency
Original:  However, the study regarding Heterogeneous Information Network (HIN) is still limited, because the existing HIN-oriented GCN methods  suffer from two deficiencies: (1) they cannot flexibly exploit all possible meta-paths , and some even require the user to specify useful ones ;
Revised:  However, the study regarding Heterogeneous Information Network (HIN) is still limited, because the existing HIN-oriented GCN methods suffer from two deficiencies: (1) they cannot flexibly explore all possible meta-paths , and some even require the user to specify useful ones ;
Edit intent: clarity
Original:  However, the study regarding Heterogeneous Information Network (HIN) is still limited, because the existing HIN-oriented GCN methods  suffer from two deficiencies: (1) they cannot flexibly exploit all possible meta-paths , and some even require the user to specify useful ones ;
Revised:  However, the study regarding Heterogeneous Information Network (HIN) is still limited, because the existing HIN-oriented GCN methods suffer from two deficiencies: (1) they cannot flexibly exploit all possible meta-paths and extract the most useful ones for a target object, which hinders both effectiveness and interpretability ;
Edit intent: meaning-changed