wanyu/IteraTeR_full_sent huggingface dataset:
Original:  mostly positively charged patches on the spike, and thereby mask the basic residues of the receptor binding domain and the basic S1/S2 site , which is unique specific to SAR-CoV-2 and is important for furin-mediated spike cleavage and the subsequent formation of its post-fusion conformation . Experiments corroborated the simulation results by showing that heparin binds the basic S1/S2 site of spike, inhibiting cleavage by furin.
Revised:  mostly positively charged patches on the spike, and thereby mask the basic residues of the receptor binding domain and the basic S1/S2 site , which is unique specific to SAR-CoV-2 and is important for furin-mediated spike cleavage and the subsequent formation of its post-fusion conformation . Experiments corroborated the simulation results by showing that heparin inhibits the cleavage of spike by furin by binding to the basic S1/S2 site of spike, inhibiting cleavage by furin.
Edit intent: clarity
Original:  mostly positively charged patches on the spike, and thereby mask the basic residues of the receptor binding domain and the basic S1/S2 site , which is unique specific to SAR-CoV-2 and is important for furin-mediated spike cleavage and the subsequent formation of its post-fusion conformation . Experiments corroborated the simulation results by showing that heparin binds the basic S1/S2 site of spike, inhibiting cleavage by furin. The simulations show that heparin can act on the hinge region responsible for the motion of the RBD between inactive closed and active open conformations of the spike. In simulations of the closed spike, heparin binds the RBD and the N-terminal domain of two adjacent spike subunits and hinders the opening. In simulations of the open spike, heparin binds similarly but induces stabilization of the hinge region and a change in RBD motion.
Revised:  mostly positively charged patches on the spike, and thereby mask the basic residues of the receptor binding domain and the basic S1/S2 site , which is unique specific to SAR-CoV-2 and is important for furin-mediated spike cleavage and the subsequent formation of its post-fusion conformation . Experiments corroborated the simulation results by showing that heparin binds the basic S1/S2 site .
Edit intent: coherence
Original:  Our results indicate that heparin is able to inhibit SARS-CoV-2 infection by allosterically hindering binding to the host cell receptor, by directly competing with binding to the host heparan sulfate proteoglycan co-receptors, and by preventing spike cleavage by furin .
Revised:  Our results indicate that heparin exerts its antiviral activity by both direct and allosteric mechanisms .
Edit intent: clarity
Original: Physics-Informed Neural Networks (PINN) are neural networks that encode the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network training . PINNs have emerged as an essential tool to solve various challenging problems, such as computing linear and non-linear PDEs, completing data assimilation and uncertainty quantification tasks .
Revised: Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network training . PINNs have emerged as an essential tool to solve various challenging problems, such as computing linear and non-linear PDEs, completing data assimilation and uncertainty quantification tasks .
Edit intent: clarity
Original: Physics-Informed Neural Networks (PINN) are neural networks that encode the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network training . PINNs have emerged as an essential tool to solve various challenging problems, such as computing linear and non-linear PDEs, completing data assimilation and uncertainty quantification tasks .
Revised: Physics-Informed Neural Networks (PINN) are neural networks that encode the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network . PINNs have emerged as an essential tool to solve various challenging problems, such as computing linear and non-linear PDEs, completing data assimilation and uncertainty quantification tasks .
Edit intent: clarity
Original: Physics-Informed Neural Networks (PINN) are neural networks that encode the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network training . PINNs have emerged as an essential tool to solve various challenging problems, such as computing linear and non-linear PDEs, completing data assimilation and uncertainty quantification tasks .
Revised: Physics-Informed Neural Networks (PINN) are neural networks that encode the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network training . PINNs have emerged as a new essential tool to solve various challenging problems, such as computing linear and non-linear PDEs, completing data assimilation and uncertainty quantification tasks .
Edit intent: clarity
Original: Physics-Informed Neural Networks (PINN) are neural networks that encode the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network training . PINNs have emerged as an essential tool to solve various challenging problems, such as computing linear and non-linear PDEs, completing data assimilation and uncertainty quantification tasks .
Revised: Physics-Informed Neural Networks (PINN) are neural networks that encode the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network training . PINNs have emerged as an essential tool to solve various challenging problems, including computing linear systems arising from PDEs, a task for which several traditional methods exist .
Edit intent: clarity
Original:  In this work, we focus  on evaluating the PINN potential to replace or accelerate traditional approaches for solving linear systems. We solve the Poisson equation, one of the most critical and computational-intensive tasks in scientific computing , with different source terms.
Revised:  In this work, we focus on evaluating the potential of PINNs as linear solvers in the case of the Poisson equation, one of the most critical and computational-intensive tasks in scientific computing , with different source terms.
Edit intent: clarity
Original:  We solve the Poisson equation, one of the most critical and computational-intensive tasks in scientific computing , with different source terms. We test and evaluate PINN performance under different  configurations (depth, activation functions, input data set distribution , and transfer learningimpact).
Revised:  We solve the Poisson equation, one of the most critical and computational-intensive tasks in scientific computing . We characterize PINN linear solvers in terms of accuracy and performance under different configurations (depth, activation functions, input data set distribution , and transfer learningimpact).
Edit intent: clarity
Original:  We test and evaluate PINN performance under different  configurations (depth, activation functions, input data set distribution , and transfer learningimpact). We show how to integrate PINN with traditional scientific computing approaches , such as multigrid and Gauss-Seidel methods.
Revised:  We test and evaluate PINN performance under different configurations (depth, activation functions, input data set distribution ). We highlight the critical role of transfer learning. Our results show that low-frequency components of the solution converge quickly as an effect of the F-principle. In contrast, an accurate solution of the high frequencies requires an exceedingly long time. To address this limitation, we propose integrating PINNs into traditional linear solvers. We show that this integration leads to the development of new solvers whose performance is on par with other high-performance solvers , such as multigrid and Gauss-Seidel methods.
Edit intent: meaning-changed
Original:  We show how to integrate PINN with traditional scientific computing approaches , such as multigrid and Gauss-Seidel methods. While the accuracy and computational performance is still a limiting factor for the direct use of PINN for solving , hybrid strategies are a viable option for the development of a new class of linear solvers combining emerging deep-learning and traditional scientific computing approaches .
Revised:  We show how to integrate PINN with traditional scientific computing approaches , such as PETSc conjugate gradient linear solvers, in terms of performance and accuracy. Overall, while the accuracy and computational performance is still a limiting factor for the direct use of PINN for solving , hybrid strategies are a viable option for the development of a new class of linear solvers combining emerging deep-learning and traditional scientific computing approaches .
Edit intent: meaning-changed
Original:  While the accuracy and computational performance is still a limiting factor for the direct use of PINN for solving , hybrid strategies are a viable option for the development of a new class of linear solvers combining emerging deep-learning and traditional scientific computing approaches .
Revised:  While the accuracy and computational performance are still a limiting factor for the direct use of PINN for solving , hybrid strategies are a viable option for the development of a new class of linear solvers combining emerging deep-learning and traditional scientific computing approaches .
Edit intent: fluency
Original:  While the accuracy and computational performance is still a limiting factor for the direct use of PINN for solving , hybrid strategies are a viable option for the development of a new class of linear solvers combining emerging deep-learning and traditional scientific computing approaches .
Revised:  While the accuracy and computational performance is still a limiting factor for the direct use of PINN linear solvers , hybrid strategies are a viable option for the development of a new class of linear solvers combining emerging deep-learning and traditional scientific computing approaches .
Edit intent: clarity
Original:  While the accuracy and computational performance is still a limiting factor for the direct use of PINN for solving , hybrid strategies are a viable option for the development of a new class of linear solvers combining emerging deep-learning and traditional scientific computing approaches .
Revised:  While the accuracy and computational performance is still a limiting factor for the direct use of PINN for solving , hybrid strategies combining old traditional linear solver approaches with new emerging deep-learning techniques are among the most promising methods for developing a new class of linear solvers combining emerging deep-learning and traditional scientific computing approaches .
Edit intent: clarity
Original:  While the accuracy and computational performance is still a limiting factor for the direct use of PINN for solving , hybrid strategies are a viable option for the development of a new class of linear solvers combining emerging deep-learning and traditional scientific computing approaches .
Revised:  While the accuracy and computational performance is still a limiting factor for the direct use of PINN for solving , hybrid strategies are a viable option for the development of a new class of linear solvers .
Edit intent: clarity
Original: While the beta-VAE family is aiming to find disentangled representations and acquire human-interpretable generative factors, like what an ICA does in the linear domain  , we propose Full Encoder : a novel unified autoencoder framework as a correspondence to PCA in the non-linear domain.
Revised: While the beta-VAE family is aiming to find disentangled representations and acquire human-interpretable generative factors, like what an ICA (from the linear domain , we propose Full Encoder : a novel unified autoencoder framework as a correspondence to PCA in the non-linear domain.
Edit intent: fluency
Original: While the beta-VAE family is aiming to find disentangled representations and acquire human-interpretable generative factors, like what an ICA does in the linear domain  , we propose Full Encoder : a novel unified autoencoder framework as a correspondence to PCA in the non-linear domain.
Revised: While the beta-VAE family is aiming to find disentangled representations and acquire human-interpretable generative factors, like what an ICA does in the linear domain ) does , we propose Full Encoder : a novel unified autoencoder framework as a correspondence to PCA in the non-linear domain.
Edit intent: meaning-changed
Original: While the beta-VAE family is aiming to find disentangled representations and acquire human-interpretable generative factors, like what an ICA does in the linear domain  , we propose Full Encoder : a novel unified autoencoder framework as a correspondence to PCA in the non-linear domain.
Revised: While the beta-VAE family is aiming to find disentangled representations and acquire human-interpretable generative factors, like what an ICA does in the linear domain , we propose Full Encoder , a novel unified autoencoder framework as a correspondence to PCA in the non-linear domain.
Edit intent: fluency
Original:  We created a toy dataset with a  non-linear system to test the Full Encoder and compare its results to VAE and beta-VAE's results .
Revised:  These qualities will make FE useful for analyzing real-life industrial non-linear systems. To validate, we created a toy dataset with a non-linear system to test the Full Encoder and compare its results to VAE and beta-VAE's results .
Edit intent: meaning-changed
Original:  However, generalizing vision-based RL policy to unknown test environments still remains as a challengingproblem. Unlike previous works that focus on training a universal RL policy that is invariant to discrepancies between test and training environment, we focus on developing an independent module to disperse interference factors irrelevant to the task, thereby providing "clean " observations for the RL policy .
Revised:  However, generalizing vision-based RL policy to unknown test environments remains challenging. Existing methods focus on training a universal RL policy that is invariant to discrepancies between test and training environment, we focus on developing an independent module to disperse interference factors irrelevant to the task, thereby providing "clean " observations for the RL policy .
Edit intent: clarity
Original:  Unlike previous works that focus on training a universal RL policy that is invariant to discrepancies between test and training environment, we focus on developing an independent module to disperse interference factors irrelevant to the task, thereby providing "clean " observations for the RL policy .
Revised:  Unlike previous works that focus on training an RL policy that is invariant to discrepancies between test and training environment, we focus on developing an independent module to disperse interference factors irrelevant to the task, thereby providing "clean " observations for the RL policy .
Edit intent: clarity
Original:  Unlike previous works that focus on training a universal RL policy that is invariant to discrepancies between test and training environment, we focus on developing an independent module to disperse interference factors irrelevant to the task, thereby providing "clean " observations for the RL policy .
Revised:  Unlike previous works that focus on training a universal RL policy that is universal to changing visual domains, whereas we focus on developing an independent module to disperse interference factors irrelevant to the task, thereby providing "clean " observations for the RL policy .
Edit intent: meaning-changed
Original:  Unlike previous works that focus on training a universal RL policy that is invariant to discrepancies between test and training environment, we focus on developing an independent module to disperse interference factors irrelevant to the task, thereby providing "clean " observations for the RL policy .
Revised:  Unlike previous works that focus on training a universal RL policy that is invariant to discrepancies between test and training environment, we focus on extracting visual foreground that is universal, feeding clean invariant vision to the RL policy .
Edit intent: clarity
Original:  Unlike previous works that focus on training a universal RL policy that is invariant to discrepancies between test and training environment, we focus on developing an independent module to disperse interference factors irrelevant to the task, thereby providing "clean " observations for the RL policy . The proposed unsupervised visual attention and invariance method (VAI) contains three key components: 1) an unsupervisedkeypoint detection model which captures semantically meaningful keypoints in observations; 2) an unsupervised visual attention module which automatically generates the distraction-invariant attention mask for each observation;
Revised:  Unlike previous works that focus on training a universal RL policy that is invariant to discrepancies between test and training environment, we focus on developing an independent module to disperse interference factors irrelevant to the task, thereby providing "clean " observations for the RL policy learner. Our method is completely unsupervised, without manual annotations or access to environment internals. Given videos of actions in a training environment, we learn how to extract foregrounds with unsupervised keypoint detection, followed by unsupervised visual attention module which automatically generates the distraction-invariant attention mask for each observation;
Edit intent: coherence
Original:  2) an unsupervised visual attention module which automatically generates the distraction-invariant attention mask for each observation; 3) a self-supervised adapter for visual distraction invariance which reconstructs distraction-invariant attention mask from observations with artificial disturbances generated by a series of foreground and background augmentations. All components are optimized in an unsupervised way, without manual annotation or access to environment internals, and only the adapter is used during inference time to provide distraction-free observations to RL policy.
Revised:  2) an unsupervised visual attention to automatically generate a foreground mask per video frame. We can then introduce artificial distractors and train a model to reconstruct the clean foreground mask from noisy observations. Only this learned model is needed during test to provide distraction-free observations to RL policy.
Edit intent: clarity
Original:  All components are optimized in an unsupervised way, without manual annotation or access to environment internals, and only the adapter is used during inference time to provide distraction-free observations to RL policy. VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode  .
Revised:  All components are optimized in an unsupervised way, without manual annotation or access to environment internals, and only the adapter is used during inference time to provide distraction-free visual input to the RL policy learner. Our Visual Attention and Invariance (VAI) method significantly outperforms the state-of-the-art on visual domain generalization, gaining 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode .
Edit intent: clarity
Original:  VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode  .
Revised:  VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode .
Edit intent: fluency
Original:  VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode  .
Revised:  VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% ( 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode .
Edit intent: clarity
Original:  VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode  .
Revised:  VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% in DeepMind Control suite benchmark and 61 to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode .
Edit intent: fluency
Original:  VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode  .
Revised:  VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% ) more cumulative rewards per episode .
Edit intent: clarity
Original:  VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode  .
Revised:  VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode on DeepMind Control (our DrawerWorld Manipulation) benchmarks. Our results demonstrate that it is not only possible to learn domain-invariant vision without any supervision, but freeing RL from visual distractions also makes the policy more focused and thus far better .
Edit intent: meaning-changed
Original: OBJECTIVE : To evaluate the association between coronavirus disease 2019 (COVID-19) diagnosis with SARS-CoV-2 variant B.1.1.7 (also known as Variant of Concern 202012/01) and the risk of hospitalisation compared to diagnosis with wildtype SARS-CoV-2 variants.
Revised: Objective : To evaluate the association between coronavirus disease 2019 (COVID-19) diagnosis with SARS-CoV-2 variant B.1.1.7 (also known as Variant of Concern 202012/01) and the risk of hospitalisation compared to diagnosis with wildtype SARS-CoV-2 variants.
Edit intent: fluency
Original: OBJECTIVE : To evaluate the association between coronavirus disease 2019 (COVID-19) diagnosis with SARS-CoV-2 variant B.1.1.7 (also known as Variant of Concern 202012/01) and the risk of hospitalisation compared to diagnosis with wildtype SARS-CoV-2 variants.
Revised: OBJECTIVE : To evaluate the relationship between coronavirus disease 2019 (COVID-19) diagnosis with SARS-CoV-2 variant B.1.1.7 (also known as Variant of Concern 202012/01) and the risk of hospitalisation compared to diagnosis with wildtype SARS-CoV-2 variants.
Edit intent: clarity