wanyu/IteraTeR_v2 huggingface dataset:
Original:  We test our model in multiple anomaly detection and class-incremental settings presenting quantitative and qualitative analysis showcasing the accuracy and the flexibility of our model for both anomaly detection and class-incremental learning .
Revised:  We test our model in multiple anomaly detection and class-incremental settings presenting quantitative and qualitative analysis showcasing the accuracy and the flexibility of our model for anomaly detection tasks .
Edit intent: clarity
Original:  Focusing on 42 million galaxy images from the latest data release of the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, we first train a self-supervised model to distil low-dimensional representations that are robust to symmetries, uncertainties, and noise in each image.
Revised:  Focusing on 42 million galaxy images from the latest data release of the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, we first train a self-supervised model to distill low-dimensional representations that are robust to symmetries, uncertainties, and noise in each image.
Edit intent: fluency
Original:  We performed an empirical evaluation to compare the PMM \ to other hierarchical forecasting methods on Australian domestic tourism data, where we obtain a 20 percent relative improvement.
Revised:  We performed an empirical evaluation to compare the PMM to other hierarchical forecasting methods on Australian domestic tourism data, where we obtain a 20 percent relative improvement.
Edit intent: fluency
Original: Hierarchical forecasting problems arise when time series compose a group structure that naturally defines aggregation and disaggregation coherence constraints for the predictions.
Revised: Hierarchical forecasting problems arise when time series have a natural group structure, and predictions at multiple levels of aggregation and disaggregation coherence constraints for the predictions.
Edit intent: clarity
Original: In several applications, besides getting a generative model of the data , we also want the model to be useful for specific downstream tasks.
Revised: Probabilistic models help us encode latent structures that both model the data and are ideally also useful for specific downstream tasks.
Edit intent: clarity
Original: GenEO ( ` Generalised Eigenvalue problems on the Overlap') is a method for computing an operator-dependent spectral coarse space to be combined with local solves on subdomains to form a robust parallel domain decomposition preconditioner for elliptic PDEs.
Revised: GenEO ( ' Generalised Eigenvalue problems on the Overlap') is a method for computing an operator-dependent spectral coarse space to be combined with local solves on subdomains to form a robust parallel domain decomposition preconditioner for elliptic PDEs.
Edit intent: fluency
Original: Only a few cherry-picked robust augmentation policies are beneficial to standard self-supervised image representation learning , despite the large augmentation family .
Revised: Despite the large augmentation family, only a few cherry-picked robust augmentation policies are beneficial to standard self-supervised image representation learning , despite the large augmentation family .
Edit intent: fluency
Original: Only a few cherry-picked robust augmentation policies are beneficial to standard self-supervised image representation learning , despite the large augmentation family .
Revised: Only a few cherry-picked robust augmentation policies are beneficial to self-supervised image representation learning , despite the large augmentation family .
Edit intent: clarity
Original: Only a few cherry-picked robust augmentation policies are beneficial to standard self-supervised image representation learning , despite the large augmentation family .
Revised: Only a few cherry-picked robust augmentation policies are beneficial to standard self-supervised image representation learning .
Edit intent: clarity
Original:  Specifically, we adapt risky augmentation policies after standard views augmented by robust augmentations, to generate harder risky view (RV).
Revised:  Specifically, we adapt heavy augmentation policies after standard views augmented by robust augmentations, to generate harder risky view (RV).
Edit intent: clarity
Original:  The risky view usually has a higher deviation from the original image than the standard robust view (SV). Unlike previous methods equally pairing all augmented views for symmetrical self-supervised training to maximize their similarities, DSSL treats augmented views of the same instance as a partially ordered set (  SV\leftrightarrow SV, SV %DIFDELCMD < \leftarrowRV%%% \leftarrowHV ), and then equips directional objective functions respecting to the derived relationships among views.
Revised:  The risky view usually has a higher deviation from the original image than the standard robust view (SV). Unlike previous methods equally pairing all augmented views to symmetrically maximize their similarities, DSSL treats augmented views of the same instance as a partially ordered set ( SV\leftrightarrow SV, SV %DIFDELCMD < \leftarrowRV%%% \leftarrowHV ), and then equips directional objective functions respecting to the derived relationships among views.
Edit intent: clarity
Original:  The risky view usually has a higher deviation from the original image than the standard robust view (SV). Unlike previous methods equally pairing all augmented views for symmetrical self-supervised training to maximize their similarities, DSSL treats augmented views of the same instance as a partially ordered set (  SV\leftrightarrow SV, SV %DIFDELCMD < \leftarrowRV%%% \leftarrowHV ), and then equips directional objective functions respecting to the derived relationships among views.
Revised:  The risky view usually has a higher deviation from the original image than the standard robust view (SV). Unlike previous methods equally pairing all augmented views for symmetrical self-supervised training to maximize their similarities, DSSL treats augmented views of the same instance as a partially ordered set ( with directions as SV\leftrightarrow SV, SV %DIFDELCMD < \leftarrowRV%%% \leftarrowHV ), and then equips directional objective functions respecting to the derived relationships among views.
Edit intent: clarity
Original:  The risky view usually has a higher deviation from the original image than the standard robust view (SV). Unlike previous methods equally pairing all augmented views for symmetrical self-supervised training to maximize their similarities, DSSL treats augmented views of the same instance as a partially ordered set (  SV\leftrightarrow SV, SV %DIFDELCMD < \leftarrowRV%%% \leftarrowHV ), and then equips directional objective functions respecting to the derived relationships among views.
Revised:  The risky view usually has a higher deviation from the original image than the standard robust view (SV). Unlike previous methods equally pairing all augmented views for symmetrical self-supervised training to maximize their similarities, DSSL treats augmented views of the same instance as a partially ordered set ( SV\leftrightarrow SV, SV %DIFDELCMD < \leftarrowRV%%% \leftarrowHV ), and then equips a directional objective function respecting to the derived relationships among views.
Edit intent: fluency
Original:  DSSL can be easily implemented with a few lines of Pseudocode and is highly flexible to popular self-supervised learning frameworks, including SimCLR, SimSiam, BYOL.
Revised:  DSSL can be easily implemented with a few lines of codes and is highly flexible to popular self-supervised learning frameworks, including SimCLR, SimSiam, BYOL.
Edit intent: clarity
Original:  The extensive experimental results on CIFAR and ImageNet demonstrated that DSSL can stably improve these frameworks with compatibility to a wider range of augmentations.
Revised:  Extensive experimental results on CIFAR and ImageNet demonstrated that DSSL can stably improve these frameworks with compatibility to a wider range of augmentations.
Edit intent: clarity
Original:  The extensive experimental results on CIFAR and ImageNet demonstrated that DSSL can stably improve these frameworks with compatibility to a wider range of augmentations.
Revised:  The extensive experimental results on CIFAR and ImageNet demonstrated that DSSL can stably improve various baselines with compatibility to a wider range of augmentations.
Edit intent: clarity
Original: We present H-NeRF, neural radiance fields for rendering and temporal (4D) reconstruction of a human in motion as captured by a sparse set of cameras or even from a monocular video.
Revised: We present neural radiance fields for rendering and temporal (4D) reconstruction of a human in motion as captured by a sparse set of cameras or even from a monocular video.
Edit intent: coherence
Original:  Our NeRF-inspired approach combines ideas from neural scene representation, novel-view synthesis, and implicit statistical geometric human representations  . H-NeRF allows to accurately synthesize images of the observed subject under novel camera views and human poses.
Revised:  Our approach combines ideas from neural scene representation, novel-view synthesis, and implicit statistical geometric human representations . H-NeRF allows to accurately synthesize images of the observed subject under novel camera views and human poses.
Edit intent: coherence
Original:  Our NeRF-inspired approach combines ideas from neural scene representation, novel-view synthesis, and implicit statistical geometric human representations  . H-NeRF allows to accurately synthesize images of the observed subject under novel camera views and human poses.
Revised:  Our NeRF-inspired approach combines ideas from neural scene representation, novel-view synthesis, and implicit statistical geometric human representations . 
Edit intent: coherence
Original:  This allows us to robustly fuse information from sparse views and , at test time, to extrapolate beyond the observed poses or views  . Moreover, we apply geometric constraints to co-learn the structure of the observed subject ( including both body and clothing ) and to regularize the radiance field to geometrical plausible solutions.
Revised:  This allows us to robustly fuse information from sparse views and generalize well beyond the poses or views . Moreover, we apply geometric constraints to co-learn the structure of the observed subject ( including both body and clothing ) and to regularize the radiance field to geometrical plausible solutions.
Edit intent: clarity
Original:  This allows us to robustly fuse information from sparse views and , at test time, to extrapolate beyond the observed poses or views  . Moreover, we apply geometric constraints to co-learn the structure of the observed subject ( including both body and clothing ) and to regularize the radiance field to geometrical plausible solutions.
Revised:  This allows us to robustly fuse information from sparse views and , at test time, to extrapolate beyond the observed poses or views . Moreover, we apply geometric constraints to co-learn the structure of the observed subject -- including both body and clothing ) and to regularize the radiance field to geometrical plausible solutions.
Edit intent: fluency
Original:  This allows us to robustly fuse information from sparse views and , at test time, to extrapolate beyond the observed poses or views  . Moreover, we apply geometric constraints to co-learn the structure of the observed subject ( including both body and clothing ) and to regularize the radiance field to geometrical plausible solutions.
Revised:  This allows us to robustly fuse information from sparse views and , at test time, to extrapolate beyond the observed poses or views . Moreover, we apply geometric constraints to co-learn the structure of the observed subject ( including both body and clothing -- and to regularize the radiance field to geometrical plausible solutions.
Edit intent: fluency
Original:  This allows us to robustly fuse information from sparse views and , at test time, to extrapolate beyond the observed poses or views  . Moreover, we apply geometric constraints to co-learn the structure of the observed subject ( including both body and clothing ) and to regularize the radiance field to geometrical plausible solutions.
Revised:  This allows us to robustly fuse information from sparse views and , at test time, to extrapolate beyond the observed poses or views . Moreover, we apply geometric constraints to co-learn the structure of the observed subject ( including both body and clothing ) and to regularize the radiance field to geometrically plausible solutions.
Edit intent: fluency
Original:  Extensive experiments on multiple datasets demonstrate the robustness and  accuracy of our approach and its generalization capabilities beyond the sparse training set of poses and views  .
Revised:  Extensive experiments on multiple datasets demonstrate the robustness and the accuracy of our approach and its generalization capabilities beyond the sparse training set of poses and views .
Edit intent: fluency
Original:  Extensive experiments on multiple datasets demonstrate the robustness and  accuracy of our approach and its generalization capabilities beyond the sparse training set of poses and views  .
Revised:  Extensive experiments on multiple datasets demonstrate the robustness and accuracy of our approach , its generalization capabilities beyond the sparse training set of poses and views .
Edit intent: coherence
Original:  Experiments are conducted to measure the time dynamics of the organisms' population level movement when reacting to certain stimulations .
Revised:  Experiments are conducted to measure the time dynamics of the organisms' population level movement when reacting to certain stimulation .
Edit intent: fluency
Original:  The discussion is presented in the Bayesian framework, where the posterior distribution of the turning kernel of the organisms population is sought after.
Revised:  The discussion is presented in the Bayesian framework, where the posterior distribution of the turning kernel of the organism population is sought after.
Edit intent: fluency
Original: Log-structured merge-trees (LSM trees) are increasingly used as the storage engines behind several data systems, many of which are deployed in the cloud.
Revised: Log-Structured Merge trees (LSM trees) are increasingly used as the storage engines behind several data systems, many of which are deployed in the cloud.
Edit intent: fluency
Original: Log-structured merge-trees (LSM trees) are increasingly used as the storage engines behind several data systems, many of which are deployed in the cloud.
Revised: Log-structured merge-trees (LSM trees) are increasingly used as the storage engines behind several data systems, frequently deployed in the cloud.
Edit intent: clarity
Original:  Similar to other database architectures, LSM trees take into account information about the expected workloads (e.g., reads vs. writes and point vs. range queries) and optimize their performances by changing tunings .
Revised:  Similar to other database architectures, LSM trees take into account information about the expected workload (e.g., reads vs. writes and point vs. range queries) and optimize their performances by changing tunings .
Edit intent: fluency
Original:  Similar to other database architectures, LSM trees take into account information about the expected workloads (e.g., reads vs. writes and point vs. range queries) and optimize their performances by changing tunings .
Revised:  Similar to other database architectures, LSM trees take into account information about the expected workloads (e.g., reads vs. writes , point vs. range queries) and optimize their performances by changing tunings .
Edit intent: fluency
Original:  Similar to other database architectures, LSM trees take into account information about the expected workloads (e.g., reads vs. writes and point vs. range queries) and optimize their performances by changing tunings .
Revised:  Similar to other database architectures, LSM trees take into account information about the expected workloads (e.g., reads vs. writes and point vs. range queries) to optimize their performance via tuning .
Edit intent: clarity
Original:  Operating in  the cloud, however, comes with a degree of  uncertainty due to multi-tenancy and the fast-evolving nature of modern applications.
Revised:  Operating in shared infrastructure like the cloud, however, comes with a degree of uncertainty due to multi-tenancy and the fast-evolving nature of modern applications.
Edit intent: clarity
Original:  Databases with static tunings discount the variability of such hybrid workloads and hence provide an inconsistent and overall suboptimal performance.
Revised:  Systems with static tuning discount the variability of such hybrid workloads and hence provide an inconsistent and overall suboptimal performance.
Edit intent: fluency
Original:  To address this problem, we introduce ENDURE -- a new paradigm for tuning LSM trees in the presence of workload uncertainty.
Revised:  To address this problem, we introduce Endure - a new paradigm for tuning LSM trees in the presence of workload uncertainty.
Edit intent: fluency
Original:  ENDURE considers a robust formulation of the throughput maximization problem, and recommends a tuning that maximizes the worst-case throughput over the neighborhood of an expected workload.
Revised:  ENDURE considers a robust formulation of the throughput maximization problem, and recommends a tuning that maximizes the worst-case throughput over a neighborhood of each expected workload.
Edit intent: clarity
Original:  Lastly, we scale up the training dataset from 60k hours to 94k hours of public audio data, and optimize its training procedure for better representation extraction .
Revised:  Lastly, we scale up the training dataset from 60k hours to 94k hours .
Edit intent: clarity
Original:  Each is individually calibrated as an impact predictor in the book Kormendy (2020), "Metrics of Research Impact in Astronomy" ( Publ Astron Soc Pac  , San Francisco).
Revised:  Each is individually calibrated as an impact predictor in the book Kormendy (2020), "Metrics of Research Impact in Astronomy" ( Astron Soc Pac , San Francisco).
Edit intent: clarity
Original:  To construct such  graph from raw visual observation , a deep nerual network named AffRel is introduced.
Revised:  To construct such a graph from raw visual observation , a deep nerual network named AffRel is introduced.
Edit intent: fluency
Original:  To construct such  graph from raw visual observation , a deep nerual network named AffRel is introduced.
Revised:  To construct such graph from raw visual observations , a deep nerual network named AffRel is introduced.
Edit intent: fluency
Original:  It consists of a Attribute and Context module, which guide the relationship learning at instance and subgraph level respectively.
Revised:  It consists of an Attribute module and a Context module, which guide the relationship learning at instance and subgraph level respectively.
Edit intent: fluency
Original:  It consists of a Attribute and Context module, which guide the relationship learning at instance and subgraph level respectively.
Revised:  It consists of a Attribute and Context module, which guide the relationship learning at object and subgraph level respectively.
Edit intent: clarity
Original:  We quantitatively validate our method on a novel manipulation relationship dataset named SMRD dataset .
Revised:  We quantitatively validate our method on a novel manipulation relationship dataset named SMRD .
Edit intent: fluency
Original:  To evaluate the performance of  proposed model and representation, both visual and physical experiments are conducted.
Revised:  To evaluate the performance of the proposed model and representation, both visual and physical experiments are conducted.
Edit intent: fluency
Original:  To evaluate the performance of  proposed model and representation, both visual and physical experiments are conducted.
Revised:  To evaluate the performance of proposed model and representation, both visual perception and physical manipulation experiments are conducted.
Edit intent: clarity
Original:  Overall, AffRel along with MRG outperforms all baselines, achieving the success rate of 88.89\% on task relationship recognition (TRR) and 73.33\% on task completion (TC) . It demonstrates its superior capability to reason about the affordance in an interactive way for the purpose of robotic manipulation.
Revised:  Overall, AffRel along with MRG outperforms all baselines, achieving the success rate of 88.89\% on task relationship recognition (TRR) and 73.33\% on task completion (TC) 
Edit intent: coherence
Original:  Although the existing utterancelevel permutation invariant training-based continuous speech separation approach has proven to be effective in various conditions, it lacks the ability to leverage the long-span relationship of utterances and is computationally inefficient due to the highly overlapped sliding windows.
Revised:  Although the original utterancelevel permutation invariant training-based continuous speech separation approach has proven to be effective in various conditions, it lacks the ability to leverage the long-span relationship of utterances and is computationally inefficient due to the highly overlapped sliding windows.
Edit intent: clarity
Original: This work studies a more general setting, in which we aim to learn a single model to parsimoniously transfer motion from a source video to any target person given only one image of the person, named as Collaborative Parsing-Flow Network (CPF-Net).
Revised: This work studies a more general setting, in which we aim to learn a single model to parsimoniously transfer motion from a source video to any target person given only one image of the person, named as Collaborative Parsing-Flow Network (CPF-Net).
Edit intent: fluency
Original:  A combination of the duality theory and the Benders decomposition is developed to solve the adaptive DRO model.
Revised:  A combination of the duality theory and the Benders decomposition is developed to solve the DRO model.
Edit intent: clarity
Original:  The extensive experiments on six benchmarks  using various video architectures show our TCPNet is clearly superior to its counterparts, while having strong generalization URL source code is publicly available.
Revised:  The extensive experiments on six benchmarks using various video architectures show our TCPNet is clearly superior to its counterparts, while having strong generalization ability. The source code is publicly available.
Edit intent: clarity
Original: Collecting hands data is important for many cognitive studies , especially for senior participantswho has no IT background.
Revised: Collecting hands data is important for many cognitive studies , especially for older participants, many of whom have no IT background.
Edit intent: clarity
Original:  During data collection process, one of the key steps is to detect whether the participants is following the instruction correctly to do the correct gestures . Meanwhile, re-searchers found a lot of problems in TAS Test hand movement data collection process , where is challenging to detect similar gestures and guarantee the quality of the collect-ed images.
Revised:  During data collection process, one of the key steps is to detect whether the participants is following the instruction correctly to do the correct gestures . 
Edit intent: coherence
Original:  We have implemented a hand gesture detector to detect the gestures per-formed in the hand movement tests , which enables us to monitor if the participants are following the instructions correctly .
Revised:  We have implemented a hand gesture detector to detect the gestures in the hand movement tests , which enables us to monitor if the participants are following the instructions correctly .
Edit intent: fluency
Original:  In this research, we have processed 20,000 images collected from TAS Test and labelled 6,450 images to detect different hand poses in the hand movement tests.
Revised:  In this research, we have processed 20,000 images collected from hand movement tests and labelled 6,450 images to detect different hand poses in the hand movement tests.
Edit intent: coherence