wanyu/IteraTeR_v2 huggingface dataset:
Original:  With  Hindmarsh-Rose model for the dynamics of single neurons, we analyze the possible patterns of dynamics in each layer separately , and report emergent patterns of activity like  anti-phase  oscillations in multi-clusters with phase regularities and enhanced amplitude and frequency with mixed mode oscillations when the connections are inhibitory.
Revised:  With Hindmarsh-Rose model for the dynamics of single neurons, we analyze the possible patterns of dynamics in each layer separately and report emergent patterns of activity like anti-phase oscillations in multi-clusters with phase regularities and enhanced amplitude and frequency with mixed mode oscillations when the connections are inhibitory.
Edit intent: fluency
Original:  With  Hindmarsh-Rose model for the dynamics of single neurons, we analyze the possible patterns of dynamics in each layer separately , and report emergent patterns of activity like  anti-phase  oscillations in multi-clusters with phase regularities and enhanced amplitude and frequency with mixed mode oscillations when the connections are inhibitory.
Revised:  With Hindmarsh-Rose model for the dynamics of single neurons, we analyze the possible patterns of dynamics in each layer separately , and report emergent patterns of activity like anti-phase oscillations in multi-clusters with phase regularities when the connections are inhibitory.
Edit intent: coherence
Original:  When they are multiplexed  with neurons of one layer coupled with excitatory synaptic coupling and neurons of the other layer coupled with inhibitory synaptic coupling, we observe transfer or selection of interesting patterns of collective behaviour between the layers , inducing anti-phase oscillations and multi-cluster oscillations .
Revised:  When they are multiplexed , with neurons of one layer coupled with excitatory synaptic coupling and neurons of the other layer coupled with inhibitory synaptic coupling, we observe transfer or selection of interesting patterns of collective behaviour between the layers , inducing anti-phase oscillations and multi-cluster oscillations .
Edit intent: fluency
Original:  When they are multiplexed  with neurons of one layer coupled with excitatory synaptic coupling and neurons of the other layer coupled with inhibitory synaptic coupling, we observe transfer or selection of interesting patterns of collective behaviour between the layers , inducing anti-phase oscillations and multi-cluster oscillations .
Revised:  When they are multiplexed with neurons of one layer coupled with excitatory synaptic coupling and neurons of the other layer coupled with inhibitory synaptic coupling, we observe transfer or selection of interesting patterns of collective behavior between the layers , inducing anti-phase oscillations and multi-cluster oscillations .
Edit intent: fluency
Original:  We also discuss how the selection of these patterns can be controlled by tuning the intra-layer or inter-layer coupling strengths or increasing the range of non-local coupling.
Revised:  We also discuss how theselection of these spatio temporal patterns can be controlled by tuning the intra-layer or inter-layer coupling strengths or increasing the range of non-local coupling.
Edit intent: clarity
Original:  We also discuss how the selection of these patterns can be controlled by tuning the intra-layer or inter-layer coupling strengths or increasing the range of non-local coupling.
Revised:  We also discuss how the selection of these patterns can be controlled by tuning the intralayer or interlayer coupling strengths or increasing the range of non-local coupling.
Edit intent: fluency
Original:  With one layer having electrical coupling while the other synaptic coupling of excitatory(inhibitory)type, we find in-phase( anti-phase ) synchronized patterns of activity among neurons in both layers.
Revised:  With one layer having electrical coupling while the other synaptic coupling of excitatory(inhibitory)type, we find in-phase( antiphase ) synchronized patterns of activity among neurons in both layers.
Edit intent: fluency
Original:  We apply computer vision algorithms to corroborate new information from the video clip that may not be readily interpreted by human eyes.
Revised:  In our analysis, we apply computer vision algorithms to corroborate new information from the video clip that may not be readily interpreted by human eyes.
Edit intent: clarity
Original:  By comparing the differential features against different video frames, our method can quantify the falling structural components by intuitively showing the directions and magnitudes of their movements.
Revised:  By comparing the differential features against different video frames, our proposed method is used to quantify the falling structural components by intuitively showing the directions and magnitudes of their movements.
Edit intent: clarity
Original:  By comparing the differential features against different video frames, our method can quantify the falling structural components by intuitively showing the directions and magnitudes of their movements.
Revised:  By comparing the differential features against different video frames, our method can quantify the falling structural components by mapping the directions and magnitudes of their movements.
Edit intent: clarity
Original:  We demonstrate the potential of this video processing methodology in investigations of catastrophic structural failures and hope our results would serve as the basis for further investigations of this and other structure collapse events.
Revised:  We demonstrate the potential of this video processing methodology in investigations of catastrophic structural failures and hope our approach may serve as a basis for further investigations of this and other structure collapse events.
Edit intent: clarity
Original:  We demonstrate the potential of this video processing methodology in investigations of catastrophic structural failures and hope our results would serve as the basis for further investigations of this and other structure collapse events.
Revised:  We demonstrate the potential of this video processing methodology in investigations of catastrophic structural failures and hope our results would serve as the basis for further investigations into structure collapse events.
Edit intent: clarity
Original:  This article showcases a pragmatic methodology to obtain preliminary  estimation of treatment effect from observational studies .
Revised:  This article proposes a pragmatic methodology to obtain preliminary estimation of treatment effect from observational studies .
Edit intent: clarity
Original:  Driven by a transparent, trust-centric methodology, the proposed COVID-Net Clinical ICU was built using a clinical dataset from Hospital Sirio-Libanes comprising of 1,925 COVID-19 patients , and is able to predict when a COVID-19 positive patient would require ICU admission with an accuracy of 96.9\% to facilitate better care planning for hospitals amidst the on-going pandemic.
Revised:  Driven by a transparent, trust-centric methodology, the proposed COVID-Net Clinical ICU was built using a clinical dataset from Hospital Sirio-Libanes comprising of 1,925 COVID-19 patient records , and is able to predict when a COVID-19 positive patient would require ICU admission with an accuracy of 96.9\% to facilitate better care planning for hospitals amidst the on-going pandemic.
Edit intent: clarity
Original:  However, in tasks modeled into parsing, like structural sentiment analysis, "dependency edges" are very sparse which hinders parser performance.
Revised:  However, in tasks modeled into parsing, like structured sentiment analysis, "dependency edges" are very sparse which hinders parser performance.
Edit intent: fluency
Original:  Thus we propose a sparse and fuzzy attention scorer with pooling layers which improves parser performance and sets the new state-of-the-art on structural sentiment analysis.
Revised:  Thus we propose a sparse and fuzzy attention scorer with pooling layers which improves parser performance and sets the new state-of-the-art on structured sentiment analysis.
Edit intent: fluency
Original:  We further explore the parsing modeling on structural sentiment analysis with second-order parsing and introduce a novel sparse second-order edge building procedure that leads to significant improvement in parsing performance.
Revised:  We further explore the parsing modeling on structured sentiment analysis with second-order parsing and introduce a novel sparse second-order edge building procedure that leads to significant improvement in parsing performance.
Edit intent: clarity
Original: We present a novel self-supervised algorithmnamedMotionHintfor monocular visual odometry (VO) that takes motion constraints into account.
Revised: We present a novel self-supervised algorithm named MotionHint for monocular visual odometry (VO) that takes motion constraints into account.
Edit intent: fluency
Original:  A key aspect of ourapproach is to use an appropriate motion model that can help existing self-supervised monocular VO (SSM-VO) algorithms to overcome issues related to the local minima within their self-supervised loss functions.
Revised:  A key aspect of our approach is to use an appropriate motion model that can help existing self-supervised monocular VO (SSM-VO) algorithms to overcome issues related to the local minima within their self-supervised loss functions.
Edit intent: fluency
Original:  Taking two existing SSM-VO systems as our baseline, we evaluate our MotionHint algorithm on the standard KITTI and EuRoC benchmark.
Revised:  Taking two existing SSM-VO systems as our baseline, we evaluate our MotionHint algorithm on the standard KITTI benchmark.
Edit intent: clarity
Original:  Experimental results show that our MotionHint algorithm can be easily applied to existing open-source state-of-the-art SSM-VO systems to greatly improve the performance on KITTI dataset by reducing the resulting ATE by up to 28.73\% . For EuRoc dataset, our method can extract the motion model.
Revised:  Experimental results show that our MotionHint algorithm can be easily applied to existing open-sourced state-of-the-art SSM-VO systems to greatly improve the performance on KITTI dataset by reducing the resulting ATE by up to 28.73\% . For EuRoc dataset, our method can extract the motion model.
Edit intent: fluency
Original:  Experimental results show that our MotionHint algorithm can be easily applied to existing open-source state-of-the-art SSM-VO systems to greatly improve the performance on KITTI dataset by reducing the resulting ATE by up to 28.73\% . For EuRoc dataset, our method can extract the motion model.
Revised:  Experimental results show that our MotionHint algorithm can be easily applied to existing open-source state-of-the-art SSM-VO systems to greatly improve the performance by reducing the resulting ATE by up to 28.73\% . For EuRoc dataset, our method can extract the motion model.
Edit intent: coherence
Original: What should a malicious user write next to fool a detection model?
Revised: What should a malicious user write next to fool a detection model?
Edit intent: fluency
Original:  Several deep learning based detection models have been created.
Revised:  Several deep learning-based detection models have been created.
Edit intent: fluency
Original:  Here we create a novel adversarial attack model against deep user sequence embedding-based classification models, which use the sequence of user posts to generate user embeddings and detect malicious users.
Revised:  Here we create a novel adversarial attack model against deep user sequence embedding based classification models, which use the sequence of user posts to generate user embeddings and detect malicious users.
Edit intent: fluency
Original:  We propose a novel end-to-end Personalized Text Generation Attack model, called PETGEN , that simultaneously reduces the efficacy of the detection model and generates posts that have several key desirable properties.
Revised:  We propose a novel end-to-end Personalized Text Generation Attack model, called PETGEN , that simultaneously reduces the efficacy of the detection model and generates posts that have several key desirable properties.
Edit intent: fluency
Original:  Specifically, PETGEN generates posts that are personalized to the user's writing style, have knowledge about a given target context, are aware of the user's historical posts on the target context, and encapsulate the user's recent topical interests.
Revised:  Specifically, PETGEN generates posts that are personalized to the user's writing style, have knowledge about a given target context, are aware of the user's historical posts on the target context, and encapsulate the user's recent topical interests.
Edit intent: fluency
Original:  We conduct extensive experiments on two real-world datasets (Yelp and Wikipedia, both with ground-truth of malicious users) to show that PETGEN significantly reduces the performance of popular deep user sequence embedding-based classification models.
Revised:  We conduct extensive experiments on two real-world datasets (Yelp and Wikipedia, both with ground-truth of malicious users) to show that PETGEN significantly reduces the performance of popular deep user sequence embedding-based classification models.
Edit intent: fluency
Original:  PETGEN outperforms five attack baselines in terms of text quality and attack efficacy in both white-box and black-box classifier settings.
Revised:  PETGEN outperforms five attack baselines in terms of text quality and attack efficacy in both white-box and black-box classifier settings.
Edit intent: fluency
Original:  Unlike previous works, our geometric controller uses the logarithmic map of SO(3) to express rotational error in the Lie algebra, allowing us to treat the manifold in a more effective and natural manner, and can be shown to be globally attractive .
Revised:  Our geometric controller uses the logarithmic map of SO(3) to express rotational error in the Lie algebra, allowing us to treat the manifold in a more effective and natural manner, and can be shown to be globally attractive .
Edit intent: clarity
Original:  We show the performance of our control scheme against highly aggressive trajectories in simulation experiments.
Revised:  We show the performance of our controller against highly aggressive trajectories in simulation experiments.
Edit intent: clarity
Original: Recently, DETR ~\mbox{ carion2020end pioneered the solution of vision tasks with transformers, it directly translates the image feature map into the object detection result.
Revised: Recently, DETR pioneered the solution of vision tasks with transformers, it directly translates the image feature map into the object detection result.
Edit intent: coherence
Original:  We further validate the generalizability of the PnP module on \textbf{panoptic segmentation and the recent transformer-based image recognition model ViT ~\mbox{ dosovitskiy2020image and show consistent efficiency gain.
Revised:  We further validate the generalizability of the PnP module on \textbf{panoptic segmentation and the recent transformer-based image recognition model ViT ~\mbox{ dosovitskiy2020image and show consistent efficiency gain.
Edit intent: fluency
Original:  We further validate the generalizability of the PnP module on \textbf{panoptic segmentation and the recent transformer-based image recognition model ViT ~\mbox{ dosovitskiy2020image and show consistent efficiency gain.
Revised:  We further validate the generalizability of the PnP module on \textbf{panoptic segmentation and the recent transformer-based image recognition model ~\mbox{ dosovitskiy2020image and show consistent efficiency gain.
Edit intent: clarity
Original:  We further validate the generalizability of the PnP module on \textbf{panoptic segmentation and the recent transformer-based image recognition model ViT ~\mbox{ dosovitskiy2020image and show consistent efficiency gain.
Revised:  We further validate the generalizability of the PnP module on \textbf{panoptic segmentation and the recent transformer-based image recognition model ViT ViT and show consistent efficiency gain.
Edit intent: clarity
Original: Class Activation Mapping (CAM) methods have recently gained much attention for weakly-supervised object localization (WSOL) tasks , allowing for CNN visualization and interpretation without training on fully annotated image datasets.
Revised: Class Activation Mapping (CAM) methods have recently gained much attention for weakly-supervised object localization (WSOL) tasks . They allow for CNN visualization and interpretation without training on fully annotated image datasets.
Edit intent: coherence
Original:  Due to convolution and downsampling/ pooling operations, these backbones yield low resolution CAMs with a down-scaling factor of up to 32, making accurate localization more difficult .
Revised:  Due to convolution and pooling operations, these backbones yield low resolution CAMs with a down-scaling factor of up to 32, making accurate localization more difficult .
Edit intent: clarity
Original:  Due to convolution and downsampling/ pooling operations, these backbones yield low resolution CAMs with a down-scaling factor of up to 32, making accurate localization more difficult .
Revised:  Due to convolution and downsampling/ pooling operations, these backbones yield low resolution CAMs with a down-scaling factor of up to 32, contributing to inaccurate localizations .
Edit intent: clarity
Original:  Interpolation is required to restore a full size CAMs, but without considering the statistical properties of the objects, leading to activations with inconsistent boundaries  and inaccurate localizations.
Revised:  Interpolation is required to restore full size CAMs, but without considering the statistical properties of the objects, leading to activations with inconsistent boundaries and inaccurate localizations.
Edit intent: fluency
Original:  Interpolation is required to restore a full size CAMs, but without considering the statistical properties of the objects, leading to activations with inconsistent boundaries  and inaccurate localizations.
Revised:  Interpolation is required to restore a full size CAMs, yet it does not consider the statistical properties of the objects, leading to activations with inconsistent boundaries and inaccurate localizations.
Edit intent: coherence
Original:  Interpolation is required to restore a full size CAMs, but without considering the statistical properties of the objects, leading to activations with inconsistent boundaries  and inaccurate localizations.
Revised:  Interpolation is required to restore a full size CAMs, but without considering the statistical properties of the objects, leading to activations with inconsistent boundaries , and inaccurate localizations.
Edit intent: fluency
Original:  In particular, we propose a trainable decoding architecture that can be connected to any CNN classifier to produce more accurate CAMs . Given an original (low resolution ) CAM, foreground and background pixels are randomly sampled for fine-tuning the decoder.
Revised:  In particular, we propose a trainable decoding architecture that can be connected to any CNN classifier to produce more accurate CAMs . Given an original low resolution CAM, foreground and background pixels are randomly sampled for fine-tuning the decoder.
Edit intent: fluency
Original:  In particular, we propose a trainable decoding architecture that can be connected to any CNN classifier to produce more accurate CAMs . Given an original (low resolution ) CAM, foreground and background pixels are randomly sampled for fine-tuning the decoder.
Revised:  In particular, we propose a trainable decoding architecture that can be connected to any CNN classifier to produce more accurate CAMs . Given an original (low resolution ) CAM, foreground and background pixels are randomly sampled to fine-tune the decoder.
Edit intent: fluency
Original:  Additional priors such as image statistics , and size constraints are also considered to expand and refine object boundaries.
Revised:  Additional priors such as image statistics and size constraints are also considered to expand and refine object boundaries.
Edit intent: fluency
Original:  Extensive experiments using three CNN backbones and six WSOL baselines on the CUB-200-2011 and OpenImages datasets, indicate that our F-CAM method yields a significant improvement in CAM localization accuracy.
Revised:  Extensive experiments , over three CNN backbones and six WSOL baselines on the CUB-200-2011 and OpenImages datasets, indicate that our F-CAM method yields a significant improvement in CAM localization accuracy.
Edit intent: clarity
Original:  F-CAM performance is competitive with state-of-art WSOL methods, yet it requires fewer computational resources during inference.
Revised:  F-CAM performance is competitive with state-of-art WSOL methods, yet it requires fewer computations during inference.
Edit intent: clarity
Original:  We propose to work with the Lie algebra (infinitesimal generators) instead of the Lie group .
Revised:  We propose to work with Lie algebras (infinitesimal generators) instead of the Lie group .
Edit intent: fluency
Original:  We propose to work with the Lie algebra (infinitesimal generators) instead of the Lie group .
Revised:  We propose to work with the Lie algebra (infinitesimal generators) instead of Lie groups .
Edit intent: fluency
Original:  Our model, the Lie algebra convolutional network (L-conv) can learn potential symmetries and does not require discretization of the group.
Revised:  Our model, the Lie algebra convolutional network (L-conv) can automatically discover symmetries and does not require discretization of the group.
Edit intent: clarity
Original:  We discuss how CNNs and Graph Convolutional Networks are related to and can be expressed as L-conv with appropriate groups.
Revised:  We discuss how CNNs and Graph Convolutional Networks can be expressed as L-conv with appropriate groups.
Edit intent: clarity
Original:  Over various datasets, our algorithm produced well balanced clusters with coherent items in, leading to high retrieval scores on this task. 
Revised:  Over various datasets, our algorithm produced well balanced clusters with coherent items in, leading to high retrieval scores on this task. .
Edit intent: fluency
Original:  and iii) large training masks, which unlocks the potential of the first two components.
Revised:  iii) large training masks, which unlocks the potential of the first two components.
Edit intent: coherence
Original:  Our model generalizes surprisingly well to resolutions that are higher than those seen at train time, and achieves this at lower parameter& compute costs than the competitive baselines.
Revised:  Our model generalizes surprisingly well to resolutions that are higher than those seen at train time, and achieves this at lower parameter& time costs than the competitive baselines.
Edit intent: clarity
Original: Tens of thousands of parent companies control millions of subsidiaries through long chains ofintermediary entities in global corporate networks.
Revised: Tens of thousands of parent companies control millions of subsidiaries through long chains of intermediary entities in global corporate networks.
Edit intent: fluency
Original:  Conversely, tens of millions of entities aredirectly held by sole owners.
Revised:  Conversely, tens of millions of entities are directly held by sole owners.
Edit intent: fluency
Original:  We propose an algorithm for identification of ultimate controllingentities in such networks that unifies direct and indirect control and allows for continuousinterpolation between the two concepts via a factor damping long paths.
Revised:  We propose an algorithm for identification of ultimate controlling entities in such networks that unifies direct and indirect control and allows for continuousinterpolation between the two concepts via a factor damping long paths.
Edit intent: fluency