wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this work , we propose a novel PIP scheme: Physical Interaction Prediction via Mental Imagery with Span Selection . PIP utilizes a deep generative model to output future frames of physical interactions among objects before extracting crucial information for predicting physical interactions by focusing on salient frames using span selection .
Revised:  In this work , we propose a novel PIP scheme: Physical Interaction Prediction via Mental Imagery with Span Selection . PIP utilizes a deep generative model to output future frames of physical interactions among objects before extracting crucial information for predicting physical interaction outcomes .
Edit intent: clarity
Original:  To evaluate our model, we propose a large-scale SPACE+ dataset of synthetic video frames, including three physical interaction events in a 3D environment.
Revised:  To evaluate our model, we further propose the large-scale SPACE+ dataset of synthetic video frames, including three physical interaction events in a 3D environment.
Edit intent: clarity
Original:  To evaluate our model, we propose a large-scale SPACE+ dataset of synthetic video frames, including three physical interaction events in a 3D environment.
Revised:  To evaluate our model, we propose a large-scale SPACE+ dataset of synthetic videos with long sequences of three prime physical interactions in a 3D environment.
Edit intent: clarity
Original:  Furthermore, PIP's span selection scheme can effectively identify the frames where physical interactions among objects occur within the generated frames , allowing for added interpretability.
Revised:  Furthermore, PIP's span selection module effectively identifies the frames indicating key physical interactions among objects occur within the generated frames , allowing for added interpretability.
Edit intent: clarity
Original:  Furthermore, PIP's span selection scheme can effectively identify the frames where physical interactions among objects occur within the generated frames , allowing for added interpretability.
Revised:  Furthermore, PIP's span selection scheme can effectively identify the frames where physical interactions among objects , allowing for added interpretability.
Edit intent: clarity
Original:  Our method processes new meshes in less than 5 [s], consistently achieves a normalised mean absolute error of \leq 1.6 [\%], and peaks at 90.5 [\%] median approximation accuracy over the held-out test set, comparing favorably to previously published work.
Revised:  Our method processes new meshes in less than 5 [s], consistently achieves a normalised mean absolute error of \leq 1.6 [\%], and peaks at 90.5 [\%] median approximation accuracy over the held-out test set, comparing favourably to previously published work.
Edit intent: fluency
Original:  This shows the feasibility of CFD surrogate modelling using mesh convolutional neural networks for hemodynamic parameter estimation in artery models.
Revised:  This demonstrates the feasibility of CFD surrogate modelling using mesh convolutional neural networks for hemodynamic parameter estimation in artery models.
Edit intent: clarity
Original:  Our framework outperforms previous state-of-the-art by a large margin, while capable of adapting to new target domain taxonomies.
Revised:  Our framework outperforms previous state-of-the-art by a large margin, while capable of adapting to target taxonomies.
Edit intent: clarity
Original: In this paper, a general nonlinear 1st-order consensus-based solution for distributed constrained convex optimization is considered for applications in network resource allocation .
Revised: A general nonlinear 1st-order consensus-based solution for distributed constrained convex optimization is considered for applications in network resource allocation .
Edit intent: clarity
Original: In this paper, a general nonlinear 1st-order consensus-based solution for distributed constrained convex optimization is considered for applications in network resource allocation .
Revised: In this paper, a general nonlinear 1st-order consensus-based solution for distributed constrained convex optimization is proposed with network resource allocation .
Edit intent: clarity
Original:  The solution is anytime feasible and models various nonlinearities to account for imperfections and constraints on the (physical model of) agents in terms of their limited actuation capabilities, e.g., quantization and saturation constraints among others.
Revised:  The solution is anytime feasible and models various nonlinearities to account for imperfections and constraints on the (physical model of) agents in terms of limited actuation capabilities, e.g., quantization and saturation constraints among others.
Edit intent: clarity
Original:  Moreover, different applications impose specific nonlinearities to the model , e.g., convergence in fixed/finite-time , robustness to uncertainties, and noise-tolerant dynamics.
Revised:  Moreover, different applications impose specific nonlinearities , e.g., convergence in fixed/finite-time , robustness to uncertainties, and noise-tolerant dynamics.
Edit intent: clarity
Original:  Our proposed distributed resource allocation protocol generalizes such nonlinear models.
Revised:  Our proposed distributed protocol generalizes such nonlinear models.
Edit intent: clarity
Original:  Putting convex set analysis together with the Lyapunov theorem, we provide a general technique to prove convergence (i) regardless of the particular type of nonlinearity  (ii) with weak network-connectivity requirement (i.e., uniform-connectivity ).
Revised:  Putting convex set analysis together with nonsmooth Lyapunov analysis, we prove convergence, (i) regardless of the particular type of nonlinearity (ii) with weak network-connectivity requirement (i.e., uniform-connectivity ).
Edit intent: clarity
Original:  Putting convex set analysis together with the Lyapunov theorem, we provide a general technique to prove convergence (i) regardless of the particular type of nonlinearity  (ii) with weak network-connectivity requirement (i.e., uniform-connectivity ).
Revised:  Putting convex set analysis together with the Lyapunov theorem, we provide a general technique to prove convergence (i) regardless of the particular type of nonlinearity , and (ii) with weak network-connectivity requirement (i.e., uniform-connectivity ).
Edit intent: coherence
Original:  Putting convex set analysis together with the Lyapunov theorem, we provide a general technique to prove convergence (i) regardless of the particular type of nonlinearity  (ii) with weak network-connectivity requirement (i.e., uniform-connectivity ).
Revised:  Putting convex set analysis together with the Lyapunov theorem, we provide a general technique to prove convergence (i) regardless of the particular type of nonlinearity (ii) with weak network-connectivity requirements ( uniform-connectivity ).
Edit intent: coherence
Original:  Although FPGAs are more power-efficient than CPUs and GPUs, a typical (DRL ) method cannot be applied since they are composed of many Logic Blocks (LBs) for high-speed logical operations but low-speed real-number operations.
Revised:  Although FPGAs are more power-efficient than CPUs and GPUs, a typical DRL method cannot be applied since they are composed of many Logic Blocks (LBs) for high-speed logical operations but low-speed real-number operations.
Edit intent: fluency
Original:  \% While academic publications that synthesize FBMC in model frameworks agree on a general process that aligns with the real market coupling process, they differ greatly in some core assumptions.
Edit intent: coherence
Original:  However, the functional quality of this regulatory approach has not been thoroughly interrogated .
Revised:  Despite the widespread turn to human oversight, these policies rest on an uninterrogated assumption: that people are able to oversee algorithmic decision-making .
Edit intent: clarity
Original:  Second,  human oversight policies legitimize government use of flawed and controversial algorithms without addressing the fundamental issues with these tools.
Revised:  Second, human oversight policies legitimize government uses of faulty and controversial algorithms without addressing the fundamental issues with these tools.
Edit intent: fluency
Original:  In light of these flaws, I propose a more rigorous approach for determining whether and how to incorporate algorithms into government decision-making.
Revised:  In light of these flaws, I propose a more stringent approach for determining whether and how to incorporate algorithms into government decision-making.
Edit intent: clarity
Original:  Second, before deploying an algorithm alongside human oversight, vendors or agencies must conduct preliminary evaluations of whether people can effectively oversee the algorithm.
Revised:  Second, before deploying an algorithm alongside human oversight, agencies or vendors must conduct preliminary evaluations of whether people can effectively oversee the algorithm.
Edit intent: fluency
Original:  We will opensource our code and trained models to reproduce the reported results .
Revised:  Code and trained models to reproduce the reported results .
Edit intent: clarity
Original: In this paper, we propose an approach to improve image captioning solutions for images with novel objects that do not have caption labels in the training dataset.
Revised: In this paper, we propose an approach to improve image captioning solution for images with novel objects that do not have caption labels in the training dataset.
Edit intent: fluency
Original:  Our approach is agnostic to model architecture, and primarily focuses on training technique that uses existing fully paired image-caption data and the images with only the novel object detection labels (partially paired data).
Revised:  Our approach is agnostic to model architecture, and primarily focuses on the training approach that uses existing fully paired image-caption data and the images with only the novel object detection labels (partially paired data).
Edit intent: clarity
Original:  We create synthetic paired captioning data for these novel objects by leveraging context from existing image-caption pairs.
Revised:  We create synthetic paired captioning data for novel objects by leveraging context from existing image-caption pairs.
Edit intent: clarity
Original:  We further re-use these partially paired images with novel objects to create pseudo-label captions that are used to fine-tune the captioning model.
Revised:  We then create pseudo-label captions for partially paired images with novel objects to create pseudo-label captions that are used to fine-tune the captioning model.
Edit intent: clarity
Original:  We further re-use these partially paired images with novel objects to create pseudo-label captions that are used to fine-tune the captioning model.
Revised:  We further re-use these partially paired images with novel objects , and use this additional data to fine-tune the captioning model.
Edit intent: clarity
Original: We present a novel method for safely navigating a robot in unknown and uneven outdoor terrains.
Revised: We present a novel method for reliable robot navigation in uneven outdoor terrains.
Edit intent: clarity
Original:  Our approach trains a novel Deep Reinforcement Learning (DRL) -based network with channel and spatial attention modules using a novel reward function to compute an attention map of the environment.
Revised:  Our approach trains a novel Deep Reinforcement Learning (DRL) -based network with channel and spatial attention modules using a novel reward function to compute an attention mask of the environment.
Edit intent: fluency
Original:  The attention map identifies regions in the environment's elevation map with high elevation gradients where the robot could have reduced stability or even flip over.
Revised:  The attention mask is used to identify reduced stability regions in the environment's elevation map with high elevation gradients where the robot could have reduced stability or even flip over.
Edit intent: clarity
Original:  We transform this attention map into a 2D navigation cost-map , which encodes the planarity (level of flatness) of the terrain . Using the cost-map, we formulate a novel method for computing local least-cost waypoints leading to the robot's goal and integrate our approach with DWA-RL, a state-of-the-art navigation method.
Revised:  We transform this attention map into a 2D navigation cost-map , which encodes the planarity (level of flatness) of the terrain . Using the cost-map, we formulate a novel method for computing local least-cost waypoints on the cost-map and compute the final dynamically feasible trajectory using another DRL-based method.
Edit intent: clarity
Original:  Our approach guarantees safe, locally least-cost paths and dynamically feasible robot velocities in highly uneven terrains.
Revised:  Our approach guarantees safe, locally least-cost paths and dynamically feasible robot velocities in uneven terrains.
Edit intent: clarity
Original:  Our hybrid approach also leads to a low sim-to-real gap, which arises while training DRL networks.
Edit intent: coherence
Original:  We evaluate our method on a real Husky robot in highly uneven real-world terrains and demonstrate its benefits.
Revised:  We evaluate our method on a Husky robot in highly uneven real-world terrains and demonstrate its benefits.
Edit intent: clarity
Original:  We evaluate our method on a real Husky robot in highly uneven real-world terrains and demonstrate its benefits.
Revised:  We evaluate our method on a real Husky robot in real-world terrains and demonstrate its benefits.
Edit intent: clarity
Original:  Since the uncontrolled agents usually demonstrate multimodal reactive behavior, the motion planner needs to solve a continuous motion planning problem under multimodal behaviorsof the uncontrolled agents , which contains a discrete element.
Revised:  Since the uncontrolled agents usually demonstrate multimodal reactive behavior, the motion planner needs to solve a continuous motion planning problem under these behaviors , which contains a discrete element.
Edit intent: clarity
Original:  The proposed branch MPC framework is tested on an overtake and lane change task and a merging task for autonomous vehicles in simulation, and on the motion planning of an autonomous quadruped robot alongside an uncontrolled quadruped in experiments.
Revised:  The proposed branch MPC framework is tested on an overtake and lane change task and a merging task for autonomous vehicles in simulation, and on the motion planning of an autonomous quadruped robot alongside an uncontrolled quadruped in experiments.
Edit intent: fluency
Original:  Experimental results show promising results for incorporating real-world data in training speech enhancement systems.
Revised:  Experiments show promising results for incorporating real-world data in training speech enhancement systems.
Edit intent: clarity
Original:  Although memes are typically humorous, recent days have witnessed an escalation of harmful memes used for trolling, cyberbullying, and abusing social entities .
Revised:  Although memes are typically humorous, recent days have witnessed an escalation of harmful memes used for trolling, cyberbullying, and abuse .
Edit intent: clarity
Original:  Detecting such harmful memes is challenging as they can be highly satirical and cryptic.
Revised:  Detecting such memes is challenging as they can be highly satirical and cryptic.
Edit intent: clarity
Original:  Moreover, while previous work has focused on specific aspects of memes such as hate speech and propaganda, there has been little work on harm in general , and only one specialized dataset for it . Here, we focus on bridging this gap.
Revised:  Moreover, while previous work has focused on specific aspects of memes such as hate speech and propaganda, there has been little work on harm in general . Here, we focus on bridging this gap.
Edit intent: coherence
Original:  Moreover, while previous work has focused on specific aspects of memes such as hate speech and propaganda, there has been little work on harm in general , and only one specialized dataset for it . Here, we focus on bridging this gap.
Revised:  Moreover, while previous work has focused on specific aspects of memes such as hate speech and propaganda, there has been little work on harm in general , and only one specialized dataset for it . Here, we aim to bridge this gap.
Edit intent: coherence
Original:  In particular, we aim to solve two novel tasks: detecting harmful memes and identifying the social entities they target.
Revised:  We focus on two tasks: (i) detecting harmful memes and identifying the social entities they target.
Edit intent: clarity
Original:  In particular, we aim to solve two novel tasks: detecting harmful memes and identifying the social entities they target.
Revised:  In particular, we aim to solve two novel tasks: detecting harmful memes , and (ii) identifying the social entities they target.
Edit intent: coherence
Original:  We further extend the recently released HarMeme dataset to generalize on two prevalent topics - COVID-19 and US politics and name the two datasets as Harm-C and Harm-P, respectively.
Revised:  We further extend a recently released HarMeme dataset to generalize on two prevalent topics - COVID-19 and US politics and name the two datasets as Harm-C and Harm-P, respectively.
Edit intent: fluency
Original:  We further extend the recently released HarMeme dataset to generalize on two prevalent topics - COVID-19 and US politics and name the two datasets as Harm-C and Harm-P, respectively.
Revised:  We further extend the recently released HarMeme dataset , which covered COVID-19 and US politics and name the two datasets as Harm-C and Harm-P, respectively.
Edit intent: clarity
Original:  We then propose MOMENTA (MultimOdal framework for detecting harmful MemEs aNd Their tArgets), a novel multimodal (text + image) deep neural model, which uses global and local perspectives to detect harmful memes.
Revised:  We then propose MOMENTA (MultimOdal framework for detecting harmful MemEs aNd Their tArgets), a novel multimodal deep neural network that uses global and local perspectives to detect harmful memes.
Edit intent: clarity
Original:  MOMENTA identifies the object proposals and attributes and uses a multimodal model to perceive the comprehensive context in which the objects and the entities are portrayed in a given meme . MOMENTA is interpretable and generalizable, and it outperforms numerous baselines .
Revised:  MOMENTA systematically analyzes the local and the entities are portrayed in a given meme . MOMENTA is interpretable and generalizable, and it outperforms numerous baselines .
Edit intent: clarity
Original: We consider over-the-air convex optimization on a d dimensional space where coded gradients are sent over an additive Gaussian noise channel with variance \sigma^2.
Revised: We consider over-the-air convex optimization on a d-dimensional space where coded gradients are sent over an additive Gaussian noise channel with variance \sigma^2.
Edit intent: fluency
Original:  We investigated the internal patterns of differently aged areca nuts randomly chosen from the same age group.
Revised:  We investigated the internal patterns of randomly chosen nuts from a same age group.
Edit intent: clarity
Original:  In this paper, we develop an integration by parts formula in the multi-dimensional Malliavin calculus, and apply it to obtain the Greeks formulae for both simple and complex continuous-time Asian options in the multi-asset situation.
Revised:  In this paper, we develop an integration by parts formula in the multi-dimensional Malliavin calculus, and apply it to obtain the Greeks formulae for continuous-time Asian options in the multi-asset situation.
Edit intent: clarity
Original:  We propose to use the conditional quasi-Monte Carlo method to smooth Malliavin Greeks, and show that the calculation of conditional expectations analytically is viable for many common Asian options.
Revised:  We propose to use the conditional quasi-Monte Carlo method to smooth Malliavin Greeks, and show that the calculation of conditional expectations analytically is viable for many types of Asian options.
Edit intent: clarity
Original:   Numerical experiments demonstrate the large efficiency improvement of the proposed method, especially for  options with discontinuous payoff functions.
Revised:  Numerical experiments demonstrate the large efficiency improvement of the proposed method, especially for Asian options with discontinuous payoff functions.
Edit intent: fluency
Original: In this paper, we present a single-pixel compressive direction of arrival (DoA) estimation technique leveraging a graph attention network (GAT) based deep-learning framework.
Revised: In this paper, we present a single-pixel compressive direction of arrival (DoA) estimation technique leveraging a graph attention network (GAT) -based deep-learning framework.
Edit intent: fluency
Original:  The physical layer compression is achieved using a coded-aperture technique, probing the spectrum of far-field sources  incident on the aperture using a set of spatio-temporally incoherent modes.
Revised:  The physical layer compression is achieved using a coded-aperture technique, probing the spectrum of far-field sources that are incident on the aperture using a set of spatio-temporally incoherent modes.
Edit intent: clarity
Original:  The coded-aperture based receiver exhibits a single-channel , replacing the conventional multichannel raster scan based solutions for DoA estimation.
Revised:  The coded-aperture based receiver exhibits a single-channel and replacing the conventional multichannel raster scan based solutions for DoA estimation.
Edit intent: fluency
Original:  The coded-aperture based receiver exhibits a single-channel , replacing the conventional multichannel raster scan based solutions for DoA estimation.
Revised:  The coded-aperture based receiver exhibits a single-channel , replacing the conventional multichannel raster scan-based solutions for DoA estimation.
Edit intent: fluency
Original:  This step eliminates the need for an additional reconstruction step and significantly simplifies the processing layer to obtain the DoA estimate .
Revised:  This step eliminates the need for an additional reconstruction step and significantly simplifies the processing layer to achieve DoA estimation .
Edit intent: clarity
Original: Deep Neural Networks (DNNs) have been utilized in various applications ranging from image classification and facial recognition to medical imagery analysis and real-time object detection.
Revised: Deep Neural Networks (DNNs) are ubiquitous and span a variety of applications ranging from image classification and facial recognition to medical imagery analysis and real-time object detection.
Edit intent: clarity
Original: Deep Neural Networks (DNNs) have been utilized in various applications ranging from image classification and facial recognition to medical imagery analysis and real-time object detection.
Revised: Deep Neural Networks (DNNs) have been utilized in various applications ranging from image classification and facial recognition to medical image analysis and real-time object detection.
Edit intent: fluency
Original:  As our models become more sophisticated and complex, the computational cost of training such models becomes a burden for small companies and individuals;
Revised:  As our models become more sophisticated and complex, the computational cost of training these models becomes a burden for small companies and individuals;
Edit intent: clarity
Original:  for this reason, outsourcing the training process has been the go-to option for such users.
Revised:  for this reason, outsourcing the training process has been the go-to option for many DNN users.
Edit intent: clarity
Original:  Unfortunately, outsourcing the training process comes at the cost of vulnerability to backdoor attacks.
Revised:  Unfortunately, this comes at the cost of vulnerability to backdoor attacks.
Edit intent: clarity