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Found 17 papers, 6 papers with code
Leveraging 3D LiDAR Sensors to Enable Enhanced Urban Safety and Public Health: Pedestrian Monitoring and Abnormal Activity Detection
The integration of Light Detection and Ranging (LiDAR) and Internet of Things (IoT) technologies offers transformative opportunities for public health informatics in urban safety and pedestrian well-being.
A Survey of Resilient Coordination for Cyber-Physical Systems Against Malicious Attacks
Furthermore, miscellaneous resilient coordination problems are discussed in this survey.
Emulating Complex Synapses Using Interlinked Proton Conductors
The memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations.
VoxelKP: A Voxel-based Network Architecture for Human Keypoint Estimation in LiDAR Data
To the best of our knowledge, \textit{VoxelKP} is the first single-staged, fully sparse network that is specifically designed for addressing the challenging task of 3D keypoint estimation from LiDAR data, achieving state-of-the-art performances.
Ranked #1 on
3D Human Pose Estimation
on Waymo Open Dataset
Is A 15-minute City within Reach in the United States? An Investigation of Activity-Based Mobility Flows in the 12 Most Populous US Cities
Enhanced efforts in the transportation sector should be implemented to mitigate the adverse effects of CO2 emissions resulting from zoning-based planning paradigms.
AGAD: Adversarial Generative Anomaly Detection
In order to address the lack of abnormal data for robust anomaly detection, we propose Adversarial Generative Anomaly Detection (AGAD), a self-contrast-based anomaly detection paradigm that learns to detect anomalies by generating \textit{contextual adversarial information} from the massive normal examples.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection
In this paper, we introduce \textit{DIA}, dissolving is amplifying.
Simulating the Power Electronics-Dominated Grid using Schwarz-Schur Complement based Hybrid Domain Decomposition Algorithm
This paper proposes a novel two-stage hybrid domain decomposition algorithm to speed up the dynamic simulations and the analysis of power systems that can be computationally demanding due to the high penetration of renewables.
Identifying Operation Equilibrium in Integrated Electricity, Natural Gas, and Carbon-Emission Markets
Carbon emission markets can play a significant role in this transition by putting a price on carbon and giving electricity producers an incentive to reduce their emissions.
Deep Decarbonization of Multi-Energy Systems: A Carbon-Oriented Framework with Cross Disciplinary Technologies
The retirement of unabated coal power plants, the plummeting cost of renewable energy technologies, along with more aggressive public policies and regulatory reforms, are occurring at an unprecedented speed to decarbonize the power and energy systems towards the 2030 and 2050 climate goals.
Semantic decomposition Network with Contrastive and Structural Constraints for Dental Plaque Segmentation
Therefore, we propose a semantic decomposition network (SDNet) that introduces two single-task branches to separately address the segmentation of teeth and dental plaque and designs additional constraints to learn category-specific features for each branch, thus facilitating the semantic decomposition and improving the performance of dental plaque segmentation.
TorMentor: Deterministic dynamic-path, data augmentations with fractals
We propose the use of fractals as a means of efficient data augmentation.
Ranked #1 on
No real Data Binarization
on DIBCO and H_DIBCO 2009
Upsampling Autoencoder for Self-Supervised Point Cloud Learning
In computer-aided design (CAD) community, the point cloud data is pervasively applied in reverse engineering, where the point cloud analysis plays an important role.
Single-Image Specular Highlight Removal via Real-World Dataset Construction
Specular reflections pose great challenges on various multimedia and computer vision tasks, e. g. , image segmentation, detection and matching.
Differentiable Data Augmentation with Kornia
In this paper we present a review of the Kornia differentiable data augmentation (DDA) module for both for spatial (2D) and volumetric (3D) tensors.
Multi-year Long-term Load Forecast for Area Distribution Feeders based on Selective Sequence Learning
It was compared with traditional methods and our previous sequence prediction method.
Learning Non-Lambertian Object Intrinsics across ShapeNet Categories
Rendered with realistic environment maps, millions of synthetic images of objects and their corresponding albedo, shading, and specular ground-truth images are used to train an encoder-decoder CNN.
