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Found 13 papers, 4 papers with code
Zero-shot Load Forecasting for Integrated Energy Systems: A Large Language Model-based Framework with Multi-task Learning
The results validate the framework's potential for accurate and transferable load forecasting in integrated energy systems, particularly beneficial for renewable energy integration and smart grid applications.
Exploring the Inquiry-Diagnosis Relationship with Advanced Patient Simulators
By inputting medical records into our patient simulator to simulate patient responses, we conduct extensive experiments to explore the relationship between "inquiry" and "diagnosis" in the consultation process.
Transformer-Based Approaches for Sensor-Based Human Activity Recognition: Opportunities and Challenges
Transformers have excelled in natural language processing and computer vision, paving their way to sensor-based Human Activity Recognition (HAR).
Enhancing Motion Variation in Text-to-Motion Models via Pose and Video Conditioned Editing
For instance, current text-to-motion models cannot generate a motion of kicking a football with the instep of the foot, since the training data only includes martial arts kicks.
Cooperative Path Planning with Asynchronous Multiagent Reinforcement Learning
In this paper, we study the shortest path problem (SPP) with multiple source-destination pairs (MSD), namely MSD-SPP, to minimize average travel time of all shortest paths.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Beyond the Snapshot: Brain Tokenized Graph Transformer for Longitudinal Brain Functional Connectome Embedding
In this work, we proposed the first interpretable framework for brain FC trajectory embedding with application to neurodegenerative disease diagnosis and prognosis, namely Brain Tokenized Graph Transformer (Brain TokenGT).
An image segmentation algorithm based on multi-scale feature pyramid network
Medical image segmentation is particularly critical as a prerequisite for relevant quantitative analysis in the treatment of clinical diseases.
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
To better answer these questions, we propose Motley, a benchmark for personalized federated learning.
Automatic Map Update Using Dashcam Videos
We propose in this paper a pipeline for initiating and updating 3D maps with dashcam videos, with a focus on automatic change detection based on comparison of metadata (e. g., the types and locations of traffic signs).
Learning-based decentralized offloading decision making in an adversarial environment
Vehicular fog computing (VFC) pushes the cloud computing capability to the distributed fog nodes at the edge of the Internet, enabling compute-intensive and latency-sensitive computing services for vehicles through task offloading.
Ajalon: Simplifying the Authoring of Wearable Cognitive Assistants
Wearable Cognitive Assistance (WCA) amplifies human cognition in real time through a wearable device and low-latency wireless access to edge computing infrastructure.
Edge-computing
Human-Computer Interaction
"How do urban incidents affect traffic speed?" A Deep Graph Convolutional Network for Incident-driven Traffic Speed Prediction
Combining above methods, we propose a Deep Incident-Aware Graph Convolutional Network (DIGC-Net) to effectively incorporate urban traffic incident, spatio-temporal, periodic and context features for traffic speed prediction.
Acrobot Swing Up with MATLAB
Numerical integration method is used to simulate the behaviour of passive robots, with the goal of showing the correctness of the complicated dynamics of the 3-link acrobot.
Systems and Control
