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Autoencoder-Based Domain Learning for Semantic Communication with Conceptual Spaces
In this work, we develop a framework for learning a domain of a conceptual space model using only the raw data with high-level property labels.
Active Foundational Models for Fault Diagnosis of Electrical Motors
To overcome this limitation, we propose a foundational model-based Active Learning framework that utilizes less amount of labeled samples, which are most informative and harnesses a large amount of available unlabeled data by effectively combining Active Learning and Contrastive Self-Supervised Learning techniques.
Foundational Models for Fault Diagnosis of Electrical Motors
However, the data distribution can vary across different operating conditions during real-world operating scenarios of electrical motors.
Knowledge-Driven Semantic Communication Enabled by the Geometry of Meaning
As our world grows increasingly connected and new technologies arise, global demands for data traffic continue to rise exponentially.
A General Framework for Uncertainty Quantification via Neural SDE-RNN
Uncertainty quantification is a critical yet unsolved challenge for deep learning, especially for the time series imputation with irregularly sampled measurements.
Latent Neural ODE for Integrating Multi-timescale measurements in Smart Distribution Grids
Under a smart grid paradigm, there has been an increase in sensor installations to enhance situational awareness.
Semantic Communication with Conceptual Spaces
Despite the fact that Shannon and Weaver's Mathematical Theory of Communication was published over 70 years ago, all communication systems continue to operate at the first of three levels defined in this theory: the technical level.
Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems
Simulation results on IEEE 37 and IEEE 123 bus test systems illustrate the efficiency of the proposed methods from the standpoint of both multi time-scale data aggregation and DSSE.
Asynchronous Multiuser Detection for SCMA with Unknown Delays: A Compressed Sensing Approach
Despite being the subject of a growing body of research, non-orthogonal multiple access has failed to garner sufficient support to be included in modern standards.
Engineering Semantic Communication: A Survey
In this survey, we aim to provide a comprehensive view of the history and current state of semantic communication and the techniques for engineering this higher level of communication.
Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming.
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization
However, there are serious limitations in current approaches such as: (1) IM formulations only consider influence via spread and ignore self activation; (2) scalability to large graphs; (3) generalizability across graph families; (4) low computational efficiency with a large running time to identify seed sets for every test network.
A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks
Graph Neural Networks (GNN) provide a powerful framework that elegantly integrates Graph theory with Machine learning for modeling and analysis of networked data.
Addressing practical challenges in Active Learning via a hybrid query strategy
Inspired by these practical challenges, we present a hybrid query strategy-based AL framework that addresses three practical challenges simultaneously: cold-start, oracle uncertainty and performance evaluation of Active Learner in the absence of ground truth.
An Information Theoretic approach to identify Dominant Voltage Influencers for Unbalanced Distribution Systems
VIS is derived analytically in a computationally efficient manner and its efficacy to identify DVI nodes is validated using the IEEE 37-node test system.
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System
Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness.
Scalable Graph Neural Network-based framework for identifying critical nodes and links in Complex Networks
To overcome these challenges, this article proposes a scalable and generic graph neural network (GNN) based framework for identifying critical nodes/links in large complex networks.
Bayesian Graph Neural Network for Fast identification of critical nodes in Uncertain Complex Networks
Most existing methods of critical node identification are based on an iterative approach that explores each node/link of a graph.
Reinforcement Learning for Mitigating Intermittent Interference in Terahertz Communication Networks
Emerging wireless services with extremely high data rate requirements, such as real-time extended reality applications, mandate novel solutions to further increase the capacity of future wireless networks.
