279 papers
This paper proposes an unsupervised prognostics framework that utilizes unlabeled run-to-failure data to simultaneously identify latent failure modes and select informative sensors, thereby enabling accurate remaining useful life prediction for autonomous deep-space habitats under multiple unknown failure conditions.
This paper proposes a data-driven framework combining control barrier functions and differentiable optimization to learn interpretable responsibility allocations, enabling autonomous agents to quantify and adjust their behaviors for safe, socially-aware multi-agent interactions.
This paper proposes a hybrid control framework that combines Deep Reinforcement Learning (DRL) with robust model-independent bounded extremum seeking to enhance the stability and adaptability of controlling nonlinear time-varying systems, demonstrating its effectiveness through numerical simulations and the automatic tuning of a particle accelerator.
This paper proposes a hierarchical reinforcement learning framework that jointly optimizes antenna tilt angles and the data collection ratio between a physical network and its digital twin to maximize user data rates while minimizing communication overhead and delay.
This paper introduces a family of mean-normalized matrix operator norms to derive width-independent smoothness bounds for deep neural networks, leading to the development of MOGA, a row/column-normalized optimizer that enables stable hyperparameter transfer across model widths and outperforms Muon in speed while maintaining competitive performance.
This paper proposes a curriculum-based continual learning framework that decomposes complex robust control problems with multiple uncertainties into sequential tasks, combining a model-based controller with deep reinforcement learning to achieve efficient, non-forgetting policy updates and successful sim-to-real transfer for automotive powertrain vibration control.
This paper introduces EMFusion, a conditional multivariate diffusion-based framework that leverages a residual U-Net with cross-attention and imputation-based sampling to provide accurate, uncertainty-quantified, frequency-selective electromagnetic field forecasts for wireless network planning, significantly outperforming existing baseline models.
This paper presents a data-driven methodology using geospatial analytics and machine learning to accurately estimate and identify the drivers of spectrum demand variations across urban regions, achieving 70% predictive accuracy to support flexible spectrum access policies for future 6G networks.
This paper presents an AI-driven, data-driven framework that utilizes validated proxies from site license and crowdsourced data to accurately forecast spectrum demand across five major Canadian cities, thereby enabling more efficient dynamic spectrum planning and resource allocation for regulators and network operators.
This paper introduces HR-GAT, a hierarchical resolution graph attention network that leverages geospatial data to predict spectrum demand with 21% higher accuracy than baseline models, effectively addressing spatial autocorrelation challenges to enable more efficient spectrum sharing and policy-making.
This paper proposes a biologically plausible, local learning framework for time-continuous neuronal networks that approximates backpropagation through time by deriving real-time error dynamics from a prospective energy function, thereby unifying and extending the Generalized Latent Equilibrium model to enable spatiotemporal credit assignment consistent with brain circuitry.
This paper proposes a physics-informed generative modeling framework that derives a differentiable, distributional traffic dynamics model from stochastic Ito-type equations, enabling the estimation of traffic density distributions, credible intervals, and congestion risks through a score network trained with denoising score matching and Fokker-Planck residual loss.
This paper proposes a unified taxonomy and evaluation framework for latent world models in automated driving, organizing design choices by latent representations and structural priors while identifying key internal mechanics and research directions to enhance robustness, generalization, and deployability.
This paper introduces Auralink SDC, an edge-deployed multi-agent AI architecture that autonomously manages electric vehicle charging infrastructure with high reliability and sub-50ms latency, achieving 78% autonomous incident resolution and 87.6% diagnostic accuracy to address the critical failure rates and slow remediation times of current cloud-centric systems.
This paper introduces the FaBRIC strategy, which integrates new scalable backward reachability algorithms with existing forward analysis techniques to significantly improve the verification of reach-avoid specifications in nonlinear neural feedback systems.
This study utilizes THz time-domain spectroscopy to characterize the frequency-dependent dielectric properties of pork skin tissue across the 0.1–11 THz range, providing critical data for modeling intra-body nanosensor networks.
This paper introduces CONQURE, an open-source co-execution framework that bridges the gap between quantum and classical computing by enabling seamless offloading of OpenMP quantum kernels to QPUs, efficient resource scheduling, and low-overhead result integration, demonstrated by achieving a 3.1X runtime reduction in parallelized VQE runs on an ion-trap device.
This study introduces and evaluates the Adiabatic Quantum Power Flow (AQPF) and Optimal Power Flow (AQOPF) algorithms, which reformulate power system analysis as discrete combinatorial optimization problems solvable by quantum and digital annealers, demonstrating their feasibility and promising scalability across various test systems from 4 to 1354 buses.
This paper proposes and validates a real-time coordinated routing strategy using connected and automated vehicles (CAVs) in mixed-traffic environments to dynamically reroute them away from dedicated bus lanes, thereby simultaneously improving bus schedule adherence and CAV travel efficiency.
This paper introduces DEBT control, a joint routing and flow-control protocol for payment channel networks that uses price-based mechanisms and gradient descent optimization to guide the network toward an optimal steady-state operating condition while providing convergence guarantees.