This study analyzes Graph Neural Networks (GNNs) for distribution system state estimation (DSSE) by employing an interpretable Graph Neural Additive Network (GNAN) and by utilizing an edge-conditioned message-passing mechanism. The architectures are benchmarked against the standard Graph Attention Network (GAT) architecture. Multiple SimBench grids with topology changes and various measurement penetration rates were used to evaluate performance. Empirically, GNAN trails GAT in accuracy but serves as a useful probe for graph learning when accompanied with the proposed edge attention mechanism. Together, they demonstrate that incorporating information from distant nodes could improve learning depending on the grid topology and available data. This study advances the state-of-the-art understanding of learning on graphs for the state estimation task and contributes toward reliable GNN-based DSSE prediction technologies.
In this work, we develop a spectral theory for hypergraph limits. We prove the convergence of the spectra of adjacency and Laplacian matrices for hypergraph sequences converging in the 1-cut metric. On the other hand, we give examples of matrix operators associated with hypergraphs whose spectra are not continuous with respect to the 1-cut metric. Furthermore, we show that these operators are continuous with respect to other cut norms.
Synthetic neuroimaging data can mitigate critical limitations of real-world datasets, including the scarcity of rare phenotypes, domain shifts across scanners, and insufficient longitudinal coverage. However, existing generative models largely rely on weak conditioning signals, such as labels or text, which lack anatomical grounding and often produce biologically implausible outputs. To this end, we introduce Cor2Vox, a cortex-grounded generative framework for brain magnetic resonance image (MRI) synthesis that ties image generation to continuous structural priors of the cerebral cortex. It leverages high-resolution cortical surfaces to guide a 3D shape-to-image Brownian bridge diffusion process, enabling topologically faithful synthesis and precise control over underlying anatomies. To support the generation of new, realistic brain shapes, we developed a large-scale statistical shape model of cortical morphology derived from over 33,000 UK Biobank scans. We validated the fidelity of Cor2Vox based on traditional image quality metrics, advanced cortical surface reconstruction, and whole-brain segmentation quality, outperforming many baseline methods. Across three applications, namely (i) anatomy-guided synthesis, (ii) simulation of progressive gray matter atrophy, and (iii) harmonization of in-house frontotemporal dementia scans with public datasets, Cor2Vox preserved fine-grained cortical morphology at the sub-voxel level. Notably, the proposed model exhibits inherent robustness to variations in cortical geometry and unseen dementia phenotypes without retraining, paving the way for disease progression modeling and robust data augmentation in rare neurodegenerative disorders.
This paper proposes a generalized active classification framework designed to classify the environment of a robot through controlled tactile interaction, with direct application to robotic disassembly of Waste Electrical and Electronic Equipment (WEEE). In this domain, purely vision-based systems frequently fail due to occlusions, dust, and reflective surfaces, making haptic identification an operational necessity. By formulating environment identification as an active sensing problem, we utilize the entropy of learned interaction models and a receding-horizon optimization to select control inputs that maximize information gain from force-torque feedback. Our approach transitions from a passive classifier to an active framework that dynamically probes the environment to resolve belief uncertainty across six distinct tactile classes, demonstrated through the high-precision detection and unfastening of screws using an industrial 6-DOF robotic arm. Experimental results on a consumer microwave demonstrate that the active framework achieves a 51% reduction in detection latency for screws compared to passive baselines and consistently outperforms expert-designed heuristics. Furthermore, the system exhibits robust zero-shot generalization to novel industrial objects, indicating that learned tactile signatures are invariant to the global object context. These results provide a scalable foundation for autonomous disassembly in high-variance environments like electronic waste recycling, while offering a broader methodology for any robotic classification task where control can be leveraged to accelerate information gain.
In response to climate goals, growing environmental awareness, and financial incentives, municipalities increasingly seek to electrify public transportation networks. We study the problem of locating stationary and dynamic inductive charging stations for electric vehicles (EVs), allowing detours from fixed transit routes and schedules. Dynamic charging, which enables energy transfer while driving, reduces space usage in dense urban areas and lowers vehicle idle times. We formulate a cost-minimization problem that considers both infrastructure and operational costs and propose an Iterated Local Search (ILS) algorithm to solve instances of realistic size. Each configuration requires solving a decomposed subproblem comprising multiple resource-constrained shortest-path problems. To do so, we employ a bidirectional label-setting algorithm with lazy dominance checks based on local bounds. On adapted benchmark instances, our approach outperforms a commercial solver by up to 57% in solution quality. We further apply our method to a real-world case study in Hof, Germany. Results indicate that, under current cost structures, calibrated from a test track in Bad Staffelstein, dynamic inductive charging is not yet cost-competitive with stationary alternatives. We quantify the value of allowing detours at up to 3.9% of the total system cost and show that integrating photovoltaics with decentralized energy storage can yield savings exceeding 20% by contributing more than 60% to the fleet’s charging demand during operations.
