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Dielectric-Anisotropy-Induced Quasi-BIC Activation for Spatial Differentiation in All-Dielectric Metasurfaces
arXiv:2606.01983v1 Announce Type: new Abstract: Quasi-BICs in dielectric metasurfaces are typically obtained through geometric symmetry breaking. Here, a 20\,nm BeS insert is placed in the gap of a symmetric TiO$_2$ nanobar pair. The anisotropy of the BeS layer ($\Delta\varepsilon \approx 0.27$) relaxes the dipole-cancellation condition and gives rise to a quasi-BIC resonance.
Design Principles for AI-Ready QCD Data with a Barrel Imaging Calorimeter Application
arXiv:2606.07667v1 Announce Type: new Abstract: Data from large physics collider experiments in Quantum Chromodynamics (QCD) research differ fundamentally from the modalities used in modern foundation models. The heterogeneity of detector readouts and their technology dependence require principled curation for cross experiment AI applications.
Japan electronics retailers Yamada, Edion plan merger to create sector giant, Nikkei Asia reports
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EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learning
arXiv:2606.05942v1 Announce Type: cross Abstract: Neural network (NN)-based nonlinear causal discovery methods recover DAG structure but leave each causal mechanism as a black box. Waxman et al. argued that extracting causal mechanisms from NN weights is ill-posed. We propose EML-CD, a framework that integrates the EML operator (capable of composing elementary functions from a single binary operator) into causal structure learning, with interpretable mechanism recovery as the primary objective.
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Application of Fractional Polynomial Model Based on Bayesian Criterion in Cerebrovascular Diseases: A Robust Framework for Occlusion Simulation and Topology Reconstruction
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Multilayer Babinet metamaterial to initiate nonreciprocal topological phenomena and generalized Faraday rotation
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Bayesian estimation of spectral parameters of the 6.7-GHz methanol maser G339.884-1.259 from GRAO observations
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Parallel Adaptive Multi-Objective Evolutionary Learning of Discretized Bayesian Network Classifiers for Clinical Data
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