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Hierarchical RBF-KAN and RBF-SKAN Architectures for Multidimensional Function Approximation and Random Field Learning

arXiv:2606.02936v1 Announce Type: new Abstract: In this manuscript, we propose and analyze hierarchical Kolmogorov--Arnold neural network architectures employing radial basis functions as activation functions for approximating deterministic functions and random field models. Specifically, we develop a hierarchical radial-basis-function Kolmogorov--Arnold network (hierarchical RBF-KAN) for multidimensional deterministic function approximation and a hierarchical radial-basis-function...

arXiv CS 7d ago

Training-Free Coverless Multi-Image Steganography with Access Control

Announce Type: replace Abstract: Coverless Image Steganography (CIS) hides information without explicitly modifying a cover image, providing strong imperceptibility and inherent robustness to steganalysis. However, existing CIS methods largely lack robust access control, making it difficult to selectively reveal different hidden contents to different authorized users. Such access control is critical for scalable and privacy-sensitive information hiding in multi-user settings.

arXiv CS 8d ago

A 65-nm Privacy-Preserving Neuromorphic Encoder With 7.13-nJ Efficiency, 2.38-Mb/mm^2 Item-Memory Density, and Federated Learning Support

arXiv:2606.09460v1 Announce Type: new Abstract: The increasing demand for privacy-preserving personal data analytics in smart assistants, wearable health monitors, and context-aware systems calls for hardware that is both energy-efficient and secure. This work presents a 65-nm privacy-preserving neuromorphic encoder that leverages transistor-level process variation as physically unclonable entropy for hyperdimensional computing. The proposed 2T-2T entropy cell enables compact,...

arXiv CS 1d ago

High-Rate Quantized Matrix Multiplication II

arXiv:2605.13768v2 Announce Type: replace Abstract: This is the second part of the work investigating quantized matrix multiplication (MatMul). In part I we considered the case of calibration-free quantization, whereas here we discuss the setting where covariance matrix $\Sigma_X$ of the columns of the second factor is available. This setting arises in the ubiquitous task of weight-only post-training quantization of LLMs.

arXiv CS 1d ago

Physicists achieve 'perfect randomness' for the first time ever

Physicists achieve 'perfect randomness' for the first time ever Physicists used quantum bits to achieve 'perfect randomness' in a world-first experiment. The results of their research could strengthen cryptography and other security systems. Researchers at ETH Zurich have demonstrated a means of generating "perfect randomness" by using entangled superconducting qubits.

Live Science 7d ago

Mutually Unbiased Bases for Variational Quantum Initialization: Basis-Union Optimality and Adaptive Family Search

arXiv:2605.16060v2 Announce Type: replace-cross Abstract: We study mutually unbiased bases (MUBs) as structured finite initialization and adaptation families for variational quantum algorithms. The main theoretical result is that, in every dimension admitting a complete set of MUBs, the complete MUB ensemble maximizes isotropic Gaussian random-Hamiltonian width among all unions of d+1 orthonormal bases in C^d. Equivalently, within this basis-union class, it gives the smallest expected...

arXiv CS 9d ago

When Three-Dimensional Conformer Ensembles Improve Molecular Property Prediction Beyond Two-Dimensional Fingerprints: A Systematic Study

arXiv:2606.08825v1 Announce Type: new Abstract: When do three-dimensional conformer ensembles improve molecular property prediction beyond two-dimensional fingerprints? We provide the first systematic, mechanistically grounded answer. Through ~1,000 experiments spanning 13 model configurations, 14 regression targets, and 2 classification targets across MoleculeNet, QM9, and MARCEL benchmarks, we discover selective complementarity: conformer ensemble statistics extracted via Distribution...

arXiv Physics 1d ago

CSI Phase Averaging for High-Sensitivity Wi-Fi Sensing in Low-Multipath Environments

arXiv:2606.07347v1 Announce Type: cross Abstract: This paper presents a low-complexity motion detection method for outdoor Wi-Fi sensing based on a model-driven approach. The method exploits the structural characteristics of the phase components in channel state information (CSI) for low-multipath propagation environments, which are generally considered disadvantageous for Wi-Fi sensing, to mitigate the phase offset errors originating from wireless devices. In addition, phase averaging...

arXiv CS 2d ago

PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning

Announce Type: new Abstract: Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images. However, real-world clients may not share a common modality basis: a visual-search client may contain image--interaction graphs but no seller descriptions, while a catalog client may provide text but no product images. We refer to this practical setting as client-level modality deficiency.

arXiv CS 1d ago

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles

arXiv:2601.22068v2 Announce Type: replace Abstract: Foundation models have become a dominant paradigm in machine learning, achieving remarkable performance across diverse tasks through large-scale pretraining. However, they often yield overconfident, uncalibrated predictions. The standard approach to quantifying epistemic uncertainty are ensembles of multiple independently trained models.

arXiv CS 9d ago