technocracy

Quantum kernel methods have been proposed as a promising approach for leveraging near-term quantum computers for supervised learning, yet rigorous benchmarks against strong classical baselines remain scarce. We present a comprehensive empirical study of quantum kernel support vector machines (QSVMs) across nine binary classification datasets, four quantum feature maps, three classical kernels, and multiple noise models, totalling 970 experiments with strict nested cross-validation. Our analysis spans four phases: (i) statistical significance testing, revealing that none of 29 pairwise […]

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arXiv:2603.18413v2 Announce Type: replace Abstract: A data analysis pipeline is a structured sequence of steps that transforms raw data into meaningful insights by integrating multiple analysis algorithms. In many practical applications, analytical findings are obtained only after data pass through several data-dependent procedures within such pipelines. In this study, we address the problem of quantifying the statistical reliability of results produced by data analysis pipelines. As a proof of concept, we focus on clustering pipelines that identify cluster […]

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arXiv:2601.12194v1 Announce Type: new Abstract: We develop an information-theoretic framework for discrete dynamics grounded in a comparison-cost functional on ratios. Given two quantities compared via their ratio (x=a/b), we assign a cost (F(x)) measuring deviation from equilibrium ((x=1)). Requiring coherent composition under multiplicative chaining imposes a d’Alembert functional equation; together with normalization ((F(1)=0)) and quadratic calibration at unity, this yields a unique reciprocal cost functional (proved in a companion paper): [ J(x) = tfrac{1}{2}bigl(x + x^{-1}bigr) – 1. […]

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The increasing complexity of neural networks poses significant challenges for democratizing FL on resource?constrained client devices. Parallel split learning (PSL) has emerged as a promising solution by offloading substantial computing workload to a server via model partitioning, shrinking client-side computing load, and eliminating the client-side model aggregation for reduced communication and deployment costs. Since PSL is aggregation-free, it suffers from severe training divergence stemming from gradient directional inconsistency across clients. To address this challenge, we propose GAPSL, a […]

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arXiv:2601.20921v1 Announce Type: new Abstract: Random access remains a central bottleneck in DNA-based data storage. Existing systems typically retrieve records by PCR enrichment or other multi-step biochemical procedures, which do not naturally support fast, massively parallel, content-addressable queries. We introduce the Holographic Bloom Filter (HBF), a probabilistic indexing primitive that stores key-pointer associations as a single high-dimensional memory vector. HBF binds a key vector and a value (pointer) vector using circular convolution and superposes bindings across all records. […]

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Radial basis function in machine learning serves as a powerful tool for handling non-linear data patterns through neural networks that use distance-based activations. This approach helps models approximate complex functions and make accurate predictions in tasks like classification and regression. This blog explains what radial basis function means, its formula, network structure, training steps, a solved example, and applications of radial basis function network. You will gain a clear understanding of how these networks work, their strengths, and […]

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Symmetry-based disentangled representation learning leverages the group structure of environment transformations to uncover the latent factors of variation. Prior approaches to symmetry-based disentanglement have required strong prior knowledge of the symmetry group’s structure, or restrictive assumptions about the subgroup properties. In this work, we remove these constraints by proposing a method whereby an embodied agent autonomously discovers the group structure of its action space through unsupervised interaction with the environment. We prove the identifiability of the true symmetry […]

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Imitation learning has achieved remarkable success in robotic manipulation, yet its application to surgical robotics remains challenging due to data scarcity, constrained workspaces, and the need for an exceptional level of safety and predictability. We present a supervised Mixture-of-Experts (MoE) architecture designed for phase-structured surgical manipulation tasks, which can be added on top of any autonomous policy. Unlike prior surgical robot learning approaches that rely on multi-camera setups or thousands of demonstrations, we show that a lightweight action […]

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