May 2026

This article presents the mathematical foundations of spiking neural networks (SNNs) in a unified formalism, with a deliberate emphasis on derivational provenance. The same neuron model is written one way in computational neuroscience textbooks, another way in machine learning papers, and a third way in the stochastic process literature. Even within a single line of work, papers absorb constants into other constants until two equations from two sources cannot be compared by inspection. We collect the core mathematics […]

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The integration of heterogeneous multi-omics data — spanning genomics, transcriptomics,proteomics, epigenomics, and metabolomics — remains one of the central open challenges incomputational biology. Existing approaches either flatten omics layers into feature matrices,losing relational structure, or adopt multilayer network formalisms that treat layers asindependent graphs coupled only by alignment edges. In this position paper we propose afundamentally different data model: a Network of Networks (NoN), in which each node of atop-levelgraphisitselfacompletegraph, definedrecursively. Thisrecursivestructurenaturallyencodes the hierarchical organisation of biological systems […]

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Differential Causal Networks (DCNs) were introduced to represent changes between two causal networks inferred under different conditions. In their original use, however, DCNs remain pairwise objects: each differential graph summarizes rewiring within a single system, while common differential structures shared across many systems remain implicit. We introduce a methodological framework for the local alignment of DCNs aimed at detecting recurrent rewiring motifs, that is, small directed differential subnetworks that reappear across multiple systems under the same contrast. The […]

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The calibrated model reproduced the overall trend of specimen-to-specimen mechanical variation observed experimentally. Predicted stiffness values were in reasonable agreement with measured data. Fracture force predictions showed moderate agreement for dynamically tested specimens (R² = 0.60), which improved to R² = 0.88 after exclusion of one statistically identified outlier. Compared with a purely linear elastic formulation, the proposed viscoelastic model demonstrated modest improvement in stiffness prediction and more substantial improvement in fracture force prediction. These findings indicate that […]

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For two decades, the security operations center has been built around a quiet lie: that detection and investigation are separate disciplines. They are not, and never were. The split exists because vendors built it that way, and organizations paid to hold two incompatible worlds together with duct tape and headcount. The consequences are now too expensive to ignore. CardinalOps 4th Annual State of SIEM Detection Risk Report showed that 18% of all SIEM rules were broken at any […]

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Hi everyone — I’m working on an open-source environment for studying sequential decision-making in manufacturing systems. The current demo is a synthetic process-window benchmark: an agent/planner selects process settings, observes noisy quality outcomes, tracks uncertainty, and recommends the next experiment. The motivation is similar to sparse-data physical systems, where each real experiment is expensive and the goal is not just prediction, but deciding what to try next. Repo: https://github.com/programmablemanufacturing/programmable-manufacturing-lab I’d appreciate feedback from the RL community on: what […]

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