technocracy

arXiv:2601.18981v1 Announce Type: new Abstract: The increasing deployment of Internet-of-Things (IoT)-enabled measurement devices in modern power systems has expanded the cyberattack surface of the grid. As a result, this critical infrastructure is increasingly exposed to cyberattacks, including false data injection attacks (FDIAs) that compromise measurement integrity and threaten reliable system operation. Existing FDIA detection methods primarily exploit spatial correlations and network topology using graph-based learning; however, these approaches often rely on high-dimensional representations and shallow classifiers, limiting their […]

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arXiv:2602.03942v1 Announce Type: new Abstract: Extracting medical decisions from clinical notes is a key step for clinical decision support and patient-facing care summaries. We study how the linguistic characteristics of clinical decisions vary across decision categories and whether these differences explain extraction failures. Using MedDec discharge summaries annotated with decision categories from the Decision Identification and Classification Taxonomy for Use in Medicine (DICTUM), we compute seven linguistic indices for each decision span and analyze span-level extraction recall of […]

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arXiv:2602.22365v1 Announce Type: new Abstract: Crowdsourcing platforms face a four-way tension between allocation quality, workforce sustainability, operational feasibility, and strategic contractor behaviour–a dilemma we formalise as the Cold-Start, Burnout, Utilisation, and Strategic Agency Dilemma. Existing methods resolve at most two of these tensions simultaneously: greedy heuristics and multi-criteria decision making (MCDM) methods achieve Day-1 quality but cause catastrophic burnout, while bandit algorithms eliminate burnout only through operationally infeasible 100% workforce utilisation.To address this, we introduce FORGE, a physics-grounded […]

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Privacy in blockchains is often known as more than just a cryptographic problem. If transaction values are hidden, sender and receiver identities are obfuscated, and proofs reveal nothing beyond validity, then privacy is secured here. In practice, this assumption breaks down, once transactions leave the wallet and enter the peer-to-peer network. Data in transit, even though protected by cryptographic security, can reveal information about the origin. Even the strongest on-chain privacy mechanisms do not prevent trackers from watching […]

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arXiv:2605.13849v1 Announce Type: new Abstract: Determining what to eat to satisfy nutritional requirements is one of the oldest optimization problems in operations research, yet existing formulations have two persistent limitations: continuous variables produce impractical fractional servings (1.7 eggs, 0.37 bananas), and hard nutrient constraints cause infeasibility when targets conflict. A systematic review of 56 diet optimization papers found that none combine integer programming with goal programming to address both issues. We propose Mixed Integer Goal Programming (MIGP) for […]

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In this tutorial, we explore how to build a fully functional background task processing system using Huey directly, without relying on Redis. We configure a SQLite-backed Huey instance, start a real consumer in the notebook, and implement advanced task patterns, including retries, priorities, scheduling, pipelines, locking, and monitoring via signals. As we move step by step, we demonstrate how we can simulate production-grade asynchronous job handling while keeping everything self-contained and easy to run in a cloud notebook […]

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arXiv:2604.13081v1 Announce Type: new Abstract: The Forward-Forward (FF) algorithm is a biologically plausible alternative to backpropagation that trains neural networks layer by layer using a local goodness function to distinguish positive from negative data. Since its introduction, sum-of-squares (SoS) has served as the default goodness function. In this work, we systematically study the design space of goodness functions, investigating both which activations to measure and how to aggregate them. We introduce top-k goodness, which evaluates only the k […]

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arXiv:2602.06246v1 Announce Type: new Abstract: We consider the problem of exactly learning an $s$-sparse real-valued Boolean polynomial of degree $d$ of the form $f:{ 0,1}^n rightarrow mathbb{R}$. This problem corresponds to decomposing functions in the AND basis and is known as taking a M”obius transform. While the analogous problem for the parity basis (Fourier transform) $f: {-1,1 }^n rightarrow mathbb{R}$ is well-understood, the AND basis presents a unique challenge: the basis vectors are coherent, precluding standard compressed sensing […]

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arXiv:2601.22204v1 Announce Type: new Abstract: Federated learning (FL) encounters substantial challenges due to heterogeneity, leading to gradient noise, client drift, and partial client participation errors, the last of which is the most pervasive but remains insufficiently addressed in current literature. In this paper, we propose FedAdaVR, a novel FL algorithm aimed at solving heterogeneity issues caused by sporadic client participation by incorporating an adaptive optimiser with a variance reduction technique. This method takes advantage of the most recent […]

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