technocracy

:::info Authors: Enes Altuncu, ea483@kent.ac.uk (University of Kent, UK) Virginia N. L. Franqueira, V.Franqueira@kent.ac.uk (University of Kent, UK) Shujun Li, S.J.Li@kent.ac.uk (University of Kent, UK) ::: Abstract Recent advancements in AI, especially deep learning, have contributed to a significant increase in the creation of new realistic-looking synthetic media (video, image, and audio) and manipulation of existing media, which has led to the creation of the new term “deepfake”. Based on both the research literature and resources in English […]

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arXiv:2604.02406v1 Announce Type: new Abstract: Measurement is essential to improving AI performance and mitigating harms for marginalized groups. As generative AI systems are rapidly deployed across geographies and contexts, AI measurement practices must be designed to support repeatable, automatable application across different models, datasets, and evaluation settings. But the drive to automate measurement can be in tension with the ability for measurement instruments to capture the expertise and perspectives of communities impacted by AI. Recent work advocates for […]

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In recent years, foundation models have become very popular due to their exceptional performance, mainly in natural language (NLP) tasks where they were first introduced. These models usually consist of hundreds of millions, or even billions, of parameters, making them resource-intensive during training and in production systems, leading to increased costs. This paper focuses on the reduction of a foundation’s model size when applied to music information retrieval (MIR) tasks. Our research combines the Branchformer architecture with SummaryMixing, […]

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arXiv:2602.05272v1 Announce Type: cross Abstract: We consider the problem of quickest changepoint detection under the Average Run Length (ARL) constraint where the pre-change and post-change laws lie in composite families $mathscr{P}$ and $mathscr{Q}$ respectively. In such a problem, a massive challenge is characterizing the best possible detection delay when the “hardest” pre-change law in $mathscr{P}$ depends on the unknown post-change law $Qinmathscr{Q}$. And typical simple-hypothesis likelihood-ratio arguments for Page-CUSUM and Shiryaev-Roberts do not at all apply here. To […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## The Long Now of the Web: Inside the Internet Archive’s Fight Against Forgetting By @zbruceli [ 18 Min read ] A deep dive into the Internet Archive’s custom tech stack. Read More. The Authorization Gap No One Wants to Talk About: Why Your API […]

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arXiv:2601.08953v1 Announce Type: new Abstract: Complex decision-making by autonomous machines and algorithms could underpin the foundations of future society. Generative AI is emerging as a powerful engine for such transitions. However, we show that Generative AI-driven developments pose a critical pitfall: fairness concerns. In robotic applications, although intuitions about fairness are common, a precise and implementable definition that captures user utility and inherent data randomness is missing. Here we provide a utility-aware fairness metric for robotic decision making […]

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arXiv:2603.23504v1 Announce Type: new Abstract: Motivated by evacuation scenarios arising in extreme events such as flooding or forest fires, we study the problem of smoothly scheduling a set of paths in graphs where connections become impassable at some point in time. A schedule is smooth if no two paths meet on an edge and the number of paths simultaneously located at a vertex does not exceed its given capacity. We study the computational complexity of the problem when […]

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arXiv:2604.02347v1 Announce Type: new Abstract: Accurate and up-to-date forecasting of the power grid’s carbon footprint is crucial for effective product carbon footprint (PCF) accounting and informed decarbonization decisions. However, the carbon intensity of the grid exhibits high non-stationarity, and existing methods often struggle to effectively leverage periodic and oscillatory patterns. Furthermore, these methods tend to perform poorly when confronted with irregular exogenous inputs, such as missing data or misalignment. To tackle these challenges, we propose FTimeXer, a frequency-aware […]

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arXiv:2601.08030v1 Announce Type: new Abstract: Extracting higher-order structures from multivariate data has become an area of intensive study in complex systems science, as these multipartite interactions can reveal insights into fundamental features of complex systems like emergent phenomena. Information theory provides a natural language for exploring these interactions, as it elegantly formalizes the problem of comparing “wholes” and “parts” using joint, conditional, and marginal entropies. A large number of distinct statistics have been developed over the years, all […]

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Generative molecular models such as diffusion models and graph neural networks are widely used in drug design. However, their black-box nature means they lack an explicit understanding of chemical rules (e.g., valency, charge, aromaticity), often generating chemically impossible structures such as pentavalent carbon. To address this issue, this paper proposes Atomic-SCS, an atom-level chemical rule scoring tool based on a symbolic approach. Atomic-SCS does not rely on data-driven training but directly applies IUPAC rules to independently score each […]

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