technocracy

arXiv:2602.16734v1 Announce Type: new Abstract: We analyze the winning coalitions that arise under Bloc voting when voters preferences are single-peaked. For small numbers of candidates and numbers of winners, we determine conditions under which candidates in winning coalitions are adjacent. We also analyze the results of pairwise contests between winning and losing candidates and assess when the winning coalitions satisfy several proposed extensions of the Condorcet criterion to multiwinner voting methods. Finally, we use Monte Carlo simulations to […]

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arXiv:2603.23520v1 Announce Type: new Abstract: Medicine is an empirical discipline refined through long-term observation and the messy, high-variance reality of clinical practice. Physicians build diagnostic and therapeutic competence through repeated cycles of application, reflection, and improvement, forming individualized methodologies. Yet outcomes vary widely, and master physicians’ knowledge systems are slow to develop and hard to transmit at scale, contributing to the scarcity of high-quality clinical expertise. To address this, we propose Med-Shicheng, a general framework that enables large […]

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Two years back, shipping a SaaS product meant throwing a bunch of cash at devs, running out of money, and then desperately crossing your fingers that your first version wouldn’t fall over on launch day. Fast forward to now, and a solo founder with a subscription to Claude and a three-day weekend can actually get something really close to the same result. That’s genuinely great for builders, but defensibility is a different story. When everyone has access to […]

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Clinical trials are characterized by high costs, extended timelines, and substantial operational risk, yet reliable prospective methods for predicting trial success before initiation remain limited. Existing artificial intelligence approaches often focus on isolated metrics or specific development stages and frequently rely on variables unavailable at the trial design phase, limiting real-world applicability. We present a hierarchical latent risk-aware machine learning framework for prospective prediction of clinical trial operational success using a curated subset of TrialsBank, a proprietary AI-ready […]

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arXiv:2601.03319v1 Announce Type: new Abstract: A photorealistic and controllable 3D caricaturization framework for faces is introduced. We start with an intrinsic Gaussian curvature-based surface exaggeration technique, which, when coupled with texture, tends to produce over-smoothed renders. To address this, we resort to 3D Gaussian Splatting (3DGS), which has recently been shown to produce realistic free-viewpoint avatars. Given a multiview sequence, we extract a FLAME mesh, solve a curvature-weighted Poisson equation, and obtain its exaggerated form. However, directly deforming […]

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Some capabilities need to be owned, while others can be acquired. The best leaders know how to make that distinction.

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This paper introduces two transfer learning methodologies for estimating nonparametric Bayesian networks under scarce data. We propose two algorithms, a constraint-based structure learning method, called PC-stable-transfer learning (PCS-TL), and a score-based method, called hill climbing transfer learning (HC-TL). We also define particular metrics to tackle the negative transfer problem in each of them, a situation in which transfer learning has a negative impact on the model’s performance. Then, for the parameters, we propose a log-linear pooling approach. For […]

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Before the “last mile” delivery driver sets off for your home, your Amazon item has moved through the middle-mile network of fulfillment centers and sort centers, which brings products close enough to customers to make our same-day or next-day shipping promises possible. For years, Amazon engineers and scientists have been pushing computational boundaries to optimize this network under uncertainty, and that push has accelerated as the network has grown more complex. What happens when a huge snowstorm closes […]

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arXiv:2601.19939v1 Announce Type: new Abstract: Oculomics – the concept of predicting systemic diseases, such as cardiovascular disease and dementia, through retinal imaging – has advanced rapidly due to the data efficiency of transformer-based foundation models like RETFound. Image-level mixed sample data augmentations, such as CutMix and MixUp, are frequently used for training transformers, yet these techniques perturb patient-specific attributes, such as medical comorbidity and clinical factors, since they only account for images and labels. To address this limitation, […]

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