technocracy

arXiv:2603.24610v1 Announce Type: new Abstract: In photoacoustic tomography (PAT), a hybrid imaging modality that is based on the acoustic detection of optical absorption from biological tissue exposed to a pulsed laser, a short pulse laser generates an initial pressure proportional to the absorbed optical energy, which then propagates acoustically and is measured on the boundary. To account for the significant signal distortion caused by acoustic attenuation in biological tissue, we model PAT in heterogeneous media using a damped […]

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arXiv:2604.01425v1 Announce Type: new Abstract: Synonymy is a widespread yet puzzling linguistic phenomenon. Absolute synonyms theoretically should not exist, as they do not expand language’s expressive potential. However, it was suggested that even if synonyms denote the same concept, they may reflect different perspectives or carry distinct cultural associations, claims that have rarely been tested quantitatively. In Hindi, prolonged contact with Persian produced many Perso-Arabic loanwords coexisting with their Sanskrit counterpart, forming numerous synonym pairs. This study investigates […]

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So it’s well known that Y Combinator owns some stake in OpenAI. But how big is that stake? This seems like devilishly difficult information to obtain. I asked around and a little birdie who knows several OpenAI investors came back with an answer: Y Combinator owns about 0.6 percent of OpenAI. At OpenAI’s current $852 billion valuation, that’s worth over $5 billion. — John Gruber, Y Combinator’s Stake in OpenAI Tags: openai, y-combinator, ai, john-gruber

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Model-based reinforcement learning (MBRL) achieves high sample efficiency by simulating future trajectories with learned dynamics and reward models. However, its effectiveness is severely compromised in sparse reward settings. The core limitation lies in the standard paradigm of regressing ground-truth scalar rewards: in sparse environments, this yields a flat, gradient-free landscape that fails to provide directional guidance for planning. To address this challenge, we propose Shaping Landscapes with Optimistic Potential Estimates (SLOPE), a novel framework that shifts reward modeling […]

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arXiv:2510.06028v2 Announce Type: replace-cross Abstract: This paper provides data-dependent bounds on the expected error of the Gibbs algorithm in the overparameterized interpolation regime, where low training errors are also obtained for impossible data, such as random labels in classification. The results show that generalization in the low-temperature regime is already signaled by small training errors in the noisier high-temperature regime. The bounds are stable under approximation with Langevin Monte Carlo algorithms. The analysis motivates the design of an […]

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arXiv:2604.15402v1 Announce Type: new Abstract: Classical symbolic protocol verification under Dolev–Yao uses binary attacker knowledge (known/unknown). This abstraction misses cumulative side-channel settings, where repeated noisy observations progressively improve attacker knowledge. We model this process with a graded attacker view (mu_Kin[0,1]), product T-norm leak updates, and finite-grid explicit-state execution in Modified Murphi. The method is optimised with exact concept-lattice attribute reducts and exposes threshold-driven safe-to-fail transitions that are not represented in corresponding binary runs under the same bounded assumptions. […]

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I’m looking for hands-on resources (courses, repos, playlists) that actually train agents from pixels, not just theory.I am thinking to buy this course on udemy Advanced AI: Deep Reinforcement Learning in PyTorch (v2). Is there any better free alternative. Anyone experienced guide me on this to go from zero → building autonomous agents? submitted by /u/These_Negotiation936 [link] [comments]

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arXiv:2603.03470v1 Announce Type: new Abstract: Bisynchronous FIFOs — hardware buffers that mediate data transfer between independent clock domains without a shared global timebase — have been designed, formally verified, and commercially deployed in silicon for over four decades. We survey this literature from Chapiro’s 1984 GALS thesis through Cummings’s Gray-code pointer techniques, Chelcea and Nowick’s mixed-timing interfaces, Greenstreet’s STARI protocol, and the 2015 NVIDIA pausible bisynchronous FIFO, and argue that this body of work constitutes a silicon-proven existence […]

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Automatically discovering formulaic alpha factors is a central problem in quantitative finance. Existing methods often ignore syntactic and semantic constraints, relying on exhaustive search over unstructured and unbounded spaces. We present AlphaCFG, a grammar-based framework for defining and discovering alpha factors that are syntactically valid, financially interpretable, and computationally efficient. AlphaCFG uses an alpha-oriented context-free grammar to define a tree-structured, size-controlled search space, and formulates alpha discovery as a tree-structured linguistic Markov decision process, which is then solved […]

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