technocracy

Artificial intelligence is advancing faster than ever before and often reaches impressive heights. The growth of AI-based startups has accelerated 14 times since 2020. AI has really penetrated any and every corner of our lives: from content creation to fintech to healthcare. However, the problem with AI is that it often doesn’t exactly do what we request of it. It often represents a black box that interprets our queries in its own way. And sometimes it can be […]

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arXiv:2602.20204v1 Announce Type: new Abstract: AI kernel compilation for edge devices depends on the compiler’s ability to exploit parallelism and hide memory latency in the presence of hierarchical memory and explicit data movement. This paper reports a benchmark methodology and corresponding results for three compiler-controlled mechanisms in an MLIR-based compilation pipeline: vectorization (Vec), multi-threading (MT) across hardware contexts, and double buffering (DB) using ping–pong scratchpad buffers to overlap DMA transfers with compute. Using Triton/Inductor-generated kernels, we present an […]

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arXiv:2602.17967v1 Announce Type: cross Abstract: Transfer learning aims to improve inference in a target domain by leveraging information from related source domains, but its effectiveness critically depends on how cross-domain heterogeneity is modeled and controlled. When the conditional mechanism linking covariates and responses varies across domains, indiscriminate information pooling can lead to negative transfer, degrading performance relative to target-only estimation. We study a multi-source, single-target transfer learning problem under conditional distributional drift and propose a semiparametric domain-varying coefficient […]

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arXiv:2601.16241v1 Announce Type: new Abstract: Respiratory monitoring is an extremely important task in modern medical services. Due to its significant advantages, e.g., non-contact, radar-based respiratory monitoring has attracted widespread attention from both academia and industry. Unfortunately, though it can achieve high monitoring accuracy, consumer electronics-grade radar data inevitably contains User-sensitive Identity Information (USI), which may be maliciously used and further lead to privacy leakage. To track these challenges, by variational mode decomposition (VMD) and adversarial loss-based encryption, we […]

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arXiv:2403.01318v3 Announce Type: replace Abstract: Motivated by the empirical observation of power-law distributions in the credits (e.g., “likes”) of viral posts in social media, we introduce a high-dimensional tail index regression model and propose methods for estimation and inference of its parameters. First, we propose a regularized estimator, establish its consistency, and derive its convergence rate. Second, we debias the regularized estimator to facilitate inference and prove its asymptotic normality. Simulation studies corroborate our theoretical findings. We apply […]

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arXiv:2601.14265v1 Announce Type: new Abstract: The integration of AI chatbots into educational settings has opened new pathways for transforming teaching and learning, offering enhanced support to both educators and learners. This study investigates the design and application of an AI chatbot as an educational tool in higher education. Designed to operate in the Greek language, the chatbot addresses linguistic challenges unique to Greek while delivering accurate, context grounded support aligned with the curriculum. The AI chatbot is built […]

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arXiv:2601.21410v2 Announce Type: replace Abstract: We introduce Statsformer, a principled framework for integrating large language model (LLM)-derived knowledge into supervised statistical learning. Existing approaches are limited in adaptability and scope: they either inject LLM guidance as an unvalidated heuristic, which is sensitive to LLM hallucination, or embed semantic information within a single fixed learner. Statsformer overcomes both limitations through a guardrailed ensemble architecture. We embed LLM-derived feature priors within an ensemble of linear and nonlinear learners, adaptively calibrating […]

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arXiv:2509.21906v2 Announce Type: replace-cross Abstract: Discrete flow models offer a powerful framework for learning distributions over discrete state spaces and have demonstrated superior performance compared to the discrete diffusion models. However, their convergence properties and error analysis remain largely unexplored. In this work, we develop a unified framework grounded in stochastic calculus theory to systematically investigate the theoretical properties of discrete flow models. Specifically, by leveraging a Girsanov-type theorem for the path measures of two continuous-time Markov chains […]

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:::info Astounding Stories of Super-Science March, 1932, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Affair of the Brains – Chapter IX: The Brain Speaks Astounding Stories of Super-Science March 1932: The Affair of the Brains – Chapter IX The Brain Speaks By Anthony Gilmore ::: A case lay revealed. At first, while it was unlit, it seemed nothing more than that: a case like those glass-sided […]

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I saw yet another “CSS is a massively bloated mess” whine and I’m like. My dude. My brother in Chromium. It is trying as hard as it can to express the totality of visual presentation and layout design and typography and animation and digital interactivity and a few other things in a human-readable text format. It’s not bloated, it’s fantastically ambitious. Its reach is greater than most of us can hope to grasp. Put some respect on its […]

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