technocracy

arXiv:2505.08371v4 Announce Type: replace-cross Abstract: We address the problem of inferring the causal direction between a continuous variable $X$ and a discrete variable $Y$ from observational data. For the model $X to Y$, we adopt the threshold model used in prior work. For the model $Y to X$, we consider two cases: (1) the conditional distributions of $X$ given different values of $Y$ form a location-shift family, and (2) they are mixtures of generalized normal distributions with independently […]

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arXiv:2601.19965v1 Announce Type: new Abstract: In industrial recommender systems, conversion rate (CVR) is widely used for traffic allocation, but it fails to fully reflect recommendation effectiveness because it ignores refund behavior. To better capture true user satisfaction and business value, net conversion rate (NetCVR), defined as the probability that a clicked item is purchased and not refunded, has been proposed.Unlike CVR, NetCVR prediction involves a more complex multi-stage cascaded delayed feedback process. The two cascaded delays from click […]

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In less than 3 pages, this tutorial covers signal processing and convolution quite thoroughly, even advanced concepts. I illustrate the techniques with the Riemann zeta function and the kernel method, along with short, home-made Python code that shows all the detailed steps, rather than based on Blackbox Python libraries. The document looks like a cheat sheet. It may be useful for scientists who need a refresher on the topic, or professionals in quantitative fields who don’t know much […]

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arXiv:2601.00221v1 Announce Type: new Abstract: The increasing complexity and interconnectedness of systems across various fields have led to a growing interest in studying complex networks, particularly Scale-Free (SF) networks, which best model real-world systems. This paper investigates the influence of clustering on the observability and controllability of complex SF networks, framing these characteristics in the context of structured systems theory. In this paper, we show that densely clustered networks require fewer driver and observer nodes due to better […]

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FLUX.2-klein-4B Pure C Implementation On 15th January Black Forest Labs, a lab formed by the creators of the original Stable Diffusion, released black-forest-labs/FLUX.2-klein-4B – an Apache 2.0 licensed 4 billion parameter version of their FLUX.2 family. Salvatore Sanfilippo (antirez) decided to build a pure C and dependency-free implementation to run the model, with assistance from Claude Code and Claude Opus 4.5. Salvatore shared this note on Hacker News: Something that may be interesting for the reader of this […]

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arXiv:2602.06184v1 Announce Type: new Abstract: Recent progress in large-scale CLIP-like vision-language models(VLMs) has greatly advanced medical image analysis. However, most existing medical VLMs still rely on coarse image-text contrastive objectives and fail to capture the systematic visual knowledge encoded in well-defined medical phenotype ontologies. To address this gap, we construct PhenoKG, the first large-scale, phenotype-centric multimodal knowledge graph that encompasses over 520K high-quality image-text pairs linked to more than 3,000 phenotypes. Building upon PhenoKG, we propose PhenoLIP, a […]

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Anthropic developed Claude AI models as part of its mission to create safe artificial intelligence systems and has thus undergone rapid technological progress. The Claude AI model’s evolution demonstrates important developments in reasoning abilities, context management, and enterprise application effectiveness. From its initial version to the current Claude AI updates, it has developed into a powerful tool, serving developers, companies, and researchers. What Are Claude AI Models? The Anthropic Claude models constitute advanced artificial intelligence systems that perform […]

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In this tutorial, we explore how we can decode linguistic features directly from brain signals using a modern neuroAI pipeline. We work with MEG data and build an end-to-end system that transforms raw neural activity into meaningful predictions, in this case, estimating word length from brain responses. We set up the environment, load and process neural events, design a custom feature extractor, and construct a structured data pipeline using NeuralSet. From there, we train a convolutional neural network […]

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arXiv:2602.23588v1 Announce Type: new Abstract: Large unimodal foundation models for vision and language encode rich semantic structures, yet aligning them typically requires computationally intensive multimodal fine-tuning. Such approaches depend on large-scale parameter updates, are resource intensive, and can perturb pretrained representations. Emerging evidence suggests, however, that independently trained foundation models may already exhibit latent semantic compatibility, reflecting shared structures in the data they model. This raises a fundamental question: can cross-modal alignment be achieved without modifying the models […]

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