arXiv:2601.06009v1 Announce Type: new Abstract: We develop a practical framework for distinguishing diffusive stochastic processes from deterministic signals using only a single discrete time series. Our approach is based on classical excursion and crossing theorems for continuous semimartingales, which correlates number $N_varepsilon$ of excursions of magnitude at least $varepsilon$ with the quadratic variation $[X]_T$ of the process. The scaling law holds universally for all continuous semimartingales with finite quadratic variation, including general Ito diffusions with nonlinear or state-dependent […]

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arXiv:2603.24730v1 Announce Type: new Abstract: The classic duck-rabbit illusion reveals that when visual evidence is ambiguous, the human brain must decide what it sees. But where exactly do human observers draw the line between ”duck” and ”rabbit”, and do machine classifiers draw it in the same place? We use semantically ambiguous images as interpretability probes to expose how vision models represent the boundaries between concepts. We present a psychophysically-informed framework that interpolates between concepts in the CLIP embedding […]

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How are you, hacker? 🪐 What’s happening in tech today, April 17, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, we present you with these top quality stories. From The Engineer in the Machine: Neo Wants to Be a Kaggle Grandmaster in a Box to Veritasium Stole $10,000 From MKBHDs Locked iPhone. Apple and Visa Knew About the Bug for 5 Years., let’s dive right in. Veritasium Stole $10,000 From MKBHDs […]

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arXiv:2606.05239v1 Announce Type: new Abstract: Diffusion models have demonstrated strong performance in time series modeling due to their ability to progressively capture complex data distributions through iterative denoising. However, existing approaches struggle with frequency-sensitive denoising, high-frequency reconstruction and balancing global trends with local dynamics. To address these limitations, we propose textbf{HyFAD}, a textbf{Hy}brid time-frequency textbf{D}iffusion model with textbf{F}requency-textbf{A}ware embedding for time series imputation. Built upon the DDPM paradigm, HyFAD adopts a coupled time-frequency diffusion framework, in which the […]

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Release: luau-wasm 0.1a0 See Publishing WASM wheels to PyPI for use with Pyodide for details. Tags: lua, webassembly, pyodide

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arXiv:2604.01247v1 Announce Type: new Abstract: We investigate multi-stage pretraining for prosody modeling in diffusion-based TTS. A speaker-conditioned dual-stream encoder is trained with masked language modeling followed by SigLIP-style cross-modal contrastive learning using mixed-phoneme batches, with an additional same-phoneme refinement stage studied separately. We evaluate intrinsic text-audio retrieval and downstream synthesis in Grad-TTS and a latent diffusion TTS system. The two-stage curriculum (MLM + mixed-phoneme contrastive learning) achieves the best overall synthesis quality in terms of intelligibility, speaker similarity, […]

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Self-supervised learning aims to learn maximally informative representations, but explicit information maximization is hindered by the curse of dimensionality. Existing methods like VCReg address this by regularizing first and second-order feature statistics, which cannot fully achieve maximum entropy. We propose Radial-VCReg, which augments VCReg with a radial Gaussianization loss that aligns feature norms with the Chi distribution-a defining property of high-dimensional Gaussians. We prove that Radial-VCReg transforms a broader class of distributions towards normality compared to VCReg and […]

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arXiv:2603.26752v1 Announce Type: new Abstract: Tight matching with the environment is key to effective robot operation in complex settings. Situated robots that build their bodies in situ (e.g. by spinning) are uniquely positioned to exploit their surroundings, yet functionalization of these structures remains an integration challenge – multimaterial spinning requires complex spinneret multiplexing, and mixture doping is limited by additive availability and chemical stability. We propose instead using materials available in the environment to functionalize in situ spun […]

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arXiv:2603.00546v1 Announce Type: new Abstract: Using Multimodal Large Language Models (MLLMs) as judges to achieve precise and consistent evaluations has gradually become an emerging paradigm across various domains. Evaluating the capability and reliability of MLLM-as-a-judge systems is therefore essential for ensuring trustworthy assessment. Existing judge benchmarks categorize samples by task types but fail to capture the fundamental judgment capabilities required for reliable evaluation. In this work, we introduce M-JudgeBench, a ten-dimensional capability-oriented benchmark designed to comprehensively assess the […]

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