Hypergraph Neural Networks (HyGNNs) have demonstrated remarkable success in modeling higher-order relationships among entities. However, their performance often degrades on heterophilic hypergraphs, where nodes connected by the same hyperedge tend to have dissimilar semantic representations or belong to different classes. While several HyGNNs, including our prior work BHyGNN, have been proposed to address heterophily, their reliance on labeled data significantly limits their applicability in real-world scenarios where annotations are scarce or costly. To overcome this limitation, we introduce […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## ISO 27001 Compliance Tools in 2026: A Comparative Overview of 7 Leading Platforms By @stevebeyatte [ 7 Min read ] Breaking down the best ISO 27001 Compliance tools in the market for 2026. Read More. The Hidden Cost of AI: Why It’s Making Workers […]

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arXiv:2604.15408v1 Announce Type: new Abstract: Token pruning methods for Vision Transformers (ViTs) promise quadratic reductions in attention FLOPs by dropping uninformative patches. Yet when pruned sequences are executed with state-of-the-art variable-length attention APIs — including FlashAttention-2’s varlen and PyTorch’s NestedTensor SDPA-the wall-clock attention latency doesn’t scale accordingly. We trace this to a dispatch-overhead bottleneck: at the short, post-pruning sequence lengths typical of ViTs (<=197 tokens), actual matrix arithmetic completes in single-digit microseconds while the host-side dispatch path consumes […]

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We introduce a reproducible, bias-resistant machine learning framework that integrates domain-informed feature engineering, nested cross-validation, and calibrated decision-threshold optimization for small-sample neuroimaging data. Conventional cross-validation frameworks that reuse the same folds for both model selection and performance estimation yield optimistically biased results, limiting reproducibility and generalization. Demonstrated on a high-dimensional structural MRI dataset of deep brain stimulation cognitive outcomes, the framework achieved a nested-CV balanced accuracy of 0.660,$pm$,0.068 using a compact, interpretable subset selected via importance-guided ranking. By […]

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Cost-cutting alone is shortsighted.

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arXiv:2601.21717v2 Announce Type: replace-cross Abstract: We establish sample complexity guarantees for estimating the covariance matrix of a strongly log-concave smooth distribution using the unadjusted Langevin algorithm (ULA). We quantitatively compare our complexity estimates on single-chain ULA with embarrassingly parallel ULA and derive that the sample complexity of the single-chain approach is smaller than that of embarrassingly parallel ULA by a logarithmic factor in the dimension and the reciprocal of the prescribed precision, with the difference arising from effective […]

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Local LLMs are great – until you have to manage them on multiple machines. Ollama makes it easy to run models like Qwen, Mistral, and Gemma on consumer hardware, but most tools stop at “chat UI”. This tutorial shows how to build a production-ready admin dashboard for Ollama that runs on Android and Desktop with about 95% shared Kotlin Multiplatform code. You will: Build a Compose Multiplatform app with a shared UI layer for Android and Desktop. Implement […]

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Continuous and global detection of large methane emissions is a crucial step for global warming mitigation. Satellite observations, such as from S5P/TROPOMI, combined with plume detection algorithms, can play a key role in this effort. However, not all TROPOMI plume detections that look like methane emission plumes are the result of actual emissions. A significant part of the plume-like features in the data are retrieval artifacts. Such artifacts could be the result of variations in elevation or albedo […]

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Bitcoin’s consensus mechanism asks: Did you demonstrate sufficient computational commitment to deserve validating this block? Proof of Usefulness asks: Does this project create demonstrable value in the world? The architectural goal is the same in both cases — produce a score that cannot be easily faked and that correlates with something real. The difference is what “something real” means. In Bitcoin, it means electricity burned. In Proof of Usefulness, it means people helped.

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A conversation with Zynga founder Mark Pincus on creating a culture of innovation.

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