technocracy

Agriculture is pivotal for the economy of a country as it is a major source of food, em-ployment and raw materials. However, challenges such as diseases, soil degradation, and water scarcity persist. Technology adoption can address these issues, improving production and quality. Machine learning enables prediction in agriculture. It opti-mizes irrigation, fertilization, and crop selection, aiding decision-making for food se-curity and crop management. This study proposes multi-model machine learning models for eleven staple (Bananas, Maize, Wheat, Cassava, Rice […]

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arXiv:2510.04970v2 Announce Type: replace Abstract: We present FLOP (Fast Learning of Order and Parents), a score-based causal discovery algorithm for linear models. It pairs fast parent selection with iterative Cholesky-based score updates, cutting run-times over prior algorithms. This makes it feasible to fully embrace discrete search, enabling iterated local search with principled order initialization to find graphs with scores at or close to the global optimum. The resulting structures are highly accurate across benchmarks, with near-perfect recovery in […]

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arXiv:2601.13776v1 Announce Type: cross Abstract: Orthogonal and 1-Lipschitz neural network layers are essential building blocks in robust deep learning architectures, crucial for certified adversarial robustness, stable generative models, and reliable recurrent networks. Despite significant advancements, existing implementations remain fragmented, limited, and computationally demanding. To address these issues, we introduce Orthogonium , a unified, efficient, and comprehensive PyTorch library providing orthogonal and 1-Lipschitz layers. Orthogonium provides access to standard convolution features-including support for strides, dilation, grouping, and transposed-while maintaining […]

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arXiv:2410.11771v3 Announce Type: replace Abstract: Many spatial models exhibit locality structures that effectively reduce their intrinsic dimensionality, enabling efficient approximation and sampling of high-dimensional distributions. However, existing approximation techniques primarily focus on joint distributions and do not provide precise accuracy control for low-dimensional marginals, which are of primary interest in many practical scenarios. By leveraging the locality structures, we establish a dimension independent uniform error bound for the marginals of approximate distributions. Inspired by the Stein’s method, we […]

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The CIA claims a classified tool called “Ghost Murmur” — built by Lockheed Martin’s Skunk Works — used quantum magnetometry + AI to detect a downed pilot’s heartbeat from 40 miles away in Iran. The rescue was real. The physics story? Multiple physicists say it violates the inverse cube law so severely it’d require detecting a signal 2 quintillion times weaker than what current lab sensors can barely pick up at 1 centimeter. The real story is probably […]

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Welcome to the latest HackerNoon Projects of the Week installment. Each week, we shine a light on standout projects from our Proof of Usefulness Hackathon—a contest built around the core question every builder should answer: Is my product actually useful in the real world? For each edition, we’ll highlight projects that demonstrate clear usefulness, technical execution, and real-world impact; all backed by data and not witty buzzwords. This week, we’re excited to share three projects that have proven […]

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Recently, TabPFN has gained attention as a foundation model for tabular data. However, it struggles to integrate heterogeneous modalities such as images and text, which are common in domains like healthcare and marketing, thereby limiting its applicability. To address this, we present the Multi-Modal Prior-data Fitted Network (MMPFN), which extends TabPFN to handle tabular and non-tabular modalities in a unified manner. MMPFN comprises per-modality encoders, modality projectors, and pre-trained foundation models. The modality projectors serve as the critical […]

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Retrieval-Augmented Generation (RAG) has become a standard technique for grounding large language models in external knowledge — but the moment you move beyond plain text and start mixing in images and videos, the whole approach starts to buckle. Visual data is token-heavy, semantically sparse relative to a specific query, and grows unwieldy fast during multi-step reasoning. Researchers at Tongyi Lab, Alibaba Group introduced ‘VimRAG’, a framework built specifically to address that breakdown. The problem: linear history and compressed […]

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arXiv:2603.01157v1 Announce Type: cross Abstract: Risk forecasts in financial regulation and internal management are calculated through historical data. The unknown structural changes of financial data poses a substantial challenge in selecting an appropriate look-back window for risk modeling and forecasting. We develop a data-driven online learning method, called the bootstrap-based adaptive window selection (BAWS), that adaptively determines the window size in a sequential manner. A central component of BAWS is to compare the realized scores against a data-dependent […]

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