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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. RESEARCH · arXiv cs.AI English(EN) · · [2 sources]

    Structured Adversarial Camouflage via Voronoi Diagrams

    Researchers have developed a new method for creating adversarial camouflage patterns using Voronoi diagrams, which optimizes seed-point locations for printable, structured patterns. This technique aims to be more visually plausible and computationally efficient than pixel-wise adversarial patches. When tested on person detection using the COCO dataset, the camouflage significantly degraded detector performance, even transferring across different detector families like YOLOv9 through YOLOv12. AI

    IMPACT This research demonstrates a novel approach to adversarial attacks that could impact the robustness of computer vision systems.

  2. RESEARCH · arXiv cs.LG English(EN) · · [2 sources]

    Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy

    Researchers have developed a new framework called Multimodality Stacking with Blockwise missing values (MSB) to address challenges in integrating multimodal datasets for clinical oncology. MSB is a late-fusion framework designed for survival analysis that can handle situations where entire data sources are unavailable for certain patient subsets. When applied to the PIONeeR study to predict progression-free survival in lung cancer patients, MSB demonstrated improved predictive performance over existing algorithms, significantly reducing the generalization gap and identifying key predictive biomarkers. AI

  3. TOOL · Mastodon — fosstodon.org English(EN) ·

    As a scikit-learn contributor, I wish this course existed when I started. The official scikit-learn MOOC, built by core devs at INRIA, teaches predictive modell

    An official scikit-learn MOOC, developed by core contributors at INRIA, is now available. This free, hands-on course utilizes Jupyter notebooks to teach predictive modeling, covering everything from preprocessing to model evaluation. It aims to build strong intuition for machine learning rather than just code execution. AI

    IMPACT Provides a structured, free resource for learning practical machine learning skills with scikit-learn.