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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.CV English(EN) · · [2 sources]

    An Uncertainty Estimation Framework for Dose Accumulation in Adaptive Radiotherapy: Application to CBCT-Guided Radiotherapy for Cervical Cancer

    Researchers have developed IMPACT-DoseAcc, a new framework for estimating cumulative radiation dose in adaptive radiotherapy (ART) for cervical cancer. This system quantifies uncertainties arising from image registration and segmentation, providing probabilistic dose-volume histograms. The framework demonstrated strong correlation between registration uncertainty and geometric error, achieving high coverage for target volumes and stabilizing dose estimates. AI

    IMPACT Improves interpretation of cumulative dose in ART, supporting more reproducible and uncertainty-informed treatment workflows.

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

    IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation

    Researchers have developed IMPACT, a new framework for robotic manipulation that improves performance in tasks requiring forceful interactions. This system decouples task planning from internal-model predictive control, allowing robots to better handle objects of varying weights and perform contact-rich tasks. Experiments show IMPACT achieves higher success rates, better generalization, and improved safety and energy efficiency compared to previous methods. AI

    IMPACT Enhances robotic capabilities in real-world manipulation tasks, potentially leading to more versatile and efficient automation.

  3. TOOL · arXiv cs.AI English(EN) ·

    IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection

    Researchers have developed a new framework called IMPACT for open-set time series anomaly detection. This method uses influence modeling to estimate the impact of individual training samples, enabling the generation of realistic unseen anomalies and the repurposing of high-influence samples for anomaly decontamination. Experiments demonstrate that IMPACT significantly outperforms existing state-of-the-art methods across various settings and contamination rates. AI

    IMPACT Enhances anomaly detection capabilities for time series data, potentially improving applications in fraud detection and system monitoring.

  4. TOOL · arXiv cs.AI English(EN) ·

    When Reviews Disagree: Fine-Grained Contradiction Analysis in Scientific Peer Reviews

    Researchers have developed a new framework called IMPACT to analyze disagreements within scientific peer reviews, moving beyond simple binary contradiction detection. This system identifies specific evidence spans and assigns graded scores for the intensity of disagreement. To make this practical, IMPACT has been distilled into a smaller language model named TIDE, which can predict contradiction evidence and intensity efficiently. AI

    When Reviews Disagree: Fine-Grained Contradiction Analysis in Scientific Peer Reviews

    IMPACT Introduces a novel method for analyzing nuanced disagreements in academic peer reviews, potentially improving the efficiency and accuracy of editorial processes.