Advancing regulatory variant effect prediction with AlphaGenome - Nature
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE1RM054VDNBMEc3eGlrRFI5TnNlQjJaM1hVY0hMYjE1dXIwemR2aFVrb2R4S3M3em4tenBzcEEyc0haVnB6ZXBEb2E4dGE1TngyOVVZQlNta1JJTDFub3hn?oc=5" target="_blank">Advancing regulatory variant effect prediction with AlphaGenome</a> <font color="#6f6f6f">Nature</font>
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M2H-MX: Multi-Task Dense Visual Perception for Real-Time Monocular Spatial Understanding
arXiv:2603.29236v1 Announce Type: new Abstract: Monocular cameras are attractive for robotic perception due to their low cost and ease of deployment, yet achieving reliable real-time spatial understanding from a single image stream remains challenging. While recent multi-task dense prediction models have improved per-pixel depth and semantic estimation, translating these advances into stable monocular mapping systems is still non-trivial. This paper presents M2H-MX, a real-time multi-task perception model for monocular spatial understanding. The model preserves multi-scale feature representations while introducing register-gated global context and controlled cross-task interaction in a lightweight decoder, enabling depth and semantic predictions to reinforce each other under strict latency
Robust and Consistent Ski Rental with Distributional Advice
arXiv:2603.29233v1 Announce Type: new Abstract: The ski rental problem is a canonical model for online decision-making under uncertainty, capturing the fundamental trade-off between repeated rental costs and a one-time purchase. While classical algorithms focus on worst-case competitive ratios and recent "learning-augmented" methods leverage point-estimate predictions, neither approach fully exploits the richness of full distributional predictions while maintaining rigorous robustness guarantees. We address this gap by establishing a systematic framework that integrates distributional advice of unknown quality into both deterministic and randomized algorithms. For the deterministic setting, we formalize the problem under perfect distributional prediction and derive an efficient algorithm t
Improving Ensemble Forecasts of Abnormally Deflecting Tropical Cyclones with Fused Atmosphere-Ocean-Terrain Data
arXiv:2603.29200v1 Announce Type: new Abstract: Deep learning-based tropical cyclone (TC) forecasting methods have demonstrated significant potential and application advantages, as they feature much lower computational cost and faster operation speed than numerical weather prediction models. However, existing deep learning methods still have key limitations: they can only process a single type of sequential trajectory data or homogeneous meteorological variables, and fail to achieve accurate forecasting of abnormal deflected TCs. To address these challenges, we present two groundbreaking contributions. First, we have constructed a multimodal and multi-source dataset named AOT-TCs for TC forecasting in the Northwest Pacific basin. As the first dataset of its kind, it innovatively integrates
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Calibrated Confidence Expression for Radiology Report Generation
arXiv:2603.29492v1 Announce Type: new Abstract: Safe deployment of Large Vision-Language Models (LVLMs) in radiology report generation requires not only accurate predictions but also clinically interpretable indicators of when outputs should be thoroughly reviewed, enabling selective radiologist verification and reducing the risk of hallucinated findings influencing clinical decisions. One intuitive approach to this is verbalized confidence, where the model explicitly states its certainty. However, current state-of-the-art language models are often overconfident, and research on calibration in multimodal settings such as radiology report generation is limited. To address this gap, we introduce ConRad (Confidence Calibration for Radiology Reports), a reinforcement learning framework for fin
Robust and Consistent Ski Rental with Distributional Advice
arXiv:2603.29233v1 Announce Type: new Abstract: The ski rental problem is a canonical model for online decision-making under uncertainty, capturing the fundamental trade-off between repeated rental costs and a one-time purchase. While classical algorithms focus on worst-case competitive ratios and recent "learning-augmented" methods leverage point-estimate predictions, neither approach fully exploits the richness of full distributional predictions while maintaining rigorous robustness guarantees. We address this gap by establishing a systematic framework that integrates distributional advice of unknown quality into both deterministic and randomized algorithms. For the deterministic setting, we formalize the problem under perfect distributional prediction and derive an efficient algorithm t
Microsoft (MSFT) Commits $1B to Thailand for Data Centres and AI - mexc.co
<a href="https://news.google.com/rss/articles/CBMiTkFVX3lxTE80SmJJTHgzZjAxNlVSMkxTUm55bTNGQ3k1cVZXaXVNWlZMZFFmNTJHWE5kOExaQ3hxLTRzRDlBWnUycUFSajkzc0gzSFBadw?oc=5" target="_blank">Microsoft (MSFT) Commits $1B to Thailand for Data Centres and AI</a> <font color="#6f6f6f">mexc.co</font>
NSSF Urges MUBS Students to Embrace AI for High-Level Functions - Nilepost News
<a href="https://news.google.com/rss/articles/CBMirAFBVV95cUxPQ1AxcDVyMnFkUThfUnVwVFgyQ2F3QlZkZVJybFhaS2lCOWE5Y0haWG5lUGxHbGJxQWoxcTB3VmFBSm92SjRwUWRCOFRNVXpxOHhDdjhEZ0N6aUE2ZHB5S2E2MnlUeFVFRTJRR05CeG5RWkgzRExwMU1UOXd2UTBQcTZDMWpURVRsLWQwWWNFQTlNVmNJT1dDM29kdkpsalF5T254dkJ1X2hqWmtD?oc=5" target="_blank">NSSF Urges MUBS Students to Embrace AI for High-Level Functions</a> <font color="#6f6f6f">Nilepost News</font>
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