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US threatens sanctions against Chinese AI models over IP theft1h◆Google launches a cheaper alternative to large AI security models like Mythos1h◆Music streamer Deezer says more than 50% of daily uploads are AI-generated3h◆Halliday’s latest smart glasses feature a much-improved display3h◆America needs to stop getting shocked by Chinese AI5h◆Advancing next-gen AI with materials science innovation6h◆Gritt exits stealth with $32 million for robots to build solar plants — then, everything else6h◆Capacity and Redundancy Trade-offs in Multi-Task Learning12h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation12h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making12h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection12h◆Supervised Reward Inference12h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization12h◆Is Progressive Disclosure All You Need for Long-Context Agents?12h◆It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability12h◆DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification12h◆Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time Collaboration12h◆Scientific reasoning does not reliably translate into scientific forecasting in frontier AI12h◆Language Triggers Hijack Language Circuits: A Mechanistic Analysis of Backdoor Behaviors in Large Language Models12h◆AI-Augmented Human Resource Management? Insights from German companies12h◆US threatens sanctions against Chinese AI models over IP theft1h◆Google launches a cheaper alternative to large AI security models like Mythos1h◆Music streamer Deezer says more than 50% of daily uploads are AI-generated3h◆Halliday’s latest smart glasses feature a much-improved display3h◆America needs to stop getting shocked by Chinese AI5h◆Advancing next-gen AI with materials science innovation6h◆Gritt exits stealth with $32 million for robots to build solar plants — then, everything else6h◆Capacity and Redundancy Trade-offs in Multi-Task Learning12h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation12h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making12h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection12h◆Supervised Reward Inference12h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization12h◆Is Progressive Disclosure All You Need for Long-Context Agents?12h◆It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability12h◆DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification12h◆Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time Collaboration12h◆Scientific reasoning does not reliably translate into scientific forecasting in frontier AI12h◆Language Triggers Hijack Language Circuits: A Mechanistic Analysis of Backdoor Behaviors in Large Language Models12h◆AI-Augmented Human Resource Management? Insights from German companies12h◆
News/model/chandra-ocr-2

chandra-ocr-2 news

3 articles mentioning chandra-ocr-2

arxivJun 18

The Chandra-Gaia Catalog of Counterparts: Resolving ambiguous Gaia matches to X-ray sources in the Chandra Source Catalog using Machine Learning

arXiv:2606.19329v1 Announce Type: cross Abstract: We present a framework to cross-match sources from the Chandra Source Catalog (CSC v2.1) with optical sources from Gaia Data Release 3. Unlike purely spatial approaches, we use source properties such as magnitudes, colors, and distances to identify t

arxivMay 29

Real-Time Retargeting Using Controllability Boundary for Chandrayaan-3 Lunar Landing

arXiv:2605.29412v1 Announce Type: cross Abstract: This paper presents the real-time retargeting guidance policy developed for the Chandrayaan-3 lunar landing mission. The baseline guidance generates approximate fuel-optimal descent trajectories, while a high-level policy enables safe retargeting to

arxivMay 19

Extracting latent representations from X-ray spectra. Classification, regression, and accretion signatures of Chandra sources

arXiv:2510.14102v2 Announce Type: replace-cross Abstract: Spectral signatures are crucial in the era of large X-ray surveys. Automatic machine learning methods have proven useful in this respect, but so far they have not been applied to large spectral datasets, such as the Chandra Source Catalog (CS

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