Smoothing Exponents and Decoupling in Semifinite von Neumann Algebras
2026-07-10T08:51:43Z•86434fc9a7dc94a5f409f11dbfed070e0f6dbbe4441a905134d2d824c1df4964
6GPOMDPPPOUAVZak-OTFSbeam-selectiondecouplingdeep-reinforcement-learningdiffusion-modelsdomain-generalizationgraph-neural-networks (GNNs) - deep-learning-acceleration (trim:human-activity-recognitioninterference-mitigationlocalizationmax-relative-entropymultiuserpilot-designquantum-informationradio-mapsrotatable-antennasensor-networksspread-pilotsthrough-the-wall-radartransformervon-neumann-algebras
What happened
Feed of nine new arXiv submissions (math/IT-related) covering theory and systems across quantum information, wireless communications, signal processing, coding, and learning: (1) "Smoothing Exponents and Decoupling in Semifinite von Neumann Algebras" — exact smoothing-exponent formula for max-relative entropy in semifinite von Neumann algebras and application to catalytic decoupling; (2) "RadioDiff-v2" — a 1D diffusion-transformer for generative angular radio maps to improve multi-beam selection and localization in 6G; (3) "Generalization Theory for Through-the-Wall Radar Human Activity Recogn
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_math_it
- Record identifier
- 86434fc9a7dc94a5f409f11dbfed070e0f6dbbe4441a905134d2d824c1df4964
- Enrichment time
- 2026-07-10T08:51:43Z
- AI-assisted enrichment
- Yes
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