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Beverly Hills-based Subject, whose streaming-style, on-demand platform provides digital curriculum and learning insights for grades 6-12, raised $28M (Natalie Breymeyer/Axios)
https://www.axios.com/pro/all-deals/2026/02/24/subject-venture-ed-tech-vistara…

Exclusive: Subject raises $28M to expand AI curriculum platform
AI education platform raises $28M as edtech sector stabilizes

Crosslisted article(s) found for physics.flu-dyn. https://arxiv.org/list/physics.flu-dyn/new
[1/1]:
- Physics Constrained Neural Collision Operators for Variable Hard Sphere Surrogates and Ab Initio ...
Ehsan Roohi, Ahmad Shoja-Sani, Stefan Stefanov
https://arxiv.org/abs/2602.21244 https://mastoxiv.page/@arXiv_physicscompph_bot/116135902638427987
- Chapman-Enskog expansion for chirally colliding disks
Ruben Lier, Pawe{\l} Matus
https://arxiv.org/abs/2602.21367 https://mastoxiv.page/@arXiv_condmatsoft_bot/116135969484479808
- Passive freeze-out of the Richtmyer-Meshkov instability
J. Strucka, et al.
https://arxiv.org/abs/2602.21375 https://mastoxiv.page/@arXiv_physicsplasmph_bot/116135979972070806
- A CFD-Based Investigation of Local Luminal Curvature as a Primary Determinant of Hemodynamic Envi...
Marcella P. A. Dallavanzi, Jos\'e L. Gasche, Iago L. Oliveira
https://arxiv.org/abs/2602.21409 https://mastoxiv.page/@arXiv_physicsmedph_bot/116135886905819251
- Unstable magnetic reconnection self-generates turbulence
Nick Williams, Alessandro De Rosis, Alex Skillen
https://arxiv.org/abs/2602.21422 https://mastoxiv.page/@arXiv_physicsplasmph_bot/116135995018666818
- Out-of-time-ordered correlators for turbulent fields: a quantum-classical correspondence
Motoki Nakata
https://arxiv.org/abs/2602.21710 https://mastoxiv.page/@arXiv_physicsplasmph_bot/116136015335973276
- Particle, kinetic and hydrodynamic models for sea ice floes. Part II: Rotating floes with nonline...
Quanling Deng, Seung-Yeal Ha, Jaemoon Lee
https://arxiv.org/abs/2602.21972 https://mastoxiv.page/@arXiv_mathph_bot/116136043517281861
- A consistent phase-averaged model of the interactions between surface gravity waves and currents
Jacques Vanneste, William R. Young
https://arxiv.org/abs/2602.21976 https://mastoxiv.page/@arXiv_physicsaoph_bot/116135993734282277
- Hydrodynamics of Dense Active Fluids: Turbulence-Like States and the Role of Advected Activity
Sandip Sahoo, Siddhartha Mukherjee, Samriddhi Sankar Ray
https://arxiv.org/abs/2602.22044 https://mastoxiv.page/@arXiv_condmatsoft_bot/116136069077151185
- Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach
Pinheiro, Guo, Menke, Joshi, Heaney, ElSheikh, Pain
https://arxiv.org/abs/2602.22188 https://mastoxiv.page/@arXiv_csLG_bot/116136497040052377
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Their season is over, but it's a winning week for the Giants: Solak on John Harbaugh's impact https://www.espn.com/nfl/story/_/id/47619832/john-harbaugh-new-york-giants-best-coaching-hire-carousel

Sparse Bayesian Deep Functional Learning with Structured Region Selection
Xiaoxian Zhu, Yingmeng Li, Shuangge Ma, Mengyun Wu
https://arxiv.org/abs/2602.20651 https://arxiv.org/pdf/2602.20651 https://arxiv.org/html/2602.20651
arXiv:2602.20651v1 Announce Type: new
Abstract: In modern applications such as ECG monitoring, neuroimaging, wearable sensing, and industrial equipment diagnostics, complex and continuously structured data are ubiquitous, presenting both challenges and opportunities for functional data analysis. However, existing methods face a critical trade-off: conventional functional models are limited by linearity, whereas deep learning approaches lack interpretable region selection for sparse effects. To bridge these gaps, we propose a sparse Bayesian functional deep neural network (sBayFDNN). It learns adaptive functional embeddings through a deep Bayesian architecture to capture complex nonlinear relationships, while a structured prior enables interpretable, region-wise selection of influential domains with quantified uncertainty. Theoretically, we establish rigorous approximation error bounds, posterior consistency, and region selection consistency. These results provide the first theoretical guarantees for a Bayesian deep functional model, ensuring its reliability and statistical rigor. Empirically, comprehensive simulations and real-world studies confirm the effectiveness and superiority of sBayFDNN. Crucially, sBayFDNN excels in recognizing intricate dependencies for accurate predictions and more precisely identifies functionally meaningful regions, capabilities fundamentally beyond existing approaches.
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Ranking the top nine NFL head coaching candidates: Which coordinators could land an open job? https://www.espn.com/nfl/story/_/id/47259365/ranking-nfl-head-coach-candidates-2026-carousel-interviews-coordinators-hirings

The Rams' offense flows through ... its running game? Solak on why it's crucial to their playoff run https://www.espn.com/nfl/story/_/id/47600318/nfl-playoffs-los-angeles-rams-run-game-kyren-williams-blake-corum-sean…

'No shirt, no shirt': Bears HC Ben Johnson's viral celebration triggers free hot dogs https://www.espn.com/nfl/story/_/id/47170519/chicago-bears-ben-johnson-shirtless-celebration-wieners-circle-free-hot-dogs

When football meets third grade curriculum: The rise of NFeLementary https://www.espn.com/nfl/story/_/id/47736879/mary-crippen-nfelementary-bijan-robinson-falcons-book-fair

Who makes Mike Macdonald's defense so good? Three Seahawks to watch in Super Bowl LX https://www.espn.com/nfl/story/_/id/47831363/seattle-seahawks-defense-mike-macdonald-defensive-scheme-keys-super-bowl

NFL coach movement intel on every team: What we're hearing on possible changes, rising candidates https://www.espn.com/nfl/story/_/id/47515440/2025-nfl-coach-coordinator-hiring-firing-candidates-intel-buzz-32-teams…