Tootfinder

Top Republicans Don't Want To Talk About The DOJ Paying Trump $10 Billion
House Speaker Mike Johnson and Senate Majority Leader John Thune told HuffPost they're not familiar with Trump's efforts to boost his compensation
https://www.huffpost.com/entry/tr…

Top Republicans Don't Want To Talk About The DOJ Paying Trump $10 Billion
House Speaker Mike Johnson and Senate Majority Leader John Thune told HuffPost they're not familiar with Trump's efforts to boost his compensation.

Want to know more about the large, colorful textiles in our Jane and Harry Willson Sculpture Garden, created by artist #RachelHayes? We did, too! Former intern Nabiha Rahman did an in-depth interview with Hayes that really illuminates her work.

Lesley Groff worked for Epstein for almost 20 years,
beginning in 2001 and ending in July 2019 when he was arrested.
Representative Robert Garcia, the top Democrat on the House oversight committee, told Good Morning America this week that Groff is
“really central to Epstein’s organization,
we want to know what she saw”.
“We have a lot of questions,” Garcia added.

Going live on Twitch with some more Windrose! 🚀
Come hang out, chat, and see where the journey takes us today. The stream is up and running—grab a drink and join the adventure!
🔴 Live now: https://www.twitch.tv/tuxramus
Good luck with the stream today! Let me know if you want to tweak the word…

Went to a tour of the University House restoration progress with Peter Kanowski today. Looking really good. Soft open of cellar bar and kitchen in June/July. Public opening on 14/15 August.
#canberra #anu

Seeing Inside the Storm: Improving Nowcasting by Integrating Meteorological Drivers
Minghui Qiu, Jun Chen, Lin Chen, Weifeng Chen, Shuxin Zhong, Zhidan Liu, Yu Zhang, Kaishun Wu
https://arxiv.org/abs/2605.24067 https://arxiv.org/pdf/2605.24067 https://arxiv.org/html/2605.24067
arXiv:2605.24067v1 Announce Type: new
Abstract: Most nowcasting systems, built on radar reflectivity, focus on current precipitation, ignoring the atmospheric precursors -- such as low-level convergence, turbulent eddies, and latent heating -- that offer a fleeting window to foresee storm birth. We introduce MeteoLogist, a physics-inspired radar intelligence framework that models the full life cycle of convection -- from its precursors to organized storm evolution. However, exploiting these precursors is non-trivial: they originate from multiple meteorological drivers -- thermodynamic, kinematic, and microphysical -- that evolve asynchronously (C1) and remain spatially fragmented (C2). To this end, MeteoLogist designs three tightly integrated components. The Physics-Tailored Encoders process radar echoes according to their intrinsic physical scales and semantics, forming thermodynamic, kinematic, and microphysical streams that capture distinct dynamical regimes. The Temporal-Phase Aligner addresses C1 by leveraging causal temporal attention to capture when and how different drivers interact and activate. The Cross-Field Spatial Aggregator addresses C2 through cross-regional fusion, aligning weak and scattered precursors across neighboring cells to expose upstream triggers and enforce spatial coherence. Evaluated on 3D-NEXRAD (2020--2022, US-wide), MeteoLogist boosts high-impact detection (CSI40) by 9.7% over strong baselines, and achieves a remarkable 37.67% gain during the storm-developing stage -- demonstrating true foresight in sensing storms before they appear. The code can be found in the supplementary material.
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