Tootfinder

If Trump embodies the theory of the “unitary executive,” it’s because conservative jurists like John Roberts and Clarence Thomas and Samuel Alito have
— along with their Republican-appointed colleagues
— effectively rewritten the Constitution to allow for the exercise of virtually unaccountable power by Republican presidents.
Yes, you can attribute some of the worst of this administration to the specific authoritarian vision of Trump and his allies.
But a good deal of…

Opinion | Now Can We Stop Talking About What a Maverick Trump Is?
The president is a normie Republican. Stop thinking otherwise.

Study: ChatGPT is Bad for Your Brain
#NowWatching

Micheal has cred, owing to a11ysupport.io:
https://bsky.app/profile/mfairchild365.bsky.social/post/3lpugshbmtk2o
But this other effort, IMO, is not the same nor as useful:

🎉🎉🎉 https://samenet.social/@mbergnordlie/114754259710178079

Boardda Mihkkal (@mbergnordlie@samenet.social)
Attached: 1 image Enstemmig høyesterettsdom I fosenaksjonistenes favør! https://www.nrk.no/trondelag/hoyesterett_-fosen-aksjonistene-kan-ikke-straffes-1.17472171 #Allheimen #NorskTut

This https://arxiv.org/abs/2412.03318 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data
Segmenting stroke lesions in MRI is challenging due to diverse acquisition protocols that limit model generalisability. In this work, we introduce two physics-constrained approaches to generate synthetic quantitative MRI (qMRI) images that improve segmentation robustness across heterogeneous domains. Our first method, $\texttt{qATLAS}$, trains a neural network to estimate qMRI maps from standard MPRAGE images, enabling the simulation of varied MRI sequences with realistic tissue contrasts. The …

Replaced article(s) found for math.NA. https://arxiv.org/list/math.NA/new
[1/1]:
- SFEM for the Lagrangian formulation of the surface Stokes problem
Charles M. Elliott, Achilleas Mavrakis

State and Memory is All You Need for Robust and Reliable AI Agents
Matthew Muhoberac, Atharva Parikh, Nirvi Vakharia, Saniya Virani, Aco Radujevic, Savannah Wood, Meghav Verma, Dimitri Metaxotos, Jeyaraman Soundararajan, Thierry Masquelin, Alexander G. Godfrey, Sean Gardner, Dobrila Rudnicki, Sam Michael, Gaurav Chopra
https://a…

State and Memory is All You Need for Robust and Reliable AI Agents
Large language models (LLMs) have enabled powerful advances in natural language understanding and generation. Yet their application to complex, real-world scientific workflows remain limited by challenges in memory, planning, and tool integration. Here, we introduce SciBORG (Scientific Bespoke Artificial Intelligence Agents Optimized for Research Goals), a modular agentic framework that allows LLM-based agents to autonomously plan, reason, and achieve robust and reliable domain-specific task ex…

It seems @… is not active on Masto, so…
https://bsky.app/profile/mfairchild365.bsky.social/post/3lpugshbmtk2o

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Towards NoahMP-AI: Enhancing Land Surface Model Prediction with Deep Learning
Mahmoud Mbarak, Manmeet Singh, Naveen Sudharsan, Zong-Liang Yang

SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting
Yiming Huang, Long Bai, Beilei Cui, Kun Yuan, Guankun Wang, Mobarakol Islam, Nicolas Padoy, Nassir Navab, Hongliang Ren
https://arxiv.org/abs/2506.23309

SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting
In contemporary surgical research and practice, accurately comprehending 3D surgical scenes with text-promptable capabilities is particularly crucial for surgical planning and real-time intra-operative guidance, where precisely identifying and interacting with surgical tools and anatomical structures is paramount. However, existing works focus on surgical vision-language model (VLM), 3D reconstruction, and segmentation separately, lacking support for real-time text-promptable 3D queries. In thi…