Tootfinder

A profile of OpenAI President Greg Brockman and his role in the company's $1.4T infrastructure buildout that's required to reach AGI (Sharon Goldman/Fortune)
https://fortune.com/2025/11/05/openai-greg-brockman-ai-infrastructure-data-c…

Meet OpenAI’s master builder: Greg Brockman is steering a $1.4 trillion infrastructure surge with stakes that go far beyond AI
As OpenAI’s president, Brockman is focused on “completing the mission” of moving beyond AI to artificial general intelligence (AGI).

Schachroboter SenseRobot bewegt reale Figuren
SenseRobot stellt Schach- und Go-Roboter her, die Figuren mit Roboterarmen selbstständig bewegen, Spielzüge erkennen und Training bis auf Meisterniveau bieten.
https://www.…

#Blakes7 Series D, Episode 04 - Stardrive
VILA: Two billion? Training manual rule one: Never play with asteroids. They're what Murphy's Law's all about.
DAYNA: Murphy's Law?
SOOLIN: Anything that can go wrong will go wrong.
https:…

Shitty Camera Challange holiday fundraiser for the Trevor project! Get one of 47 mystery cameras!
More info:
https://bsky.app/profile/shittychallenge.bsky.social/post/3m7ettgsskc2t

REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis
Alec K. Peltekian, Halil Ertugrul Aktas, Gorkem Durak, Kevin Grudzinski, Bradford C. Bemiss, Carrie Richardson, Jane E. Dematte, G. R. Scott Budinger, Anthony J. Esposito, Alexander Misharin, Alok Choudhary, Ankit Agrawal, Ulas Bagci
https://arxiv.org/abs…

REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis
Mixture-of-Experts (MoE) architectures have significantly contributed to scalable machine learning by enabling specialized subnetworks to tackle complex tasks efficiently. However, traditional MoE systems lack domain-specific constraints essential for medical imaging, where anatomical structure and regional disease heterogeneity strongly influence pathological patterns. Here, we introduce Regional Expert Networks (REN), the first anatomically-informed MoE framework tailored specifically for med…

WOW: WAIC-Optimized Gating of Mixture Priors for External Data Borrowing
Shouhao Zhou, Qiuxin Gao, Chenqi Fu, Yanxun Xu
https://arxiv.org/abs/2510.05085 https://

WOW: WAIC-Optimized Gating of Mixture Priors for External Data Borrowing
The integration of external data using Bayesian mixture priors has become a powerful approach in clinical trials, offering significant potential to improve trial efficiency. Despite their strengths in analytical tractability and practical flexibility, existing methods such as the robust meta-analytic-predictive (rMAP) and self-adapting mixture (SAM) often presume borrowing without rigorously assessing whether, how, or when integration is appropriate. When external and concurrent data are discor…

Perdido numa ilha deserta, o português avista uma moça agarrada num
barril, a uns duzentos metros da praia. Imediatamente, ele pula na
šgua para ajudš-la. Assim que chegam na ilha e a moça recupera a sua
consciência, ela diz:
-- Agora, para mostrar a minha gratidão, eu vou lhe dar uma coisa
que acho que você não vê hš muito tempo.
E o portuga:
-- Não vš me dizer que tem chopp dentro do barril!

Erst vor wenigen Wochen wurde hier mit großem Aufwand eine der neuen touristischen Infostelen der Stadt Dresden aufgestellt. Nun ist diese plötzlich wieder weg. Die Frage ist warum? Stand die etwa Holgers Riesenrad im weg, welches hier demnächt aufgestellt wird? Wenn ja: Zahlt Holger den Auf- & Abbau?

Pivotal CLTs for Pseudolikelihood via Conditional Centering in Dependent Random Fields
Nabarun Deb
https://arxiv.org/abs/2510.04972 https://arxiv.org/pdf/2…

Pivotal CLTs for Pseudolikelihood via Conditional Centering in Dependent Random Fields
In this paper, we study fluctuations of conditionally centered statistics of the form $$N^{-1/2}\sum_{i=1}^N c_i(g(σ_i)-\mathbb{E}_N[g(σ_i)|σ_j,j\neq i])$$ where $(σ_1,\ldots ,σ_N)$ are sampled from a dependent random field, and $g$ is some bounded function. Our first main result shows that under weak smoothness assumptions on the conditional means (which cover both sparse and dense interactions), the above statistic converges to a Gaussian \emph{scale mixture} with a random scale det…

Series C, Episode 03 - Volcano
TARRANT: No, it's the heat.
DAYNA: No, can't you hear it?
TARRANT: Not a thing.
DAYNA: I can. Maybe it's in my mind... Cally?
TARRANT: What does it say, this voice?
DAYNA: It-it says to go forward, there.
https://blake.torpidity.net/m/303/599