Tootfinder

Cannot emphasize enough how much we need people with experience as public defenders and civil rights lawyers to run for political office.
At a time of authoritarianism, their skepticism of power is critical.
https://bsky.app/profile/davidmenschel.bsky.social/post…

Uniform large deviation principles and averaging principles for stochastic Burgers type equations with reflection
Huijie Qiao
https://arxiv.org/abs/2506.15443

Uniform large deviation principles and averaging principles for stochastic Burgers type equations with reflection
This work concerns about stochastic Burgers type equations with reflection. First of all, by means of the equicontinuous uniform Laplace principle, we prove the Freidlin-Wentzell uniform large deviation principle for these equations uniformly on bounded sets. Then based on this result, we establish the Dembo-Zeitouni uniform large deviation principle for these equations uniformly on compact sets. Finally, an averaging principle result for these equations is obtained through the time discretizat…

Functional Change Point Detection via Adjacent Deviation Subspace
Luoyao Yu, Long Feng, Xuehu Zhu
https://arxiv.org/abs/2506.15143 https://

Functional Change Point Detection via Adjacent Deviation Subspace
This paper develops the concept of the Adjacent Deviation Subspace (ADS), a novel framework for reducing infinite-dimensional functional data into finite-dimensional vector or scalar representations while preserving critical information of functional change points. To identify this functional subspace, we propose an efficient dimension reduction operator that overcomes the critical limitation of information loss inherent in traditional functional principal component analysis (FPCA). Building up…

Skew-Induced Insertion Loss Deviation (SILD) and FOM_SILD: Metrics for Quantifying P/N Skew Effects in High-Speed Channels
David Nozadze, Zurab Kiguradze, Amendra Koul, Mike Sapozhnikov
https://arxiv.org/abs/2506.15105

Skew-Induced Insertion Loss Deviation (SILD) and FOM_SILD: Metrics for Quantifying P/N Skew Effects in High-Speed Channels
The rise of AI workloads and growing data center demands have driven the need for ultra-high-speed interconnects exceeding 200 Gb/s. As unit intervals (UI) shrink, even a few picoseconds of P/N skew can degrade serializer-deserializer (SerDes) performance. Traditional methods for quantifying skew fall short in capturing its impact. We introduce two new metrics: 1) Skew-Induced Insertion Loss Deviation (SILD) and 2) its complementary Figure of Merit (FOM_SILD), analytically developed to assess P…

It all fits. Suppose you’re corporate leadership. You’re laying people off / refusing to hire because you have no clue what things will look like even 1 or 2 years in the future, and you are •terrified•.
Meanwhile, your investors, your gullible trend-chasing investors, are already mentally dividing companies into “pre-AI” and “post-AI.” And not only do you have no idea how to be “post-AI,” but you have no idea whether you’ll be solvent next year. What do you tell them?!??
Obviously you tell them you’re not hiring because you’re having such incredible success with AI! Maybe even you’re going to lay off all the senior people! They’ll love that!
Now you’re not failing and flailing — you’re leading the march into the future! Yay!

Hey, #meat folks! I got this sirloin steak to refrigerator temperature and I'm debating if I need to trim it any more than it is. Would you recommend removing that fat cap?

Large Deviations of First Passage Times of Branching Random Walks in $\mathbb{R}^d$: Asymptotics and Algorithms
Jose Blanchet, Wei Cai, Shaswat Mohanty, Zhenyuan Zhang
https://arxiv.org/abs/2506.15072

Large Deviations of First Passage Times of Branching Random Walks in $\mathbb{R}^d$: Asymptotics and Algorithms
We investigate the large deviation probabilities of first passage times (FPT) of discrete-time supercritical non-lattice branching random walks (BRWs) in $\mathbb{R}^d$ where $d\geq 1$. Here, the FPT refers to the first time the BRW enters a ball of radius one with a distance $x$ from the origin, conditioned upon survival. We then apply the spine decomposition technique to construct an asymptotically optimal polynomial-time algorithm for computing the lower large deviation probabilities of the …

As @pluralistic writes here, “the American right is a brittle coalition:”
https://pluralistic.net/2024/07/14/fracture-lines/
We imagine the right as monolithic at our peril. Reply guys love, love, love to lecture me about how These People are all the same. Here’s the thing: they are not, and imagining they are is self-defeating. The right has been •incredibly• effective at finding fracture points on the left and jamming a crowbar into them. It’s time we did the same.
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