Tootfinder

Rarefaction-induced inflation and similarity breakdown of hypersonic bow shocks over a circular cylinder
Ehsan Roohi, Ahmad Shoja-Sani
https://arxiv.org/abs/2605.17099 https://arxiv.org/pdf/2605.17099 https://arxiv.org/html/2605.17099
arXiv:2605.17099v1 Announce Type: new
Abstract: Rarefied hypersonic bow shocks over blunt bodies inflate as the Knudsen number increases, but it remains unclear whether this inflation is a simple shift and broadening of one common shock layer or a multi-scale change of the macroscopic and internal-energy fields. We address this question using direct simulation Monte Carlo (DSMC) data for Mach-10 flow over a circular cylinder in argon and nitrogen over \(Kn_\infty \approx 0.01\)--\(1\), together with a Mach-number sweep at \(Kn_\infty=0.01\). At low rarefaction, a ray-based density-gradient ridge gives a reproducible bow-shock location and agrees with an independent schlieren-based shock-wave-detection method. As \(Kn_\infty\) increases, this ridge is replaced by a broad kinetic compression layer, so the high-Knudsen cases are analysed using profile-based standoff and thickness metrics rather than by imposing a visual shock line. The Knudsen- and Mach-number sweeps separate two mechanisms. At fixed \(M_\infty\), the continuum normal-shock density ratio provides a useful low-rarefaction reference compression scale, whereas the measured standoff growth is governed primarily by the kinetic mean free path; the effective density thickness shows an intermediate minimum before increasing in the diffuse regime. At fixed low \(Kn_\infty\), changing \(M_\infty\) mainly changes compression strength and curvature, preserving a coherent attached-layer structure. Density-registered profiles and shock-attached proper orthogonal decomposition (POD) show that, within the present maximum-density-gradient registration, density becomes nearly rank one, whereas Mach number and thermal variables retain independent modal content. Rarefied bow-shock inflation is therefore a coupled compression--relaxation process, not a single-scale rescaling of a continuum-like shock.
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Solutocapillary instability in slipping falling films
Sanghasri Mukhopadhyay, S\'everine Millet, Bastien Di Pierro, Asim Mukhopadhyay
https://arxiv.org/abs/2605.17519 https://arxiv.org/pdf/2605.17519 https://arxiv.org/html/2605.17519
arXiv:2605.17519v1 Announce Type: new
Abstract: We present a comprehensive framework for gravity-driven, surfactant-laden thin films flowing over slippery substrates, elucidating how wall slip modifies the coupled hydrodynamics and interfacial transport. A long-wave model is formulated with a conservative bulk-surface mass balance and a Navier slip condition. The Orr-Sommerfeld eigenvalue problem governs the linear regime, while a weighted-residual model captures the nonlinear evolution over a range of equilibrium surfactant coverages, Marangoni strengths, and adsorption kinetics. The analysis predicts a non-monotonic variation of the critical Reynolds number with equilibrium coverage, exhibiting a maximum at intermediate $\Gamma_e$, and a slip-induced transition from single- to double-hump solitary structures with increasing Marangoni number, accompanied by attenuated capillary ripples. Under fast adsorption kinetics, the surface field homogenizes, preserving the mean film shape and flux while flattening both the surface concentration $\Gamma$ and the bulk inventory $\chi h\phi$. A spurious interfacial mass growth reported by Pascal et al.(PRF, 2019) and D'Alessio et al.(JFM, 2020) is resolved through a revised surface balance ensuring strict conservation. Wall slip thus emerges as a key control parameter, reducing viscous resistance and mitigating Marangoni back-stress. The slip parameter $\beta$ is a useful control knob for surfactant-laden films. Slip prevents fragile multi-hump bound states, promoting a single broad crest or an almost flat, uniform sheet by carefully bonding $\beta$ to wave selection, ripple damping, and the bulk-surface surfactant balance.
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OpenAI confirms it will follow President Trump's EO that asks AI companies to allow the US government to assess their models' capabilities before release (Michael Considine/CNBC)
https://www.cnbc.com/2026/06/05/openai-trump-ai-model-review-order.html

OpenAI says it will comply with Trump's order requiring AI model reviews before release
George Osborne, OpenAI's Head of Countries told CNBC that governments "have a big role to play in how this technology is used and deployed."

The health information of 500,000 members of the UK's health data project, UK Biobank, were offered for sale online in China, the government has confirmed.
https://www.bbc.com/news/articles/c4g515n57zyo?at_medium=RSS&at_campaign=rss

Newspaper headlines: 'Stop sharing data with China' and 'Shoot and kill'
A data breach at Biobank and Trump's new orders to US troops in the Middle East lead Friday's papers.

So one of the authors is Nicholas Carlini, who works for Anthropic. This is basically an ad for the three letter agencies to use Claude. It massively over-promises compared to what the actual paper says.
But, it is important. First, this is really about silencing people. The threat of identification is designed to make people afraid to talk online. There's a massive asymmetry between the fascists and the people. The fascists are weird racists and pedophiles who are obsessed with control. No one likes them. No one likes their ideas, because their ideas are creepy and bad.
When they talk about their ideas, that people should be murdered or kidnaped based on their skin color, that there should be a national dress code, that people's sex lives should be monitored, that children should be treated like objects that are owned by the parent (specifically, one parent), that people with different skin color or uteri should be considered as livestock, people fucking hate it because it's awful. When we talk about our ideas, that everyone should be able to eat and take care of themselves, that people who can't take care of themselves should be taken care of, that we should live in a society that values life, that we should live in harmony with nature, people like those ideas. When fascists out us for talking about those ideas, people support us. When we out people who are working as fascist goons those people have to face social consequences.
Everyone hates these people. The US government is currently less popular than it has ever been. The only way they can keep power is by making everyone think that they aren't extraordinarily unpopular. The only way to do that, the way authoritarian have always done it, is to make everyone afraid to talk.
But, yes, what this paper is saying is actually kind of bad. It looks like people who don't take any precautions at all in separating identities can be identified about 30% of the time (based on the results). It's unclear how this will actually work in the real world. Larger corpses will probably have more data, making connecting things easier.
This isn't as good as a human trying to dox someone. It's not going to work as well. It may only work in a small number of cases. There will be false positives (just like there are with people doing the work). It's probably not cheaper than hiring people. But it does mean that you can just dump money into a machine that has no ethical framework and get data out. That's the point. It's hard to find humans who will do evil shit like help dictatorships target human rights activists, but if a machine can do it for twice the price then it's a better deal for the dictatorship.
For most people, you just shouldn't care. This isn't for you. As long as you keep doing what you're doing, and you can keep everyone else doing what they're doing, then there aren't enough resources to actually target you. Even if they know who you are, there are just too many people who hate them and too few goons.
For people who might actually be targeted, there are a lot of things. First, keep in mind what you're putting into anonymous accounts. Any feature that's connected to your real life is a feature that can be extracted to identify you. This has always been true, it just may be easier to find now. Your identities should be totally siloed. It's also harder to identify you if you're writing anonymously as a collective. Collectives are better anyway because they can help check your thinking. When you write as a collective, you can help clean up each other's personal details and language. A collective develops its own voice, which is distinct from individual contributors. If you do this, and you also present your work as being from one "person," then it becomes even harder for anyone (systems or individuals) to really figure it out.
I'm not going to do a full deep dive on this because I just don't have time, but your existing threat model should *already cover these threats* if you need to make sure your writing remains anonymous.
This paper doesn't present any novel methodologies. It just extracts a bunch of features, which a human would extract as notes, and tries to correlate those between identities, which is how human researchers work. Linguistic forensics were mentioned (not by name) in the paper, but the actual methodology doesn't actually seem to use them.
So a thing with less ethics can do a worse job for more money (when adjusted for the real, not investor deflated, price of tokens). It's worth knowing. It's not the end of the world, but it is a good reminder to check your threat model and make sure it's up to date.

PoDAR: Power-Disentangled Audio Representation for Generative Modeling
Alejandro Luebs, Mithilesh Vaidya, Ishaan Kumar, Sumukh Badam, Stephen W. Bailey, Matthew Bendel, Jose Sotelo, Xingzhe He
https://arxiv.org/abs/2605.10084 https://arxiv.org/pdf/2605.10084 https://arxiv.org/html/2605.10084
arXiv:2605.10084v1 Announce Type: new
Abstract: The performance of audio latent diffusion models is primarily governed by generator expressivity and the modelability of the underlying latent space. While recent research has focused primarily on the former, as well as improving the reconstruction fidelity of audio codecs, we demonstrate that latent modelability can be significantly improved through explicit factor disentanglement. We present PoDAR (Power-Disentangled Audio Representation), a framework that utilizes a randomized power augmentation and latent consistency objective to decouple signal power from invariant semantic content. This factorization makes the latent space easier to model, which both accelerates the convergence of downstream generative models and improves final overall performance. When applied to a Stable Audio 1.0 VAE with an F5-TTS generator, PoDAR achieves about a $2\times$ acceleration in convergence to match baseline performance, while increasing final speaker similarity by 0.055 and UTMOS by 0.22 on the LibriSpeech-PC dataset. Furthermore, isolating power into dedicated channels enables the application of CFG exclusively to power-invariant content, effectively extending the stable guidance regime to higher scales.
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Anthropic says "some routine tasks like coding and debugging" on Fable 5 "will fall back to Opus 4.8" in "the near term" as it works to "reduce false positives" (@anthropicai)
https://x.com/anthropicai/status/2072163884430229756

Anthropic (@AnthropicAI) on X
Claude Fable 5 will be available again globally tomorrow. After a series of productive conversations with the US government, we're redeploying the model with a new set of classifiers to target and block more cybersecurity tasks. In the near term, some routine tasks like coding

Crosslisted article(s) found for physics.comp-ph. https://arxiv.org/list/physics.comp-ph/new
[1/2]:
- Interpolation of Microscale Stress and Strain Fields Based on Mechanical Models
Wenzhe Shan, Udo Nackenhorst
https://arxiv.org/abs/2104.09749
- Joint discovery of governing partial differential equations from multi-source datasets by competi...
Hao Xu, Siyu Lou, Yuntian Chen, Dongxiao Zhang
https://arxiv.org/abs/2606.30699 https://mastoxiv.page/@arXiv_csLG_bot/116843565775934283
- LinApart3: efficient algorithm for multivariate partial fraction decomposition with linear denomi...
L. Fek\'esh\'azy, A. Kardos
https://arxiv.org/abs/2606.30708 https://mastoxiv.page/@arXiv_hepph_bot/116843629511193799
- Introducing AuriGLOBES: the effect of compressive tides, compact object-induced mass loss, and si...
Pablo Contreras Guerra, Robert J. J. Grand, Marta Reina-Campos, Claudio Dalla Vecchia
https://arxiv.org/abs/2606.30746 https://mastoxiv.page/@arXiv_astrophGA_bot/116843732310368458
- Time-dependent adaptive mesh refinement solver for the Gross-Pitaevskii-Poisson equations
Iv\'an \'Alvarez-Rios
https://arxiv.org/abs/2606.30827 https://mastoxiv.page/@arXiv_astrophGA_bot/116843767701812779
- Computed materials proposals depart from the structural memory of experimental discovery
Dan Nguyen, Karen Cao, Brian Chu, Nick Lemoff, Paul Kienzle, William Ratcliff II
https://arxiv.org/abs/2606.30967 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/116843665854969922
- An Enhanced RPA-LDA Model for Ion Stopping Power from Cold Matter to High-Energy Density Plasmas:...
Thomas A. Mehlhorn, Ming Feng Gu, Igor Golovkin
https://arxiv.org/abs/2606.30978 https://mastoxiv.page/@arXiv_physicsplasmph_bot/116843597824839239
- Full-Wave Green's-Function Modeling of Collective Single-Photon Emission in Non-Markovian Open-Sy...
Hyunwoo Choi, Jisang Seo, Junwoo Gim, Bowoo Jang, Weng C. Chew, Dong-Yeop Na
https://arxiv.org/abs/2606.31317 https://mastoxiv.page/@arXiv_quantph_bot/116843791099390803
- Side-Chain Tuning of Thermal-Expansion Crossover in Metal-Organic Frameworks
Wei Qiu, Penghua Ying
https://arxiv.org/abs/2606.31417 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/116843748631678308
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