Tootfinder

Don't miss today's Metacurity for the latest on how Washington is taking giant steps to address AI security, along with other critical infosec developments you should know, including
--White House AI testing framework arrives, but key details remain secret,
--Congress probes OpenAI incident as calls for stronger AI oversight grow,
--China's open AI push fuels geopolitical debate,
--Banks press ahead with AI agents,
--US eyes China data center tech ban,
--Apple renews legal fight over UK encryption demands,
--Telegram mysteriously disappears from Apple's App Store,
--Malware can hijack Google passkeys,
--N-able warns of active attacks exploiting N-central flaw,
--Samsung pulls smart TV apps that turned homes into proxy networks,
--River Bank says stolen ransomware data was deleted by attackers,
--Congress moves to extend lifetime identity protection for OPM victims,
--AI now powers most cybercrime in Africa,
--State CISOs report falling confidence amid rising AI threats,
--AI agent security startup Zenity raises $125m in Series C,
--Visa buys BioCatch in $2.4b AI fraud defense play,
--Stop being an AI "meat proxy,"
--ICE's expanding digital dragnet raises alarms,
--FBI agent accused of stealing seized cryptocurrency
https://www.metacurity.com/ai-watch-ai-security-moves-to-washingtons-center-stage/

Hierarchical neural integration of musical structure during expert performance https://www.biorxiv.org/content/10.64898/2026.07.20.738980v1 "Responses in the motor network, default mode network, and hippocampus were strongly impacted by scrambling, indicating that they…

Hierarchical neural integration of musical structure during expert performance
Music, which is organized hierarchically (notes, phrases, sections), provides an ideal model for studying fine-grained motor sequence production. However, it is unknown how musicians' brains integrate tonal structure over multiple timescales during real-life performance. Here, we scrambled an unfamiliar Tchaikovsky piano suite at four timescales (every 1/2/8 measures, or fully intact) and asked expert pianists to play (sightread) all four versions in the fMRI scanner. Responses in the motor net…

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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How the fly holds a single goal: normalization, not selection, in Drosophila FC2
Gioele Nanni, Christopher Lee
https://arxiv.org/abs/2607.18969 https://arxiv.org/pdf/2607.18969 https://arxiv.org/html/2607.18969
arXiv:2607.18969v1 Announce Type: new
Abstract: A walking fly steers toward a goal direction, held as a bump of activity across the FC2 neurons of the fan-shaped body. These neurons also inhibit one another over distance, more strongly the farther apart they are, a feedback proposed to keep the fly on a single goal. We asked, from the connectome, what circuit produces this inhibition, and whether it lets FC2 actively choose one goal among competitors (a winner-take-all) or simply keeps a goal set elsewhere as one clean bump. Tracing the wiring in a single FlyWire brain, we find the inhibition is almost entirely global: four FB5A cells inhibit every FC2 neuron roughly equally, with a smaller, distance-dependent contribution from hDelta interneurons and a negligible direct component. A ring-attractor winner-take-all (the kind the compass uses) requires local recurrent excitation that the FC2 wiring lacks, so this geometry cannot build one; and across a range of dynamical models, including a spiking network, no version of the circuit locks onto a winner at the connectome-scaled reference coupling. FC2 therefore normalizes an externally set goal rather than selecting it, with FB5A likely acting as the global normalizer, much as the APL neuron does in the mushroom body. We are explicit about two open points: a different mechanism, mutual inhibition between two competing goals (which hDelta supplies), could in principle select at very strong coupling, and we bound rather than exclude it; and FB5A's inhibitory identity is a low-confidence prediction of the connectome's transmitter classifier, not yet measured, and likely not GABAergic. We then ask where the goal is actually set: the connectome nominates an upstream hDelta network and rules out the leading proposed alternative, whose neurons supply under 0.2% of FC2's input. Finally, we propose a direct experiment, silencing FB5A while imaging FC2, that would test the account.
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HBO Max received 122 Emmy nominations, Netflix landed 111, Disney received 111 overall, Apple scored 87, its most-ever, and Amazon landed 39 for Prime and MGM (Peter White/Deadline)
https://deadline.com/2026/07/2026-emmy-nominations-netflix-hb…

‘Hacks’ & ‘The Pitt’s Help HBO Max Take Emmy Noms Crown Over Netflix As Apple Lands Most Nominations Ever
Which companies came out ahead in the 2026 Emmy nominations? We break down the biggest winners and losers among networks and streamers.

Hard Guarantees at a Measured Price: Entropy-Stable Learned Finite Volumes for Compressible Flow
Denis Gueyffier (ONERA -- Institut Polytechnique de Paris)
https://arxiv.org/abs/2607.20171 https://arxiv.org/pdf/2607.20171 https://arxiv.org/html/2607.20171
arXiv:2607.20171v1 Announce Type: new
Abstract: Learned solvers for compressible flow are usually compared to classical methods at equal mesh resolution rather than at equal computational cost, and they typically offer no guarantee that their solutions remain physically admissible. We present a learned finite volume scheme for the two-dimensional Euler equations on unstructured meshes, admissible by construction and with an entropy-stable interior flux. We evaluate it under protocols fixed before any computation: frozen thresholds, falsification clauses, negative controls, a factor decomposition of the learned components, and an iso-cost comparison against the refined classical baseline. The decomposition produced the central result: the guarantee machinery alone, with both learned heads switched off (the unlearned skeleton), is the strongest scheme at equal mesh on every periodic case. At equal wall-clock cost the picture inverts into a map. Learning pays robustly only on the wall case whose boundary-condition type it never saw (10.8%). Its periodic gains flip sign with the evaluation draw ( 10% on one held-out case, -12% on the hardest). The skeleton is the only method whose iso-cost gain never changes sign, at a measured overhead of 1.74x per step. The guaranteed variant completes 36 of 36 rollouts, Mach extrapolation and unseen wall included, with zero negativity events. We fix the guaranteed scheme's one remaining out-of-distribution weakness, Mach extrapolation, at inference time: with scale-invariant network inputs, a specific-entropy floor, and no retraining, the corrected arm overtakes the unconstrained arm on one Mach case, cuts its deficit on the other by a third, passes the skeleton on the unseen wall, and keeps the guarantee. A spatial gate closes the loop: activating the heads only near the walls beats both the skeleton and the corrected arm, and transfers unchanged to a second wall geometry.
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STSBench: A Large-Scale Dataset for Modeling Neuronal Activity in the Dorsal Stream of Primate Visual Cortex
Ethan B. Trepka, Ruobing Xia, Shude Zhu, Sharif Saleki, Danielle Abreu Lopes, Stephen J. Ni\~no Cital, Konstantin F. Willeke, Mindy Kim, Tirin Moore
https://arxiv.org/abs/2607.15631 https://arxiv.org/pdf/2607.15631 https://arxiv.org/html/2607.15631
arXiv:2607.15631v1 Announce Type: new
Abstract: The primate visual system is typically divided into two streams - the ventral stream, responsible for object recognition, and the dorsal stream, responsible for encoding spatial relations and motion. Recent studies have shown that convolutional neural networks (CNNs) pretrained on object recognition tasks are remarkably effective at predicting neuronal responses in the ventral stream, shedding light on the neural mechanisms underlying object recognition. However, similar models of the dorsal stream remain underdeveloped due to the lack of large scale datasets encompassing dorsal stream areas. To address this gap, we present STSBench, a dataset of large-scale, single neuron recordings from over 2,000 neurons in the superior temporal sulcus (STS), a nearly 50-fold increase over existing dorsal stream datasets, collected while Rhesus macaques viewed thousands of unique, natural videos. We show that our dataset can be used for benchmarking encoding models of dorsal stream neuronal responses and reconstructing visual input from neural activity.
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