Tootfinder

Radiometric Thermal Imaging Dataset of Laboratory Rats with Anatomical Segmentation Masks
Dima Bykhovsky, Evyatar Chaimoff, Pe'er Eden, Tom Simkin, Oshrit Hoffer, Shahar Cohen, Bar Eilat Yogev, Gal Levi, Noa Efroni, Doron Todder, Hagit Cohen
https://arxiv.org/abs/2608.03481 https://arxiv.org/pdf/2608.03481 https://arxiv.org/html/2608.03481
arXiv:2608.03481v1 Announce Type: new
Abstract: Infrared thermography provides a contact-free, restraint-free method to record surface temperatures. It serves as a valuable marker for thermoregulatory responses in laboratory animal stress and pharmacology research. However, the analysis of these images is currently bottlenecked by the manual delineation of anatomical regions. To date, no public dataset has provided paired radiometric thermal frames of rats with pixel-level body-part labels. We present a dataset of 1,655 quality-controlled radiometric thermal frames from 25 laboratory rats. Each frame is paired with a dense four-class anatomical segmentation mask (background, head, body, and tail) and the raw $480 \times 640$ temperature matrix (rows $\times$ columns) in degrees Celsius. This ensures every label is registered directly to the physical temperature it describes rather than a color-mapped rendering. The frames originate from two pharmacological cohorts where interventions alter thermoregulation in opposite directions: ethanol, which induces peripheral vasodilation, and ketamine, which affects central thermoregulation. This provides a wide and physiologically diverse range of surface temperature regimes. Aggregated across the dataset, the per-class temperatures follow a head~$>$~body~$>$~tail ordering in physical units. To demonstrate that the data support pixel-level segmentation directly from the radiometric channel, we present an exploratory U-Net segmentation pipeline that attains a subject-level cross-validated mean intersection-over-union of $0.895 \pm 0.006$. The dataset provides a reuse-ready benchmark for thermal semantic segmentation and for downstream physiological and stress-phenotyping analyses.
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NavSight in the wild: understanding real-world use of a mobile augmented reality application for people with low vision in outdoor navigation #NavSight, "a research pr…

NavSight in the Wild: Understanding Real-World Use of a Mobile Augmented Reality Application for People with Low Vision in Outdoor Navigation
The ability to navigate outdoors safely and independently is crucial yet challenging for people with low vision (PLV). While various augmented reality (AR) systems for low vision have been designed and evaluated in ideal lab environments, no research has investigated their real-world feasibility and challenges. We present NavSight, a mobile AR application that assists PLV in outdoor navigation by recognizing important outdoor objects (e.g., curb, vehicle) and rendering real-time visual augmenta…

from my link log —
CSS: the bomb inside your inbox.
https://portswigger.net/research/css-the-bomb-inside-your-inbox
saved 2026-08-22 h…

CSS:the bomb inside your inbox
Gareth Heyes - gareth.heyes@portswigger.net - @garethheyes It's quite common for webmail clients to render untrusted CSS in a trusted UI. They attempt to make this safe using CSS sanitization. In this

Isomorphic Emergence of Lorentz and Gauge Symmetries--A Constructive Interpretation Based on Continuum Mechanics
Ke-Xia Jiang
https://arxiv.org/abs/2608.06244 https://arxiv.org/pdf/2608.06244 https://arxiv.org/html/2608.06244
arXiv:2608.06244v1 Announce Type: new
Abstract: Lorentz symmetry and gauge symmetry constitute the mathematical cornerstones of modern physics, yet their ultimate physical origins remain elusive. From the standpoint of a constructive interpretation, this paper demonstrates that both symmetry structures can emerge isomorphically from a unified classical source: dynamical constraints on wave-packet excitations in a continuous elastic substrate medium (SM). Neither symmetry is posited as fundamental, nor does their emergence rely on quantization. Three core results are established. First, taking the transverse wave speed of a homogeneous, isotropic SM as the benchmark under a conventionalist synchronization scheme, Minkowski-type spacetime arises isomorphically, with Lorentz transformations as effective coordinate transformations between inertial frames. This reinterprets the Michelson--Morley null result: observers are composite wave-packet excitations, and medium-induced kinematic corrections are encoded in their measurement frameworks. Second, for wave packets endowed with $SU(N)$ intrinsic symmetry, the requirement of identity invariance drives the SM to spontaneously generate gauge fields. This requires invariant equivalence classes of intrinsic states under propagation, yielding gauge structures isomorphic to Yang-Mills theory. The gauge group $SU(N)$ is uniquely determined by the number of stable normal modes supported by the medium. Third, through the same conventionalist measurement protocols, inhomogeneous distributions of the SM emerge isomorphically as curved spacetime geometry, governed by Einstein-type field equations from variational extremization of the medium's deformation free energy. This paper advances a unified interpretive account of the physical origin underlying the mathematical structures of both Relativity and the Standard Model.
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Feature-Guided Diffusion for Non-Differentiable Inverse Rendering
Andrei-Timotei Ardelean, Michael Fischer, Tim Weyrich, Tom\'a\v{s} Iser
https://arxiv.org/abs/2607.17411 https://arxiv.org/pdf/2607.17411 https://arxiv.org/html/2607.17411
arXiv:2607.17411v1 Announce Type: new
Abstract: Inverse rendering is traditionally solved via differentiable renderers and gradient descent, which requires substantial problem-specific engineering and is prone to getting stuck in local minima due to ambiguities. Derivative-free approaches alleviate engineering requirements, but often heavily depend on a good problem initialization. In this work, we propose Feature-Informed Diffusion Evolution (FIDE), a fully black-box framework that requires no gradients or specific initialization: the renderer is treated as an opaque function whose only requirement is to produce images. Our key insight is feature guiding: rather than reducing each candidate rendering to a scalar loss value, we use a Vision Transformer (ViT) to extract dense visual features from it. We subsequently use these features to train a diffusion-based candidate proposal model, allowing the network to use visual cues to predict parameters that would match the target image. The candidate solutions proposed by this diffusion model are then refined in a closed loop with a CMA evolution strategy, continuously narrowing the proposal region as optimization progresses. We validate across diverse inverse problems from path tracing, vector splines, Voronoi shaders, and robotics, and demonstrate that feature-guiding substantially improves convergence over scalar-loss baselines and reliably escapes local minima where gradient-based methods stall.
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#BigTech #Billionaires #EatTheRich #Security #Win10 #TechSupport
So some press covered Zuckerberg's mega yacht not responding to a distress call and rightfully saying the Law Of the Sea requires aid to be rendered if possible.
Now, there's apparently a Win10 zero-day vulnerability in the wild. A vulnerability that Microsoft HAS A FIX FOR.
And yet they won't allow you to run it if you haven't paid up for support.
They should be REQUIRED TO RELEASE the fix to all.