Tootfinder

On Tuesday, Santa Barbara County supervisors turned their attention to vaccination rates,
responding to a May 29 grand jury report that praised strong coverage in local schools but pointed to troubling blind spots.
The report
— “Do Vaccination Rates in Santa Barbara County Create a Public Health Risk?” 
— noted that while more than 95 percent of county students are vaccinated,
officials lack reliable data on two groups:
homeschooled children
and incar…

Santa Barbara Supervisors Weigh Vaccine Gaps as Federal Policy Unravels
Supervisors respond to grand jury report revealing lack of reliable data on homeschooled children and incarcerated people.

Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentation
Patrick Schmidt, Vasileios Belagiannis, Lazaros Nalpantidis
https://arxiv.org/abs/2509.17702

Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentation
Autonomous robotic systems applied to new domains require an abundance of expensive, pixel-level dense labels to train robust semantic segmentation models under full supervision. This study proposes a model-agnostic Depth Edge Alignment Loss to improve Weakly Supervised Semantic Segmentation models across different datasets. The methodology generates pixel-level semantic labels from image-level supervision, avoiding expensive annotation processes. While weak supervision is widely explored in tr…

Cool Chemistry Trick Could Transform Vinegar Into a Powerful Weapon Against Superbugs
https://gizmodo.com/cool-chemistry-trick-could-transform-vinegar-into-a-powerful-weapon-against-superbugs-2000662684

♫No novo tempo
Apesar dos perigos
Da força mais bruta
Da noite que assusta
Estamos na luta
Pra sobreviver
Pra sobreviver
Pra sobreviver
Pra que nossa esperança
Seja mais que vingança
Seja sempre um caminho
Que se deixa de herança♫
https://www.youtube.com/watch?v=jHEfuVacAeU

Meta launches the American Technology Excellence Project, a super PAC to fight AI policy bills at the state level; it previously launched a California PAC (Ashley Gold/Axios)
https://www.axios.com/2025/09/23/meta-superpac-ai-regulation

Exclusive: Meta launches super PAC to fight AI regulation
Meta is targeting what it sees as onerous AI and tech policy bills across the country.

Detection of non-absolute separability in quantum states and channels through moments
Bivas Mallick, Saheli Mukherjee, Nirman Ganguly, A. S. Majumdar
https://arxiv.org/abs/2508.15700

Detection of non-absolute separability in quantum states and channels through moments
In quantum information and computation, generation of entanglement through unitary gates remains a significant and active area of research. However, there are states termed as absolutely separable, from which entanglement cannot be created through any non-local unitary action. Thus, from a resource-theoretic perspective, non-absolutely separable states are useful as they can be turned into entangled states using some appropriate unitary gates. In this work, we propose an efficient method to det…

Explainable AI-Enhanced Supervisory Control for Robust Multi-Agent Robotic Systems
Reza Pirayeshshirazinezhad, Nima Fathi
https://arxiv.org/abs/2509.15491 https://

Explainable AI-Enhanced Supervisory Control for Robust Multi-Agent Robotic Systems
We present an explainable AI-enhanced supervisory control framework for multi-agent robotics that combines (i) a timed-automata supervisor for safe, auditable mode switching, (ii) robust continuous control (Lyapunov-based controller for large-angle maneuver; sliding-mode controller (SMC) with boundary layers for precision and disturbance rejection), and (iii) an explainable predictor that maps mission context to gains and expected performance (energy, error). Monte Carlo-driven optimization pro…

Weakly-Supervised Learning for Tree Instances Segmentation in Airborne Lidar Point Clouds
Swann Emilien C\'eleste Destouches, Jesse Lahaye, Laurent Valentin Jospin, Jan Skaloud
https://arxiv.org/abs/2508.15646

Weakly-Supervised Learning for Tree Instances Segmentation in Airborne Lidar Point Clouds
Tree instance segmentation of airborne laser scanning (ALS) data is of utmost importance for forest monitoring, but remains challenging due to variations in the data caused by factors such as sensor resolution, vegetation state at acquisition time, terrain characteristics, etc. Moreover, obtaining a sufficient amount of precisely labeled data to train fully supervised instance segmentation methods is expensive. To address these challenges, we propose a weakly supervised approach where labels of…

Towards Sharper Object Boundaries in Self-Supervised Depth Estimation
Aur\'elien Cecille, Stefan Duffner, Franck Davoine, R\'emi Agier, Thibault Neveu
https://arxiv.org/abs/2509.15987

Towards Sharper Object Boundaries in Self-Supervised Depth Estimation
Accurate monocular depth estimation is crucial for 3D scene understanding, but existing methods often blur depth at object boundaries, introducing spurious intermediate 3D points. While achieving sharp edges usually requires very fine-grained supervision, our method produces crisp depth discontinuities using only self-supervision. Specifically, we model per-pixel depth as a mixture distribution, capturing multiple plausible depths and shifting uncertainty from direct regression to the mixture w…

Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising
Shen Cheng, Haipeng Li, Haibin Huang, Xiaohong Liu, Shuaicheng Liu
https://arxiv.org/abs/2509.16091 https://…

Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising
In this work, we present Blind-Spot Guided Diffusion, a novel self-supervised framework for real-world image denoising. Our approach addresses two major challenges: the limitations of blind-spot networks (BSNs), which often sacrifice local detail and introduce pixel discontinuities due to spatial independence assumptions, and the difficulty of adapting diffusion models to self-supervised denoising. We propose a dual-branch diffusion framework that combines a BSN-based diffusion branch, generati…