Tootfinder

macOS 26 Tahoe review: Liquid Glass has small regressions in usability and accessibility but the upgrade has enough iterative changes like automated Shortcuts (Andrew Cunningham/Ars Technica)
https://arstechnica.com/gadgets/2025/09/macos-26-tahoe-the-ars-techn…

macOS 26 Tahoe: The Ars Technica Review
Liquid Glass brings translucent sheen to the typical batch of iterative changes.

This is awesome! 🤩
From: @…
https://mastodon.social/@arstechnica/114851881549053554

Ars Technica (@arstechnica@mastodon.social)
Attached: 1 image Two guys hated using Comcast, so they built their own fiber ISP Brothers-in-law use construction knowledge to compete against Comcast in Michigan. https://arstechnica.com/tech-policy/2025/07/two-guys-hated-using-comcast-so-they-built-their-own-fiber-isp/?utm_brand=arstechnica&utm_social-type=owned&utm_source=mastodon&utm_medium=social

Raspberry Pi
"This time, because the changes to the underlying architecture are so significant, we are not suggesting any procedure for upgrading a Bullseye image to Bookworm; any attempt to do this will almost certainly end up with a non-booting desktop and data loss."
Mein Raspy 3:
😎

There's this fascination with what science is discovering about unexpected ecosystems, coupled with dismay at the changes we are causing without intent, thought, or (unfortunately) care. Until it's too late to stop what we are doing, to our own detriment

There's this fascination with what science is discovering about unexpected ecosystems, coupled with dismay at the changes we are causing without intent, thought, or (unfortunately) care. Until it's too late to stop what we are doing, to our own detriment

SynergAI: Edge-to-Cloud Synergy for Architecture-Driven High-Performance Orchestration for AI Inference
Foteini Stathopoulou, Aggelos Ferikoglou, Manolis Katsaragakis, Dimosthenis Masouros, Sotirios Xydis, Dimitrios Soudris
https://arxiv.org/abs/2509.12252

SynergAI: Edge-to-Cloud Synergy for Architecture-Driven High-Performance Orchestration for AI Inference
The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) has significantly heightened computational demands, particularly for inference-serving workloads. While traditional cloud-based deployments offer scalability, they face challenges such as network congestion, high energy consumption, and privacy concerns. In contrast, edge computing provides low-latency and sustainable alternatives but is constrained by limited computational resources. In this work, we introduce Synerg…

Layer-specific changes in sensory cortex across the lifespan in mice and humans #7T

Layer-specific changes in sensory cortex across the lifespan in mice and humans - Nature Neuroscience
The principal layer architecture of the sensory cortex is altered with aging. The authors show that overall thinning of the primary somatosensory cortex is driven by deep layer degeneration but that layer IV is more pronounced in old age.

Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
Robiul Islam, Dmitry I. Ignatov, Karl Kaberg, Roman Nabatchikov
https://arxiv.org/abs/2509.14078

Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
This study investigates classifier performance across EEG frequency bands using various optimizers and evaluates efficient class prediction for the left and right hemispheres. Three neural network architectures - a deep dense network, a shallow three-layer network, and a convolutional neural network (CNN) - are implemented and compared using the TensorFlow and PyTorch frameworks. Results indicate that the Adagrad and RMSprop optimizers consistently perform well across different frequency bands,…

RMFAT: Recurrent Multi-scale Feature Atmospheric Turbulence Mitigator
Zhiming Liu, Nantheera Anantrasirichai
https://arxiv.org/abs/2508.11409 https://arxiv…

RMFAT: Recurrent Multi-scale Feature Atmospheric Turbulence Mitigator
Atmospheric turbulence severely degrades video quality by introducing distortions such as geometric warping, blur, and temporal flickering, posing significant challenges to both visual clarity and temporal consistency. Current state-of-the-art methods are based on transformer and 3D architectures and require multi-frame input, but their large computational cost and memory usage limit real-time deployment, especially in resource-constrained scenarios. In this work, we propose RMFAT: Recurrent Mu…

$S^2M^2$: Scalable Stereo Matching Model for Reliable Depth Estimation
Junhong Min, Youngpil Jeon, Jimin Kim, Minyong Choi
https://arxiv.org/abs/2507.13229

$S^2M^2$: Scalable Stereo Matching Model for Reliable Depth Estimation
The pursuit of a generalizable stereo matching model, capable of performing across varying resolutions and disparity ranges without dataset-specific fine-tuning, has revealed a fundamental trade-off. Iterative local search methods achieve high scores on constrained benchmarks, but their core mechanism inherently limits the global consistency required for true generalization. On the other hand, global matching architectures, while theoretically more robust, have been historically rendered infeas…