Tootfinder

Diese Werkzeuge sollen Kunstwerke vor KI schützen – doch die lassen sich umgehen
Tools wie Nightshade sollen Kunst vor KI schützen, doch eine neue Methode namens Lightshed unterläuft diese Schutzmaßnahmen.

Astrophysicist Proposes Paperclip-Sized Spacecraft Could Travel at Lightspeed to a Black Hole - Slashdot
https://science.slashdot.org/story/25/08/10/2124249/astrophysicist-proposes-paperclip-sized-spacecraft-could-travel-at-lightspeed-to-a-black-hole

Lightweight Deep Unfolding Networks with Enhanced Robustness for Infrared Small Target Detection
Jingjing Liu, Yinchao Han, Xianchao Xiu, Jianhua Zhang, Wanquan Liu
https://arxiv.org/abs/2509.08205

Lightweight Deep Unfolding Networks with Enhanced Robustness for Infrared Small Target Detection
Infrared small target detection (ISTD) is one of the key techniques in image processing. Although deep unfolding networks (DUNs) have demonstrated promising performance in ISTD due to their model interpretability and data adaptability, existing methods still face significant challenges in parameter lightweightness and noise robustness. In this regard, we propose a highly lightweight framework based on robust principal component analysis (RPCA) called L-RPCANet. Technically, a hierarchical bottl…

Q&A with OpenAI COO Brad Lightcap on GPT-5, its dynamic reasoning, defining AGI, scaling vs. post-training, hallucinations, enterprise adoption, and more (Alex Kantrowitz/Big Technology)
https://www.

OpenAI COO Brad Lightcap: GPT-5's Capabilities, Why It Matters, and Where AI Goes Next
A conversation about what OpenAI's long-anticipated flagship model tells us about different forms intelligence and AI's trajectory from here.

🇺🇦 Auf radioeins läuft...
Lucy Dacus:
🎵 Ankles
#NowPlaying #LucyDacus
https://lucydacus.bandcamp.com/track/ankles
https://open.spotify.com/track/4VhbsGXRGDncpi79aiX8eE

Ich habe „Views“ von Marc-Uwe Kling angehört. https://lesetagebu.ch/eintrag/72331

Eintrag von jan_j: „Views“ von Marc-Uwe Kling
Dein digitales Bücherregal.

For those fretting about Microsoft's inevitable further enshittification of the already shit Github, I can report (having ditched it the day Microsoft announced its acquisition) that Forgejo (#libre

LightSwitch: Multi-view Relighting with Material-guided Diffusion
Yehonathan Litman, Fernando De la Torre, Shubham Tulsiani
https://arxiv.org/abs/2508.06494 https://

LightSwitch: Multi-view Relighting with Material-guided Diffusion
Recent approaches for 3D relighting have shown promise in integrating 2D image relighting generative priors to alter the appearance of a 3D representation while preserving the underlying structure. Nevertheless, generative priors used for 2D relighting that directly relight from an input image do not take advantage of intrinsic properties of the subject that can be inferred or cannot consider multi-view data at scale, leading to subpar relighting. In this paper, we propose Lightswitch, a novel …

First-order State Space Model for Lightweight Image Super-resolution
Yujie Zhu, Xinyi Zhang, Yekai Lu, Guang Yang, Faming Fang, Guixu Zhang
https://arxiv.org/abs/2509.08458 http…

First-order State Space Model for Lightweight Image Super-resolution
State space models (SSMs), particularly Mamba, have shown promise in NLP tasks and are increasingly applied to vision tasks. However, most Mamba-based vision models focus on network architecture and scan paths, with little attention to the SSM module. In order to explore the potential of SSMs, we modified the calculation process of SSM without increasing the number of parameters to improve the performance on lightweight super-resolution tasks. In this paper, we introduce the First-order State S…

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices
Sizhen Bian, Mengxi Liu, Vitor Fortes Rey, Daniel Geissler, Paul Lukowicz
https://arxiv.org/abs/2507.07949

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices
Human Activity Recognition (HAR) on resource-constrained wearable devices demands inference models that harmonize accuracy with computational efficiency. This paper introduces TinierHAR, an ultra-lightweight deep learning architecture that synergizes residual depthwise separable convolutions, gated recurrent units (GRUs), and temporal aggregation to achieve SOTA efficiency without compromising performance. Evaluated across 14 public HAR datasets, TinierHAR reduces Parameters by 2.7x (vs. TinyHA…