Tootfinder

heise | Die schärfste VR-Brille für Sim-Fans: Pimax Crystal Super im Test
Die Pimax Crystal Super stellt knapp 30 Millionen Pixel dar, das entspricht fast 8K. Wir testen, ob die PC-VR-Brille auch abseits des Datenblatts überzeugt.

"Motorcyclist on shopping trip arrested amid Labour’s crackdown on undocumented migrants"
https://www.theguardian.com/uk-news/2025/sep/02/motorcyclist-on-shopping-trip-arrested-london-amid-labours-crac…

🧢 Modeling the World in 280 Characters
#graphics

Modeling the World in 280 Characters | Codrops
An exploration of the mindset, methods, and motivations behind crafting tiny, expressive shaders that combine code, art, and constraint.

game_thrones: Game of Thrones coappearances
Network of coappearances of characters in the Game of Thrones series, by George R. R. Martin, and in particular coappearances in the book "A Storm of Swords." Nodes are unique characters, and edges are weighted by the number of times the two characters' names appeared within 15 words of each other in the text.
This network has 107 nodes and 352 edges.
Tags: Social, Fictional, Weighted

CircuitHunt: Automated Quantum Circuit Screening for Superior Credit-Card Fraud Detection
Nouhaila Innan, Akshat Singh, Muhammad Shafique
https://arxiv.org/abs/2508.21366 https:…

CircuitHunt: Automated Quantum Circuit Screening for Superior Credit-Card Fraud Detection
Designing effective quantum models for real-world tasks remains a key challenge within Quantum Machine Learning (QML), particularly in applications such as credit card fraud detection, where extreme class imbalance and evolving attack patterns demand both accuracy and adaptability. Most existing approaches rely on either manually designed or randomly initialized circuits, leading to high failure rates and limited scalability. In this work, we introduce CircuitHunt, a fully automated quantum cir…

Honestly, #emoji and icons in #Unicode are a true horror.
Yeah, sure. It's great that you don't have to use <img/> anymore and you can just paste a random Unicode character. You can get graphics into fields where only text was originally intended (like bug summaries). Even better, you can now easily get cool colorful icons on terminal with almost no effort.
However, it is an #accessibility nightmare. People are now encoding *information* in random graphical symbols. Symbols that require huge fonts to render, or huge character tables to describe.
Yeah, a bare <img/> carrying information sucks. However, you can add a *meaningful* alt-text to the image, and accessibility tools can use that text to provide meaningful context. Like "bug fix".
However, emojis and icons are symbolic. The best you can get is some description like "hammer and wrench", so people can kinda figure out that it's probably a "bug fix". Or maybe it was a "maintenance task"? Or you'll get a "unknown character 0x1F6E0". And I'm sure people will surely enjoy cross-referencing a "legend" of such "unknown characters".

🇺🇦 Auf radioeins läuft...
Crooked Colours:
🎵 Come Back To You
#NowPlaying #CrookedColours
https://crookedcolours.bandcamp.com/track/come-back-to-you
https://open.spotify.com/track/5pl2iAMtxTwUIHGJ3yTYkL

Neural Hamilton--Jacobi Characteristic Flows for Optimal Transport
Yesom Park, Shu Liu, Mo Zhou, Stanley Osher
https://arxiv.org/abs/2510.01153 https://arx…

Neural Hamilton--Jacobi Characteristic Flows for Optimal Transport
We present a novel framework for solving optimal transport (OT) problems based on the Hamilton--Jacobi (HJ) equation, whose viscosity solution uniquely characterizes the OT map. By leveraging the method of characteristics, we derive closed-form, bidirectional transport maps, thereby eliminating the need for numerical integration. The proposed method adopts a pure minimization framework: a single neural network is trained with a loss function derived from the method of characteristics of the HJ …

Christian Gonzalez doesn't practice with hamstring injury, timetable for return uncertain for Patriots CB

https://www.cbssports.com/nfl/news/christi

Reinforcement Learning for Optimizing Large Qubit Array based Quantum Sensor Circuits
Laxmisha Ashok Attisara, Sathish Kumar
https://arxiv.org/abs/2508.21253 https://

Reinforcement Learning for Optimizing Large Qubit Array based Quantum Sensor Circuits
As the number of qubits in a sensor increases, the complexity of designing and controlling the quantum circuits grows exponentially. Manually optimizing these circuits becomes infeasible. Optimizing entanglement distribution in large-scale quantum circuits is critical for enhancing the sensitivity and efficiency of quantum sensors [5], [6]. This paper presents an engineering integration of reinforcement learning with tensor-network-based simulation (MPS) for scalable circuit optimization for op…