Tootfinder

Improving Robustness of AlphaZero Algorithms to Test-Time Environment Changes
Isidoro Tamassia, Wendelin B\"ohmer
https://arxiv.org/abs/2509.04317 https://

Improving Robustness of AlphaZero Algorithms to Test-Time Environment Changes
The AlphaZero framework provides a standard way of combining Monte Carlo planning with prior knowledge provided by a previously trained policy-value neural network. AlphaZero usually assumes that the environment on which the neural network was trained will not change at test time, which constrains its applicability. In this paper, we analyze the problem of deploying AlphaZero agents in potentially changed test environments and demonstrate how the combination of simple modifications to the stand…

🇺🇦 #NowPlaying on KEXP's #DriveTime
Alvvays:
🎵 Plimsoll Punks
#Alvvays
https://alvvays.bandcamp.com/track/plimsoll-punks-2
https://open.spotify.com/track/5cVC8YxqA51DztRmHmisvr

falls jemand aus münster und umzu am 22.11. lust auf eine gute stunde schöne musik hat: 17 uhr st. petri!
» https://www.blaeservereinigung-albachten.de/bva_aktuelles.html 🎶🎶🎶

Lechrauschen.
...at the place where the river Lech emerges from the Alps and drops into a narrow rocky canyon just outside Füssen... The canyon was formed around 12,000 years ago at the end of the Würm glaciation.
(Recorded last Friday for #WaterfallWednesday :)
#Video

We've just rejigged the alpaca festival website and welcome all critical feedback ! https://2025.algorithmicpattern.org/sheffield/

Learning Neural Decoding with Parallelism and Self-Coordination for Quantum Error Correction
Kai Zhang, Situ Wang, Linghang Kong, Fang Zhang, Zhengfeng Ji, Jianxin Chen
https://arxiv.org/abs/2509.03815

Learning Neural Decoding with Parallelism and Self-Coordination for Quantum Error Correction
Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation. Neural network decoders like AlphaQubit have demonstrated significant potential, achieving higher accuracy than traditional human-designed decoding algorithms. However, existing implementations of neural network decoders lack the parallelism required to decode the syndrome stream generated by a superconducting logical qubit in real time. Moreover, integrating AlphaQubit with sliding window-based para…

Deep Hedging Under Non-Convexity: Limitations and a Case for AlphaZero
Matteo Maggiolo, Giuseppe Nuti, Miroslav \v{S}trupl, Oleg Szehr
https://arxiv.org/abs/2510.01874 https://

Deep Hedging Under Non-Convexity: Limitations and a Case for AlphaZero
This paper examines replication portfolio construction in incomplete markets - a key problem in financial engineering with applications in pricing, hedging, balance sheet management, and energy storage planning. We model this as a two-player game between an investor and the market, where the investor makes strategic bets on future states while the market reveals outcomes. Inspired by the success of Monte Carlo Tree Search in stochastic games, we introduce an AlphaZero-based system and compare i…

Hunting for Axions in REactor neutrino COherent scattering Detection Experiment
Wei Dai, Yuanlin Gong, Guanhua Gu, Liangliang Su, Li Wang, Lei Wu, Yongcheng Wu, Litao Yang
https://arxiv.org/abs/2509.01538

Hunting for Axions in REactor neutrino COherent scattering Detection Experiment
Nuclear power plants are not only vital sources of clean energy but also powerful facilities for probing new physics beyond the Standard Model. Due to the intense gamma-ray flux and an appropriate energy conditions, they are particularly well-suited for searches of light hypothetical particles such as sub-MeV axions and axion-like particles (ALPs). In this work, we propose to search for the ALPs in the REactor Neutrino COherent scattering Detection Experiment (RECODE), where two low-threshold, …

Enhancing Zero-Shot Pedestrian Attribute Recognition with Synthetic Data Generation: A Comparative Study with Image-To-Image Diffusion Models
Pablo Ayuso-Albizu, Juan C. SanMiguel, Pablo Carballeira
https://arxiv.org/abs/2509.02161

Enhancing Zero-Shot Pedestrian Attribute Recognition with Synthetic Data Generation: A Comparative Study with Image-To-Image Diffusion Models
Pedestrian Attribute Recognition (PAR) involves identifying various human attributes from images with applications in intelligent monitoring systems. The scarcity of large-scale annotated datasets hinders the generalization of PAR models, specially in complex scenarios involving occlusions, varying poses, and diverse environments. Recent advances in diffusion models have shown promise for generating diverse and realistic synthetic images, allowing to expand the size and variability of training …

🇺🇦 #NowPlaying on KEXP's #MorningShow
Pink Floyd:
🎵 Fearless
#PinkFloyd
https://jamallbufford.bandcamp.com/track/pink-floyd-fearless
https://open.spotify.com/track/7AalBKBoLDR4UmRYRJpdbj