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Be Simple - Don’t Be Clever - Code Rust
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🦀 #rust

Be Simple | corrode Rust Consulting
The phone buzzes at 3 AM. You roll out of bed, open your laptop, and see this in the logs: th…

Get Creative With These Simple Strategies  #creativity #inspiration https://muz4now.com/2024/get-creative-

Get Creative With These Simple Strategies 
Stan Stewart - @muz4now

KAN-SR: A Kolmogorov-Arnold Network Guided Symbolic Regression Framework
Marco Andrea B\"uhler, Gonzalo Guill\'en-Gos\'albez
https://arxiv.org/abs/2509.10089 https:…

KAN-SR: A Kolmogorov-Arnold Network Guided Symbolic Regression Framework
We introduce a novel symbolic regression framework, namely KAN-SR, built on Kolmogorov Arnold Networks (KANs) which follows a divide-and-conquer approach. Symbolic regression searches for mathematical equations that best fit a given dataset and is commonly solved with genetic programming approaches. We show that by using deep learning techniques, more specific KANs, and combining them with simplification strategies such as translational symmetries and separabilities, we are able to recover grou…

Sunflower Chimney bee (Diadasia enavata — according to iNaturalist) on a Sunflower. Chimney bees are solitary, ground dwelling pollinators common to Central and North America. LA Arboretum, Arcadia, California, USA. September,2025. #laarboretum #bee

#HTTP is not simple
https://daniel.haxx.se/blog/2025/08/08/http-is-not-simple/

Applications of the Liouville symplectic form on the cotangent bundle of a loop group
Indranil Biswas, Michi-aki Inaba, Arata Komyo, Swarnava Mukhopadhyay, Masa-Hiko Saito
https://arxiv.org/abs/2508.09714

Applications of the Liouville symplectic form on the cotangent bundle of a loop group
Let $G$ be a semisimple, simply connected, affine algebraic group defined over $\mathbb C$. Consider the Liouville symplectic structure on the total space $T^*G((t))$ of the cotangent bundle of the loop group $G((t))$, where $t$ is a formal parameter. We show that the Liouville symplectic structure on $T^*G((t))$ induces the symplectic structures on the moduli stack of framed principal Higgs $G$-bundles on a compact connected Riemann surface $X$ and also on the moduli spaces of framed $G$-conne…

Online Robust Planning under Model Uncertainty: A Sample-Based Approach
Tamir Shazman, Idan Lev-Yehudi, Ron Benchetit, Vadim Indelman
https://arxiv.org/abs/2509.10162 https://…

Online Robust Planning under Model Uncertainty: A Sample-Based Approach
Online planning in Markov Decision Processes (MDPs) enables agents to make sequential decisions by simulating future trajectories from the current state, making it well-suited for large-scale or dynamic environments. Sample-based methods such as Sparse Sampling and Monte Carlo Tree Search (MCTS) are widely adopted for their ability to approximate optimal actions using a generative model. However, in practical settings, the generative model is often learned from limited data, introducing approxi…

Top draft pick Jihaad Campbell gives update on Eagles return, has simple goal as he prepares for rookie season

https://www.cbssports.com/nfl/news/top-d…

To voksne og tre barn har fanga 1,9 kg blåbær

MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
Yuchen Zhu, Wei Guo, Jaemoo Choi, Guan-Horng Liu, Yongxin Chen, Molei Tao
https://arxiv.org/abs/2508.10684 h…

MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
We study the problem of learning a neural sampler to generate samples from discrete state spaces where the target probability mass function $π\propto\mathrm{e}^{-U}$ is known up to a normalizing constant, which is an important task in fields such as statistical physics, machine learning, combinatorial optimization, etc. To better address this challenging task when the state space has a large cardinality and the distribution is multi-modal, we propose $\textbf{M}$asked $\textbf{D}$iffusion $\te…