Tootfinder

Oriented diameter of graphs with diameter $4$ and given maximum edge girth
Jifu Lin, Lihua You
https://arxiv.org/abs/2507.23517 https://arxiv.org/pdf/2507.…

Oriented diameter of graphs with diameter $4$ and given maximum edge girth
Let $G$ be a bridgeless graph. We introduce the maximum edge girth of $G$, denoted by $g^*(G)=\max\{l_G(e)\mid e\in E(G)\}$, where $l_G(e)$ is the edge girth of $e$, defined as the length of the shortest cycle containing $e$. Let $F(d,A)$ be the smallest value for which every bridgeless graph $G$ with diameter $d$ and $g^*(G)\in A$ admits a strong orientation $\overrightarrow{G}$ such that the diameter of $\overrightarrow{G}$ is at most $F(d,A)$. Let $f(d)=F(d,A)$, where $A=\{a\in \mathbb{N}\mi…

Stronger Directed Low-Diameter Decompositions with Sub-Logarithmic Diameter and Separation
Bernhard Haeupler, Richard Hlad\'ik, Shengzhe Wang, Zhijun Zhang
https://arxiv.org/abs/2509.24565

Stronger Directed Low-Diameter Decompositions with Sub-Logarithmic Diameter and Separation
This paper significantly strengthens directed low-diameter decompositions in several ways. We define and give the first results for separated low-diameter decompositions in directed graphs, tighten and generalize probabilistic guarantees, and prove new independence results between (far away) edges. Our results are the first to give meaningful guarantees for decompositions with small diameters $D = Ω(\log\log n)$ in contrast to the state of the art that only applies to super-logarithmic diame…

After trading barbs online with former Russian leader Dmitri A. Medvedev,
Donald Trump ordered nuclear submarines to be positioned in “the appropriate regions” near Russia “just in case.”
https://www.forbes.com/sites/mar…

Nuclear Submarines Moved Near Russia On Trump’s Order
Trump’s online war of words with Russia’s Dmitri Medvedev led to military action on Friday.

The 14 and 15 year olds are competing next. #DemoDerby

NSPDI-SNN: An efficient lightweight SNN based on nonlinear synaptic pruning and dendritic integration
Wuque Cai, Hongze Sun, Jiayi He, Qianqian Liao, Yunliang Zang, Duo Chen, Dezhong Yao, Daqing Guo
https://arxiv.org/abs/2508.21566

NSPDI-SNN: An efficient lightweight SNN based on nonlinear synaptic pruning and dendritic integration
Spiking neural networks (SNNs) are artificial neural networks based on simulated biological neurons and have attracted much attention in recent artificial intelligence technology studies. The dendrites in biological neurons have efficient information processing ability and computational power; however, the neurons of SNNs rarely match the complex structure of the dendrites. Inspired by the nonlinear structure and highly sparse properties of neuronal dendrites, in this study, we propose an effic…

Browns say WR Tillman, KR Carter out 'weeks' https://www.espn.com/nfl/story/_/id/46423254/browns-say-wr-cedric-tillman-kr-deandre-carter-weeks

#DonnaDErrico always claims to be unable to swim and terrified of water, but she filmed several scenes like this in her years in the Baywatch Universe.
Baywatch Nights 2x08 - Last Breath
#RandomBaywatch

🇺🇦 #NowPlaying on KEXP's #AstralPlane
The Dandy Warhols:
🎵 Every Day Should Be a Holiday
#TheDandyWarhols
https://music.dandywarhols.com/track/every-day-should-be-a-holiday
https://open.spotify.com/track/3eJmOEtymeK04MsDPHW4ES

DyMoDreamer: World Modeling with Dynamic Modulation
Boxuan Zhang, Runqing Wang, Wei Xiao, Weipu Zhang, Jian Sun, Gao Huang, Jie Chen, Gang Wang
https://arxiv.org/abs/2509.24804 …

DyMoDreamer: World Modeling with Dynamic Modulation
A critical bottleneck in deep reinforcement learning (DRL) is sample inefficiency, as training high-performance agents often demands extensive environmental interactions. Model-based reinforcement learning (MBRL) mitigates this by building world models that simulate environmental dynamics and generate synthetic experience, improving sample efficiency. However, conventional world models process observations holistically, failing to decouple dynamic objects and temporal features from static backg…

Spiking Decision Transformers: Local Plasticity, Phase-Coding, and Dendritic Routing for Low-Power Sequence Control
Vishal Pandey, Debasmita Biswas
https://arxiv.org/abs/2508.21505

Spiking Decision Transformers: Local Plasticity, Phase-Coding, and Dendritic Routing for Low-Power Sequence Control
Reinforcement learning agents based on Transformer architectures have achieved impressive performance on sequential decision-making tasks, but their reliance on dense matrix operations makes them ill-suited for energy-constrained, edge-oriented platforms. Spiking neural networks promise ultra-low-power, event-driven inference, yet no prior work has seamlessly merged spiking dynamics with return-conditioned sequence modeling. We present the Spiking Decision Transformer (SNN-DT), which embeds Lea…