Tootfinder

Cuomo pronounces it [edit: actually I can't remember how Cuomo pronounces it, I just remember that he pronounces it incorrectly]. Sliwa pronounces it "mandami". Is this just a power thing?
Also, Sliwa's a huge weirdo, but the "slapping fannies and killing grannies" line was solid.

Mayoral Candidate Goes on Bonkers ‘Slapping Fannies’ Rant Against Rival
Curtis Sliwa held nothing back as he unloaded both barrels on his mayoral rivals.

"U.S. total consumption of fossil fuels is no longer falling. Importantly, it should be clarified this pattern was already emerging before Trump took office. Another reminder: Trump has only been in office for 8 months. So, the vast majority of changes to fossil fuel use and decarbonization are yet to come."
Slipping into Darkness - by Gregor Macdonald

Slipping into Darkness
Monday 18 August 2025

Weak TransNet: A Petrov-Galerkin based neural network method for solving elliptic PDEs
Zhihang Xu, Min Wang, Zhu Wang
https://arxiv.org/abs/2506.14812 http…

Weak TransNet: A Petrov-Galerkin based neural network method for solving elliptic PDEs
While deep learning has achieved remarkable success in solving partial differential equations (PDEs), it still faces significant challenges, particularly when the PDE solutions have low regularity or singularities. To address these issues, we propose the Weak TransNet (WTN) method, based on a Petrov-Galerkin formulation, for solving elliptic PDEs in this work, though its framework may extend to other classes of equations. Specifically, the neural feature space defined by TransNet (Zhang et al.,…

Contribution of expert aggregation to temperature prediction part II: Second order bounds with sleeping experts
L\'eo Pfitzner (GMAP), Olivier Wintenberger (SU), Olivier Mestre (GMAP)
https://arxiv.org/abs/2506.15216

Contribution of expert aggregation to temperature prediction part II: Second order bounds with sleeping experts
In this paper we improve on the temperature predictions made with (online) Expert Aggregation (EA) [Cesa-Bianchi and Lugosi, 2006] in Part I. In particular, we make the aggregation more reactive, whilst maintaining at least the same root mean squared error and reducing the number of large errors. We have achieved this by using the Sleeping Expert Framework (SEF) [Freund et al., 1997, Devaine et al., 2013], which allows the more efficient use of biased experts (bad on average but which may be go…

Language learning has been part of me since high school. I'm solid in 2 non-English languages, crappy but survivable in 2 others. I've played with & started learning others many times.
I'm real busy rn, but language learning could be a fun thing to do for myself & make me feel like I'm still me.
But I'm stumped about my language picks. I learnt the obvious European languages in school; later tried key Asian languages. What do I want to do now?
African languages? I won't be getting a chance to use them much in Aus, & I'm unlikely to get to a stage where I can read literature.
I tried Slovenian/Slovene on a whim & really love it, but I'll never go there. Is the practical but unfun answer grind out more kanji/hanzi? Or is whimsically learning a language spoken by only 2.5 million people reasonable? I will continue struggling through with Ukrainian, 'cause I think it's important.
#LanguageLearning

A Biased Random Key Genetic Algorithm for Solving the Longest Run Subsequence Problem
Christian Blum, Pedro Pinacho-Davidson
https://arxiv.org/abs/2508.14020 https://

A Biased Random Key Genetic Algorithm for Solving the Longest Run Subsequence Problem
The longest run subsequence (LRS) problem is an NP-hard combinatorial optimization problem belonging to the class of subsequence problems from bioinformatics. In particular, the problem plays a role in genome reassembly. In this paper, we present a solution to the LRS problem using a Biased Random Key Genetic Algorithm (BRKGA). Our approach places particular focus on the computational efficiency of evaluating individuals, which involves converting vectors of gray values into valid solutions to …

Queen Domination by SAT Solving
Taha Rostami, Curtis Bright
https://arxiv.org/abs/2508.11945 https://arxiv.org/pdf/2508.11945

Queen Domination by SAT Solving
The queen domination problem asks for the minimum number of queens needed to attack all squares on an $n\times n$ chessboard. Once this optimal number is known, determining the number of distinct solutions up to isomorphism has also attracted considerable attention. Previous work has introduced specialized and highly optimized search procedures to address open instances of the problem. While efficient in terms of runtime, these approaches have not provided proofs that can be independently verif…

Mitigating sloppiness in joint estimation of successive squeezing parameters
Priyanka Sharma, Stefano Olivares, Devendra Kumar Mishra, Matteo G. A. Paris
https://arxiv.org/abs/2506.15638

Mitigating sloppiness in joint estimation of successive squeezing parameters
When two successive squeezing operations with the same phase are applied to a field mode, reliably estimating the amplitude of each is impossible because the output state depends solely on their sum. In this case, the quantum statistical model becomes sloppy, and the quantum Fisher information matrix turns singular. However, estimation of both parameters becomes feasible if the quantum state is subjected to an appropriate scrambling operation between the two squeezing operations. In this work, …

I was at my desk, & I thought Teddi was sleeping on the floor next to me, but she was wide awake. Just a-starin’ at me. Upside down, just starin’. ❤️ #DogsOfMastodon

e-boost: Boosted E-Graph Extraction with Adaptive Heuristics and Exact Solving
Jiaqi Yin, Zhan Song, Chen Chen, Yaohui Cai, Zhiru Zhang, Cunxi Yu
https://arxiv.org/abs/2508.13020

e-boost: Boosted E-Graph Extraction with Adaptive Heuristics and Exact Solving
E-graphs have attracted growing interest in many fields, particularly in logic synthesis and formal verification. E-graph extraction is a challenging NP-hard combinatorial optimization problem. It requires identifying optimal terms from exponentially many equivalent expressions, serving as the primary performance bottleneck in e-graph based optimization tasks. However, traditional extraction methods face a critical trade-off: heuristic approaches offer speed but sacrifice optimality, while exac…