Tootfinder

With Josh Barro trending, here's my takedown of his views on transit in Dissent ten years ago. https://www.dissentmagazine.org/blog/the-sovereign-consumer-pushed-off-the-train/

The Sovereign Consumer, Pushed Off the Train - Dissent Magazine
Establishment economists insist that they don't second-guess consumer preferences. You are, they say, your own best judge of what's good for you. But not if you like to travel by train.

This https://arxiv.org/abs/2307.08780 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Discounted-Sum Automata with Multiple Discount Factors
Discounting the influence of future events is a key paradigm in economics and it is widely used in computer-science models, such as games, Markov decision processes (MDPs), reinforcement learning, and automata. While a single game or MDP may allow for several different discount factors, nondeterministic discounted-sum automata (NDAs) were only studied with respect to a single discount factor. It is known that every class of NDAs with an integer as the discount factor has good computational prop…

GOP operative: “Hard to imagine two people with more checkered national reputations among MAGA base than Musk and DeSantis."
'Stood tall and took the hits': DeSantis email praises Musk in the midst of Trump feud - Alternet.org
https://www.alternet.org/desantis-musk-trump/?utm_source=dlvr.it&utm_medium=mastodon

When will people realize that writing "-10% discount" is actually equivalent to stating a 10% surcharge and not a discount at all? sigh.

Electromagnetism: an intrinsic approach to Hadamard's method of descent
Giuliano Angelone, Elisa Ercolessi, Paolo Facchi, Rocco Maggi, Giuseppe Marmo, Saverio Pascazio, Francesco V. Pepe
https://arxiv.org/abs/2506.05255

Electromagnetism: an intrinsic approach to Hadamard's method of descent
We present a systematic geometric framework for the dimensional reduction of classical electromagnetism based on the concept of descent along vector fields of invariance. By exploring the interplay between the Lie derivative and the Hodge star operator, we implement descent conditions on differential forms that reduce Maxwell's equations in four-dimensional spacetime to electromagnetic theories in lower dimensions. We also consider multiple descent along pairwise commuting vector fields of inva…

Achieving Linear Speedup and Near-Optimal Complexity for Decentralized Optimization over Row-stochastic Networks
Liyuan Liang, Xinyi Chen, Gan Luo, Kun Yuan
https://arxiv.org/abs/2506.04600

Achieving Linear Speedup and Near-Optimal Complexity for Decentralized Optimization over Row-stochastic Networks
A key challenge in decentralized optimization is determining the optimal convergence rate and designing algorithms to achieve it. While this problem has been extensively addressed for doubly-stochastic and column-stochastic mixing matrices, the row-stochastic scenario remains unexplored. This paper bridges this gap by introducing effective metrics to capture the influence of row-stochastic mixing matrices and establishing the first convergence lower bound for decentralized learning over row-sto…

"#Diversity Game für Lehrpersonal: Lernspiel für schwierige Gesprächssituationen mit Studierenden" der Universität des Saarlandes:
https://www.uni-saarland.de/aktuell/divers

This https://arxiv.org/abs/2505.04947 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

DFPL: Decentralized Federated Prototype Learning Across Heterogeneous Data Distributions
Federated learning is a distributed machine learning paradigm through centralized model aggregation. However, standard federated learning relies on a centralized server, making it vulnerable to server failures. While existing solutions utilize blockchain technology to implement Decentralized Federated Learning (DFL), the statistical heterogeneity of data distributions among clients severely degrades the performance of DFL. Driven by this issue, this paper proposes a decentralized federated prot…

Taliban Broadens Crackdown With Detention Of Critical Religious Scholars
https://www.rferl.org/a/afghanistan-taliban-islamic-clerics-dissent/33434004.html

Multilevel Bregman Proximal Gradient Descent
Yara Elshiaty, Stefania Petra
https://arxiv.org/abs/2506.03950 https://arxiv.org/pdf/250…

Multilevel Bregman Proximal Gradient Descent
We present the Multilevel Bregman Proximal Gradient Descent (ML-BPGD) method, a novel multilevel optimization framework tailored to constrained convex problems with relative Lipschitz smoothness. Our approach extends the classical multilevel optimization framework (MGOPT) to handle Bregman-based geometries and constrained domains. We provide a rigorous analysis of ML BPGD for multiple coarse levels and establish a global linear convergence rate. We demonstrate the effectiveness of ML BPGD in th…