Tootfinder

Markovian projections for functionals of It\^o semimartingales with jumps
Martin Larsson, Shukun Long
https://arxiv.org/abs/2506.00762 https://

Markovian projections for functionals of Itô semimartingales with jumps
Given an Itô semimartingale $X$, its Markovian projection is an Itô semimartingale $\widehat{X}$, with Markovian differential characteristics, that matches the one-dimensional marginal laws of $X$. One may even require certain functionals of the two processes to have the same fixed-time marginals, at the cost of enhancing the differential characteristics of $\widehat{X}$ but still in a Markovian sense. In the continuous case, the definitive result on existence of Markovian projections was obt…

Bayesian and Markovian classical feedforward for discriminating amplitude damping channels
Milajiguli Rexiti, Stefano Mancini
https://arxiv.org/abs/2506.01851

Bayesian and Markovian classical feedforward for discriminating amplitude damping channels
We address the issue of multishot discrimination between two qubit-amplitude damping channels by invoking a simple adaptive protocol that employs Helstrom measurement at each step and classical information feedforward. We contrast the performance of Bayesian and Markovian strategies. We show that the former is only slightly advantageous for a limited parameters' region.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models
Yifan Hao, Chenlu Ye, Chi Han, Tong Zhang
https://arxiv.org/abs/2506.01919

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models
Transformer based models have shown remarkable capabilities in sequence learning across a wide range of tasks, often performing well on specific task by leveraging input-output examples. Despite their empirical success, a comprehensive theoretical understanding of this phenomenon remains limited. In this work, we investigate the layerwise behavior of Transformers to uncover the mechanisms underlying their multi-task generalization ability. Taking explorations on a typical sequence model, i.e, H…

Bernstein-Markov measures and Toeplitz theory
Siarhei Finski
https://arxiv.org/abs/2506.01610 https://arxiv.org/pdf/2506.01610…

Bernstein-Markov measures and Toeplitz theory
We prove that Toeplitz operators associated with a Bernstein-Markov measure on a compact complex manifold endowed with a big line bundle form an algebra under composition. As an application, we derive a Szegő-type spectral equidistribution result for this class of operators. A key component of our approach is the off-diagonal asymptotic analysis of the Bergman kernel, also known as the Christoffel-Darboux kernel.

Drawdowns, Drawups, and Occupation Times under General Markov Models
Pingping Zeng, Gongqiu Zhang, Weinan Zhang
https://arxiv.org/abs/2506.00552 https://…

Drawdowns, Drawups, and Occupation Times under General Markov Models
Drawdown risk, an important metric in financial risk management, poses significant computational challenges due to its highly path-dependent nature. This paper proposes a unified framework for computing five important drawdown quantities introduced in Landriault et al. (2015) and Zhang (2015) under general Markov models. We first establish linear systems and develop efficient algorithms for such problems under continuous-time Markov chains (CTMCs), and then establish their theoretical convergen…

Schnelles Wachstum, alte Infrastruktur:
«Im Fall Mumbais komme dazu, dass die Stadt sehr tief liegt – nur elf Meter über dem Meeresspiegel – und sie deshalb für Hochwasser anfällig sei. Das Problem sei „systemisch“, so Stadtplaner Kukreja, beginnend bei einer Planung, die künftige Klimafaktoren nicht berücksichtige, gefolgt von unzureichender Umsetzung und mangelnder Regulierung.»

@… Have you considered asking a tiny Markov chain, trained on the works of arseholes on 4chan, to respond for you?

US-Analysten: Smartphone-Verkäufe wegen US-Zollpolitik unter Druck 
Die Markforscher von IDC haben ihre Verkaufs-Prognose für Smartphones nach unten korrigiert. Ein Grund ist die wachsende Verunsicherung der US-Verbraucher.

Speeding Up Hyper-Heuristics With Markov-Chain Operator Selection and the Only-Worsening Acceptance Operator
Abderrahim Bendahi, Benjamin Doerr, Adrien Fradin, Johannes F. Lutzeyer
https://arxiv.org/abs/2506.01107

Speeding Up Hyper-Heuristics With Markov-Chain Operator Selection and the Only-Worsening Acceptance Operator
The move-acceptance hyper-heuristic was recently shown to be able to leave local optima with astonishing efficiency (Lissovoi et al., Artificial Intelligence (2023)). In this work, we propose two modifications to this algorithm that demonstrate impressive performances on a large class of benchmarks including the classic Cliff$_d$ and Jump$_m$ function classes. (i) Instead of randomly choosing between the only-improving and any-move acceptance operator, we take this choice via a simple two-state…

A universal constraint for relaxation rates for quantum Markov generators: complete positivity and beyond
Dariusz Chru\'sci\'nski, Frederik vom Ende, Gen Kimura, Paolo Muratore-Ginanneschi
https://arxiv.org/abs/2505.24467

A universal constraint for relaxation rates for quantum Markov generators: complete positivity and beyond
Relaxation rates are key characteristics of quantum processes, as they determine how quickly a quantum system thermalizes, equilibrates, decoheres, and dissipates. While they play a crucial role in theoretical analyses, relaxation rates are also often directly accessible through experimental measurements. Recently, it was shown that for quantum processes governed by Markovian semigroups, the relaxation rates satisfy a universal constraint: the maximal rate is upper-bounded by the sum of all rat…