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Adaptive dynamical systems arise in a multitude of contexts, e.g., optimization, control, communications, signal processing, and machine learning. A precise characterization of their fundamental limitations is therefore of paramount…

Information Theory · Computer Science 2010-10-13 Maxim Raginsky

It is shown that Vop\v{e}nka's Principle (VP) can restore almost the entire ZF over a weak fragment of it. Namely, if EST is the theory consisting of the axioms of Extensionality, Empty Set, Pairing, Union, Cartesian Product,…

Logic · Mathematics 2023-03-28 Athanassios Tzouvaras

Based on the probability distribution observed in complex systems and an assumption that the probability distributions of complex systems satisfy a new generalized multiplication, it is proved that the statistical theory of complex systems…

Statistical Mechanics · Physics 2015-06-25 Jincan Chen , Tie Liu , Zhifu Huang , Guozhen Su

We call for a theory of the particle-scale structure of materials that is based on the general notion of information rather than its special case of symmetry. An inherent limitation to the symmetry-based understanding of structure is…

Materials Science · Physics 2024-08-13 Glenn D. Hibbard , John Çamkıran

The article addresses some open questions about the relations between the topological weak mixing property and the transitivity of the map $f\times f^2 \times...\times f^m$, where $f\colon X\ra X$ is a topological dynamical system on a…

Dynamical Systems · Mathematics 2014-05-06 Dominik Kwietniak , Piotr Oprocha

Real-world processes often generate data that are a mix of categorical and numeric values that are recorded at irregular and informative intervals. Discrete token-based approaches are limited in numeric representation capacity while methods…

Machine Learning · Computer Science 2025-06-02 Andrew J. Loza , Jun Yup Kim , Shangzheng Song , Yihang Liu , Joseph J. Y. Sung , R Andrew Taylor , Dennis L. Shung

A general theory for computing information transfers in nonlinear stochastic systems driven by deterministic forcing and additive and/or multiplicative noises, is presented. It extends the Liang-Kleeman framework of causality inference to…

Chaotic Dynamics · Physics 2023-10-12 Carlos A. Pires , David Docquier , Stéphane Vannitsem

In this work, we present a variety of novel information-theoretic generalization bounds for learning algorithms, from the supersample setting of Steinke & Zakynthinou (2020)-the setting of the "conditional mutual information" framework. Our…

Machine Learning · Statistics 2023-06-16 Ziqiao Wang , Yongyi Mao

Detecting weak, systematic distribution shifts and quantitatively modeling individual, heterogeneous responses to policies or incentives have found increasing empirical applications in social and economic sciences. Given two probability…

Statistics Theory · Mathematics 2024-03-29 YoonHaeng Hur , Tengyuan Liang

In our previous arXiv papers (more systematically the informational conception is presented in the paper "The Information as Absolute", 2010) it was rigorously shown that Matter in our Universe - and Universe as a whole - are some…

General Physics · Physics 2011-05-05 S. V. Shevchenko , V. V. Tokarevsky

We introduce a generalization to the lottery ticket hypothesis in which the notion of "sparsity" is relaxed by choosing an arbitrary basis in the space of parameters. We present evidence that the original results reported for the canonical…

Machine Learning · Computer Science 2021-07-27 Ibrahim Alabdulmohsin , Larisa Markeeva , Daniel Keysers , Ilya Tolstikhin

We introduce the framework of Bayesian relative belief that directly evaluates whether or not the experimental data at hand supports a given hypothesis regarding a quantum system by directly comparing the prior and posterior probabilities…

This paper will focus on the process of 'fusing' several observations or models of uncertainty into a single resultant model. Many existing approaches to fusion use subjective quantities such as 'strengths of belief' and process these…

Artificial Intelligence · Computer Science 2020-07-28 Shawn C. Eastwood , Svetlana N. Yanushkevich

We present an exact treatment of the thermodynamics of physical systems in the framework of the generalized uncertainty principle (GUP). Our purpose is to study and compare the consequences of two GUPs that one implies a minimal length…

High Energy Physics - Theory · Physics 2016-03-11 M. Abbasiyan-Motlaq , P. Pedram

Self-stabilization is a strong property that guarantees that a network always resume correct behavior starting from an arbitrary initial state. Weaker guarantees have later been introduced to cope with impossibility results: probabilistic…

Distributed, Parallel, and Cluster Computing · Computer Science 2009-09-29 Stéphane Devismes , Sébastien Tixeuil , Masafumi Yamashita

A famous conjecture of Graham asserts that every set $A \subseteq \mathbb{Z}_p \setminus \{0\}$ can be ordered so that all partial sums are distinct. Although this conjecture was recently proved for sufficiently large primes by Pham and…

Combinatorics · Mathematics 2026-02-24 Simone Costa , Stefano Della Fiore

We present a fluctuation theorem for quantum bipartite systems in which the subsystems exchange information with each other. Our information fluctuation theorem includes correlations by introducing a quantum mechanical mutual information…

Quantum Physics · Physics 2020-06-24 Jung Jun Park , Sang Wook Kim , Vlatko Vedral

In the conventional formulation, it is broadly accepted that simultaneous measurability and commutativity of observables are equivalent. However, several objections have been claimed that there are cases in which even nowhere commuting…

Quantum Physics · Physics 2011-11-28 Masanao Ozawa

We prove a weak iterated invariance principle for a large class of non-uniformly expanding random dynamical systems. In addition, we give a quenched homogenization result for fast-slow systems in the case when the fast component corresponds…

Dynamical Systems · Mathematics 2025-02-11 Davor Dragicevic , Yeor Hafouta

Ensuring generalization to unseen environments remains a challenge. Domain shift can lead to substantially degraded performance unless shifts are well-exercised within the available training environments. We introduce a simple robust…

Machine Learning · Computer Science 2021-10-20 Yilun Xu , Tommi Jaakkola