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We introduce an information-theoretic framework that views learning as universal prediction under log loss, characterized through regret bounds. Central to the framework is an effective notion of architecture-based model complexity, defined…

Machine Learning · Computer Science 2025-11-04 Meir Feder , Ruediger Urbanke , Yaniv Fogel

The structure of real-world networks is usually difficult to characterize owing to the variation of topological scales, the nondyadic complex interactions, and the fluctuations in the network. We aim to address these problems by introducing…

Social and Information Networks · Computer Science 2019-09-25 Quoc Hoan Tran , Van Tuan Vo , Yoshihiko Hasegawa

When plane waves diffract through fractal-patterned apertures, the resulting far-field profiles or diffractals also exhibit iterated, self-similar features. Here we show that this specific architecture enables robust signal processing and…

Optics · Physics 2015-09-17 Matthew Moocarme , Luat T. Vuong

Complex systems can be described at myriad different scales, and their causal workings often have multiscale structure (e.g., a computer can be described at the microscale of its hardware circuitry, the mesoscale of its machine code, and…

Information Theory · Computer Science 2025-04-22 Erik Hoel

Through extensive studies of dynamical system modeling cellular growth and reproduction, we find evidence that complexity arises in multicellular organisms naturally through evolution. Without any elaborate control mechanism, these systems…

Adaptation and Self-Organizing Systems · Physics 2009-10-31 Chikara Furusawa , Kunihiko Kaneko

The formalism of the scattering matrix is applied to describe the transmission properties of multilayered structures with deep variations of the refractive index and arbitrary arrangements of the layers. We show that there is an exact…

Optics · Physics 2011-09-22 Victor Grigoriev , Fabio Biancalana

Many complex systems satisfy a set of constraints on their degrees of freedom, and at the same time, they are able to work and adapt to different conditions. Here, we describe the emergence of this ability in a simplified model in which the…

Disordered Systems and Neural Networks · Physics 2007-05-23 Ginestra Bianconi , Roberto Mulet

Subject of research is complex networks and network systems. The network system is defined as a complex network in which flows are moved. Classification of flows in the network is carried out on the basis of ordering and continuity. It is…

Physics and Society · Physics 2017-02-13 Olexandr Polishchuk

This document is both a synthesis of current notions about complex systems, and a practical approach description. A disambiguation is proposed and exposes possible reasons for controversies related to causation and emergence. Theoretical…

Adaptation and Self-Organizing Systems · Physics 2008-03-05 Nicolas Brodu

Implicit Computational Complexity makes two aspects implicit, by manipulating programming languages rather than models of com-putation, and by internalizing the bounds rather than using external measure. We survey how automata theory…

Logic in Computer Science · Computer Science 2015-02-05 Clément Aubert

Except for crystalline or random structures, an agreed definition of complexity for intermediate and hence interesting cases does not exist. We fill this gap with a notion of complexity that characterises shapes formed by any finite number…

General Relativity and Quantum Cosmology · Physics 2024-05-14 Julian Barbour , Zaza Doborjginidze , Tim Koslowski , Hemant Shukla

Resilience is a system's ability to maintain its function when perturbations and errors occur. Whilst we understand low-dimensional networked systems' behavior well, our understanding of systems consisting of a large number of components is…

Systems and Control · Electrical Eng. & Systems 2021-09-08 Giannis Moutsinas , Mengbang Zou , Weisi Guo

Low-rank matrix approximations are often used to help scale standard machine learning algorithms to large-scale problems. Recently, matrix coherence has been used to characterize the ability to extract global information from a subset of…

Machine Learning · Statistics 2010-09-07 Mehryar Mohri , Ameet Talwalkar

Real complex systems are not rigidly structured; no clear rules or blueprints exist for their construction. Yet, amidst their apparent randomness, complex structural properties universally emerge. We propose that an important class of…

Attempts to replicate probabilistic reasoning in expert systems have typically overlooked a critical ingredient of that process. Probabilistic analysis typically requires extensive judgments regarding interdependencies among hypotheses and…

Artificial Intelligence · Computer Science 2013-04-15 Marvin S. Cohen

Numerical analysts might be expected to pay close attention to a branch of complexity theory called information-based complexity theory (IBCT), which produces an abundance of impressive results about the quest for approximate solutions to…

Numerical Analysis · Mathematics 2025-10-20 Beresford N. Parlett

Spreadsheets are widely used in industry, even for critical business processes. This implies the need for proper risk assessment in spreadsheets to evaluate the reliability and validity of the spreadsheet's outcome. As related research has…

Software Engineering · Computer Science 2017-04-06 Thomas Reschenhofer , Bernhard Waltl , Klym Shumaiev , Florian Matthes

Link prediction is an open problem in the complex network, which attracts much research interest currently. However, little attention has been paid to the relation between network structure and the performance of prediction methods. In…

Social and Information Networks · Computer Science 2014-10-28 Xu Feng , Jichang Zhao , Ke Xu

The theory of patterns formation for a reaction-diffusion system defined on a multiplex is developed by means of a perturbative approach. The intra-layer diffusion constants act as small parameter in the expansion and the unperturbed state…

Statistical Mechanics · Physics 2015-06-22 Malbor Asllani , Daniel M. Busiello , Timoteo Carletti , Duccio Fanelli , Gwendoline Planchon

In the presence of modeling errors, the mainstream Bayesian methods seldom give a realistic account of uncertainties as they commonly underestimate the inherent variability of parameters. This problem is not due to any misconception in the…

Applications · Statistics 2020-05-19 Omid Sedehi , Costas Papadimitriou , Lambros S. Katafygiotis