24 papers · ranked by Valyu relevance
Brani Vidaković
Kolmogorov complexity of a finite binary word reflects both algorithmic structure and the empirical distribution of symbols appearing in the word. Words with symbol frequencies far from one half have smaller combinatorial richness and therefore appear less complex under the standard definition. In this paper an…
Akshat Sharma
The maximization of statistical complexity has long been associated with the emergence of probability distributions lying between perfect order and complete disorder. While previous studies have shown that complexity-maximizing distributions exhibit a two-level structure in finite discrete systems, an analogous unified…
Dario Javier Zamora
> Quantifying complexity in physical systems remains a fundamental challenge, and many proposed measures fail to capture the structural features that intuitive or theoretical considerations would demand. Among them, the L´opez-Ruiz–Mancini–Calbet (LMC) statistical complexity has been widely cited due to its simplicity…
Cooper Jacobus
The data-driven characterization of the "complexity" present in dynamical systems remains an open problem with broad applications across the physical sciences. We investigate the "structural complexity" of the 2D ferromagnetic Ising model, a paradigmatic system exhibiting a second-order phase transition at a certain…
James P. Crutchfield, Antonio M. Scarfone
We show that mixtures comprising multicomponent systems typically are much more structurally complex than the sum of their parts; sometimes, infinitely more complex. We contrast this with the more familiar notion of statistical mixtures, demonstrating how statistical mixtures miss key aspects of emergent hierarchical…
Athokpam Langlen Chanu, S Amrutha, Pravabati Chingangbam, Changbom Park
As statistical systems, galaxies exhibit a rich interplay between organized structure and stochastic fluctuations across a broad range of spatial scales. This duality motivates the need for quantitative frameworks capable of capturing their morphological complexity. The ordinal patterns framework, along with its…
Sébastien Berquet, Norberto M. Grzywacz, Sarah Marzen, John Beggs + 2 more
Perceived complexity is a key component of sensory brain function as it indicates the number of resources necessary to process incoming information. A recently proposed measure of perceived complexity defined it as normalized Shannon entropy. However, the proposal used probability distributions estimated from the…
Jeff Edmonds, Ming Li
This paper does not aim to prove new mathematical theorems or claim a fundamental unification of physics and information, but rather to provide a new pedagogical framework for interpreting foundational results in algorithmic information theory. Our focus is on understanding the profound connection between entropy and…
Anna Kravchenko, Andrey A Bagrov, Mikhail I Katsnelson, Veronica Dudarev
While intuitive for humans, the concept of visual complexity is hard to define and quantify formally. We suggest adopting the multiscale structural complexity (MSSC) measure, an approach that defines structural complexity of an object as the amount of dissimilarities between distinct scales in its hierarchical…
Boumediene Hamzi, Marianne Clausel, Kamal Dingle, Marcus Hutter + 2 more
Spurious correlations between time series are a persistent problem: simple, low-complexity patterns are abundant, so unrelated series can easily exhibit high Pearson correlation. We argue that Kolmogorov complexity-a series’ resistance to compression-provides a principled diagnostic for flagging such cases. We prove an…
Roxana Peña-Mendieta, Ania Mesa-Rodríguez, Daniel Estevez-Moya, José Rafael de la Horra + 3 more
The classification of trajectories in two dimensions was done through an entropic analysis of their coded representation. The steps include discretising the trajectory into an 8-symbol code using the Freeman procedure. The resulting sequence is amenable to entropic analysis. Kolmogorov-Sinai entropy, effective…
Dibyajyoti Mohanta, Manish Dwivedi, Hiranmay Maity, Debaprasad Giri
Polymer association in confined spaces governs diverse phenomena from protein aggregation to DNA condensation. We investigate the sequence-level mechanisms underlying this behavior through exact enumeration of two confined AB-type block copolymers on a two-dimensional lattice, revealing how sequence complexity controls…
Francesco G. Rinaldi, Eugenio Piasini
To make sense of a noisy world, living beings constantly face decisions between competing interpretations for ambiguous sensory data. This process parallels statistical model selection, where most frameworks, like the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), are based on a…
Giulia Parodi, Giorgia Zanini, Linda Collo, Donatella Di Lisa + 3 more
Three-dimensional in vitro neuronal cultures have emerged as promising platforms for modelling human brain function and disease under controlled conditions. However, their ability to recapitulate in vivo-like complexity and rich dynamics remains underexplored. In this study, we developed and characterized…
Zoe Leyva-Acosta, Eduardo Acuña Yeomans, Francisco Hernández-Quiroz, Ming Li
Algorithmic complexity is a foundational notion in theoretical computer science, but its incomputability has led to two families of practical estimators: compression-based and program-execution-based (e.g., the Coding Theorem Method, CTM). Despite widespread use, the correspondence between these paradigms remains…
Patricia Lamirande, Mia Brunetti, Terry Easlick, Fatemeh Beigmohammadi + 1 more
Mechanistic mathematical models have been used extensively to provide a deeper understanding of biological mechanisms, including unveiling the regulation of tumour growth and its response to various treatments. However, given the breadth of biological regulatory mechanisms, these models are frequently large and thus…
Authors not listed
Gibbs’ paradox—the apparent discontinuity in mixing entropy for gases of varying similarity and the seeming reversibility of mixing-separation cycles—has resisted fully satisfactory resolution for 150 years. We present a solution based on categorical state theory, which posits that physical con f igurations are…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Authors not listed
Quantitative Structure Activity Relationship (QSAR) remains an effective tool for early-stage chemical modelling and virtual screening in drug design. The advancements in this field are led by two core paradigms, 1) descriptor engineering, where complex fixed-length vectors of compounds are generated and conventional…
Alex N Popinga, Jack Forman, Dmitri Svetlov, Huy Vo + 1 more
Biological data is prone to both intrinsic and extrinsic noise and variability between experimental replicas. That same stochasticity and heterogeneity can carry information about underlying biochemical mechanisms but, if not incorporated in modeling and probabilistic inference, can also bias parameter estimates and…
Xun Gu
Rapid growth of entire genome data has revolutionized the field of phylogenomics, i.e., the problem of tree of life. Substantial studies demonstrated that genome phylogeny can be inferred based upon the generalized gene content approach. Two simple types were widely-used: the first-order gene content (J=1) for the…
Authors not listed
A framework for catalysis based on categorical aperture selection rather than temporal acceleration is presented. Traditional catalysis theory describes catalysts as agents that accelerate reactions by lowering activation energies, implicitly treating time as the fundamental variable and reaction rate enhancement as…
Jacob A. Parker, Alexandre L.S. Filipowicz, Kristen Li, Vijay Balasubramanian + 2 more
Human decision-making behavior varies widely across individuals and task conditions. This variability is often interpreted in terms of different suboptimal decision strategies, but the principles that govern these suboptimalities remain poorly understood. We propose that some of these suboptimalities can be understood…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…