21 papers · ranked by Valyu relevance
Joseph Starkey, Robin L. Carhart-Harris, Andrea Pigorini, Lino Nobili + 1 more
Application of complexity measures to neurophysiological time series has seen increased use in recent years to identify neural correlates of global states of consciousness. Lempel-Ziv complexity is currently the de-facto complexity measure used in these investigations. However, by simply counting the number of…
Nima Dehghani
Complexity science offers a wide range of measures for quantifying unpredictability, structure, and information. Yet a systematic conceptual organization of these measures is still missing. We present a unified framework that locates statistical, algorithmic, and dynamical measures along three orthogonal…
Xingyu Chen, Ruiqi Zhang, Lin Liu
Higher-order U-statistics abound in fields such as statistics, machine learning, and computer science, but are known to be highly time-consuming to compute in practice. Despite their widespread appearance, a comprehensive study of their computational complexity is surprisingly lacking. This paper aims to fill that gap…
George Datseris, Kristian Agasøster Haaga, Alessandro Mengarelli
In the nonlinear timeseries analysis literature, countless quantities have been presented as new “entropy” or “complexity” measures, often with similar roles. The ever-increasing pool of such measures makes creating a sustainable and all-encompassing software for them difficult both conceptually and pragmatically. Such…
Charles Alexandre Bédard, Geoffroy Bergeron, Paul M. B. Vitányi
We suggest a quantitative and objective notion of emergence. Our proposal uses algorithmic information theory as a basis for an objective framework in which a bit string encodes observational data. A plurality of drops in the Kolmogorov structure function of such a string is seen as the hallmark of emergence. Our…
Qian-Yuan Tang, Weitong Ren, Jun Wang, Kunihiko Kaneko
The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database (AlphaFold DB), we perform comparative analyses of the proteins of…
А. Н. Семенов, Alexander Shen, Nikolay Vereshchagin
The last theme of Kolmogorov's mathematics research was algorithmic theory of information, now often called Kolmogorov complexity theory. Kolmogorov played a crucial role in its creation, though he was not the first who suggested measuring the amount of information in a finite object using theory of algorithms, as the…
Ilmun Kim, Matey Neykov, Sivaraman Balakrishnan, Larry Wasserman
This paper is concerned with the problem of conditional independence testing for discrete data. In recent years, researchers have shed new light on this fundamental problem, emphasizing finite-sample optimality. The non-asymptotic viewpoint adapted in these works has led to novel conditional independence tests that…
Rafael C. Núñez, Gregory R. Hart, Michael Famulare, Christopher Lorton + 1 more
Since the coining of the term phylodynamics, the use of phylogenies to understand infectious disease dynamics has steadily increased. As methods for phylodynamics and genomic epidemiology have proliferated and grown more computationally expensive, the epidemiological information they extract has also evolved to better…
Hector Zenil, James A. R. Marshall, Jesper Tegnér
Being able to objectively characterize the intrinsic complexity of behavioral patterns resulting from human or animal decisions is fundamental for deconvolving cognition and designing autonomous artificial intelligence systems. Yet complexity is difficult in practice, particularly when strings are short. By numerically…
Sébastien Berquet, Hassan Aleem, Norberto M. Grzywacz, Takuya Yamano
When evaluating sensory stimuli, people tend to prefer those with not too little or not too much complexity. A recent theoretical proposal for this phenomenon is that preference has a direct link to the Observed Fisher Information that a stimulus carries about the environment. To make this theory complete, one must…
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…
Michael R. Powers, Jiaxin Xu
Parametric statistical methods play a central role in analyzing risk through its underlying frequency and severity components. Given the wide availability of numerical algorithms and high-speed computers, researchers and practitioners often model these separate (although possibly statistically dependent) random…
Thomas Chambon, Jean-Loup Guillaume, Jeanne Lallement, Éloi Bossé
Predicting how an individual will perceive the visual complexity of a piece of information is still a relatively unexplored domain, although it can be useful in many contexts such as for the design of human-computer interfaces. We propose here a new method, called Information Complexity Ranking (ICR) to rank objects…
Authors not listed
Sequence is the critical determinant of macromolecular function, yet current polymer design approaches often optimize monomer composition and ratios while ignoring sequence. This creates poorly defined design spaces for active learning that miss the vast combinatorial landscape of sequence possibilities. We introduce…
Thijs Janzen, Rampal S. Etienne
Phylogenetic trees are believed to contain a wealth of information on diversification processes. Comparing phylogenetic trees is not straightforward due to their high dimensionality. Researchers have therefore defined a wide range of one-dimensional summary statistics. However, it remains unexplored to what extent…
Robert Reischke
Confidence contours in parameter space are a helpful tool to compare and classify determined estimators. For more intricate parameter estimations of non-linear nature or complex error structures, the procedure of determining confidence contours is a statistically complex task. For polymer chemists, such particular…
Christian Tönsing, Bernhard Steiert, Jens Timmer, Clemens Kreutz + 1 more
'Mark Alber'] Likelihood ratios are frequently utilized as basis for statistical tests, for model selection criteria and for assessing parameter and prediction uncertainties, e.g. using the profile likelihood. However, translating these likelihood ratios into p-values or confidence intervals requires the exact form of…
Ruben Sanchez-Garcia, Dávid Havasi, Gergely Takács, Matthew C. Robinson + 3 more
Compound availability is a critical property for design prioritization across the drug discovery pipeline. Historically, and despite their multiple limitations, compound-oriented synthetic accessibility scores have been used as proxies for this problem. However, the size of the catalogues of commercially available…
Ruben Sanchez-Garcia, Dávid Havasi, Gergely Takács, Matthew C. Robinson + 3 more
Compound availability is a critical property for design prioritization across the drug discovery pipeline. Historically, and despite their multiple limitations, compound-oriented synthetic accessibility scores have been used as proxies for this problem. However, the size of the catalogues of commercially available…
Ruben Sanchez-Garcia, Dávid Havasi, Gergely Takács, Matthew C. Robinson + 3 more
Compound availability is a critical property for design prioritization across the drug discovery pipeline. Historically, and despite their multiple limitations, compound-oriented synthetic accessibility scores have been used as proxies for this problem. However, the size of the catalogues of commercially available…