27 papers · ranked by Valyu relevance
David Van Horn
I gratefully acknowledge the support of the following people, groups, and institutions, in no particular order: Matthew Goldfield. Jan Midtgaard. Fritz Henglein. Matthew Might. Ugo Dal Lago. Chung-chieh Shan. Kazushige Terui. Christian Skalka. Shriram Krishnamurthi. Michael Sperber. David McAllester. Mitchell Wand.…
Jarosław Klamut, Ryszard Kutner, Zbigniew R. Struzik
Recently, it has been argued that entropy can be a direct measure of complexity, where the smaller value of entropy indicates lower system complexity, while its larger value indicates higher system complexity. We dispute this view and propose a universal measure of complexity that is based on Gell-Mann’s view of…
Alexander F. Siegenfeld, Yaneer Bar‐Yam
The standard assumptions that underlie many conceptual and quantitative frameworks do not hold for many complex physical, biological, and social systems. Complex systems science clarifies when and why such assumptions fail and provides alternative frameworks for understanding the properties of complex systems. This…
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…
Dizhen Ma, Shaobo He, Kehui Sun, Miguel A. Fuentes
Properly measuring the complexity of time series is an important issue. The permutation entropy (PE) is a widely used as an effective complexity measurement algorithm, but it is not suitable for the complexity description of multi-dimensional data. In this paper, in order to better measure the complexity of…
Jurgen Riedel, Chris P. Barnes
In this study we examine the emergence of complex biological patterns through the lens of reaction-diffusion systems. We introduce two novel complexity metrics, Diversity of Number of States (DNOS) and Diversity of Pattern Complexity (DPC), which aim to quantify structural intricacies in pattern formation, enhancing…
Andrew Adamatzky, Nic Roberts, Raphael Fortulan, Noushin Raeisi Kheirabadi + 5 more
'Noushin Raeisi Kheirabadi' 'Panagiotis Mougkogiannis' 'Michail-Antisthenis Tsompanas' 'Genaro J. Martínez' 'Georgios Ch. Sirakoulis' 'Alessandro Chiolerio'] The colloid cellular automata do not imitate the physical structure of colloids but are governed by logical functions derived from them. We analyze the space-time…
Frank Emmert-Streib, Enrico Scalas
Background The evaluation of the complexity of an observed object is an old but outstanding problem. In this paper we are tying on this problem introducing a measure called statistic complexity. Methodology/Principal Findings This complexity measure is different to all other measures in the following senses. First, it…
Anurag Dutta, K. Lakshmanan, John Harshith, Aditya Ramamoorthy
—Time Complexity is an important metric to compare algorithms based on their cardinality. The commonly used, trivial notations to qualify the same are the Big-Oh, Big-Omega, Big-Theta, Small-Oh, and Small-Omega Notations. All of them, consider time a part of the real entity, i.e., Time coincides with the horizontal…
Adrian Krzyzanowski, Axel Pahl, Michael Grigalunas, Herbert Waldmann
The fraction of sp3 hybridised carbons (Fsp3) and the fraction of stereogenic carbons (FCstereo) are two widely employed scores of molecular complexity with a strong link to biologically relevant features such as frequency, potency and selectivity of protein binding. However, due to their simplistic nature, they do not…
Stuart M. Marshall, Alastair R. G. Murray, Leroy Cronin
One thing that discriminates living things from inanimate matter is their ability to generate similarly complex or non-random structures in a large abundance. From DNA sequences to folded protein structures, living cells, microbial communities and multicellular structures, the material configurations in biology can…
Héctor Zenil
One of the most important aims of the fields of robotics, artificial intelligence and artificial life is the design and construction of systems and machines as versatile and as reliable as living organisms at performing high level human-like tasks. But how are we to evaluate artificial systems if we are not certain how…
Florian M. Gartner, Isabella R. Graf, Erwin Frey
Time efficiency of self-assembly is crucial for many biological processes. Moreover, with the advances of nanotechnology, time efficiency in artificial self-assembly becomes ever more important. While structural determinants and the final assembly yield are increasingly well understood, kinetic aspects concerning the…
Hsuan-Hao Chao, Han-Ping Huang, Sung-Yang Wei, Chang Francis Hsu + 2 more
The complexity of biological signals has been proposed to reflect the adaptability of a given biological system to different environments. Two measures of complexity—multiscale entropy (MSE) and entropy of entropy (EoE)—have been proposed, to evaluate the complexity of heart rate signals from different perspectives.…
Bar Y. Peled, Vikas Kumar Mishra, Avishy Carmi
—A cellular automaton is presented whose governing rule is that the Kolmogorov complexity of a cell's neighborhood may not increase when the cell's present value is substituted for its future value. Using an approximation of this two-dimensional Kolmogorov complexity the underlying automaton is shown to be capable of…
Peter Grassberger
We review possible measures of complexity which might in particular be applicable to situations where the complexity seems to arise spontaneously. We point out that not all of them correspond to the intuitive (or "naive") notion, and that one should not expect a unique observable of complexity. One of the main problems…
Hector Zenil, Narsis A. Kiani, Francesco Marabita, Yue Deng + 4 more
It remains fundamentally unclear how to reprogram complex evolving systems. Here, we introduce a conceptual framework and an interventional calculus to steer and manipulate systems based on their intrinsic algorithmic probability using the universal principles of the theory of computability and algorithmic information.…
Jan Žižka
Software systems are expansive, exhibiting behaviors characteristic of complex systems, such as self-organization and emergence. These systems, highlighted by advancements in Large Language Models (LLMs) and other AI applications developed by entities like DeepMind and OpenAI showcase remarkable properties. Despite…
Anamika Agrawal, Michael A. Buice
The simple linear threshold units used in many artificial neural networks have a limited computational capacity. Famously, a single unit cannot handle non-linearly separable problems like XOR. In contrast, real neurons exhibit complex morphologies as well as active dendritic integration, suggesting that their…
Tiasa Mondol, Daniel G. Brown, Ercan Kuruoglu
We build an analysis based on the Algorithmic Information Theory of computational creativity and extend it to revisit computational aesthetics, thereby, improving on the existing efforts of its formulation. We discuss Kolmogorov complexity, models and randomness deficiency (which is a measure of how much a model falls…
Asad Malik
There is a cognitive limit in Human Mind. This cognitive limit has played a decisive role in almost all fields including computer sciences. The cognitive limit replicated in computer sciences is responsible for inherent Computational Complexity. The complexity starts decreasing if certain conditions are met, even…
Eric Smith, Harrison B. Smith, Jakob Lykke Andersen
We consider problems in the functional analysis and evolution of combinatorial chemical reaction networks as rule-based, or three-level systems. The first level consists of rules, realized here as graph-grammar representations of reaction mechanisms. The second level consists of stoichiometric networks of molecules and…
Grzegorz Skoraczyński, Mateusz Kitlas, Błażej Miasojedow, Anna Gambin
Modern computer-assisted synthesis planning tools provide strong support for this problem. However, they are still limited by computational complexity. This limitation may be overcome by scoring the synthetic accessibility as a pre-retrosynthesis heuristic. A wide range of machine learning scoring approaches is…
Daniel Barter, Evan Walter Clark Spotte-Smith, Nikita S. Redkar, Shyam Dwaraknath + 2 more
Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are unintuitive, and products are unknown. Here we report new methods of CRN generation and analysis that overcome these…
Brittany Story, Biswajit Sadhu, Henry Adams, Aurora Clark
Recent work (J. Chem. Phys. 154, 114114) has demonstrated that sublevelset persistent homology provides a compact representation of the complex features of an energy landscape in $3N$-dimensions. This includes information about all transition paths between local minima (connected by critical points of index > 1), and…
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…
Authors not listed
Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical…