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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 46 through 60 of 118 found.

Cosmomics: A Genomic Source Code in Procreative Effect

Cosmic Code > Algorithms

Berges, Jurgen. Scaling Up Quantum Simulations. Nature. 569/339, 2019. . A Heidelberg University physicist lauds a paper Self-Verifying Variational Quantum Simulation of Lattice Models by eleven University of Innsbruck researchers in the same issue (Kokail, 569/355) about a composite digital-analog computational method which can span and join quantum and classical phases. Once again this complementarity is found to work best.

It is difficult to carry out and verify digital quantum simulations that use many quantum bits. A hybrid device based on a digital classical computer and an analog quantum processor suggests a way forward.

Cosmic Code > Algorithms

Cardinot, Marcos, et al. Evoplex: A Platform for Agent-Based Modeling on Networks. SoftwareX. 9/199, 2019. We cite this entry from the National University of Ireland, Galway and University of Maribor, Slovenia (Matjaz Perc) as an example of how computer code programs can likewise be found to take on these ubiquitous complexity formats.

Agent-based modeling and network science have been used extensively to advance our understanding of emergent collective behavior in systems that are composed of a large number of simple interacting individuals or agents. Evoplex meets this need by providing a fast, robust and extensible platform for developing agent-based models and multi-agent systems on networks. Each agent is represented as a node and interacts with its neighbors, as defined by the network structure. Evoplex is ideal for modeling complex systems, for example in evolutionary game theory and computational social science. (Abstract excerpt)

Cosmic Code > Algorithms

Fernandez, Jose and Francisco Vico. AI Methods in Algorithmic Composition. Journal of Artificial Intelligence Research. Volume 48, 2013. This entry by University of Malaga, Spain computer scientists is cited in A. Wagner’s Life Finds a Way (2019) to show how evolution seems guided by source programs which can be modeled by artificial neural networks. By such perceptions, the natural presence of iterative cellular automata and self-similar patterns can be noticed. Its mathematical form and flow also appear as a musical or written composition. In regard, are we coming upon an proactive ecosmos which is composing itself by way of sapient species as our global own? Please visit F. Vico’s website to read about his “Melomics” or genetics of melody project.

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence. (Abstract)

The purpose of this survey is to review and bring together existing research on a specific style of Computational Creativity: algorithmic composition. Interpreted literally, algorithmic composition is a self-explanatory term: the use of algorithms to compose music. (1)

Cosmic Code > Algorithms

Sloss, Andrew and Steven Gustafson. 2019 Evolutionary Algorithm Review. arXiv:1906.08870. Bellevue, WA software scientists post a thorough survey as the field of artificial intelligence, broadly conceived, becomes ever more biological in its basis. By turns, life’s genetically programmed development is broached as an “Idealized Darwinism.” Section 5.1 is an Auto-Constructive Evolution, while 5.2 is Deep Neuroevolution and 5.3 Self-Replicating Neural Networks.

In this review, we explore a new taxonomy of evolutionary algorithms and classifications that look at five main areas: the ability to manage the control of the environment with limiters, how to explain and repeat the search process, understandings of input and output causality within a solution, the ability to manage algorithm bias due to data or user design, and lastly, and how to add corrective measures. As many reviews of evolutionary algorithms exist, after motivating this new taxonomy, we briefly classify a broad range of algorithms and identify areas of future research. (Abstract excerpt)

Cosmic Code > 2015 universal

Aschwanden, Markus. Self-Organized Criticality in Solar and Stellar Flares. arXiv:1906.05840. The Lockheed Martin, Palo Alto astrophysicist and leading researcher of cosmic SOC phenomena (search) finds that seemingly unpredictable extreme events are actually very rare or not at all, so that a prior mathematical model (Sornette, et al, search) for them does not apply to spacescape dynamics. We cite to note the total presence of SOC just being found everywhere.

We search for outliers in extreme events of statistical size distributions of astrophysical data sets, motivated by the {Dragon-King hypothesis} of (Didier) Sornette, which suggests that the most extreme events in a statistical distribution may belong to a different population, and thus may be generated by a different physical mechanism, in contrast to the strict power law behavior of self-organized criticality models. Identifying such disparate outliers is important for space weather predictions. However, we find that Dragon-King events are not common in solar and stellar flares. Consequently, small, large, and extreme flares remain scale-free with a single physical mechanism. (Abstract excerpt)

Cosmic Code > 2015 universal

Chialvo, Dante, at al. Controlling a Complex System near its Critical Point via Temporal Correlations. arXiv:1905.11758. Argentinian and NIH, USA (Dietmar Plenz) neuroscientists provide a further theoretical and evidential basis for a dynamically creative nature which seeks to ever “tune itself” to an optimum state of complementary balance. See also by DC, et al How Ants Move: Individual and Collective Scaling Properties at 1707.07135 and Critical Fluctuations in Proteins Native State at 1601.03420 for a far afield example for this constant propensity.

A wide variety of complex systems exhibit large fluctuations both in space and time that often can be attributed to the presence of some kind of critical phenomena. Under such critical scenario it is well known that the properties of the correlation functions in space and time are two sides of the same coin. Here we test whether systems exhibiting a phase transition could self-tune to its critical point taking advantage of such correlation properties. We describe results in three models: the 2D Ising ferromagnetic model, the 3D Vicsek flocking model and a small-world neuronal network model. Since the results rely on universal properties they are expected to be relevant to a variety of other settings. (Abstract)

Cosmic Code > 2015 universal

Filatov, Denis and Alexey Lyubushin. Stochastic Dynamical Systems Always Undergo Trending Mechanisms of Transition to Criticality. Physica A. Volume 521, 2019. Sceptica Science, UK and Russian Academy of Sciences physicists post a theoretical affirmation as to why and how nature’s active phenomena has an intrinsic attraction to reach a critically poised state. See also A Method for Identification of Critical States of Open Stochastic Dynamical Systems by D. Filatov in Journal of Statistical Physics (165/4, 2016).

We study the transition of stochastic dynamical systems to critical states. We begin from employing two independent quantitative methods of time series analysis, first-order detrended fluctuation analysis and multivariate canonical coherence analysis. We find out that there are two different mechanisms of the transition to criticality: the first mechanism is consistent with that observed in some biological dynamical systems and associated with a growth of the energies at low frequencies in the power spectrum, whereas the second mechanism is new and governed by a decay of the energies at high frequencies. Despite this difference, we show that both mechanisms lead to a loss of chaoticity in the system’s behavior and result in a more deterministic evolution of the system as a whole. The obtained results allow hypothesis that in stochastic dynamical systems of any nature the transition to a critical state is always realized through a trending nonlinear process. (Abstract)

Cosmic Code > 2015 universal

Zarepour, Mahdi, et al. Universal and Non-Universal Neural Dynamics on Small World Connectomes. arXiv:1905.05280. Five Argentine complexity theorists including Dante Chialvo propose novel ways to quantify and understand nature’s propensity to seek and reside at a critically poised state. As the Abstract notes, this advance is achieved by joining active cerebral phenomena with common network topologies which serves to reveal optimal invariant behaviors. If to view altogether within this “connect-omic” motif, it well suggests that the uniVerse to us course is essentially genetic in kind.

Evidence of critical dynamics has been recently found in both experiments and models of large scale brain dynamics. The understanding of the nature and features of such critical regime is hampered by the relatively small size of the available connectome, which prevent among other things to determine its associated universality class. To circumvent that, here we study a neural model defined on a class of small-world network that share some topological features with the human connectome. We found that varying the topological parameters can give rise to a scale-invariant behavior belonging either to mean field percolation universality class or having non universal critical exponents. Overall these results shed light on the interplay of dynamical and topological roots of the complex brain dynamics. (Abstract)

Cosmic Code > networks

Mariani, Manuel, et al. Nestedness in Complex Networks: Observation, Emergence, and Implications. Physics Reports. Volume 813, 2019. Ecological theorists based in China and Switzerland including Jordi Bascompte post a 140 page, 400 reference affirmation of nature’s evolutionary developmental genesis, as long sensed, by way of combining smaller entities (biomolecules, cells, species) within larger, reciprocally beneficial, whole systems. Akin to the major transitions scale and others, life’s long recurrent emergence from microbes to meerkats to a metropolis gains its 2020 sophisticated mathematical quantification.

The observed architecture of ecological and socio-economic networks differs significantly from that of random networks. From a network science standpoint, non-random structural patterns observed in real networks calls for an explanation of their emergence and systemic consequences. This article focuses on one of these patterns: nestedness. Given a network of interacting nodes, nestedness is a tendency for nodes to interact with subsets of the interaction partners of better-connected nodes. Nestedness has been found in diverse as ecological mutualistic organizations, world trade, inter-organizational relations, among many other areas. We survey results from variegated disciplines, including statistical physics, graph theory, ecology, and theoretical economics. (Abstract excerpt)

Perhaps one of the most intriguing features of real networks is the existence of common structural and dynamical patterns in a large number of systems from various domains of science, nature, and technology. The pervasiveness of structural patterns from various fields makes it possible to analyze them with a common set of tools. A popular example of such widespread patterns is the heavy-tailed degree distribution. Power-law degree distributions (often termed as ‘‘scale-free") have been reported in many different systems, ranging from social and information networks to protein–protein interaction networks. The ubiquity of heavy-tailed degree distributions has motivated studies aimed at unveiling plausible mechanisms that explain their emergence, understanding their implications for spreading, network robustness, synchronization phenomena, etc. (3)

Cosmic Code > networks

Moreno, Yamir and Matjaz Perc, eds. Focus on Multileyer Networks. New Journal of Physics. Circa 2018,, 2019. University of Zaragoza, Spain and University of Maribor, Slovenia physicists open a special collection with this title, as the quote notes. We note, for example, Inter-Layer Competition in Adaptive Multiplex Network by Elena Pitsik (20/075004) and Communicability Geometry of Multiplexes by Ernesto Estrada (21/015004, 2019).

In the later past century and early 2000's, the availability of data about real-world systems made it possible to study the topology of large networks. This work has revealed the structure, dynamics and functions of complex networks, as well as new models for synthetic networks. During the last 5 years, also backed up by new results, scientists have realized that many systems and processes cannot be described with single-layer nets since they have a multilayer geometry made up of many layers. The study of these multiplex networks has pointed out that their structure, dynamics, and evolution exhibit non-trivial relationships and interdependencies that give rise to new phenomena. (Scope)

Cosmic Code > networks

Serafino, Matteo, et al. Scale-Free Networks Revealed from Finite-Size Scaling. arXiv:1905.09512. Organic physicists based in Italy, the USA and UK including Amos Maritan and Guido Caldarelli describe a method to analyze common features of natural linkages such as protein interactions, technological computer hyperlinks, and informational citation and lexical networks. As a result, spontaneous self-organization to a critical-like state then becomes evident, which seems to hold across all manner of net topologies.

Cosmic Code > networks

Stone, Lewi, et al. Network Motifs and Their Origins. PLoS Computational Biology. April, 2019. As the Abstracts notes, LS, Tel Aviv University, Yael Artzy-Randrup, University of Amsterdam and the veteran ecologist Daniel Simberloff, University of Tennessee provide a once and present review of this common feature of life’s dynamic physiology and anatomy.

Modern network science is a new and exciting research field that has transformed the study of complex systems over the last 2 decades. Of much interest is the identification of small “network motifs” embedded in a larger network and that indicate the presence of evolutionary design principles or have an overly influential role on system-wide dynamics. Motifs are patterns of interconnections, or subgraphs that appear in an observed network more often than in compatible randomized networks. Here, we argue that the same concept and tools for the detection of motifs were well known in the ecological literature into the last century, a fact that is generally not recognized. We review the early history of network motifs, their evolution in the mathematics literature, and their recent rediscoveries. (Abstract)

Systems Evolution: A 21st Century Genesis Synthesis

Quickening Evolution

Newman, Stuart. Inherency of Form and Function in Animal Development and Evolution. Frontiers in Physiology. Online June 19, 2019. As the Abstract describes, the New York Medical College cell biologist continues to advance his deep insights by which to appreciate life’s iterative anatomical and physiological emergence as arising from innate physical propensities. See also Inherency and Homomorphy in the Evolution of Development by SAN in Current Opinion in Genetics & Development (Vol. 37, August 2019).

I discuss recent work on the origins of morphology and cell-type diversification in Metazoa – collectively the animals – and propose a scenario for how these two features became integrated by way of a third set of cellular pattern formation processes. These inherent propensities to generate familiar forms and cell types are exhibited by present-day organisms. The structural motifs of animal bodies and organs, e.g., multilayered, hollow, elongated and segmented tissues, internal and external appendages, branched tubes, and modular endoskeletons, result from the recruitment of “generic” physical forces and mechanisms such as adhesion, contraction, polarity, chemical oscillation, and diffusion. Cellular pattern, mediated by released morphogens interacting with biochemically responsive and excitable tissues, drew on inherent self-organizing processes in proto-metazoans to transform clusters of holozoan cells into animal embryos. (Abstract excerpt)

Quickening Evolution

Ostachuk, Agustin. What is It Like to be a Crab? A Complex Network Analysis of Eucaridan Evolution. Evolutionary Biology. 46/2, 2019. In an entry which can exemplify a 2020s genesis synthesis, a National University of La Plata, Argentina biologist achieves a systems explanation of how this common crustacean came to form and develop. As the quotes say, a novel inclusion and application of nature’s pervasive topologies then provides a better explanation of how their skeletal carapace came to be. The result is seen as robust enough to be carried across Metazoan creatures because it implies an independent, generic anatomy and physiology. As the author noted in 2015 (search) new insights into an actual recapitulation process may also accrue. The entry illustrates a prime theoretical revision in our midst whence life’s long ascent from universe to us becomes braced by this vital, genetic-like mathematical source.

Eucaridan evolution involved a process starting from a body organization characterized by an elongate and cylindrical cephalothorax, a well-developed abdomen composed of swimming appendages, ending in a tail fan formed by flattened uropods and a telson. This process would lead to a body organization characterized by a shortened and depressed cephalothorax, and a reduced and ventrally folded abdomen. In this work, the evolution of the superorder Eucarida was studied using complex networks. A new definition of crab and its carcinization are given based on the results obtained. The evolution of the crab implied the formation of a triadic structure with high closeness centrality which represented a stable hierarchical core buried or enclosed in the topological structure of the network with its integrated and robust topology. (Abstract excerpts)

In this work, crustacean external morphology was abstracted as a network in which each individual morphological feature was considered as a node, and the edges among these nodes were established based on their physical connections. This representation and abstraction of the crab morphology as a network is considered to capture the evolutionary and developmental structural information of the whole organism. It represents the characteristic structure of a given organism, its architectural plan or Bauplan. The analysis of these different and successive plans yielded important results regarding the evolutionary trend of this group. In summary, this trend was involved an increase in complexity, integration and robustness. These models of crustaceans through complex network theory revealed important, unexpected and surprising features of their evolutionary process. (204)

Quickening Evolution > Intel Ev

Duran-Nebreda, Salva and George Bassel. Plant Behavior in Response to the Environment. Philosophical Transactions of the Royal Society B. 374/20190370, 2019. . In a special Liquid Brains, Solid Brains issue (search Forrest), University of Birmingham, UK botanists describe how even floral vegetation can be seen to embody and avail a faculty of cognitive intelligence for their benefit.

Information processing and storage underpins many biological processes of vital importance to organism survival. Like animals, plants also acquire, store and process environmental information relevant to their fitness, and this is particularly evident in their decision-making. The control of plant organ growth and timing of their developmental transitions are carefully orchestrated by the collective action of many connected computing agents, the cells, in what could be addressed as distributed computation. Here, we discuss some examples of biological information processing in plants, with special interest in the connection to formal computational models drawn from theoretical frameworks. (Abstract)

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