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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 110 found.


Cosmomics: A Genomic Source Code in Procreative Effect

Cosmic Code > 2015 universal

Bansal, Kanika, et al. Cognitive Chimera States in Human Brain Networks. . April, 2019. Six system neurophysicists with postings across the USA from Columbia University to UC Santa Barbara provide an illustrated overview to date about how much cerebral activities as a elf-organized criticality are manifestly distinguished by a dynamic interplay of relatively order and disorder. Since this chimeric system is then to reside in both modes at the same time, this overall effect is dubbed a Metastable condition.

The human brain is a complex dynamical system, and how cognition emerges from spatiotemporal patterns of activity remains an open question. As brain regions interact to perform cognitive tasks, patterns of partial synchrony can be observed forming chimera states. We propose that these dual modes present a cognitively informed, chimera-based framework to explore how large-scale brain architecture affects its function. Our results suggest a classification of cognitive systems into four groups with differing levels of subject and regional variability that reflect their different functional roles. (Abstract excerpt)

Using a novel, chimera-based framework, we explored the dynamical states that emerge across large-scale cognitive systems following the spread of a targeted regional stimulation. We identified three distinct dynamical states — coherent, chimera, and metastable — that arise as a function of the structural connectivity of the stimulated regions. A core result across all analyses is the variety in frequency and distribution of the observed dynamical states. Chimera states are the most pervasive state to emerge following regional stimulation. This likely reflects the foundational role that partial synchrony serves in large-scale brain function to enable the intricate balance between segregated and integrated neural processing. (8)

Cosmic Code > 2015 universal

Ganaie, Mudasir, et al. Identification of Chimera using Machine Learning. arXiv:2001.08985. We cite this entry by Indian Institute of Technology complexity scientists as an example of how new AI techniques with their basis in cerebral cognition can now reveal the propensity of all manner of natural systems to be attracted to and perform best at an active poise of a more or less orderly balance. A notable feature is that any instance can be seen to exist in both states at the same moment.

Coupled dynamics on network models have provided much insight into complex spatiotemporal patterns from many large-scale real-world complex systems. Chimera, a state of coexistence of incoherence and coherence, is one such pattern which has drawn attention due to its common presence, especially in neuroscience. We describe an approach to characterize chimeras using machine learning techniques, namely random forest, oblique random forests via multi-surface proximal support vector machines. We demonstrate high accuracy in identifying the coherent/incoherent chimera states from given spatial profiles. (Abstract excerpt)

Cosmic Code > 2015 universal

Helmrich, Stephan, et al. Signatures of Self-Organized Criticality in an Ultracold Atomic Gas. Nature. 577/481, 2020. In a paper appropriately published in the first month of this binocular year, University of Heidelberg, Cal Tech, and University of Koln physicists contribute to the ubiquitous occurrence of self-similar, critically poised states everywhere. The subject case here is elemental gases where such exemplary features appear even at these frigid, quantum extremes. See also Singular Charge Fluctuations at a Magnetic Quantum Critical Point and Quantum Spin Liquids in Science for January 17, 2020. Two decades into the 21st century, a Worldwide Discovery of a Organic, Procreative UniVerse does seem well underway, if we might be of a mind to ask and see.

Self organisation provides an elegant explanation for how complex structures emerge and persist throughout nature with remarkably similar scale-invariant properties. While this can be captured by simple models, the connection to real-world systems is difficult to test. Here we identify three key signatures of self-organised criticality in the dynamics of a dissipative gas of ultracold atoms and provide a first characterisation of its universal properties. We show that population decay drives the system to a stationary state that is independent of the initial conditions and exhibits scale invariance and a strong response to perturbations. This establishes a practical platform for investigating self-organisation phenomena and non-equilibrium universality with much experimental access to the microscopic details of the system. (Abstract)

Cosmic Code > 2015 universal

Kalinin, Nikita, et al. Self-organized Criticality and Pattern Emergence through the Lens of Tropical Geometry. Proceedings of the National Academy of Sciences. I115/E8135, 2018. National Research University, St. Petersburg, IBM Watson Research Center, University of Toulouse, Institute of Science and Technology, Austria, and CINVESTAV, Mexico system mathematicians provide another way to perceive and quantify nature’s constant propensity to reach an a balance beam of more or less relative order in every topological form and function. In actuality, each instantiated complement of the dual, reciprocal condition then resides in both modes at once (particle/wave). See also Introduction to Tropical Series by the authors at arXiv:1706.03062.

A simple geometric continuous model of self-organized criticality (SOC) is proposed. This model belongs to the field of tropical geometry and appears as a scaling limit of the classical sandpile model. We expect that our observation will connect the study of SOC and pattern formation to other fields (such as algebraic geometry, topology, string theory, and many practical applications) where tropical geometry has already been successfully used. (Significance)

Cosmic Code > 2015 universal

Lugo, Haydee, et al. Chimera and Anticoordination States in Learning Dynamics. arXiv:1812.05603. We cite this entry by Spanish economists because they go on to suggest ways that this recently realized tendency of physical, electronic and neural systems to become poised between more or less order could be similarly evident in personal and social activities. Its early on, but we add that by perceptive extensions like this, its traditional version best known as yin/yang dynamics can at last gain a modern, 21st century scientific confirmation.

In many real-life situations, individuals are motivated to achieve both social acceptance or approval and strategic objectives of coordination. Since these modes may take place in different environments, a two-layer network works well for its analysis. From an evolutionary approach, we focus on asymptotic solutions for all-to-all interactions across and inside the layers and for initial distributions of strategies. We report the existence of chimera states in which two collective states coexist in the same network. We trace back the emergence of chimera states and global anticoordination states to the agents inertia against social pressure. (Abstract excerpt)

The coexistence of coherent and incoherent states has received much attention as an intriguing manifestation of collective behaviour. This interesting behaviour was first observed by Kuramoto et al. and then named it as chimera state. Although the literature about chimera states started with the study of interacting populations of oscillators in dynamical systems, it has been dizzily expanded to many fields in physics, chemistry, biology, etc. Also in social systems, situations of two interacting populations in which one exhibits a coherent or synchronized behaviour while the other is incoherent or desynchronized are commonly observed. This phenomena has also been addressed from the conceptual framework of chimera states. (1)

In the context of coordination in social systems, our contribution brings a more realistic insight about the consequences of a collective behavior that makes a distinction between social and strategic objectives. This collective behavior may lead herding societies to chimera states and skeptical societies to polarized states of anti-coordination. (12)

Cosmic Code > 2015 universal

Zur Bonsen, Alexander, et al. Chimera States in Networks of Logistic Maps with Hierarchical Connectivities. arXiv:1711.03287. Technical University of Berlin system physicists including Anna Zakharova and Eckehard Scholl go on to describe fractal self-similar bifurcations which these node/link scalar topologies seem to be attracted to.

Cosmic Code > networks

Nature Network Collection. www.nature.com/collections/adajhgjece. A new collection series from across the many premier Nature publishing group journals. Some typical entries are The Multilayer Nature of Ecological Networks, Network Neuroscience, and Spatial Scaling of Species Interaction Networks.

Network science is now a mature research field, whose growth was catalysed by the introduction of the ‘small world’ network model in 1998. Networks give mathematical descriptions of systems containing containing many interacting components, including power grids, neuronal networks and ecosystems. This collection brings together selected research, comments and review articles on how networks are structured (Layers & structure); how networks can describe healthy and disordered systems (Brain & disorders); how dynamics unfold on networks (Dynamics & spread); and community structures and resilience in networks (Community & resilience).

Cosmic Code > networks

Boguna, Marian, et al. Network Geometry. arXiv:2001.03241. Six senior complexity scientists including Dmitri Krioukov and Shlomo Havlin offer a January 2020 posting which couldl be a bidecadal capsule of how much studies of nature’s innate node/link multiplex anatomy and physiology has been found in vivifying evidence from physical depths and galactic clusters and to evolutionary bodies, brains, groupings and onto economies and cultures. This entry describes how “fractal self-similarities, diffusion dynamics, and functional modularity” have been found from a chemical-space renormalization to cellular communities across life’s biota, as shown in intricate displays. Into the 2010s, an increasing implication is the presence of an independent, mathematic source in exemplary manifestation at each and every scale and instance. See also Geometric Origins of Self-Similarity in the Evolution of Real Networks by this group at 1912.00704 and Scale-free Networks Revealed from Finite-size Scaling at 1805.09512.

Networks are natural geometric objects. Yet the discrete metric structure of shortest path lengths in a network is not the only reservoir of geometric distances. Other forms of network-related topologies are continuous latent spaces underlying many networks, and the effective geometry induced by dynamical processes. A growing amount of evidence shows that the three approaches are well related. Network geometry is thus quite efficient in discovering hidden symmetries, such as scale-invariance, and other fundamental physical and mathematical properties, along with a variety of applications from the understanding how the brain works to routings in the Internet. Here, we review theoretical and practical developments in network geometry in the last two decades, and offer perspectives on future research for this novel complexity frontier. (Abstract)

Cosmic Code > networks

Liu, Chuang, et al. Computational Network Biology. Physics Reports. December, 2019. A seven member international team posted in China, Switzerland and the USA (Ruth Nussinov, National Cancer Institute) provide an 80 page tutorial across scientific techniques and real applications as life’s intricate anatomy and physiology becomes understood by these revolutionary 2010s features.

Biological entities are involved in intricate and complex interactions, in which uncovering the biological information from the network concepts are of great significance. In this review, we summarize the recent developments of this vital, copious field, first introducing various types of biological network structural properties. We then review the network-based approaches, ranging from metrics to machine-learning methods, and how to use these algorithms to gain new insights. We highlight the application in neuroscience, human disease, and drug developments and discuss some major challenges and future directions. (Abstract excerpt)

Cosmic Code > networks

Mokhlissi, Raihana, et al. The Structural Properties and Spanning Trees Entropy of the Generalized Fractal Scale-Free Lattice. Journal of Complex Networks. Online August, 2019. RM, Dounia Lotfi, and Mohamed El Marraki, Mohammed V University, Rabat, Morocco and Joyati Debnath, Winona State University, USA mathematicians post a sophisticated description of nature’s innate geometries. While invisible, their linkages are truly present as they unite and vivify all the overt objects and entities.

Enumerating all the spanning trees of a complex network is theoretical defiance for mathematicians, electrical engineers and computer scientists. In this article, we propose a generalization of the Fractal Scale-Free Lattice and study its structural properties. As its degree distribution follows a power law, we prove that the proposed generalization does not affect the scale-free property. In addition, we use equivalent transformations to count the number of spanning trees in the generalized Fractal Scale-Free Lattice. Finally, in order to evaluate the robustness of the generalized lattice, we compute and compare its entropy with other complex networks. (Abstract)

Dr. Joyati Debnath is a Full Professor of Mathematics and Statistics at Winona State University. She received an M. S. in Pure Mathematics and Ph. D. in Applied Mathematics from Iowa State University. She received numerous Honors and Awards including the Best Teaching Award from Iowa State University, and the Outstanding Woman of Education Award. Dr. Debnath has research interest in the areas of Topological Graph Theory, Integral Transform Theory, Partial Differential Equations and Boundary Value Problem, Associations of Variables, Discrete Mathematics, and Software Engineering Metrics. (WSU page)

Cosmic Code > networks

Rak, Rafal, et al. Universal Features of Mountain Ridge Networks on Earth. Journal of Complex Networks. May, 2019. We cite this entry by Polish systems geophysicists including Jaroslaw Kwapien and Stanislaw Drozdz (search) as another instance of how every phenomenal aspect is being found to exhibit the generative presence of fractal, self-similar, multiplex topologies. These late 2010s abilities strongly imply and represent an independent mathematical source program which manifests at every scale and instance from quantum inflation to our deep bicameral brains.

In this paper, we analyse different mountain ranges by means of a network approach so to reveal grasp essential features of their branching structure. We employ a fractal method as especially good at describing properties of rough objects and surfaces. We study ridge network structure by way of empirical elevation data from the Shuttle Radar Topography Mission across mountain ranges from different geological periods and geographical locations. We observe that the topographic networks do display fractal scales of the mountain ranges and by another view show the power-law degree distributions. Since the various aretes differ in many properties, these values seem to be universal for Earthly mountainous terrains. (Abstract excerpt, edits)

Cosmic Code > networks

Testolin, Alberto, et al. Deep Learning Systems as Complex Networks. Journal of Complex Networks. Online June, 2019. University of Padova physicists including Samir Suweis exemplify this historic synthesis, two decades into the 21st century, whence many diverse fields come together and reinforce each other. Herein self-organizing complexities are present in both cerebral architectures and physical substrates and thus serve to unite the disparate phases. See also Emergence of Network Motifs in Deep Neural Networks by this group in Entropy (22/204, 2020).

Thanks to the availability of large digital datasets and much computational power, deep learning algorithms can learn representations of data over multiple levels of abstraction. These machine-learning methods have aided challenging cognitive tasks such as visual object recognition, speech processing, natural language understanding and automatic translation. Deep belief networks (DBNs) can also discover intricate structures in large datasets in an unsupervised way. While these self-organizing systems apply within the framework of statistical mechanics, their internal functioning and emergent dynamics remains opaque. In this article, we propose to study DBNs using complex network techniques to gain insights into the structural and functional properties of the computational graph resulting from the learning process. (Abstract edits)


Cosmic Code > networks

Zheng, Muhua, et al. Geometric Origins of Self-Similarity in the Evolution of Real Networks. arXiv:1912.00704. MZ, Marian Boguna and Angeles Serrano, University of Barcelona, along with Guillermo Garcia-Perez, University of Turku contribute to integrations of nature’s universe to human multiplex connectivities with deeper physical principles.

One of the aspirations of network science is to explain the growth of real networks, often through the sequential addition of new nodes that connect to older ones. However, many real systems evolve through the branching of basic units, whether those be scientific fields, countries, or species. Here, we provide empirical evidence for self-similar branching growth in real networks and present the Geometric Branching Growth model, which is designed to predict evolution and symmetries. The model produces multiscale unfolding of a network in a sequence of scaled-up replicas. (Abstract excerpt)

In the context of network science, growth is often modeled through the sequential addition of new nodes that connect to older ones by preferential attachment. Here, we take an alternative approach and explore the relation between branching growth and geometric renormalization to explain self-similar network evolution. Renormalization in networks, based on statistical physics, acts as an inverse branching process by coarse-graining nodes. Thus, branching growth can be seen as an inverse renormalization transformation: an idea that was introduced in using a purely topological approach to reproduce the structure of fractal networks, where fractality was interpreted as an evolutionary drive towards robustness. (2)

Systems Evolution: A 21st Century Genesis Synthesis

Quickening Evolution

Manrubia, Susanna, et al. From Genotypes to Organisms: State of the Art and Perspectives of a Cornerstone in Evolutionary Dynamics. arXiv:2002:00363. Eighteen coauthors including Jose Cuesta, Sebastian Ahnert, Lee Altenbery, Paulien Hogeweg, Ard Louis, and Joshua Payne (search each) post a 44 page composite paper with 383 references from a CECAM (search) workshop at the University of Zaragoza in March 2019. The endeavor was an attempt to meld rapidly moving fields such as RNA and protein structures, gene regulatory and metabolic networks, computational algorithms, synthetic biology and so on as they may come together to explain how a phenotype creature arises or “maps” from a genomic source. A notice of “universal” occurrences is apparent, along with much evidence that generative forces are indeed in play before any selective effects. Into 2020, this is a good example of a filling in and acknowledgement of a “natural genesis” that this website has long sought to document.

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is a major missing piece in a fully predictive theory of evolution. Though we are far from achieving a complete picture of these relationships, our understanding of simpler aspects such as structures induced in the space of genotypes by sequences traced to molecular genotype-phenotype maps has revealed important facts about the dynamical description of evolutionary processes. Empirical evidence supporting such relevant features as phenotypic bias is growing as well, while the synthesis of concept and experiment leads to questioning the nature of evolutionary dynamics. This work reviews with a critical and constructive attitude our current knowledge of how genotypes map onto phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. (Abstract excerpt)

In other words, natural selection can only act on variation that has been pre-sculpted by the GP map. (14) We have identified a patchwork of processes that in principle are able to shape the variational properties of the GP map for phenotypes at the level of whole organisms, where complex integration leaves us unable to derive the properties from physical first-principles. This is an area in which evolutionary theory needs much greater development. At levels of complexity where reductionist modelling is impossible, we have surveyed efforts that attempt to analyse how evolutionary processes shape the GP map. The body of results described, while not a fully fleshed-out theory, is sufficient to demonstrate that this process-based approach can inform a research program for the GP map at the whole organism level. (32)

Quickening Evolution > Biosemiotics

Marcello, Barbieri. The Semantic Theory of Language. Biosystems. January, 2020. The University of Ferrera embryologist has been a veteran contributor (search) to the biosemiotic view that living systems are most distinguished by a series of code-like activities. But this vital perspective still seems to be in a formative phase as it morphs into various interpretations. The paper opens by saying that since Aristotle language has served to link sounds and meaning by way of phonetic and cognitive aspects. As the Abstract cites, recently N. Chomsky added a nuance that Marcello doesn’t approve. In his broader scope, harking back to C. Peirce (1839-1914), the founder of a semiotic philosophy, a further revision is proposed to sort all this out into the 2020s. An emphasis is put on three main genetic, neural and symbolic codes, which are then coordinated with the unique human feature that babies are born in such an immature state that they require a long post period to mature.

Traditional linguistics was based on the idea that language links sounds and meaning. Later on due to Noam Chomsky, this view has been replaced by the idea that children learn a language because of an innate mechanism to do so. But there is still no evidence that such a device exists. Another process is the ability of higher animals to interpret what goes on in the world, which is not based on fixed rules but on a process that Charles Peirce called abduction. This allows us to generalize into the semantic view of language, a theory that language is an activity which gives meaning to sounds. This can give us a new framework for studying the origin of language without resorting to a certain device. Herein, the origin of language is compared with the origin of life and of mind, because those mega-transitions generated the three code families that we find in Nature – organic neural and cultural. (Abstract excerpt)

Abductive reasoning (also called abduction, or abductive inference), is a form of logical inference which starts with an observation or set of observations and then seeks to find the simplest and most likely explanation for the observations. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it.

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