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Displaying entries 31 through 45 of 102 found.

Ecosmomics: A Survey of Animate Complex Network Systems

Battiston, Federico, et al. Network beyond Pairwise Interactions: Structure and Dynamics. Physics Reports. June, 2020. As network science enters the 2020s, an eight person team from across Europe and the USA including Vito Latora and Alice Patania posts a 109 page, 734 reference tutorial on the “higher-order representation of networks.” These further insights and appreciations involve features such as simplical homology, complexes, motifs, spreading dynamics, evolutionary games and more. With this expansive theory in place, an array of social, biologic, neural and ecological applications are reviewed. See also Growing Scale-Free Simplexes by K. Kovalinko, et al at arXiv:2006.12899. In regard, we record still another 21st century revolutionary discovery of a genesis nature as it reaches mature verification.

The complexity of many biological, social and technological systems stems from the richness of the interactions among their units. Over the past decades, complex systems have been described as networks whose interacting pairs of nodes are connected by links. Yet, in human communication, chemical reactions and ecological systems, interactions can occur in groups of three or more nodes. Here, we present an overview of the emerging field of networks beyond pairwise interactions. We discuss the methods to represent higher-order interactions and present different frameworks used to describe them. We review ways to characterize the structure of these systems such as random and growing simplicial complexes, bipartite graphs and hypergraphs. We conclude with a summary of empirical applications, providing an outlook on current modeling and conceptual frontiers. (Abstract excerpt)

Caetano-Anolles, Gustavo, et al. Emergence of Hierarchical Modularity in Evolving Networks Uncovered by Phylogenomic Analysis. Evolutionary Bioinformatics. 15/1, 2019. University of Illinois, Heidelberg Institute for Theoretical Studies, Gebze Technical University, Turkey, Seoul National University, and European Molecular Biology Laboratory, Germany system biologists (search lead author) advance their phylogenomomic project by showing how this theoretical approach can provide novel explanations of life’s anatomic physiology.

Networks describe how parts associate with each other to form integrated systems which often have modular and hierarchical structure. In biology, network growth involves two processes, one that unifies and the other that diversifies. Here, we propose a biphasic (bow-tie) theory of module emergence. In the first phase, emerging, self-organized interactions join nodal parts into modular linkages. In the second phase, modular variants become components for a new generative cycle of higher level organization. Remarkably, phylogenomic analyses uncover this emergence in the rewiring of metabolomic and transcriptome-informed metabolic networks, the nanosecond dynamics of proteins, and evolving metabolism, elementary functionomes, and protein domain networks. (Abstract)

Gysi, Deisy and Katja Nowick. Construction, Comparison and Evolution of Networks in Life Sciences and Other Disciplines. Journal of the Royal Society Interface. May, 2020. University of Leipzig and Free University of Berlin bioinformatic scholars (View GDs website, who is now with AL Barabasi’s group at Northeastern University) offer a broad survey of the 21st century network revolution that ALB and Reka Albert (search) initiated around 2000. Through the 2010s, almost every physical, biological and social phase has become reconceived, filled out and invigorated by these scale-free connective dynamics. The paper opens with glossary terms such as centrality and clustering to an extent that the common multiplex linkages appear to actively exist on their independent own.

This heretofore unknown anatomy and physiology is then noted from protein, metabolic, genomic, and neural realms onto an evolutionary presence and role so as to join living systems in modular scales. A further topical series covers science learning, cultural media, finance and more. It closes by saying that the same forms and functions can now be seen to repeat in kind at every stage which Geoffrey West cited as a “Universality of Networks” in Niall Ferguson’s Networld 2020 TV special.

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through disciplines such as social sciences, finance and computational gastronomy to present commonalities and differences in how networks change and can be analysed. (Abstract)

Li, Aming, et al. Evolution of Cooperation on Temporal Networks. Nature Communications. 11/2259, 2020. By a novel application of network science to social activities, an eight person team from Peking University, Northeastern University, Harvard Medical School and Princeton University (Simon Levin) illuminates a deeper natural basis for beneficial behaviors to both members and groups. As the quotes says, these heretofore unknown features can aid better explanations and usage.

Population structure is a key determinant in fostering cooperation among naturally self-interested individuals in microbial populations, social insect groups, and human societies. Prior research has focused on static structures, and yet most interactions are changing in time and form a temporal network. Surprisingly, we find that network temporality actually enhances the evolution of cooperation relative to comparable static networks, despite the fact that bursty interaction patterns generally impede. We resolve this tension by a measure which quantifies the amount of temporality in a network, so to reveal an intermediate level that boosts cooperation. (Abstract excerpt)

Explaining the evolution of durable, widespread cooperative behaviour in groups of self-interested individuals has been a challenge since the time of Darwin. In response, researchers have turned to the critical role played by the underlying interaction networks, in which nodes represent individuals and links represent interactions. It has been shown that the nontrivial population structures represented by both homogeneous and heterogeneous networks permit the formation of stable clusters of cooperators (altruists), with higher individual payoffs while also resisting defectors (egoists). As such, both theoretical analysis and behavioural experiments point to network structure as a key ingredient for the emergence of cooperation. (2)

Liu, Xueming, et al. Network Resilience. arXiv:2007.14464. Six theorists from Chinese Universities and Rensselaer Polytechnic Institute including Jianxi Gao and Boleslaw Szymanski post a 113 page, 859 reference 2020 tutorial about this pervasive ability of natural node/link complexities to restore and maintain themselves. Typical sections are Tipping Points in Ecological Networks, Phase Transitions in Biological Networks, Behavior Transitions in Animal and Human Networks and Resilience, Robustness and Stability. In the midst of epochal perils, this entry reports a concurrent worldwise finding of a revolutionary genesis ecosmos with its own bigender genomic code.

Many systems on our planet are known to shift abruptly and irreversibly from one state to another when they are forced across a "tipping point," such as mass extinctions in ecological networks, cascading failures in infrastructure systems, and social convention changes in human and animal networks. Such a regime shift demonstrates a system's ability to adjust activities so to retain its basic functionality in the face of internal or external changes. Only in recent years by way of network theory and lavish data sets, have complexity scientists been able to study real-world multidimensional systems, early warning indicators and resilient responses. This report reviews resilience function and regime shift of complex systems in domains such as ecology, biology, social systems and infrastructure. (Abstract excerpt)

The nature and the world in which we live are filled with changes and crises. Examples are the global pandemic of the novel coronavirus, the catastrophe in east Africa caused by the infestation by desert locusts, and the 2019 bushfire in Australia that burned through some 10 million hectares of land. In addition, these threats and crisises are not independent but related with one another. For example, the Australia bushfire and locust swarms are linked to the oscillations of the Indian Ocean Dipole, which is one aspect of the growing of the global climate change. How the nature or societies response to such threats and crises is defined by their resilience, which characterizes the ability of a system to adjust its activity to retain its basic functionality in the face of internal disturbances or external changes. (2)

Hillberry, Logan, et al. Entangled Quantum Cellular Automata (QCA), Physical Complexity, and Goldilocks Rules. arXiv:2005.1763. We cite this entry by a nine member team based at UT Austin, CalTech, and the University of Padova including Nicole Yunger Halpern as a current example of how disparate classical and quantum domains along with mathematic computations are joining up and cross-informing on the way to a phenomenal synthesis. The Abstract and quote convey a essential sense of the frontier project. So into the 2020s can we begin to realize (again) that an extant nature does have its own encoded reality and procreative purpose that we peoples can philosophize about? A good part of the project would be to translate the arcane terms into a human-familial image, which is what this site attempts to do.

Cellular automata are interacting classical bits that display diverse behaviors, from fractals to random-number generators to Turing-complete computation. We introduce entangled quantum cellular automata subject to Goldilocks rules, tradeoffs of the kind underpinning biological, social, and economic complexity. Tweaking digital and analog quantum-computing protocols generates persistent entropy fluctuations; robust dynamical features, including an entangled breather; and network structure and dynamics consistent with complexity. Present-day quantum platforms---Rydberg arrays, trapped ions, and superconducting qubits---can implement Goldilocks protocols, which generate quantum many-body states with rich entanglement and structure. Moreover, the complexity studies reported here underscore an emerging idea in many-body quantum physics: some systems fall outside the integrable/chaotic dichotomy. (Abstract)

We have discovered a physically potent feature of entangled quantum cellular automata: the emergence of complexity under Goldilocks rules. Goldilocks rules balance activity and inactivity. This tradeoff produces new, highly entangled, yet highly structured, quantum states. These states are persistently dynamic and neither uniform nor random. (14) Moreover, we have demonstrated that our QCA time-evolution protocols are implementable in extant digital and analog quantum computers. (14)

Kaznatcheev, Artem. Evolution is Exponentially More Powerful with Frequency-Dependent Selection. . An Oxford University computer scientist posts a latest appreciation of how living systems appear to evolve and develop as stochastic explore and educate optimization processes. In any event, the insight admits a deep mathematical presence of operative programs, which are modified along the way. See also Computational Complexity as an Ultimate Constraint on Evolution by AK in Genetics (212/245, 2019).

In 2009 (Leslie) Valiant (search) proposed to treat Darwinian evolution as a special kind of computational learning by way of statistical queries which represent a genotype’s fitness over a distribution of challenges. His model noted various environments that are “adaptable-to” from those that are not, but omits vital ecological interactions between different evolving agents. Here I extend an algorithmic Darwinism to include the ecological exigiences of frequency-dependent selection as a population-dependent bias and develop a game landscape view of evolution so to generalize the popular fitness landscape. The evolutionary game dynamics of finite populations are essential for finding a short adaptive path to the global fitness peak during the second stage of the adaptation process. This highlights the rich interface between computational learning theory, evolutionary games, and long-term evolution. (Abstract excerpt)

Artem K. website Prior to Oxford, I was at the Department of Integrated Mathematical Oncology at Moffitt Cancer, and at McGill University where I developed my interest in evolutionary dynamics, computer science, mathematical oncology and computational learning theory. In regard, I marvel at the world through algorithmic lenses. My theoretical work aims to ground the extended evolutionary synthesis in algorithmic game theory, computational learning theory, and combinatorial optimization.

Chen, Hongjia, et al. Scaling Laws and Dynamics of Hashtags on Twitter. arXiv:2004.12707. University of Sydney systems linguists including Eduardo Altmann discern yet another case of nature’s universal mathematic patterns and dynamics at formative presence even is these hyper-active Internet communications.

In this paper we quantify the statistical properties and dynamics of the frequency of hashtag use on Twitter. Hashtags are special words used in social media to attract attention and to organize content. Looking at the collection of hashtags used in a period of time, we identify the scaling laws for their frequency distribution (Zipf's law), the number of unique hashtags as a function of sample size (Heaps' law), and the fluctuations around expected values (Taylor's law). While these scaling laws appear to be universal, their volume and nature depends strongly on time, with bursts at the minute scale, fat-tailed noise, and long-range correlations. Here we view hashtags as memes and quantify emerging properties of their collective interaction including scaling laws and time scales. (Excerpt)

Gromov, Vasilii and Anastasia Migrina. A Language as a Self-Organized Critical System. Complexity. November, 2017. Oles Honchar National University, Ukraine mathematicians lay out a theoretical basis by which even this human cultural communicative quality appears to express nature’s universal middle way propensity.

A natural language (herewith texts generated by native speakers) is considered as a complex system. Namely, the authors hypothesize that such dynamic languages are self-organized critical systems and that their texts are “avalanches” flowing through word cooccurrence graphs. The respective statistical distributions of the number of words in English and Russian languages are calculated from a corpora of literary texts and sets of social media messages. The analysis found that the number of words in the texts obeys power-law distribution. (Abstract excerpt)

Pareyon, Gabriel. On Musical Self-Similarity. Google Author and Title. 2011. This 568 page posting is a University of Helsinki doctoral thesis by the Mexican polymath scholar. As one compares with other 2010s work that finds language to exhibit self-organized network forms, so it may actually be that the complements of musical score and of written script can take on an innate harmony and balance. As I access in 2020, in this fraught year might we people finally be able to appreciate the music of the spheres and to read natural creation as a literate testament?

Self-similarity, a concept taken from mathematics, is becoming a keyword in musicology. Although a polysemic term, self-similarity refers to multi-scalar feature repetition in a set of relationships, and as an indication of musical ‘coherence’ and ‘consistency’. This thesis provides a theory of musical meaning in the context of inter-semiosis, that is, its translation from one cognitive domain to another (e.g. from mathematics to music, or to speech forms). The notion of analogy is used through its classic definitions: proportion and paradigm, so to discern likeness and affinity criteria. Using quantitative–qualitative methods, a parallel study of different modalities of musical self-similarity is presented. Furthermore, connecting Charles Peirce’s synechism with Mandelbrot’s fractality is one of the main developments of the project. (Abstract excerpt)

Takahashi, Takuya and Yasuo Ihara. Quantifying the Spatial Pattern of Dialect Words Spreading from a Central Population. Journal of the Royal Society Interface. July, 2020. We cite this entry by University of Tokyo biolinguists as an example of how network topologies which are applied to brain architectures can also characterize ever-changing linguistic patterns. This iconic method could go onto similar genetic systems, quantum nets, everywhere else. While one might muse over a “book” about natural creation, by virtue of many papers like this, the whole ecosmos could actually appear as a wo/manuscript narrative which we peoples are made and meant to learn to read and write.

Some dialect words are shared among geographically distant groups of people without close interaction. Such a pattern may indicate the current or past presence of a cultural centre exerting a strong influence on peripheries. Here we develop a model of linguistic diffusion within a population network to quantify the distribution of variants created at the central population. Equilibrium distributions of word ages are obtained for idealized networks and for a realistic network of Japanese prefectures. Our model successfully replicates the observed pattern, supporting the notion that a centre–periphery social structure underlies the emergence of concentric patterns. (Abstract excerpt)

For a mathematical treatment of geographical patterning of dialect variants in the presence of the centre–periphery structure, we need a model considering linguistic influences among multiple groups of people. One commonly used framework is the gravity model, in which the mutual influence of two centres (towns, cities, etc.) is assumed to be proportional to the product of their populations and inversely proportional to the squared distance between them. This model predicts that linguistic features first diffuse from city to city, skipping the rural area in between. (2)

Buendia, Victor, et al. Feedback Mechanisms for Self-Organization to the Edge of a Phase Transition. arXiv:2006.03020. University of Granada, Columbia University, and Rutgers University bioscientists including Migeul Munoz continue to explore and finesse the various ways that nature’s newly found propensity to seek and attain an optimum dynamic balance between reciprocal modes or stages can be seen to take and express.

Scale-free outbursts of activity are commonly observed in physical, geological, and biological systems. The idea of self-organized criticality (SOC) suggests that natural systems can self-tune to a critical state with its concomitant power-laws and scaling. Theoretical progress now explains SOC by relating its critical properties to those of a non-equilibrium phase transition that separates an active state in which dynamical activity reverberates indefinitely, from an absorbing or quiescent state where activity eventually ceases. Here, we consider a related concept: self-organized bistability (SOB). We review similarities and differences between SOC and SOB under a common theoretical framework, and discuss "self-organized quasi-criticality" and "self-organized collective oscillations", with the aim of providing feedback mechanisms for self-organization to the edge of a phase transition. (Abstract excerpt)

In summary, we have reviewed within a common and unified framework different types of mechanisms for the self-organization to the vicinity of phase transitions. We hope that this work help clarify the literature on the subject, and contribute to new and exciting developments in physics and other disciplines. This could be especially important in biology, where the idea that living systems can obtain important functional advantages by operating at the edge of two alternative/complementary types of phases/state has attracted a great deal of attention and excitement. (21)

Chialvo, Dante, et al. Controlling a Complex System near Its Critical Point via Temporal Correlations. Nature Scientific Reports. 10/12145, 2020. Argentine systems neuroscientists along with Dietmar Plenz, NIMH, USA press on with more reasons and evidence that animate phenomena of many kinds from proteins to neural nets does appear to seek and arrive at a best balance of openness to changing environs while sustaining an orderly consistency. So again we ask and wonder that as scientific studies continue to illume a common “sweet spot” between complementary opposites, however might this natural knowledge be applied to human political parties whence presently conserve and create modes are fatally locked in mutual battle?

Many complex systems exhibit large fluctuations both across space and over time. These activities have often been linked to some kind of critical phenomena, where it is well known that the emerging correlation functions in space and time are closely related to each other. Here we test whether time correlation properties allow systems exhibiting a phase transition to self-tune to their critical point. We describe results in three models: the 2D Ising ferromagnetic model, the 3D Vicsek flocking model and a small-world neuronal network model. We demonstrate that feedback from the autocorrelation function shifts the system towards its critical point. Our results rely on universal properties of critical systems and are expected to be relevant to a variety of other settings. (Abstract)

The last decade has witnessed an escalating interest in complex biological phenomena at all levels including macroevolution, neuroscience at different scales, and molecular biology. The observed complexity in nature is often traced to critical phenomena because it resembles the complexity found for critical dynamics in models and theory. However such resemblances are not enough to attribute criticality as the mechanism behind all forms of natural complexity. Even though out of equilibrium generic scale invariance can arise without fine-tuning of control parameters, it is found that biological systems operate in special regions of control parameter space which are critical in the sense that they separate phases of different dynamical behavior. More specifically, it seems that many biological systems reach a “sweet spot” where they attain maximal sensitivity to changes in the environment, while maintaining internal order. (1)

Friston, Karl, et al. Parcels and Particles: Markov Blankets in the Brain. arXiv:2007.09704. We cite this entry from researchers based at University College London Wellcome Centre along with a companion posting Is the Free-energy Principle a Formal Theory of Semantics? by Maxwell Ramstead, et al (2007.09291). While cast in technical jargon they emphasize an active complementarity of neuronal parts and modular wholes, aka reciprocal segregation and integration, or separate and come together dynamic phases. As these cerebral processes empower a predictive brain, they are seen to reside in a far-from-equilibrium, self-organized critical state.

Nosonovsky, Michael and Prosun Roy. Scaling in Collodial and Biological Networks. Entropy. 22/6, 2020. We cite this contribution by University of Wisconsin bioengineers as another good example of how worldwide collaborations are finding a consistency of active topologies which form into similar nested recurrences across material, biochemical, cellular, metabolic to neural and communicative domains. By a philoSophia 2020 vision, a revolutionary organic genesis ecosmos seems well underway to being quantified.

Scaling and dimensional analysis is applied to networks that describe various physical systems. Some of these networks possess fractal, scale-free, and small-world properties. First, we consider networks arising from granular and colloidal systems due to pairwise interaction between the particles. Many networks found in colloidal science possess self-organizing properties and/or self-organized criticality. Then, we discuss the allometric laws in branching vascular networks, artificial neural networks, cortical neural networks, as well as immune networks. Scaling relationships in complex networks of neurons, which are organized in the neocortex in a hierarchical manner, suggest that the characteristic time constant is independent of brain size when interspecies comparison is conducted. The information content, scaling, dimensional, and topological properties of these networks are discussed. (Abstract excerpt)

The brain networks possess many characteristics typical to other networks, including over‐frequency and power‐law activities, avalanches, small‐world, scale‐free, and fractal topography. It is particularly interesting to look for the correlation between the spatial distribution (for example, hubs) and temporal organization (frequency spectrum) of human brain cognitive activities. Such research is being conducted by many groups, for example, the study of the DMN during such activities as the comprehension of a text in a natural language versus contemplating it (the “language of thought”). The information content of the neural networks can be studied using the standard characteristics of the information theory, such as the Shannon entropy. It may provide ways to distinguish between DNA‐encoded information and information generated during the embryonal and post‐embryonal development, which may be driven by the self‐organizing process. (22)

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