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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 1 through 15 of 50 found.


Earth Learn: A Planetary Prodigy Comes to Her/His Own Knowledge

A Learning Planet > Original Wisdom > Mythic Animism

Nelson, Melissa and Daniel Shilling, eds. Traditional Ecological Knowledge: Learning from Indigenous Practices for Environmental Sustainability. Cambridge: Cambridge University Press, 2018. In these late mechanist years with our house on fire and no water to use, male history has ended up far removed from a spiritual sense of an animate, personified, encoded nature. Into the 2010s, this rare, unique collection revives and enhances original wisdom so that it might advise Earthlings once more so to save persons and planet. Some chapters are Native Science by Gregory Cajete (search), Toward a Philosophical Understanding of TEK and Ecofeminism by Joan McGregor, The Radiant Life with Animals by Linda Hogan and Indigenous Peoples and Cultural Sustainability by Rebecca Tsosie.

This book examines the importance of Traditional Ecological Knowledge (TEK) and how it can provide models for a time-tested form of sustainability needed in the world today. The essays explore TEK through cases of environmental sustainability from multiple tribal and geographic locations in North America and beyond. Grounded in an understanding of the profound relationship between biological and cultural diversity, this book surveys a holistic and broad disciplinary approach to sustainability, including language, art, and ceremony, as critical ways to maintain healthy human-environment relations.

A Learning Planet > Original Wisdom > Mythic Animism

Redvers, Nicole, et al. Indigenous Natural and First Law in Planetary Health. Challenges. 11/2, 2020. In this MDPI online journal, a worldwise array of eight coauthors from the University of North Dakota, Nulungu Research Institute, Australia, Bond University Australia, Ogiek People’s Development Program, Kenya, Wangari Githaiga & Co,, Nairobi, Sydney Law School, and Nakazdli Whut’en, British Columbia, and the Arctic Indigenous Wellness Foundation, Canada provide a current binocular vision to sight and light a way forward to a better living Earthsphere future.

Indigenous Peoples associate their own ways with the laws of the natural world, which are formally known as Natural or First Law. These laws come from the Creator and the Land through our ancestral stories. Since colonization, Indigenous Peoples’ Natural Laws have been forcibly replaced by modern-day laws that do not take into account the sacred relationship between the Earth and all of her inhabitants. The force of societies who live outside of Natural Law has caused modern-day devastions. Pandemics, global environmental and climate change, are all result from not holding to the interconnected wisdom of the universe. Here we discuss an Indigenous paradigm and worldview with implications for planetary health and ecological movements around the globe. (Abstract)

A Learning Planet > The Spiral of Science > deep

Kelleher, John. Deep Learning. Cambridge: MIT Press, 2019. John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at the Technological University Dublin provides another up-to-date survey that is wide-ranging in scope along with in depth examples.

In the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible, concise and comprehensive introduction to the artificial intelligence revolution and its techniques. He explains, for example, how deep learning enables data-driven decisions by identifying and extracting patterns from large datasets, how it learns from large, complex data sets and much more. He describes important deep learning architectures such as autoencoders, recurrent neural networks, as well as such recent developments as Generative Adversarial Networks.

A Learning Planet > The Spiral of Science > deep

Sejnowski, Terrence. The Unreasonable Effectiveness of Deep Learning in Artificial Intelligence. Proceedings of the National Academy of Science. 117/30033, 2020. The senior Salk Institute neurobiologist introduces a Colloquium on the Science of Deep Learning as this AI neural net frontier goes rapidly forward. Some papers are Emergent Linguistic Structure in Artificial Neural Networks and Algorithms as Discrimination Detectors.

Deep learning networks have been trained to recognize speech, caption photographs, and translate text between languages. Although applications of deep learning networks to real-world problems have become ubiquitous, a deep understanding of why they are so effective lags behind. Paradoxes in their training and effectiveness are being investigated by way of the geometry of high-dimensional spaces. A mathematical theory would illuminate how they function, assess the strengths and weaknesses of network architectures, and more. (Abstract excerpt)

A Learning Planet > Mindkind Knowledge

Gebhart, Thomas and Russell Funk. The Emergence of Higher-Order Structure in Scientific and Technological Knowledge Networks. arXiv:2009.13620. University of Minnesota computer and management scientists contribute a significant entry for several reasons. They proceed to view scientific endeavors as another dynamic complex process which presently seems to be going on by itself. As a result, this worldwise enterprise can be found to exhibit similar node/edge multi-network features as everywhere else. By virtue of algebraic topology and persistent homology methods, an affinity with our own cerebral cognition becomes evident. A graphic page shows an array of common modular, community, paths, central, density, assortativity and other features. We note that these mathematic approaches are used in other areas from galactic clusters (Pranov) to neuroscience (Bassett). Thus the paper provides strong support to date for the premise of this website that an emergent sapiensphere is indeed coming to her/his own revolutionary knowledge.

The growth of science and technology is a recombinative process, wherein new discoveries and inventions are built from prior knowledge. Network science has recently emerged as a framework for measuring the structure and dynamics of knowledge. While helpful, existing approaches struggle to capture the global properties of the underlying networks. Here we use algebraic topology methods to characterize the higher-order structure of knowledge networks across scale. We observe rapid growth in the higher-order structure in many scientific and technological fields, which is not observable by traditional networks. Up to a point, increases in higher-order structure are associated with better outcomes, as measured by the novelty and impact of papers and patents. (Abstract excerpt)

Ecosmos: A Revolutionary Organic Habitable UniVerse

Animate Cosmos > Quantum Cosmology

Bojowald, Martin. Foundations of Quantum Cosmology. Online: IOP Publishing, 2020. This latest volume by Penn State University theoretical physicist offers a wide-ranging survey along with in-depth mathematical aspects. Its chapters are Universe on Large and Small Scales, Covariance, Quantum Corrections, Minispace Models, Quantum Gravity, and Inhomogeneous Spacetimes.

Animate Cosmos > Quantum Cosmology > cosmos

Wilding, Georg, et al. Persistent Homology of the Cosmic Web. arXiv:2011.12851. For a paper to appear in the MNRAS, University of Groningen, Duke Kunshan University, China and Perimeter Institute, Canada astrophysicists cite a Hierarchical Topology in ΛCDM Cosmologies (see below) as it becomes lately realized (after finding all the parts) that the celestial raiment is suffused with interconnective networks amenable to geometric mathematics as everywhere else. Just as quantum phenomena, life’s evolution and neural brains, so the whole ecosmos appears to be similarly graced and unified. We note, in this fateful year, how awesome and indicative is it that we collaborative, valiant peoples can consider and attain such findings.

Using a set of ΛCDM simulations of cosmic structure formation, we study the evolving connectivity and changing topological structure of the cosmic web. The cosmic web READ topology can be quantified by the evolution of Betti number curves and feature persistence diagrams of the three structural classes: matter concentrations, filaments and tunnels, and voids. By viewing cosmic webs over time time, the link between their multiscale topology and the hierarchical buildup of cosmic structure can be constructed. The sharp apexes in the diagrams are then related to key transitions in the formation process. A self-similar character is found due to the cosmic web's hierarchical buildup. (Abstract excerpt)

The ΛCDM (Lambda cold dark matter) model is a cosmic representation wherein the universe contains three major components: a cosmological constant denoted by the Greek Λ and associated with dark energy; the postulated cold dark matter;; and third, ordinary matter. (Wilipedia)

Animate Cosmos > Quantum Cosmology > Gaia

Chao, Keng-Hsien, et al. Lava Worlds: From Early Earth to Exoplanets. arXiv:2012.07337. In our late day of global collaborations and knowledge accumulation, by way of 400 references, University of Hawaii astronomers including Eric Gaidos can proceed to reconstruct and quantify how our fittest biosphere came to have its certain hyperactive crustal substance. The retrospective endeavor considers thermal energies, atmospheric material transport, tidal forces, gravity effects and more to attain both a conceptual version for Earth, and a model which can then be applied to vicarious exoworlds.

The magma ocean concept was conceived to explain the geology of the Moon, global oceans of silicate melt could be a "lava world" phase of rocky planet accretion, and persist on planets around other stars. Magma oceans could be a defining stage in forming a core, a crust, initiation of tectonics, and of an atmosphere. This review describes the energetic basis of magma oceans and lava lakes on Earth and Io and their evidence throughout the Solar System. It describes research on theoretical and observed exoplanets that could host extant lava worlds and ways to detect and characterize them. (Abstract excerpt)

Animate Cosmos > Quantum Cosmology > quantum CS

Deutsch, Ivan. Harnessing the Power of the Second Quantum Revolution. PRX Quantum. 1/020101, 2020. In this new APS journal, the director of the University of New Mexico’s Center for Quantum Information and Control describes how 2ist century informative and technical advances driven by incentives for faster computational abilities, copious data streams and more have led to a realization, in contrast to a 20th century opacity, that a radical familiar, treatable understanding and avail of this deepest realm is now going forward.

The second quantum revolution has been built on a foundation of fundamental research at the intersection of physics and information science, giving rise to a quantum information science (QIS). The quest for new knowledge and understanding drove the development of second-wave quantum technologies, including computers, sensors, and communication systems. Under what conditions then can we well apply quantum complexity and for what potential applications? Here I review how curiosity-driven research has led to radical new theories and technologies essential for further progress. (Abstract excerpt)

Animate Cosmos > Quantum Cosmology > quantum CS

Farrow, Tristan, et al. A Measurable Physical Theory of Hyper-Correlcation beyond Quantum Mechanics. Physica Scripta. 96/1, 2021. We cite this entry by National University of Singapore, Oxford University and Sogang University, Seoul physicists including Vlatko Vedral as an example of what these theoretical conceptions seem to be coming upon. Their latest implications suggest that a deeper realm of phenomenal reality exists which is necessary to attain a full explanation. See also Spacetime as a Tightly Bound Quantum Crystal by V. Vedral at arXiv:2009.10836. and his Frontiers of Quantum Physics group website at oxfordquantum.web.ox.ac.uk.

A characteristic of quantum mechanics is entanglement of correlations between particles irrespective of their locations. This property, called non-locality, has no classical analogue. Over the past few years, quantum physicists have reached a consensus that we lack a physical theory to account for a class of states whose non-local character exceeds the bounds allowed by quantum mechanics. We propose an extension of the Schrödinger equation with non-linear terms so to relax Born's rule, an axiom of quantum mechanics, that accounts for such hyper-correlated states. (Abstract excerpt)

Physicists postulate the existence of a physical law that goes beyond quantum mechanics, which could lead to a modification of certain axioms underpinning quantum theory. The discovery of quantum mechanics at the dawn of the twentieth century led to major breakthroughs, from nuclear physics, microelectronics to quantum computing, which, by contrast to Newtonian physics, became known as modern physics. Quantum mechanics gives the most accurate description of microscopic objects like atoms and molecules. (1)

Animate Cosmos > Quantum Cosmology > exouniverse

He, Yang-Hui. Universes as Big Data. ArXiv:2011.14442. In this latest essay, a City University of London mathematician with many Chinese and international colleagues adds a new dimension to physical theories by noticing ways that current deep neural net learning algorithmic procedures are gaining much avail and service to cosmological studies. As a consequence, universal nature can be seen to take on a textual, and indeed a cerebral character and quality. See also He’s book length The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning at 1812.02893, search this eprint site for more papers.

We review how string theory first led theoretical physics to precise problems in algebraic and differential geometry, and thence to computational geometry in the last decade or so, and, in the last few years, to data science. Using the Calabi-Yau landscape as a starting-point, we consider recent progress in machine-learning applied to the sifting through of possible universes from compactification, as well as wider problems in geometrical engineering of quantum field theories. In parallel, we discuss machine-learning mathematical structures and how they may apply from mathematical physics, to geometry, to representation theory, and to number theory. (Abstract excerpt)

Animate Cosmos > Organic > Biology Physics

Ghosh, Subhadip, et al. Enzymes as Active Matter. Annual Review of Condensed Matter. Vol. 12, 2020. Enzyme: a substance produced by a living organism which acts as a catalyst to bring about a specific biochemical reaction. Penn State biochemists contribute a further notice of this natural spontaneity in effect for metabolic processes. Are we persons “condensed Matter” or is the physical ecosmos coming to life. See also Stem Cell Populations as Self-Renewing Many-Particle Systems by David Jorg, et al in this same volume for another instance.

Nature has designed multifaceted cellular structures to support life. Cells contain a vast array of enzymes that collectively perform tasks by harnessing energy from chemical reactions. In the past decade, detailed investigations on enzymes that are freely dispersed in solution have revealed a concentration-dependent enhanced diffusion and chemotactic behavior during catalysis. The purpose of this article is to review the different classes of enzyme motility and discuss the possible mechanisms as gleaned from experimental observations and theoretical modeling. (Ghosh Abstract excerpt)

This article reviews the physical principles of stem cell populations as active many-particle systems that are able to self-renew, control their density, and recover from depletion. We illustrate the statistical hallmarks of homeostatic mechanisms from stem cell transient large-scale oscillation dynamics during recovery to the scaling behavior of clonal dynamics and front-like boundary propagation during regeneration. (Jorg Abstract)

Animate Cosmos > Organic > Universal

Jackson, Holly, et al. Using Heritability of Stellar Chemistry to Reveal the History of the Milky Way. arXiv:2011.06453. For a paper to appear in the Monthly Notices of the Royal Astronomical Society, an international, interdisciplinary team from MIT, University of Diego Portales, Chile (Paula Jofre, search), Cambridge University, and the University of Surrey (Robert Foley) continue to perceive and advance evident comparisons between biological and astrophysical evolutionary patterns and processes.

Since chemical abundances are inherited between generations of stars, we use them to trace the evolutionary history of our Galaxy. We present a robust methodology for creating a phylogenetic tree, a biological tool often used to study heritability. Combining our phylogeny with information on stellar ages and dynamical properties, we reconstruct the shared history of 78 stars in the solar neighborhood. The branching pattern in our tree supports a scenario in which the thick disk is an ancestral population of the thin disk. In this paper, we demonstrate how a biological, phylogenetic perspective can help study key processes that have contributed to the evolution of the Milky Way. (Abstract excerpt)

In any evolutionary analysis, there are two components – the actual phylogeny of the constituent lineages, and the exosystem context,. The former is reconstructed from the heritable traits of the organisms, and the latter by the environment shaping the phylogeny. In galaxy evolution, the traits or variables contributing to heritable component are those encoding the chemical pattern of the stars. Variables that reflect the context include those that describe the dynamical situation of the star, such as interactions with the bar and spiral arms and/or the galaxy’s external setting. (2)

Animate Cosmos > Information > Quant Info

Fuchs, Christopher and Andrei Khrennikov. Quantum Information Revolution. Foundations of Physics. 50/12, 2020. The University of Massachusetts, Boston and Linnaeus University, Sweden theoretical physicists introduce a special issue which is mostly a 21st century retrospect of the Quantum Bayesian, aka QBism, endeavor to appreciate and factor in the participatory presence of late observers such as ourselves. As this reconception goes forward, within its universe to human trajectory it is vital to understand our phenomenal agency.

Animate Cosmos > Intelligence

Vanchurin, Vitaly. The World as a Neural Network. arXiv:2008.01540. A Russian-American, University of Minnesota physicist continues his frontier studies about a cognitive property that the whole cosmos appears to possibly have. As informed by AI deep machine learning advances, a working hypothesis is that on the most fundamental level the dynamics of the entire universe is described by a microscopic neural network which undergoes a learning evolution. A further result that this edification can be seen in a thermodynamic way as opposed to dissipative entropy. This knowledge gaining counter course is then dubbed a second law of learning. See also Towards a Theory of Machine Learning by VV at 2004.09280.

We discuss a possibility that the entire universe on its most fundamental level is a neural network. We identify two different types of dynamical degrees of freedom: "trainable" variables (e.g. bias vector or weight matrix) and "hidden" variables (e.g. state vector of neurons). We first consider stochastic evolution of the trainable variables to argue that near equilibrium their dynamics is well approximated by Madelung equations and by Hamilton-Jacobi equations. This shows that the trainable variables can indeed exhibit classical and quantum behaviors with the state vector of neurons representing the hidden variables. This shows that the learning dynamics of a neural network can indeed exhibit approximate behaviors described by both quantum mechanics and general relativity. We also discuss a possibility that the two descriptions are holographic duals of each other. (Abstract excerpt)

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