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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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II. Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Actual Factual Knowledge

B. The Spiral of Science: Manican to American to Earthicana Phases

Hull, David. Science as a Process. Chicago: University of Chicago Press, 1988. A philosopher places science within an evolutionary context whereby it is seen to develop by variation and selection as it explores the natural cosmos.

Jackson, E. Atlee. The Unbounded Vistas of Science. Complexity. 5/5, 2000. A lengthy essay on the rising “metamorphosis” in physics and science in general due to a new recognition of nonlinear phenomena.

The first monumental change of “science” was that the Newtonian paradigm, in the sense of the deterministic power of mathematics to lead to a predictive empirical knowledge of nature, was shattered about a century ago by the insights that Poincare developed, using his new topological characterizations of dynamics. (36) It is this new dimension of dynamics in science that will entirely change the scientific perspective of nature. (37)

Johnson-Groh, Mara, et al. From Exoplanets to Quasars: Detection of Potential Damped Lyman Alpha Absorbing Galaxies Using Angular Differential Imaging. arXiv:1609.00384. We cite this entry by University of Victoria, BC and National Research Council Canada astronomers from many similar contributions to convey the incredible expanse of mid 2010s collaborative scientific research. Who are we homo, anthropo, Terra, geo, and cosmo sapiens to be able to altogether achieve nature’s self-description? See also for example Cosmological Perturbations in the 5D Holographic Big Bang Model (1703.00954) whence Earthlings study and consider entire universes.

The advantages of angular differential imaging (ADI) has been previously untested in imaging the host galaxies of damped Lyman alpha (DLA) systems. In this pilot study, we present the first application of ADI to directly imaging the host galaxy of the DLA seen towards the quasar J1431+3952. K-band imaging of the field surrounding J1431+3952 was obtained on the Gemini North telescope with the adaptive optics system and a laser guide star. The likely identification of the absorbing galaxy is discussed, and we conclude that the galaxy with the largest impact parameter and highest stellar mass is unlikely to be the host, based on its inconsistency with the impact parameter relation and inconsistent photometric redshift. (Abstract excerpt)

Ju, Harang, et al. The Network Structure of Scientific Revolutions. arXiv:2010.08381. Seven University of Pennsylvania scholars from neuroscience to history to physics including Danielle Bassett apply “network revolution” (Barabasi 2012) models which by now define node and link geometry in active response (see Cynthia Siew 2019) to analyze the historic formation of our library of natural cosmos and of human congress. With regard to an iconic Wiklpedia, an array of modular networks are composed of articles whence each node is a single site, and edges are hyperlinks to other relevant entries. As a result, and in tune with our own premise, knowledge learning proceeds on its own as a constant growth process. (Since it is said this goes on by seeking and filling in gaps, we add that it also resembles a self-organizing complex adaptive system.) Herein a further apt analogy is cited in the form of a genetic process by way of core nodes, relative mutations, and new interlinks.

Philosophers of science have long considered how collaborative scientific knowledge grows. Empirical validation has been challenging due to limitations in collecting and systematizing historical records. Here, we capitalize on Wikipedia as the largest online encyclopedia by which we are to formulate knowledge as growing networks of articles and their hyperlinked inter-relations. We demonstrate that concept networks grow not by expanding from their core but rather by creating and filling gaps. Moreover, we observe how network modules reveal a temporal signature in structural stability across scientific subjects. In a dynamic network model of scientific discovery, data-driven conditions underlying breakthroughs depend just as much on identifying uncharted gaps as on advancing solutions. (Abstract edits)

The findings described herein reveal that human knowledge grows by filling gaps in knowledge, perhaps driven by the collective curiosity of individual scientists through inward and outward exploration and gradual modifications to network structure. Moreover, knowledge discovered while creating and filling knowledge gaps is likely to be more influential and more frequently awarded in the scientific community. Our mathematical formulations of historical data pave the way to describe, understand, and even potentially guide scientific progress for individuals and funding agencies. Furthermore, our findings provide a data-driven approach to identifying novel contributions, especially those by underrepresented groups whose works are typically devalued yet are vital for vibrant scientific innovation. (6)

Kasim, Muhammad, et al. Up to Two Billion Times Acceleration of Scientific Simulations with Deep Neural Architecture Search. arXiv:2001.08055. Thirteen scientists from Oxford, Yale and Seville including Duncan Watson-Parris have come up with a method to dramatically speed up, facilitate and advance data-intensive studies from cosmic to genomic to atomic fields. The computational technique is called Deep Emulator Network Search (DENSE) which works better and faster than those designed and trained by hand. See also AI Shortcuts Speed Up Simulations by Matthew Hutson in Science (367/728, 2020) for a review.

Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability. Here we describe a way to build accurate emulators even with a limited number of training data. The method greatly accelerates simulations in 10 scientific cases including astrophysics, climate science, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. We anticipate this work will aid researchers involved with large simulations, allow extensive parameters exploration, and enable new, previously unfeasible computational discovery. (Abstract excerpt)

Keller, Evelyn Fox. A Feeling for the Organism. San Francisco: Freeman, 1983. A perceptive biography of Nobel laureate geneticist Barbara McClintock that illuminated her relational, empathic methods of thinking like a corn plant.

Keller, Evelyn Fox. Organisms, Machines, and Thunderstorms: A History of Self-Organization, Part One. Historical Studies in the Natural Sciences. 38/1, 2008. The MIT philosopher of science achieves a perceptive survey of engagements from Immanuel Kant to Stuart Kauffman to understand and express a lively, fecund nature that innately appears to generate itself. But this later course has mainly held to mechanistic and cybernetic abstractions. Part Two: Complexity, Emergence, and Stable Attractors, in volume 39/1, 2009, further situates this revolution from general systems theory to non-equilibrium thermodynamics (Prigogine) and onto its 1990s and early 2000s synthesis and maturing credence. From this vantage, a solution to Kant’s 18th century quandary of how living, thinking beings could arise from “inorganic” mechanism appears within reach, but still in need of meaningful translations from these technicalities.

Over the last quarter century, the term "self-organization" has acquired a currency that, notwithstanding its long history, has been taken to signal a paradigm shift, and perhaps even a scientific revolution, introducing a new Weltanschauung in fields as diverse as mathematics, physics, biology, ecology, cybernetics, economics, sociology, and engineering. (Part One, 45)

Khoury, Justin. Dark Matter Superfluidity. arXiv:2109.10928. We cite this entry by a University of Pennsylvania astrophysicist as a current example of collective Earthuman abilities to plumb and explore any depth and reach of cosmological phenomena. One wonders what kind of extant reality, as may become evident to us, proceeds to evolve its own facility of self-revelation and description. For whatever reason is this universal learning process going on. See also Boyle, Latham and Neil Turok. Two-Sheeted Universe, Analyticity and the Arrow of Time by Latham Boyle and Neil Turok at 2109.06204 and The Universe as a Driven Quantum System by Jose Vieira at 2109.01660 for other such scientific studies as they spiral to this global genius.

In these lectures I describe a theory of dark matter superfluidity developed in the last few years. The dark matter particles are axion-like, with masses of order eV. They Bose-Einstein condense into a superfluid phase in the central regions of galaxy halos. The superfluid phonon excitations in turn couple to baryons and mediate a long-range force (beyond Newtonian gravity). Thus the dark matter and modified gravity phenomena represent different phases of a single underlying substance, unified through the well-studied physics of superfluidity. (Abstract)

Kim, Edward and Robert Brunner. Star-Galaxy Classification Using Deep Convolutional Neural Networks. arXiv:1608.04369. We cite this entry by a University of Illinois physicist and an astronomer to show how an international collaborative community is uses cerebral methods to analyze vast amounts of data. In regard, one might assume a nascent global brain learning on its own. See also Deep Recurrent Neural Networks for Supernovae Classification by Tom Charnock and Adam Moss at 1606.07442 for another example.

Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine to automatically learn the features directly from data, minimizing the need for input from human experts. We present a star-galaxy classification framework that uses deep convolutional neural networks (ConvNets) directly on the reduced, calibrated pixel values. Using data from the Sloan Digital Sky Survey (SDSS) and the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS), we demonstrate that ConvNets are able to produce accurate and well-calibrated probabilistic classifications that are competitive with conventional machine learning techniques. Future advances in deep learning may bring more success with current and forthcoming photometric surveys, such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope (LSST), because deep neural networks require very little, manual feature engineering. (Abstract)

Kostic, Andrija, et al. Machine-driven Searches for Cosmological Physics. Astronomy & Astrophysics. July 5, 2021. We cite this entry by MPI Astrophysics, Stockholm University, Niels Bohr Institute, and Sorbonne University researchers as a current example of how these novel computational facilities are opening a new empowerment by which our Earthkind survey can describe and quantify, as we seem unbeknownst, made and meant to carry forth. In regard, our home planet seems to be lately graced with a collaborative noosphere able to learn, know and discover on her/his own.

We present maps revealing the expected information content of large-scale structures concerning cosmological physics. These maps can guide the optimal retrieval of relevant physical information with targeted cosmological searches. This achievement has become feasible through the recent development of a causal inference method that is based on the physics of cosmic structure formation. The results presented in this work elucidate the inhomogeneous distribution of cosmological information in the Universe. . This study paves a new way forward to perform efficient searches for the fundamental physics of the Universe, where search strategies are become refined with new cosmological data sets within an active learning framework. (Abstract excerpt)

Krenn, Mario and Anton Zeilinger. Predicting Research Trends with Semantic and Neural Networks with an Application in Quantum Physics. arXiv:1906.06843. University of Vienna, Center for Quantum Science and Technology physicists MK, now a postdoc (search) at the University of Toronto, and AZ, an esteemed theorist since the 1970s (see Wikipedia and his website), apply their polymath acumen to this subject field. Circa 2019, it becomes evident that global scientific endeavors are going on by own their worldwide selves, independent of individual contributors. A Semantic Network software program is proposed by which to data mine the vast resultant literature such as this eprint site. An actual cerebral process (sapiensphere) coming to her/his own knowledge remains to be seen. With daily threats of a climate, and/or nuclear Armageddon, an agreed advent of a planetary phase of salutary edification, a natural discovery in our midst, is vitally necessary. See also Quantum Teleportation in High Dimensions by the authors and Chinese colleagues at 1906.09697.

The growing number of publications in all scientific disciplines can no longer be comprehended by a single human person. As a consequence, researchers have to specialize in sub-disciplines, which makes it challenging to uncover connections beyond the own field of research. In regard, access to structured knowledge from a large document corpus could help advance the frontiers of science. Here we demonstrate a method to build a semantic network from scientific literature, which we call SemNet. We use SemNet to predict future trends and to inspire new seeds of ideas in science. In SemNet, scientific knowledge is represented as an evolving node/link network using the content of 750,000 scientific papers published since 1919. Finally, we consider possible future developments and implications of our findings. (Abstract excerpt)

krenn, Mario, et al. Artificial Intelligence and Machine Learning for Quantum Technologies. Physical Review A.. 107/010101, 2023. MPI Science of Light physicists present an illustrated review to date of these Earthhuman and computational capabilities going forward which seem to so readily empower this new planetary phase.


In recent years the dramatic progress in machine learning has begun to impact many areas of science and technology. In our perspective article, we explore how quantum technologies are benefiting from this revolution. We showcase in illustrative examples of how scientists in the past few years have started to use machine learning and more broadly methods of artificial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feedback strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation. (Abstract)

This perspective article will be concerned with shining a spotlight on how techniques
of classical machine learning (ML) and artificial intelligence (AI) hold great promise for improving quantum technologies in the future. A wide range of ideas have been developed at this interface between the two fields during the past five years; see Fig. 1. Whether one tries to understand a quantum state through measurements, discover optimal feedback strategies or quantum protocols, or design new experiments, machine learning can yield efficient solutions, optimized performance and, in the best cases, even new insights. (1)

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