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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

Harding, Sandra, ed. The Postcolonial Science and Technology Reader. Durham, NC: Duke University Press, 2011. A bandied phrase nowadays is “the West and the Rest.” As affirmed herein, of course the other continents have equally valid scientific and cultural traditions, which impoverished, imperiled, westerners could avail to their advantage. Four sections: Counterhistories, Other Cultures’ Sciences, Residues and Reinventions, and Moving Forward: Possible Pathways, proceed to so advise. A sample contribution might be “Mining Civilizational Knoweldge” by Susantha Goonatilake.

Hardwicke, Tom, et al. Calibrating the Scientific Ecosystem through Meta-Research. Annual Review of Statistics. 7/11, 2020. As a big data tsunami engulfs quantum to genomic to astromic fields, Meta-Research Innovation Center Berlin and Stanford University scholars scope out ways to reorient and empower methods that can distill evidential patterns and findings. See also in this volume 21st Century Statistical and Computational Challenges in Astrophysics by Eric Feigelson, et al.

Modern astronomy has been rapidly increasing our ability to see deeper into the universe as it acquires enormous samples of cosmic populations. Gaining astrophysical insights from these datasets requires a wide range of sophisticated statistical and machine learning methods. Bayesian inference, central to linking astronomical data to nonlinear astrophysical models, addresses problems in solar physics, properties of star clusters, and exoplanet systems. The field of astrostatistics needs increased collaboration and joint development of new methodologies. Together, they will draw more astrophysical insights into astronomical populations and the cosmos itself.

While some scientists study insects, molecules, brains, or clouds, other scientists study science itself. Meta-research, or research-on-research, is an active discipline that investigates efficiency, quality, and bias in the scientific ecosystem, which is under some attack today. We introduce a translational framework that involves (a) identifying problems, (b) investigating problems, (c) developing solutions, and (d) evaluating solutions. In each of these areas, we review key meta-research endeavors and discuss examples of prior and ongoing work. (Abstract excerpt)

Hasan, Farhanul, et al.. Filaments of The Slime Mold Cosmic Web and How They Affect Galaxy Evolution. arXiv:2311.01443. We note this entry by New Mexico State University, UC, Santa Cruz, Yale University, Hebrew University, and Simon Fraser University computational astrophysicists for its innovative application of a common organism-based search procedure as a good method to also study all manner of celestial phenomena, as the title cites and the quotes describe. Our planatural interest is a 21st century implication that a revolutionary ecosmic genesis seems to be amenable to the same genetic algorithms in each and every space and time.

We present a novel method for identifying cosmic web filaments by using the IllustrisTNG (below) galactic simulations. We compare the cosmic density field from the Delaunay Tessellation Field Estimator (DTFE) and Monte Carlo Physarum Machine (MCPM), which is inspired by the slime mold organism program which identifies filaments with higher fidelity reconstruction. Our results indicate that most galaxies are quenched and gas-poor near high-line density filaments at z<=1. We discuss applying our method to galaxy surveys. to elucidate the large-scale structure of galaxy formation. (Excerpt)

Galaxy evolution can be analyzed in the context of the universe’s large-scale structure, known as the ”cosmic web.” This structure consists of an interconnected network of filaments, which are bridges of intergalactic matter, and nodes, which are dense intersections of filaments where the cosmic density distribution is highest. In this paper, we present a new technique for reconstructing the cosmic web from galaxy catalogs. Our approach uses a novel model called the Monte Carlo Physarum Machine to estimate the cosmic density field. MCPM is inspired by the feeding habits of the biological organism Physarum polycephalum or slime mold, which generates highly efficient interconnected networks when searching for food. This behavior has been used in various fields from neuroscience to civil engineering. (1)

Perhaps even more exciting is that our new method can be applied to observational datasets to identify the cosmic web in the real universe. With rich datasets of from state-of-the-art current and upcoming observatories such as SDSS, DESI, Subaru PFS, JWST, Euclid, SPHEREx, and Roman, we will be in a position to identify filaments across most of cosmic time. This has the potential to unlock a rich array of investigations in research areas of extragalactic astrophysics and cosmology. (25)

The IllustrisTNG project is a series of cosmologica simulations of galaxy formation. TNG aims to illuminate the physical processes to understand when and how galaxies evolve into the structures that are observed in the night sky, and to make predictions for current and future observational programs.

Hayes, Brian. Undisciplined Science. American Scientist. July/August, 2004. Through the 18th century, physics reigned as the foundational science, of which aspects such as astronomy and botany were a particular kind. In the 19th and 20th centuries, separate subdisciplines proliferated into the hundreds. A grand 21st century reconvergence is underway because physics has come to characterize nature in terms of algorithmic computation because these generic statistical models apply equally well from atoms to societies to galaxies. Rather than a “postage stamp” collection, biological, cultural and celestial realms can be known as expressions of the same universal patterns and processes.

Heng, Kevin. The Nature of Scientific Proof in the Age of Simulations. American Scientist. May-June, 2014. The University of Bern, Center for Space and Habitability, Exoplanets and Exoclimes Group leader, describes the novel occurrence, after rationalism and empiricism, of a “third, modern way of testing and establishing scientific truth.” This is possible by the use of computational networks to mathematically simulate cosmoses, galaxies, planetary atmospheres, and so on. In such “synthetic universes” one can study, as not before, the formations of worlds from atomic to celestial realms. For a reference, visit the Millennium Simulation Project of the MPI for Astrophysics. And it is a sure sign of our revolutionary times when a professorship in planetary “habitability” exists.

Hey, Tony, et al, eds. The Fourth Paradigm: Data-Intensive Scientific Discovery. http://research.microsoft.com/en-us/collaboration/fourthparadigm, 2009. An e-book accessed on December 15, 2009 (re the New York Times Science Tuesday for that day) from Microsoft Research about computer capabilities that can process huge amounts of data from, e.g., the atom colliders, neuroscience, or multiple telescopes. An initial Experimental phase millennia ago is succeeded by Theoretical centuries since Newton, a Computational mode of the past years, and now this fourth Data Exploration or eScience faculty. A typical chapter might be “From Web 2.0 to the Global Database” by Timo Hannay or “Instrumenting the Earth: Next-Generation Sensor Networks and Environmental Science” by Michael Lehning, et al. But with its usual 10:1 men to women authors, the chapters highlight a “particle” emphasis as if via a left, half brain unable to imagine any abiding patterns or creation that the myriad pieces might fit together to reveal. To reflect, are we in fact seeing a radically new stage of human scientific inquiry, as if a worldwide cerebral faculty, beginning to think and learn on its own?

A good book review by physicist turned open source advocate Michael Neilsen appears in Nature (December 10, 2009), from which the next quote. On his blog can be found recent talks and thoughts that relate to his onw 2011 book Reinventing Discovery. But are we stuck in a mindset that there is nothing extant TO find? How might our worldwide mindkind, namely Mary and Charles Earthwin, finally both meld data and vision, part and whole, so as to realize a genesis universe?

Hundreds of projects in fields ranging from genomics to computational linguistics to astronomy demonstrate a major shift in the scale at which scientific data are taken, and in how they are processed, shared and communicated to the world. Most significantly, there is a shift in how researchers find meaning in data, with sophisticated algorithms and statistical techniques becoming part of the standard scientific toolkit. (722) The most interesting theme that emerges here is a vision of an increasingly linked web of information: all of the world's scientific knowledge as one big database. (722)

Heylighen, Francis and Katarina Petrovic. Foundations of ArtScience. Foundations of Science. 26/2, 2021. Free University of Brussels scholars (search FH) propose a frontier synthesis of these iconic cultural approaches, which as the Abstracts cites, could be attributed to our complementary brain hemispheres, each with a significant, vital contribution to make. This project is vital today as the two modes are far apart so that a male mechanistic, Ptolemaic physics rules, with any feminine sense of an organic image and message is excluded.

While art and science went on side-by-side during the Renaissance, their methods and perspectives parted leading to a long separation between the "two cultures". Recently, A collaboration between artists and scientists is promoted by the ArtScience movement to join the intuitive, imaginative ways of art and a rational, rule-governed science. Science and art are united in their creative investigation, where coherence, pattern and meaning play a vital role in the development of concepts. According to the standard view, science seeks an understanding that is universal, objective and unambiguous, while art focuses on unique, subjective and open-ended experiences. Both offer prospect and coherence, mystery and complexity, albeit with science preferring the former and art, the latter. (Abstract excerpt)

Ho, Matthew, et al. LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology. arXiv:2402.05137. We note this paper by fifteen coauthors with postings in France, Korea, the USA and UK to report and convey an advancing reciprocal synthesis of personal guidance and computational abilities as public scientific endeavor enter a collaborative Earthwise era.

This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline as a codebase for rapid, user-friendly, machine learning (ML) knowledge in astrophysics and cosmology. The program includes software for neural architectures, training schema, priors, and density estimators adaptable to any research workflow. We present real applications such as estimating galaxy cluster masses from X-ray photometry; inferring cosmology from matter power spectra, gravitational wave signals; and semi-analytic models of galaxy formation. We also include comparisons of other methods as well as discussions about the ML inference in astronomical sciences. (Excerpt)

Hoekstra, Alfons, et al. Multiscale Computing for Science and Engineering in the Era of Exascale Performance. Philosophical Transactions of the Royal Society A. Vol.377/Iss.2142, 2019. In a theme issue Multiscale Modelling, Simulation and Computing from the Desktop to the Exascale, a British, Swiss and German team including Peter Coveney and Simon Zwart survey this late 2010s orders of magnitude increase with its concurrent advent of voluminous informational inputs. Typical papers are Big Data: the End of the Scientific Method, Assessing the Scales in Numerical Weather and Climate Predictions and Multi-Scale High-Performance Computing in Astrophysics.

Exascale computing refers to computing systems capable of at least one exaFLOPS, or a billion billion (quintillion) calculations per second. Such capacity represents a thousandfold increase over the first petascale computer that came into operation in 2008. One exaflop is a thousand petaflops or a quintillion floating point operations per second. (Wikipedia)

Holton, Gerald. Thematic Origins of Scientific Thought. Cambridge: Harvard University Press, 1982. A major work which examines “the roots of complementarity” as conceived by Niels Bohr and his Copenhagen school. Bohr went on to see this as a universal principle beyond the quantum which applied at biological, psychological and social planes.

Howell, Owen, et al. Machine Learning as Ecology. Journal of Statistical Physics. 53/33, 2020. In a Machine Learning and Statistical Physics section, Boston University and Boston College physicists including Pankaj Mehta scope out a newly evident affinity between these novel computational methods and natural ecosystem activities. By technical finesses, parallels are found to occur by way of common “algorithmic” processes. An informative cross-transfer from each field builds the case which then reveals a universality from cognitive to flora/fauna to physical phases as they become a fertile ground.

Machine learning methods have had spectacular success on numerous problems. Here we show that a prominent class of learning algorithms, aka support vector machines (SVM), have a natural interpretation in terms of ecological dynamics. We use these ideas to design new online SVM algorithms that exploit ecological invasions, and benchmark performance using the MNIST dataset. Our work provides a new ecological lens through which we can view statistical learning and opens the possibility of designing ecosystems for machine learning. (Abstract)

Support Vector Machines is a mathematical method used for classification and regression problems. It can solve linear and non-linear practical applications. Its operative algorithm creates a line or a hyperplane which separates the data into classes.

Huertan-Company, M. and F. Lanusse. The Dawes Review 10: The Impact of Deep Learning for the Analysis of Galaxy Surveys. arXiv:2210.01813. Universidad de La Laguna, Tenerife, Spain astronomers post an extensive review as these collaborative, computational studies presently spiral on up to an Earthropo sapience. Some 500 references indicate how much our intrinsic endeavors to quantify and describe a celestial spacescape have a planetary cast. See also Gravothermal Collapse of Self-Interacting Dark Matter Halos as the Origin of Black Holes in Milky Way Satellites by Tamar Meshveliani, et al (University of Iceland) at arXiv:2210.01817 as another transitional example.

As these data flows grow, here we review the main applications of deep learning for galaxy surveys so far. We report that the applications are becoming more diverse and deep learning is used for computer vision estimates of galaxy properties, along with cosmological models.. Some common challenges are cited before moving to the next phase of deployment in the processing of future surveys; e.g. uncertainty quantification, interpretability, data labeling, and so on as the endeavor shifts from training and simulations, so to become a common practice in astronomy. (Excerpt)

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