II. Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Twintelligent Gaiable Knowledge

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

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)

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)

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