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

Dai, Zhenyu, et al.. Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter. arXiv:2303.14090. Here is another 2023 instance whereby our Earthkinder cosmological studies are shifting and rising to a global genius phase, but properly by our human guidance. As the quotes say, in this plane, University of Houston, University of Edinburgh and University of the Western Cape, RSA astrotheorists appear to have awesome abilities to explore and learn about all wide and deep reaches

Physics-informed neural networks (PINNs) have become a vital framework for predictive models that combine statistical patterns with domain knowledge. Hydrodynamic simulations are a core constituent of modern cosmology, but the computations are time-consuming. This paper presents the first application of physics-informed neural networks to baryons by combining advances in algorithmic architectures. By extracting baryonic properties from cosmological simulations, our results have improved accuracy based on dark matter haloes, metallicity relations, and scatter distributions. (Abstract excerpt)

The ΛCDM model, coined the standard model of cosmology due to its widespread adoption and explanatory power, plays a crucial role in modern cosmology and astrophysics. Galaxy formation evolution occur within virialized structures resulting from density perturbations in the early Universe, subjected to gravitational collapse. This leads to large-scale structure in
the form of a cosmic web evolving from a somewhat smooth starting
point, with dark matter halos as gravitationally bound over densities of the postulated main contributor to the matter content of the Universe. (1)

Our experiments demonstrate that PINNs as a revolutionary endeavor, offer a way to directly uncover novel baryonic properties and inform galactic dark matter halos. As such, they can be used to complete cosmological N-body simulations based on full hydrodynamic simulation suites. Our present framework is applicable to large-scale cosmological simulations and contributes to the use of modern machine learning in astrophysics. (13)

De Arruda, Henrique, et al. Knowledge Acquisition: A Complex Networks Approach. arXiv:1703.00366. University of Sao Paulo computational physicists Henrique, Filipi Silva, Luciano Costa and Diego Amancio show how the scientific process itself which discovers these dynamic natural topologies can be quantified as an iconic exemplar.

Complex networks have been found to provide a good representation of the structure of knowledge, as understood in terms of discoverable concepts and their relationships. In this context, the discovery process can be modeled as agents walking in a knowledge space. Recent studies proposed more realistic dynamics, including the possibility of agents being influenced by others with higher visibility or by their own memory. However, rather than dealing with these two concepts separately, as previously approached, in this study we propose a multi-agent random walk model for knowledge acquisition that incorporates both concepts. In order to evaluate our approach, we use a set of network models and two real networks, one generated from Wikipedia and another from the Web of Science. (Abstract excerpt)

Dieleman, Sander, et al. Rotation-Invariant Convolutional Neural Networks for Galaxy Morphology Prediction. arXiv:1503.07077. Since the Galaxy Zoo (Google) crowdsourcing project to characterize astronomical topologies has limitations, Ghent University and University of Minnesota researchers lay out a deep neural network method to further empower the communal project. See also Teaching a Machine to See: Unsupervised Image Segmentation and Categorization Using Growing Neural Gas and Hierarchical Clustering at 1507.01589 and Machine Learning Based Data Mining for Milky Way Filamentary Structure Reconstruction, 1505.06621, for similar cognitive algorithms. Might one imagine the actual formation of a global thinking brain?

Dijksterhuis, E. J. The Mechanization of the World Picture. New York: Oxford University Press, 1961. The standard work on the transition from a cosmos as sensitive organism to particulate mechanism due to the analytical, reductive method.

Dong, Yuxiao, et al. A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations. arXiv:1704.05150. From our late vantage Microsoft Research, Redmond, WA scholars survey the past 115 years of scientific activities and findings. A progression then becomes evident from an early incipient, individual stage, mainly in the US, UK, and Germany, to expansions after WWII to Japan, Israel, onto China, India, Australia, and lately over oceans and continents. But as the Abstract cites, and our website avers, into the 21st century an historic change has occurred from loners and small groups to large international teams fostered by the vast Internet. But we add it has not yet dawned, here or anywhere, that this transition to a worldwide sapiensphere could actually be learning and discovering on her/his own.

Progress in science has advanced the development of human society across history, with dramatic revolutions shaped by information theory, genetic cloning, and artificial intelligence, among the many scientific achievements produced in the 20th century. In this work, we study the evolution of scientific development over the past century by presenting an anatomy of 89 million digitalized papers published between 1900 and 2015. We find that science has benefited from the shift from individual work to collaborative effort, with over 90\% of the world-leading innovations generated by collaborations in this century, nearly four times higher than they were in the 1900s. We discover that rather than the frequent myopic- and self-referencing that was common in the early 20th century, modern scientists instead tend to look for literature further back and farther around. Finally, we also observe the globalization of scientific development from 1900 to 2015, including 25-fold and 7-fold increases in international collaborations and citations, respectively. (Abstract)

In this work, we study the evolution of science over the past century according to three dimensions. First, we examine the evolving process of collaborations between scientists of different career-ages, institutions, and countries. Second, we characterize referencing behavior over time, with an emphasis on both its individual and collective dynamics. Finally, we investigate the rise and fall of scientific impact across the planet since 1900. Our study is performed on a large-scale scholarly dataset comprised of more than 89 million publications, 795 million citations, and 1.23 billion collaboration relationships spanning from 1900 to 2015, making this the largest-scale and longest-spanning analysis yet performed on academic data. (1)

Draelos, Timothy, et al. Neurogenesis Deep Learning. arXiv:1612.03770. We note this posting by Sandia National Laboratory computational neuroscientists as another report about how neural, machine, algorithmic, computational, and probabilistic procedures are being applied from cosmology to chemistry to social media. See also for example Deep Learning with Dynamic Computation Graphs at arXiv:1702.02181.

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. (Abstract)

Dworkin, Jordan, et al. The Emergent Integrated Network Structure of Scientific Research. PLoS One. 14/4, 2019. A guiding premise for this website is a worldwide intellectual endeavor which is lately gaining revolutionary knowledge by its own sapient self. Its mission is to gather, report and document copious findings from cosmos to creativity. Here University of Pennsylvania neuroresearchers JD, Russ Shinohara, and Danielle Bassett indeed perceive an independent global learning process via many cumulative personal contributions. From their network neuroscience expertise, the dynamic process may appear to take on a cerebral topology. In regard, the prescient noosphere of Vladimir Vernadsky, Pierre Teilhard, and others in the last century seems at last in full manifestation.

Scientific research is often seen as individuals and small teams striving for disciplinary advances. Yet as a whole, this endeavor more closely resembles a complex system of natural computation, in which information is obtained, generated, and disseminated more effectively than by isolated individuals. But the structure of this integrated, innovative landscape of scientific ideas is not well understood. Here we use network science to map the landscape of interconnected topics covered in the multidisciplinary journal Proceedings of the National Academy of Sciences since 2000. In regard, nodes represent topics of study and edges give the degree to which they occur in the same papers. The network displays small-world architecture, with dense connectivity within scientific clusters and sparse connectivity between clusters. Broadly, this work suggests that complex and dynamic patterns of knowledge emerge from scientific research, and that structures reflecting intellectual integration may be beneficial for obtaining scientific insight. (Abstract excerpt)

Eisler, Riane. Toward an Empathic Science. Chaisson, Eric and T.-C. Kim, eds. The Thirteenth Labor.. Amsterdam: Gordon and Breach, 1999. Eisler recommends an educational approach that is not skewed to male warfare and control but founded upon humanistic and communal values.

Elliott, Kevin, et al. Conceptions of Good Science in Our Data-Rich World. BioScience. 66/10, 2016. In this lead journal of the American Institute of Biological Sciences, Michigan State University naturalists and philosophers consider a revised methodology suitable for a 21st century proliferation of informational bytes which require ways to constrain, organize them so as to convey significant findings.

Scientists have been debating for centuries the nature of proper scientific methods. Currently, criticisms being thrown at data-intensive science are reinvigorating these debates. However, many of these criticisms represent long-standing conflicts over the role of hypothesis testing in science and not just a dispute about the amount of data used. Here, we show that an iterative account of scientific methods developed by historians and philosophers of science can help make sense of data-intensive scientific practices and suggest more effective ways to evaluate this research. We use case studies of Darwin's research on evolution by natural selection and modern-day research on macrosystems ecology to illustrate this account of scientific methods and the innovative approaches to scientific evaluation that it encourages. We point out recent changes in the spheres of science funding, publishing, and education that reflect this richer account of scientific practice, and we propose additional reforms. (Abstract)

Fernandez, Elisco. Taking the Relational Turn. Biosemiotics. 3/2, 2010. A succinct study of this epochal, welling shift. The vested “nominalist” view that only objects exist, implicit for the past centuries of science, is now in eclipse by a reassembly of nature in many areas (albeit necessary to first find all the pieces) into viable, dynamic systems. Such interrelated networks are then seen to be suffused by an informational essence as they constantly communicate. In support, prescient precursors of the Relational Biology of Robert Rosen, the Relational Quantum Mechanics of Carlo Rovelli, and Charles Peirce’s semiotic philosophy are enlisted.

A cluster of similar trends emerging in separate fields of science and philosophy points to new opportunities to apply biosemiotic ideas as tools for conceptual integration in theoretical biology. I characterize these developments as the outcome of a “relational turn” in these disciplines. They signal a shift of attention away from objects and things and towards relational structures and processes.

Relational developments in biology bear a telling similarity to a parallel relational turn presently manifest in the philosophy of science, rooted in the philosophy of physics and mathematics and in different varieties of structural and informational realism. The recognition of the relational nature of reality within these disciplines entails a tacit repudiation of nominalistic biases in science that have hindered the reception of semiotic conceptions in biology.

Ferris, Timothy. The Science of Liberty: Democracy, Reason, and the Laws of Nature. New York: HarperCollins, 2010. The science journalist here astutely draws parallels between the experimental pursuit of natural and technical knowledge and a social milieu that permits and endorses such free pursuit. The term “liberal,” which Ferris often employs, is equated in this case with a popular openness to creative inquiry. (Other broad uses, such as by FDR and JFK, would include social justice or tolerant welfare, now under virulent attack from the right.) The result is a cogent chronicle of the past centuries of scientific advance, seen to go hand in hand with progressive democratic societies. I found his chapter on Academic Antiscience about the postmodern ridicule of such endeavors to be most lucid amongst this murky episode. The next chapter, One World, goes on to offer a succinct review of Islamic theologies. But, as a spate of new books, e.g. Sean M. Carroll, Chris Impey, and Marcelo Gleiser, take as a default and I wonder if clear thinker TF realizes this, the whole project is undercut, as per the quote, by the acceptance, even acquiescence, of a pointless, moribund nature, a random physical cosmos with no place for people or knowledge.

So to wonder, “Why am I here?” is to ask the wrong question. Nothing requires that you or I exist, or that the human species exist; it’s just that so long as there is life on Earth some creatures will exist, and you and I happen, at present, to be among them. Evolution reveals that human got here the way everything else got here, through a long historical process of accident and selection. (263)

Floris Cohen, H. The Scientific Revolution. Chicago: University of Chicago Press, 1994. A treatise that integrates many sources to equate science with a precise “mathematization” whence numbers predominate over words in the natural text, which then resulted in a machine model. By the 18th century, researchers had lowered their expectations from overarching explanations to collecting voluminous minutiae of data.

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