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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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Genesis Vision
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Organic Universe
Earth Life Emerge
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Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 1 through 15 of 108 found.

> Geonativity

Anwar, Sayeed, et al. Self-organized bistability on globally coupled higher-order networks.. arXiv:2401.02825. In these 2020s complex system studies proceed to delve deeper and uncover vital new features. Here Indian Statistical Institute, Kolkata, KU Leuven, Belgium and Immanuel Kant Baltic Federal University, Kaliningrad neuroscientists including Nikita Frolov add a significant notice of nature’s tendency to seek and reside at an optimum state between two opposite but reciprocal modes of being. This middle way poise achieves a bilateral resonance rather than a total fixation on one or the other poles.

Self-organized bistability (SOB) stands as a critical behavior for the systems adjusting themselves to this dynamic balance. Recently, SOB has been found in a scale-free network as a recurrent transition to a global synchronization. Here, we extend the theoretical boundaries to a higher-order network. We use statistical data from spontaneous synchronized events to demonstrate the crucial role SOB plays in initiating and terminating temporary synchronized events. (Excerpt)

Multistability is a prevalent phenomenon observed in both man-made and real-world systems, characterized by consistent stable states. This poise plays a crucial role in regulating processes in living systems operating on different scales, from organ system interactions to neural synchronization. Typically, in normal conditions, neural activity in the brain demonstrates distinct power-law (scale-free) distributed avalanches, which is indicative of underlying self-organized criticality. (1)

To summarize, here we have reported a theoretical investigation of SOB by a globally coupled Kuramoto network with higher-order interaction. Our study reveals that the interplay between consumption and recovery rates results in a region of critical bistable dynamics. Within this regime, the critical dynamics allow for a self-sustaining toggling from the state of incoherence to coherence. (8)

> Geonativity

Gao, Chong-Yu and Jun-Jie Wei. Scale-invariant Phenomena in Repeating Fast Radio Bursts and Glitching Pulsars. arXiv:2401.13916. As the Abstract says, Purple Mountain Observatory, Chinese Academy of Sciences and University of Science and Technology of China, Hefei astrophysicists report a seemingly ubiquitous tendency for active astronomical phenomena to persist in a dynamic self-similar criticality. See also Distributions of energy, luminosity, duration, and waiting times of gamma-ray burst pulses with known redshift detected by Fermi/GBM at arXiv:2401.14063 and The Self-organized Criticality Behaviors of Two New Parameters in SGR J1935+2154 at arXiv:2401.05955.

The recent discoveries of a glitch/antiglitch accompanied by fast radio burst (FRB)-like bursts from the Galactic magnetar SGR J1935+2154 have revealed the physical connection between the two. In this work, we study the statistical properties of radio bursts from the hyperactive repeating source FRB 20201124A. We confirm that the probability density fluctuations of energy, peak flux, duration, and waiting time well follow the Tsallis q-Gaussian distribution. Similar scale-invariant property can be found in PSR B1737--30's glitches. These statistical features can be well understood within the same physical framework of self-organized criticality systems. (Excerpt)

> Geonativity

Song, Tiancheng, et al. Unconventional Superconducting Quantum Criticality in Monolayer WTe2.. arXiv:2303.06540. Into the mid 2020s, fifteen Princeton University and National Institute for Materials Science and Nanoarchitectonics, Japan researchers proceed to find an inherent tendency for optimum critical behavior even in this substantial realm. See also Self-similarity of the third type in ultra relativistic blastwave by Tamar Faran, et al at arXiv:2402.07978 for another instance of deeply ingrained critical behavior.

The superconductor to metal transition in two dimensions (2D) provides a platform for study quantum phase transitions (QPTs) and critical phenomena but many questions remain. Extending Nernst experiments down to millikelvin temperatures, we identify a superconducting quantum critical point (QCP) in spin Hall insulator made of tungsten ditelluride (WTe2). These findings, which have no prior analogue, call for careful examinations of the mechanism of the QCP, including the possibility of a QPT between ordered phases in the monolayer. Our experiments open a new avenue for studying quantum critical matter. (Abstract)

Our Planatural Edition: A 21st Century PhiloSophia, Earthropo Ecosmic PediaVersion

The Genesis Vision > Historic Precedents

Robledo, Alberto and Carlos Velarde. rA Half-Century Research Footpath in Statistical Physics. arXiv:2401.06181. Universidad Nacional Autónoma de México physicists provide a unique retrospective of a combined sequence of achievements on a long winding walk toward a unified synthesis. The nonlinear dynamics section alone has a dozen sections which stand as a good review of the subject.

We give an account of condensed matter and complex system studies that span five decades by links to access abstracts and full texts of a select publications. The topics, techniques and outcomes reflect evolving interests of the community along with the use of analogies in distinctive ways. The studies have been grouped into thirty sets and these, in turn, placed into three collections according to the main approach: stochastic processes, density functional theory, and nonlinear dynamics. We refer to our main surmise: Athe validity of ordinary statistical mechanics and the pertinence of (Constantino) Tsallis statistics. (Excerpt)

The Genesis Vision > News

Hickey, Ravmond. Life and Language Beyond Earth.. Cambridge, UK: Cambridge University Press, 2022. An emeritus professor at the University of Duisburg and Essen, Germany and the University of Limerick, Ireland writes his comprehensive, up to date, survey of life’s evolutionary development both on our home bioworld and across analogous interstellar realms. The well researched and written text assumes that habitable planets will hold to a mainly similar Darwinian creaturely course as occurred on Earth. His especial emphasis is then a persistent appearance of communicative and language-like faculties for diverse social group viability. Visit the author’s website at raymondhickey.com for chapter abstracts and more. Here next is its full table of contents.

Part I. Introduction: 1. Approaching the topic; 2. Looking beyond Earth; 3. Striving to understand; Part II. The Universe We Live In: 4. Trying to grasp size; 5. Star formation and planets; 6. The likelihood of life; 7. Possible conditions on an exoplanet?; 8. How and where to look for exolife; 9. The limits of exploration; 10. Assessing probabilities; Part III. Our Story on Earth: 11. The slow path of evolution; 12. How does the whole work?; 13. The road to Homo sapiens; 14. The rise of human societies; Part IV. The Runaway Brain: 15. The brain-to-body ratio; 16. How brains develop; 17. Our cognition; 18. Consciousness; 19. Artificial intelligence; Part V. Language, our Greatest Gift: 20. Looking at language; 21. Talking about language; 22. The view from linguistics; 23. The language faculty and languages; 24. Language and the brain; 25. Acquiring language; 26. Humans and animals; Part VI. Life and Language, Here and Beyond: 27. Preconditions for life; 28. What might exolife be like?; 29. Looking for signs of life; 30. The issue of first contact; 31. Language beyond Earth; 32. How human language arose; 33. The language of exobeings; 34. Looking forward.

The Genesis Vision > News

Kukarni, Suman, et al.. Information content of note transitions in the music of J. S. Bach. Physical Reviews Research. 6/013136, 2024. University of Pennsylvania systems scholars including Chris Lynn and Dani Bassett post an innovative appreciation that symphonic and melodious compositions are suffused with and arranged by multiplex networks. The paper reviews of a technical basis which is graphically illustrated. After many centuries the actual presence of natural rhythms is mathematically quantified and published in a Physics journal. See also Unsupervised cross-domain translation via deep learning and application to music-inspired protein designs by Markus Buehler in Patterns. (4/3, 2023) and Cells and sounds by Michael Spitzer in Progress in Biophysics and Molecular Biology (186, January 2024). If olny we could hear and listen to the song of the cell and of the ecosmos.

Music has a complex structure that expresses emotion and conveys information. Here we study a musical piece by way of networks formed by notes (nodes) and their transitions (edges). Thus we view compositions by J. S. Bach through the lens of network science, information theory, and statistical physics over a wide range of fugues and choral pieces. In turn, we consider human neural networks that enable efficient communication via heterogeneity and clustering. Taken together, our findings shed light on both Bach's work and further studies of complexities, creativity, and more. (Abstract excerpt)’

We hope that our framework inspires more exchanges between physics, cognitive science, and musicology. On a broader scale, our project investigates how information in complex systems is conceptually contained. To conclude, we highlight a number of exciting directions for future inquiry and outline ways in which our approach can be expanded upon and improved. (10) By providing an example of a comprehensive analysis of musical melodies, our version complements the rich study of language, music, and art as dynamic complex multiplex systems. Finally, a quantitative treatment of the patterns and motifs inspire analogies between music and other fields of science such as including understanding protein structures and designing organic materials. (12)

The Genesis Vision > News

Manrique, Pedro, et al. Non-equilibrium physics of multi-species assembly: From inhibition of fibrils in biomolecular condensates to growth of online distrust.. arXiv:2312.08609. George Washington University theorists including Neil Johnson (search PM, NJ) post an innovative, wide correspondence between biomolecules and sociopeople as they/we intersect, crosstalk and come together. See also Multi-Species Cohesion: Humans, machinery, AI and beyond by this group at arXiv:2401.17410. Once again a common affinity is evident across these widest reaches which then implies deeper a physical origin.

Self-assembly is a key process in living systems from the microscopic biological level (e.g. proteins into fibrils in a human cell) to the macroscopic societal level (e.g. humans into common-interest social media). The components in such systems) are highly diverse, and so are the self-assembled structures that they form. But there is no theory of how they arise from a multi-species pool. Here we provide a simple model which trades myriad chemical and human details for a transparent analysis, in good agreement. It reveals a new inhibitory role for biomolecular condensates against dangerous amyloid fibrils, as well as a kinetic reason so much distrust has now beset the internet. The nonlinear dependencies that we uncover suggest real-world control strategies to buffer and better these processes. (Excerpt).

Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Actual Factual Knowledge

A Learning Planet > The Spiral of Science

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)

A Learning Planet > Mindkind Knowledge > deep

Li, Qing, et al. Progress and Opportunities of Foundation Models in Bioinformatics. arXiv:2402.04286. Chinese University of Hong Kong and BioMap, Beijing computer scientists provide a wide-ranging perspective on this mid 2020s synthesis of a Bioinformatic approach, whose journal goes back to 1985, and these novel AI neural net, large language models as they become amenable.

Bioinformatics has witnessed a paradigm shift with the increasing integration of artificial intelligence (AI) and the adoption of foundation models (FMs). These AI techniques have addressed prior issues in bioinformatics such as scarce annotations and of data noise. FMs are adept at handling large-scale, unlabeled data, which has allowed them to achieve notable results in downstream validation tasks. The primary goal of this survey is to conduct a systematic investigation and summary of FMs in bioinformatics, tracing their evolution, current research status, and the methodologies employed. Finally, we outline potential development paths and strategies for FMs in future biological research. (Excerpt)

A Learning Planet > Mindkind Knowledge > CI

Casadei, Roberto. Artificial Collective Intelligence Engineering: A Survey of Concepts and Perspectives. Artificial Life. 29/4, 2025. In a Special Issue on Lifelike Computing Systems, a University of Bologna surveys an array vital aspects by which nature’s steady propensity to become smarter, learned, solve problems, find better ways, by forming conversational groupings.

Collectiveness is an important property of many natural and artificial systems. By way of a large number of individuals, it is possible to produce intelligent collective. Indeed, collective intelligence is often a design goal of engineered computation motivated by technoscientific trends like the Internet of Things, swarm robotics, and crowd computing. The challenge is to identify, place in a common structure, and connect the different areas and methods through intelligent collectives. This article covers preliminary notions, fundamental concepts, identifies opportunities for researchers on artificial and computational collective intelligence engineering.

A Learning Planet > Mindkind Knowledge > CI

Friston, Karl, et al. Designing ecosystems of intelligence from first principles. Collective Intelligence. January, 2024. As the AI frontier opens wide, twenty neuroscholars from the UK, USA, Canada, Germany, Australia and the Netherlands coauthor a proposed approach and plan at this outset to respectfully orient, guide and enhance a safe, viabe way forward. Their endeavor concurs with Pierre Levy’s paper Semantic Computing with IEML herein (2/4, 2023) to build in dedicated programs for this purpose.

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade. Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making in which people are integral participants by way of a shared intelligence. This vision is premised on active inference which is a means of adaptive behavior that can be read as a physics of intelligence, self-organization, and as self-evidencing. We consider communication protocols needed to enable such a knowledge ecosystem. (Excerpt)

Active inference offers a formal definition of intelligence for AI research that entails the beliefs of agents and groups which allows us to write down the self-organizing over several scales. The result is AI that “scales up” the way nature does: by aggregating individual minds and their contextual knowledge into “nested intelligences”. (2) Once intelligence at each scale supervenes on, or emerges from, simpler phases, the multi-scale view of natural acumen implies a recursive structure in which the same functional motif recurs in ramified forms via more complex agents. (3)

Conclusion: Our proposal for stages of development for active inference as an artificial intelligence technology The aim of this white paper is a vision of research and development in the field of artificial intelligence for the next decade. We have proposed active inference as a technology uniquely suited to the collaborative design of an ecosystem of natural and synthetic sense-making, in which humans are integral participants by way of shared intelligence. The Bayesian mechanics that follows led us to define intelligence as the accumulation of evidence for an agent’s generative model of their sensed world—also known as self-evidencing (search Hohwy}. (12)

A Learning Planet > Mindkind Knowledge > CI

Mengers, Vito, et al. Leveraging Uncertainty in Collective Opinion Dynamics with Heterogeneity. arXiv:2402.03354. We note this entry by Technische Universit at Berlin, Humboldt Universit and University of Konstanz system scholars including Pawel Romanczuk in this section for its broad theoretic recognition of how prevalent a consistent tendency to move toward and form viable groupings across natural and social occasions actually is

Natural and artificial collectives exhibit complex, heterogeneous behaviors across its dimensions. We investigate two effects of such collective opinion dynamics: the agents' prior information and network neighbors. To study these, we introduce uncertainty as an additional aspect.. By quantifying this for each agent, we can adaptively weigh their individual against social information. These opportunities for improved performance and observability suggest the importance of uncertainty both for the study of natural and the design of artificial heterogeneous systems. (Excerpt)

Individuals in collectives are exposed to a flow of incoming information from their neighbors, be it in a school of fish, a network of sensors, a swarm of robots, or a group of humans. Models of opinion dynamics not only suggest a mechanism for how individuals incorporate this information but also provide a way to design equivalent artificial systems. The inter-individual variations of agents can manifest in behavioral traits, position in the network, information access, or self-confidence. (1)

A Learning Planet > Mindkind Knowledge > CI

Wheatley, Thalia. The Emerging Science of Interacting Minds. Perspectives on Psychological Science. November, 2023. In a special issue on The Psychology of Collectives, the Dartmouth College psychologist and director of its Consortium for Interacting Minds joins a welling chorus which quantifies and advocates this spiral moment which can, at last, appreciate and facilitate the value of common wisdom.

For over a century, psychology has focused on the mental processes of a single individuals, but humans rarely navigate the world in isolation. A person’s successful development, traits, dispositions, are due to family and many friends. Social interaction makes us who we are, how we think, and our behavior. Here we discuss issues that have limited a robust science of how minds engage, commune, and new approaches beginning to address these aspects. A deep understanding of the human mind requires studying the public contextual milieu within which it originates and proceeds. (Abstract)

In this article, we discuss prior research that notes the vital consequences of social interaction for individuals and collectives. We then address why the study of interaction itself—the meeting of minds that co-constitutes thought and behavior—has been empirically neglected despite its lifelong importance. Finally, we explain why it seems we are on the expansive cusp of a conceptual and methodological advance that will refocus the field on the importance of social interactions for the purpose of understanding the human mind. (1)

Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet, Incubator Lifescape

Animate Cosmos

Chon-Torres, Ovtavio, et al. Astrobiocentrism: reflections on challenges in the transition to a vision of life and humanity in space. \. International Journal of Astrobiology. February, 2024. Universidad de Lima, ICTP, Trieste, Italy, Lund University, Umeå University, Sweden. King’s College London, CSIC-UCM, Madrid, Universitat Bern, Bern and Bethany College, KS, USA astroscholars including Julian Chela-Flores and David Dunér introduce an engaging, mid 2020s, appreciation of a life-friendly, conducive ecosmos either by an evolutionary genesis on Earth-like analogs, or by human expansion into and colonization of the nearer and further galactic ezpanse.

Astrobiocentrism is a vision that places us in a confirmation of life in the universe, either as a second genesis or as an expansion of humanity in space. Unlike biocentrism or ecocentrism, the astrobiocentric view is not limited to the Earth-centric perspective for it incorporates a multi-, inter- and transdisciplinary understanding. Therefore, the aim of this paper is to be a reflection on the astrobiocentric issues related to the challenges and problems of the discovery of life in the universe. Here we explore some aspects of the transition from biogeocentrism, astrobio-semiotics, homo mensura, moral community, planetary sustainability and astrotheology perspectives.

Animate Cosmos > Organic > quantum CS

Jiang, Jinzhe, et al. Strong generalization in quantum neural networks.. Quantum Information Processing. Vol. 22. Art 428, 2023. We cite this entry by nine Inspur Electronic Information Industry Co., Jinan, China engineers as an example of how generic neural net algorithms can easily be applied to quantum phenomena. An observation might then be how similar, Rosetta ecosmos-like, whence all these procedures can be readily interchanged.

Generalization is an important feature of neural networks (Nns) as it indicates their ability to predict new and unknown data. However, classical Nns tend to overfit due to their nonlinear character, which limits generalizations. Our method combines quantum computing with Nns so to form quantum neural networks (Qnn). We show that Qnns perform almost the same on the training dataset and test dataset without overfitting. To validate our proposal, we simulate three Qnn models on public datasets and demonstrate that they have much better generalizations than classical Nns. (Excerpt)

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