Tadic, Bosiljka and Roderick Melnik. Fundamental interactions in self-organized critical dynamics on higher-order networks. arXiv:2404.06175.. Jozef Stefan Institute, Ljubljana, Slovenia and Wilfrid Laurier University; Waterloo, Canada (search each) contribute further theoretic findings of how pervasive nature’s propensity to reside at this synchronous optimum title state seems to be. In this case they discern its self-similar presence in multiplex connectivities such as cerebral phenomena. See also Self-organized dynamics beyond scaling of avalanches by Bosiljka Tadic, et al at arXiv:2403.15859.

In complex systems, higher-order connectivity is often revealed in the geometry of networked units. Such systems show signatures of self-organized criticality, a non-equilibrium collective behaviour associated with long-range correlations and scale invariance. Here, we intertwine features of higher-order geometry and self-organized critical dynamics as responsible for the emergence of new properties on a larger scale as occurs in brains. We provide an overview of collective dynamics phenomena, such as the synchronization of phase oscillators. (Excerpt)

Volkening, Alexandria. Volkening, Alexandria. Methods for quantifying self-organization in biology: a forward-looking survey. arXiv:2407.10832. A Purdue University mathematician contributes a latest tutorial chapter for an interdisciplinary audience which presents various approaches for qualitative data studies across a range of applications. See a prior paper by AV, Linking discrete and continuous models of cell birth and migration, at arXiv:2308.16093.

rom flocking birds to schooling fish, organisms interact to form collective dynamics across the natural world. Self-organization is present at smaller scales as well: cells interact and move during development to produce patterns in fish skin. For all these examples, scientists are interested in the individual behaviors informing spatial group dynamics and the patterns that will emerge due to agent interactions. A current issue is that models of self-organization are qualitative and need pattern data to include quantitative information. In this tutorial chapter, I survey some methods for quantifying self-organization, including order parameters, pair correlation functions, and techniques from topological data analysis. (Abstract)

Pattern formation driven by the interactions of agents is found across the natural and social world, spanning the population scale to the intracellular scale. Large-scale examples include pedestrian movements, honeybee aggregation, schooling fish, and marching locusts. In the domain of cells and tissues, neural-crest cell migration, color in fish skin, and wound healing are examples of self-organization. At smaller scales, proteins and filaments regulate transport and shape within cells. Studying such complex systems often leads to qualitative pattern data in the form of images. Being able to quantify these spatial data opens the door to a broader perspective on self-organization and makes complex systems more amenable to interdisciplinary investigation. (1)

Andrea V. was a programme participant in the Mathematics of Movement: an interdisciplinary approach to mutual challenges in animal ecology and cell biology (Google) symposium at the Isaac Newton Institute for Mathematical Sciences, Cambridge, autumn 2023. She gratefully acknowledges a travel grant from the Association for Women in Mathematics.

zhang, Lu, et al.. A mathematical framework for understanding the spontaneous emergence of complexity applicable to growing multicellular systems.. PLoS Computational Biology. June, 2024. June is in bloom with papers like this by Peking University, Chinese Academy of Sciences, Peking-Tsinghua Center for Life Sciences, University of Hong Kong, and Shenzhen Institutes of Advanced Technology researchers who conclude that their sophisticate findings can imply the deep presence of intrinsic topological forces guided by genetic information. If of a mind, a set course can be seen to unfold as it forms embryonic cells as if one situational cue. Altogether a novel sense of an independent, preordained, life-bearing process is achieved.

In embryonic development and organogenesis, cells sharing identical genetic codes acquire diverse gene expression states in a reproducible spatial distribution for multi-cellular formation. To understand the spontaneous growth of complexity, we constructed a division-decision model, simulating the growth of cells with similar genetic networks from a single cell. Our findings highlight role of cell division in providing positional cues, escorting the system toward states rich in information. This study is a forward step in comprehending developmental intricacies, paving the way for quantitative formulations to construct synthetic multicellular patterns. (Excerpt)

Embryonic development shapes our bodies from a single cell, determining the placement of the head and tail. But how do cells, all sharing the same genetic code, precisely know what to become? Our mathematical model allows us to envision your information asbeing provided by your neighbors and your mom.. Then, appropriate regulatory networks such as lateral inhibition can transform this input into diverse yet robust gene expressions. This novel procedure helps us see the rules behind spontaneously growing complexity, guiding us to create new patterns of cells. (Author)

Dehghani, Nema and Gianluca Caterina.. Physical computing: a category theoretic perspective on physical computation and system compositionality.. Journal of Physics: Complexity. 5/3, 2024. MIT and Endicott College, Beverly, MA physicists explore how an emphasis on algebraic topologies can advance the active pursuit of natural programs. The paper opens opens with historic precedents from Leibniz, Turing, many more, to the present day.

This paper introduces a category theory-based framework (Wikipedia) as a way to redefine physical computing in light of quantum computing and non-standard computing systems. By integrating classical definitions within this broader perspective, the paper demonstrates how the compositional nature and relational structures of physical computing systems can be coherently formalized this way. This approach not only encapsulates recent formalisms in this field but also offers a structured method to explore the dynamic interactions within these systems.

Gandolfi, Daniela, et al. Information Transfer in Neuronal Circuits: From Biological Neurons to Neuromorphic Electronics.. Intelligent Computing.. 3/0059, 2024. Seven Biomedical, Metabolic and Neural Sciences, University of Modena researchers describe these latest advances toward and optimum phase of cerebral AI facilities and cognitive faculties. See also Brain-inspired computing systems: a systematic literature review by Mohamadreza Zolfagharinejad, et al in the European Physical Journal B (Vol. 97/Art. 70, 2024) for more info.

The advent of neuromorphic electronics is on its way to revolutionize the concept of computation. Recent studies have shown how materials, architectures and devices can achieve brain-like computation with limited power consumption and high energy efficiency. In this paper, we report similarities between biological, simulated, and artificially microcircuits in terms of information transfer from a computational perspective. We analyzed a mutual transfer at the synapses between mossy fibers and granule cells by the relationship between pre- and post-synaptic variability. We then extended our study to memristor synapses that embed rate-based learning rules to validate for neuromorphic hardware. (Excerpt)

Zenil, Hector, et al. Algorithmic Information Dynamics: A Computational Approach to Causality with Applications to Living Systems.. Cambridge, UK: Cambridge University Press, 2023. For some two decades Hector Zenil has been involved in innovative studies of natural program-like mathematics with colleagues such an Gregory Chaitin (search). See the arXiv preprint site for later entries such as Decoding Geometric Properties in Non-Random Data from First Information-Theoretic Principles (2405.07803.) This present volume with coauthors Narsis Kiani and Jesper Tegner represents a current overview, explanation and frontier.

Biological systems are extensively studied as interactions forming complex networks. Reconstructing causal knowledge from, and principles of, these networks from noisy and incomplete data is a challenge in the field of systems biology. Based on an online course hosted by the Santa Fe Institute Complexity Explorer, this book introduces the field of Algorithmic Information Dynamics, a model-driven approach to the study and service of dynamical phenomena. A theoretical and methodological framework guides an exploration and generate computable candidate models able to explain complex adaptive systems from physics to cell biology to cognitive sciences.

Hector Zenil is a senior researcher at the Alan Turing Institute, Department of Chemical Engineering and Biotechnology, Cambridge University and leads the Algorithmic Dynamics Lab at the Karolinska Institute in Sweden.
Narsis A. Kiani at the Algorithmic Dynamics Lab, Center for Molecular Medicine, Karolinska Institute and Jesper Tegnér is a Professor of Bioscience and Computer Science at King Abdullah University of Sciences and Technology.

De les Coves, Gemma, et al. Universality and Complexity in Natural Languages: Mechanistic and Emergent.. C:/Users/Author/Downloads/preprints202402.1330.v1.pdf.. As this year becomes distinguished by integral syntheses across every realm and occasion, polyscholars GC, University of Innsbruck, Bernat Corominas-Murtra, Graz University, Germany and Ricard Sole, Universitat Pompeu Fabra, Barcelona prepost a subject instance whence spoken and textual communications, broadly conceived, can likewise be seen to inhere an exemplary universality of complex network systems. As the quotes say, these recursive, fractal-like attributes also refer to morphogenetic programs and traced onto physical phase transitions. As a summary it is wondered if Gottfried Leibniz’ proposal of an alphabetic Characteristica Universalis might at last be fulfilled

Human language exemplifies a complex system formed by multiple scales of description. Its origins and content have been well studied from grammatical standards to statistical analyses of word usage, which are seen imply universal patterns shared by all languages. Yet, a cohesive perspective remains elusive. In this paper we provide a basic structure of universality, and define recursion as a special case. We note generative grammars of formal languages (Chomsky) on the path toward universality and compare mathematical properties. We arrive at Zipf's law as a complexity attractor with a relation to common writing systems and Turing computations. Overall, we find two forms of universality, mechanistic and emergent, and cite some connections between them.

If anything characterizes linguistic phenomena is its ubiquity and diversity, spanning research fields and scales. Linguistics, psychology, population genetics [9], artificial intelligence, network science, statistical physics, evolutionary biology and cognitive science provide frameworks encompassing some aspect of language complexity. Yet, such explanations are not independent, but their relations give rise to a complex net of relations, as illustrated in Figure 1 (see next quote) exhibiting a hierarchy of such representations ranging from minimal components to the socio-cultural domain. (1)

Figure 1. Human language is a multiscale phenomenon with hierarchical levels from phonemes at the microscale, grammar and sentences at the mesoscale, and sociocultural dynamics at the macroscale. These phases include symbols to build the basic units of language (signs or words), the rules to organise words in sentences (grammar), neural substrate of these processes, and the role played by cultural change networks. One can approach the presence of universals in natural languages from the perspective of writing systems, whose history experienced marked transitions, or machines and recursion. (3)

The enscripted writing of languages was a major historic innovation which arose independently in various cultures and became a cornerstone of civilizations. The emergence of writing required a fortunate combination of already-present brain circuits; similarly, the transition to a reading brain involved a blend of contingency and inevitability. In this process, a revolutionary turn occurred: The transition to alphabets, by which as a small inventory of basic symbols can be combined to form syllables and words. As we shall discuss very soon, this transition can be seen as a jump to universality. (12)

Gonda, Tomas, et al. A Framework for Universality in Physics, Computer Science, and Beyond.. arXiv:2307.06851. This is a specific notice to date by University of Innsbruck and Technical University of Munich mathematicians including Gemma De les Coves as our 21st century worldly scientific revolution comes to realize a common evidential occurrence across the atomic, cosmic and personal infinities. A main emphasis on computational methods is then found to hold for quantum spin models, linguistic grammar, neural networks, and elsewhere. See also an introductory overview by this group at arXiv:2406.16607.

Turing machines and spin models share a notion of universality according to which some simulate all others. Is there a theory of universality that captures this notion? We set up a categorical framework for universality which includes as instances universal Turing machines, universal spin models, NP completeness, top of a preorder, denseness of a subset, and more. By identifying necessary conditions for universality, we show that universal spin models cannot be finite. We also characterize when universality can be distinguished from a trivial one and use it to show that universal Turing machines are non-trivial in this sense. Our framework allows not only to compare universalities within each instance, but also instances themselves.

Chojnacki, Leilee, et al. Chojnacki, Leilee, et al. Gravitational wave analogues in spin nematics and cold atoms. Physical Review B. 109/L220407, 2024. Theory of Quantum Matter Unit, Okinawa Institute of Science and Technology, University of Tokyo, Keio University, Japan and Rice University physicists cleverly draw upon an apparent affinity between an atomic state and gravity waves as a way to study this celestial phenomena. See also Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature by Andrei Constantin, et al at arXiv:2408.11065 for another current instance. Once again, into the mid-2020s, 80 years after WWII, a steady, recurrent consilience is becoming evident across the widest infinities.

Many large-scale phenomena in our Universe, such as gravitational waves, are difficult to reproduce in laboratory settings. However, parallels with condensed matter systems can provide an alternative experimental accessibility. Here we show how spin nematic phases provide a low-energy route for accessing the physics of linearized gravity. We show at the level of the action that the low-energy effective field theory describing a spin nematic is in correspondence with that of linearized gravity. We then cite a microscopic model of a spin-1 magnet whose excitations in the low energy limit disperse, massless spin-2 Bosons which are in one-to-one correspondence with gravitational waves.

Thus far, several condensed matter systems have been suggested to mimic features of gravity, with much focus on reproducing the effects of curved spacetimes. Tensor analogs leading to rich gravitational phenomena exist, and have been measured, in the context of superfluid 3He. Acoustic analogs of gravitational phenomena were suggested and later measured, with further promising experimental can didates in semimetals, in quantum Hall systems, in optics [10,11] and in cold atoms. In this Letter, we identify a parallel between gravitational waves and the Goldstone modes of quantum spin nematics, and suggest two routes for their experimental real ization.. (1)

Cugini, Davide, et al. Universal emergence of local Zipf's law.. arXiv:2407.15946. University of Pavia, Italy and University College London physicists provide a latest explanation for and verification of this implicate, scalar, power law, recurrent phenomena.

A plethora of natural and socio-economic phenomena share a striking statistical regularity whereby the magnitude of elements decreases with a power law as a function of their position in a ranking of magnitude. Such regularity is commonly known as Zipf's law, and plenty of problem-specific explanations for its emergence have been provided in different fields. Yet, a full explanation for its ubiquity is currently lacking. In this paper, we demonstrate from first principles that Zipf's behavior naturally occurs as a local approximation to the order statistics generated by any ranking process. We validate our results with several relevant examples.

Benegas, Gonzalo, et al. Genomic Language Models: Opportunities and Challenges.. arXiv:2407.11435. UC Berkeley bioinformatic researchers scope out a latest common confluence as these two main life and mind codifications proceed to gain their necessary essential affinity. See also Linguistics-based formalization of the antibody language as a basis for antibody language models by Mai Ha Vu et al in Nature Computational Science (4/412, 2024) and A Benchmark Dataset for Multimodal Prediction of Enzymatic Function Coupling DNA Sequences and Natural Language by Yuchen Zhang, et al at arXiv:2407.15888 for companion work.

Large language models (LLMs) are having transformative impacts across a wide range of scientific fields, particularly in the biomedical sciences. Just as the goal of Natural Language Processing is to understand sequences of words, a major objective in biology is to understand biological sequences. Genomic Language Models (gLMs), which are LLMs trained on DNA sequences, have the potential to significantly advance our understanding of genomes and how DNA elements at various scales interact to give rise to complex functions. In this review, we showcase this potential by highlighting key applications of gLMs, including fitness prediction, sequence design, and transfer learning. (Abstract)

Life's Corporeal Evolution Develops, Encodes and Organizes Itself: An Earthtwinian Genesis Synthesis

García-Valdecasas, Miguel and Terrence Deacon. Origins of biological teleology: how constraints represent ends. Synthese. August, 2024. University of Navarra, Spain and a UC Berkeley (search) anthropologists propose a latest integrity of biomolecular autopoietic processes with personal purpose across evolution as a further basis for life’s oriented course.

To naturalize the concept of teleological causality in biology one needs to specify how the causality of organisms is distinct from designed artifacts or the increase of entropy. Historically, this oriented view has been based on an analogy with purposeful action. In this regard, to bridge the gap between biology and human agency we describe a simple molecular process called autogenesis that shows how complementary self-organizing processes can give rise to higher-order relations that resemble goal-like dispositions. Because the autogenic model is described in sufficient detail to be empirically realizable, it provides a proof of principle for a basic form of teleological causality.

Our molecular model described by autogenesis satisfies the five criteria for teleological causality in Section 1. First, its target-direction is not reducible to external factors, but due to the holistic constraints on its self-organizing processes that maintain its discrete individuality. Second, is constitutive because the linkage between these reciprocal constraint-generating processes by a molecule that makes them co-dependent. Third, it is disposed to prevent its component self-organizing processes from reaching their terminal states. Fourth, it is normative, so as to maintain the causal capacity of which it is its own beneficiary. And fifth, its hologenic nature can impose what might be considered the general description of an end onto new physical substrates. (24)

Demoulin, Catherine, et al. Demoulin, Catherine, et al. Oldest thylakoids in fossil cells directly evidence oxygenic photosynthesis. Nature. 625/529, 2024. Early Life Traces & Evolution, University of Liège astrobiologists post a further example of the analytic depths that our late Earthuman intelligence can now achieve as our global genius proceeds with a whole scale retrospect description. One is moved to ask whom is this emergent personsphere prodigy ready and able to carry out this project for which our late transitory phase seems made to do. Why does an apparent self-making cocreation need to achieve its own retrospect, recorded description.

Today oxygenic photosynthesis is unique to cyanobacteria and their plastid relatives within eukaryotes. The accumulation of O2 profoundly modified the redox chemistry of the Earth and the evolution of the biosphere, with complex life. Here we report the oldest direct evidence of thylakoid membranes in a parallel-to-contorted arrangement within the cylindrical microfossils Navifusa majensis from Australia. This discovery allows the identification of early oxygenic photosynthesizers, and the importance of examining the ultrastructure of fossil cells to decipher their palaeobiology and early evolution.

Thylakoids are membrane-bound compartments inside chloroplasts and cyanobacteria. They are the site of the light-dependent reactions of photosynthesis.

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