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Displaying entries 31 through 45 of 57 found.

Ecosmomics: Independent Complex Network Systems, Computational Programs, Genetic Ecode Scripts

buehler, Markus. Self-Organizing Graph Reasoning Evolves into a Critical State for Continuous Discovery Through Structural-Semantic Dynamics. arXiv:2503.18852. The MIT materials scientist continues to develop and post his complex systems theories which here achieve a strongest, definitive description and endorsement of a natural universality of self-organized critical, optimum state. This phenomenal performance is evident in neural and cognitive domains and everywhere else, so it seems. In regard, this 2025 entry could exemplify how 21st century worldwise studies of nature’s inherent, mathematic procreativity have reached a robust, integral veracity. See also Sparks: Multi-Agent Artificial Intelligence Model Discovers Protein Design Principles by Alireza Ghafarollahi, and Markus Buehler at arXiv:2504.19017 for more.

We report new insights into how agentic graph reasoning systems spontaneously evolve toward a critical state which is quantified by a Critical Discovery Parameter. The whole system exhibits scale-free and small-world topological features, which reinforces a self-organized criticality. These results establish clear parallels with critical phenomena in physical, biological, and cognitive complex systems by way of an entropy-based principle governing adaptability and innovation. (Excerpts)

The Critical Discovery Principle mirrors natural and artificial complex systems. In biology, gene regulatory and protein interaction networks maintain stability through inter-dynamics driven by migration, mutation, and invasion to ensure resilience and adaptability. In social and economic contexts, innovation networks balance structural viability with semantic exploration. Finally, AI systems balance exploitation of knowledge with exploration by semantically novel opportunities, embodying the universal criticality. (5, 6)

These interdisciplinary parallels suggest that the persistent semantic basis and critical discovery dynamics are not isolated phenomena, but manifestations of a deep organizing agency across complex adaptive systems. (6) Specifically, analogous to biological networks and physical systems exhibiting scale-invariant phenomena near critical points, we anticipate that the reasoning graph will form nested communities characterized by hierarchies and scale-free connectivity distributions. (6)

Mendes, José Fernando. Complexities: An Open Access Journal for the Field of Complex Systems. Complexities. 1/1, 2025. As the quotes say, this posting introduces a new MDPI journal on nature’s intrinsic, universal, mathematical interconnective animations. A further incentive is to then be able to apply such findings to mitigate intense public health and welfare problems.

Complex systems are embedded everywhere in natural and artificial processes from ecosystems to social networks. Their phenomena is defined by interconnectivity, non-linear behaviors, and emergent properties, where the whole exceeds the sum of its parts. Our intent in this unique, dedicated journal is to provide a platform for innovative research in areas such as systems theory, network science, non-linear dynamics, computational complexity, and quantum complexity. Advanced computational tools and extensive datasets now exist for modeling emergence, multi-agent interactions and finding hidden patterns. At the same time, global issues such as pandemics and economies benefit from interdisciplinary collaboration and innovative approaches.

Prof. Dr. José Fernando Mendes is a distinguished Portuguese physicist known work in network theory and complex systems. He earned his PhD from the University of Porto and currently holds a Full Professorship at the University of Aveiro. He served as President of the Complex Systems Society (2021–2024) and currently directs the Institute i3N-Aveiro.

Barjuan, Laia, et al. The multiscale self-similarity of the weighted human brain connectome. PLoS Computational Biology. April, 2025. Universitat de Barcelona and Jiangsu University, China neuroscientists including M. Angeles Serrano describe their comprehensive theoretic and empirical findings to date of a cerebral faculty distinguished by local and global recurrent patterns and a best critical balance. This exemplary affirmation can now be traced to and grounded in major physical principles. We then want to highlight this report as a prime instance of the mid 2020s achievement of a universal synthesis.

Anatomical connectivity between brain regions can be mapped to a network representation known as the connectome by way of links, weights, resilience and functions. Yet, these features are not fully understood. In this paper, we elucidate the architecture of multiscale neural nets from empirical data sets to reveal a fractal-like self-similarity in every occasion. This commonality is based on a theoretical renormalization model across all geometric scales. The observed symmetry also represents a signature of criticality states. (Excerpts)

The multiscale self-similarity of human connectomes, along with their modular organization, ensures that spectra representing weak ties are invariant across scales. Critical systems often demonstrate such fractal behavior, reflecting their organization as poised between various phases. Self-organized criticality is seen in non-equilibrium systems with many degrees of freedom which leads us to an evolutionary criticality. (14)

Delabays, Robin, et al. Hypergraph reconstruction from dynamics.. Nature Communications.. 16/2691, 2025. As our cognizance and understanding of nature’s actual organic anatomy and physiology grows apace, University of Applied Sciences of Western Switzerland, Eindhoven University of Technology, The Netherlands. ETH Zürich, and the Santa Fe Institute theorists (search RD) introduce another procedure by which to parse these real-world multi-intricate social webworks.

Many methods can now infer the pairwise network structure of an interconnected system from its collective dynamics. Here, we contribute to higher level realms by way of an inference algorithm which reconstructs hypergraphs and simplicial complexes from time-series data. We first benchmark on synthetic data from Kuramoto and Lorenz phases and then use it to describe how non-pairwise interactions shape macroscopic brain dynamics. (Excerpt)

Agüera y Arcas, Blaise, et al. Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction. arXiv:2406.19108. Google Paradigms of Intelligence Team and University of Chicago conduct exercises that show how certain program languages as they run appear to have a tendency to spontaneously biocomplexify, evolve and emerge lifeward. These digital propensities are then seen to accompany precursor autocatalytic reactions.

The fields of Origin of Life and Artificial Life both question what life is and how it emerges from a distinct set of "pre-life" dynamics. One common feature is a marked shift in dynamics when self-replication appears. In this paper we propose some answers by studying computational substrates by way of programming languages and machine instruction sets. We show that when non self-replicating programs are placed in a conducive environment, that self-replicators tend to arise. We also show how increasingly complex dynamics then continue to emerge. (Excerpt)

Aerts, Diederik, et al. Identifying Quantum Mechanical Statistics in Italian Corpora.. International Journal of Theoretical Physics. 64/136, 2025. As the Free University of Brussels and University of Udine team (search DA) apply their insights to human realms, in this case they report an intrinsic, thematic presence in literary editions. Our natural philoSophia interest then makes note of a textual quality being attributed to nature’s deepest essence.

We present a theoretical and empirical investigation of the statistical behaviour of the words produced by human language. To this aim, we analyse the word distribution of various texts of Italian language selected from a specific literary corpus. We generalise a theory elaborated by ourselves to identify “quantum mechanical statistics.” These results confirm patterns in English language works whereby words together as a consequence of their meaning. (Excerpt)

Camps, Jean-Baptiste, et al. Camps, Jean-Baptiste, et al. On the transmission of texts: written cultures as complex systems.. arXiv:2505.19246. Ecole nationale des chartes Paris, University Paris-Saclay and Ben Guerir, Morocco computational philologists provide a 2020s expansion and deepening of this academic pursuit by recasting literature as an exemplary manifestation of nonlinear, creative phenomena. By turns, our human literary corpora can actually be appreciated and parsed as a natural narrative.

Our knowledge of past cultures relies on recovering and parsing written material. While philologists like us reconstruct text phylogenies, the sources of prior genealogies is an open issue. Here we rethink literary transmission through a complexity science approach which integrates stochastic modelling, computer simulations, and data analysis, akin to statistical physics and evolutionary biology. Thus, we design general models that can account for diachrony and other aspects such as the extinction of branches or trees. (Excerpt)

Finally, the commonality of our models makes it applicable not only to medieval texts, but to any type of cultural transmission. Further investigations should include Western Medieval literature along with other time periods and geographical areas. In this sense, our work calls for the emergence of a new research endeavor dedicated to evolutionary modelling of cultural transmission. (15)

Gong, Wen. A New Exploration into Chinese Characters: From Simplification to Deeper Understanding. arXiv:2502.19428. A City University of Macau scholar posts a unique synthesis across millennia of oriental wisdom, mathematics and geometry and our 2025 intelligent cybersphere frontiers. The achievement is a luminous preview of a Earthuman (East/west, South/north) spatial and temporal textual universality.

This paper presents a novel approach to Chinese characters through the lens of physics, networks, and natural systems. Computational analysis of over 6,000 characters served to identify 422 elemental logographs which exhibit properties of emergent complexity, self-organization, and adaptive resilience. By viewing Chinese characters as a living system, this research can reveal how human cognition organizes and transmits knowledge. This perspective, combined with AI approaches, promises to transform language education from knowledge gain to meaning discovery.

This paper represents a collaborative effort between the author, and a team of AI assistants including Claude 3.3 from Anthropic, Gemini 2 from Google and Qwen2.5-Max from Alibaba. The fusion of human knowledge and software development, physics, and Chinese language with AI capabilities enables the novel perspectives and methodologies presented in this work.

Natural Growth Patterns in Character Evolution Just as natural systems evolve from simple to complex through predictable patterns, Chinese characters follow a similar organic development. The Fibonacci sequence provides an elegant metaphor for how complexity emerges from simplicity in systematic ways. The accompanying images of Fibonacci patterns in nature - from sunflowers to nautilus shells and fern fronds to spiral galaxies - reveal this universal principle of growth and organization. (22)

Dedication This work is dedicated to the late Professor T.D. Lee whose efforts opened doors for many Chinese students to pursue studies in the United States as a bridge between Eastern and Western traditions. His vision and support have enabled countless scholars like myself to contribute to global discourse.

Aschwanden, Markus and Felix Scholkmann. Power Laws Associated with Self-Organized Criticality. arXiv:2505.00748. In this latest posting, Lockheed Martin, Palo Alto and ETH Zurich mathematicians extend and apply some two decades of celestial findings (search MA) of nature’s dynamic proclivity onto an emergent Earthly array from life’s animations to cerebral knowledge. By so doing, another salient proof this year of an universe to humanverse is established.

We investigate the relevance of self-organized criticality (SOC) models in empirical datasets drawn from statistical observations in astrophysics, geophysics, biophysics, sociophysics and informatics. We study 25 interdisciplinary phenomena with event detection and power law methods. We find that the phenomena of solar flares, earthquakes, and forest fires are consistent with the theoretical predictions, while the size distributions of other phenomena are not as conclusive. (Abstract)

What is common to all SOC processes is the fractality, physical scaling laws, power law-like size distributions, and the evolution from an exponential-growth phase to a diffusive-decay phase. In the following we discuss SOC properties grouped by science disciplines (astrophysics, geophysics, biophysics, sociophysics, informatics), as enumerated in Table 1. (6)

SOC processes in biophysics have been proposed such as protein-interaction yeast networks, the metabolism bacterium Escherichia coli and the number of species per genus of mammals. The scale-free topology of protein interaction and metabolic networks and their balance between robustness and fragility are characteristics of SOC. In terms of species, ecological systems could exhibit SOC due to the dynamics of interactions (competition, cooperation) between them and their environment. (7)

Sociophysics is a novel discipline that uses mathematical tools or physical models to understand human groups which can be defined by citizenship, power customers, soldiers in wars, religions, financial net worth and so on. The evolution of a SOC process can be associated with an event which displays an instability or exponential-growth phase and a diffusive decay process. Differences in the power law slope size have been noticed for countries, genocides, famines, epidemics, and climates. (7)

Informatics SOC phenomena have been discovered in literature as well as in global networks by way of computer software. Such studies have found that vocabularies, book sales, family names, citation of papers and more exhibit SO criticalities. The last group of SOC in the discipline of informatics deals with global networks such as telephone calls, internet usage, HTTP size, emails sent and hyperlinks to websites. (8)

Duan, Qihao, et al. JanusDNA: A Powerful Bi-directional Hybrid DNA Foundation Model.. arXiv:2505.17257. We cite this entry by Berlin Institute of Health, Shenzhen Technology University, Carnegie Mellon University and MPI Heart and Lung Research system scientists including Benjamin Wild as another example of how easily a cross-translation of AI textual algorithms and genetic code-scripts can be made, as a Rosetta ecosmos becomes increasingly legible.

Large language models (LLMs) have joined with natural language processing and are now being applied to genetic sequences. However, adapting to complex genomic interactions requires modeling long-range dependencies within DNA sequences, but DNA is inherently bidirectional, which regulates transcription. Here we introduce JanusDNA, the first foundation model built upon a pretraining paradigm that combines the optimization efficiency of autoregression as it faces in both directions. (Excerpt)

Liang, Wang. Human Genome Book: Words, Sentences and Paragraphs. arXiv:2501.16982. In these mid 2020s when an AI Large Language publicity fills the cyberair, a Huazhong University of Science and Technology, China physicist lays out a whole scale translation of life’s hereditary endowment in full literary and textual terms. See also Find Central Dogma Again by LW at arXiv:2502.06253 whereby these AI methods are able to “rediscover” basic genetic principles and DNA and Human Language: Epigenetic Memory and Redundancy in Linear Sequence by Li Yang and Dongbo Wang at arXiv:2503.23494 for a similar review.

A consideration of the genome as a book with equivalents of words, sentences, and paragraphs has been often proposed. Recently, large language models have provided a novel approach, whereby we can train a foundational model capable of transferring from English to DNA sequences. We were then able to translate a human genome by segments and tokens into a "book" comprised of genomic "words," "sentences," and "paragraphs."

Zhang, Fan, et al. CellVerse: Do Large Language Models Really Understand Cell Biology?. arXiv:2505.07865. As AI capabilities become adapted to genetic studies, CUHK-Shenzhen, Yale University, Peking University, Tsinghua University, UCLA and Westlake University, China researchers specify a more advanced, viable version. Altogether, one can observe a growing sense of nature’s universal ecosmome to geonome textuality and familementarity.

Recent studies have shown ways to model single-cell data as natural languages by leveraging large language models (LLMs) for understanding cell biology. Here we introduce CellVerse, a unified language-centric question-answering method that integrates single-cell multi-omics data to analyze annotation (cell-level), drug prediction (drug-level), and perturbations (gene-level). CellVerse offers the first large-scale empirical demonstration that challenges still remain in applying LLMs to cell biology. (Excerpt)

Recent advances in LLMs have applied them to scientific domains, including mathematics, chemistry, and biology. Galactica strives to unify knowledge representation, whereas AlphaCode expands into protein synthesis. Our work contributes to this emerging field by connecting LLMs with biological data to evaluate and improve LLM capabilities in real-world biological contexts. (3)

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

Hudnall, Kevin and Raissa D’Souza. What does the tree of life look like as it grows? Evolution and the multifractality of time.. Journal of Theoretical Biology. 607/112121, 2025. UC Davis bioscholars propose an innovative perception of life’s long creaturely, branching development after Darwin’s archetypal elm as an instantiation of an iterative self-similar fractal geometry. The article comes with an Animation video to depict such an arboreal growth on its mathematic way toward mammals and peoples. Our philoSophic interest continues on to evident implications in our Earthwinian era whereby life’s episodic emergence appears to have a phenomenal,
independent, orthogenetic essence. (See also Dragon kings in self-organized criticality systems at arXiv:2308.02658 for earlier work by Raissa D'Souza.)

By unifying foundational principles of modern biology, we develop a mathematical basis for growing tree of life. Contrary to the static phylogenetic tree, our model shows that life’s track is more like a Cantor dust where each stage is a fractal form. As a result, this variegated course is nested, dualistic and stochastic. Altogether, its shape appears as a random iterated function that generates convexly related sequences of Metazoan species. The multifractal nature implies that, for any two living entities, the time interval from their last common ancestor to the present moment is a fractal curve. (Excerpts)

Our anatomical view is obtained from three prime features. The first is a nested scale which follows from descent-with-modification. The second is duality as biological sets transition between singularities and populations. The third aspect is a random facet whereby phylogeny is a stochastic process. Hence the natural tree of life as a whole is multifractal in that it consists of many distinct monofractals. (11)

Raissa D'Souza is Associate Dean for Research of the College of Engineering and a Professor of Computer Science at the University of California, Davis and on the Science Board at the Santa Fe Institute. See her Wikipedia page for much more. Kevin Hudnall is in Biological Systems Engineering Graduate Group at UC Davis.

Attwater, James, et al. Trinucleotide substrates under pH–freeze–thaw cycles enable open-ended exponential RNA replication by a polymerase ribozyme. Nature Chemistry. May, 2025. Seven MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, UK add further explanations as to how early RNA nucleotides got themselves untangled, sorted out and on their long journey to Oxford and our vital retrospect.

RNA replication is considered a key process in the origins of life. However, both enzymatic RNA replication cycles are impeded by strand separation issues arising from the stability of RNA duplexes. Here we show that RNA trinucleotide triphosphates can overcome this by binding to RNA strands for replication by a polymerase ribozyme. Partial ribozyme self-replication alongside generation of new RNA sequences occurred, which then drifted towards primordial codons. (Excerpt)

Bunn, Hayley, et al. Laboratory Rotational Spectroscopy Leads to the First Interstellar Detection of Deuterated Methyl Mercaptan. Astrophysical Journal Letters. 980/L13, 2025. This entry by MPI Extraterrestrial Physics, Université Paris-Saclay, University of Saskatchewan and University of Copenhagen astroscientists is an example of mid-decade sophisticated instrumentation and techniques which are able to quantify critical precursors on the evident course to viable rudiments and replicant evolution.

We report an extensive rotational spectroscopic analysis of singly deuterated methyl mercaptan (CH2DSH) using both millimeter and far-infrared synchrotron spectra to achieve a global torsional analysis of the three lowest torsional substates of this species. This is the first interstellar detection of a deuterated sulfur-bearing complex organic molecule and an important step toward understanding the chemical origin of sulfur-based prebiotics.

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