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Displaying entries 31 through 45 of 121 found.

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

Bohl, Abigail, et al. Probing the Limits of Habitability: A Catalog of Rocky Exoplanets in the Habitable Zone. arXiv:2501.14054. Cornell University astrophysicists including Lisa Kaltenegger propose and scope out an initial catalog to begin our planned galactic neighborhood cavnas.

Several ground and space based searches have increased the known exoplanets to nearly 6000. While most are highly unlike our Earth, a rocky world in a stellar Habitable Zone (HZ) can provide locales for life in the cosmos. However, a tabulation that observers can use to investigate does not yet exist. In regard, we identify 67 rocky worlds in an empirical HZ and 38 in a narrower 3D-model HZ. This first population will help shape search strategies with the JWST, the Extremely Large Telescope, and Habitable Worlds Observatory. (Abstract)

Livio, Mario and Jack Szostak. Is Earth Exceptional?: The Quest for Cosmic Life.. New York: Basic Books, 2024. A unique pairing of a literate physicist and a chemistry laureate share and combine their latest understandings of extraterrestrial and prebiotic occasions of habitable occasions of minimal living, sensory systems. Since their extensive, referenced survey extends through 2023, although the evidence augurs for an especial significance, an answer conclusion remains in abeyance. But as Szostak’s describes his 21st century biochemical studies, into 2024 and now 2025 it seems that an ordained course from universe to us is indeed unfolding on its own..

Mario Livio is an astrophysicist who worked with the Hubble Space Telescope. He is also author of seven books, including The Golden Ratio. Jack Szostak is a professor of chemistry at the University of Chicago, where he leads the Center for the Origin of Life. He shared a 2009 Nobel Prize for his research.

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

Andersen, Benjamin, et al. Evidence of universal conformal invariance in living biological matter. Nature Reviews Physics.. March, 2025. Eight computational physicists posted at the University of Copenhagen, University of Lisbon, University of Lausanne and University of Sheffield press on with mid 2020s complexity studies so to provide another theoretical perspective upon nature’s universal, recurrent lawfulness. A notable difference is that they begin with physical inorganic materials which are seen to likewise express organized structures known as conformal invariance. The project continues to biological phases where the view prompts a further way to perceive a constant viable criticality. Our planatural philoSophia take is to then suggest that these many current integral insights we record seem just now coming to their phenomenal realization and factual discovery.

The emergent dynamics of collective cellular movement depend on how cells interact and move across biological systems. Here we report experimental evidence of a universal feature in the patterns of flow that spontaneously emerge in cellular groups. Specifically, we show that the flows generated by dog kidney cells, human breast cancer cells and pathogenic bacteria exhibit robust conformal invariance. This constant recurrence reveals that the macroscopic features of living biological matter exemplify universal translational, rotational and scale symmetries that are independent of their microscopic constituents. (Abstract)

Although many attempts have been made to model the patterns of collective movement made by organisms, we still lack a general unifying theory. In contrast, the study of complex interactions between the components that make up inanimate materials has led to common behaviours near critical regimes. The principles that give rise to this universality have been described using the framework of conformal field theory, which predicts how shapes and angles of structures are locally conserved across different systems. (1)

In this paper, we experimentally demonstrate that the patterns of collective movement observed in different types of living matter exhibit common characteristics that transcend the properties of the cells from which they are composed. We show that many instances including colonies of pathogenic bacteria, groups of collective kidney cells and breast cancer cells, spontaneously generate flows that exhibit a universal conformal invariance described by the percolation universality class. (2)

These results suggest that the theories used to describe conformally invariant structures might have a much broader range of applications. Although our results do not necessarily indicate that the collective movement we observe is operating at the critical point of a phase transition, many different biological systems are thought to be poised near criticality which allows them to easily switch between different states. Our findings, thus, imply that the mathematics to study conformally invariant structures could also lead to new methods to detect and understand critical phenomena in biology. (5)

Hayes, Thomas, et al. Simulating 500 million years of evolution with a language model. Science. January 16, 2025. Twenty-four coauthors at EvolutionaryScale, NYC, Arc Institute, Palo Alto and UC Berkeley post a 70 page paper which is an extensive statement of this unified synthesis of genomic code-scripts, protein (AlphaFold) linguistics and written language content to date. An array of companion works have appeared such as Sequence modeling and design from molecular to genome scale with Evo in Science (November 15, 2024), Rapid in silico directed evolution by a protein language model with EVOLVEpro in Science (November 21, 2024) and The Poetry Fan Who Taught an LLM to Read and Write DNA by Ingrid Wickelgren in Quanta (February 5, 2025, herein). Altogether a deep and wide frontier is just opening by virtue of collaborative translations of a common natural textuality that graces and informs from a biological ecomos to our Earthuman selves.

More than three billion years of evolution have produced an image of biology encoded into the space of natural proteins. Here we show that language models trained at scale on evolutionary data can create novel proteins with beneficial properties. We present ESM3, a multimodal generative language model that reasons over the sequence, structure, and function of proteins. Among the generations that we synthesized, we found a bright fluorescent protein at a far distance from known prior versions, which is equivalent to simulating five hundred million years of evolution. (Abstract)

The proteins that exist today have developed over billions of years of natural evolution, passing through a vast evolutionary sieve. In parallel experiments conducted over geological time, nature creates random mutations and applies selection, filtering proteins by their myriad sequences, structures, and functions. Gene sequencing surveys of Earth’s natural diversity illuminate patterns of variation across life. A consensus is developing that there is a fundamental language of protein biology that can be understood using language models. (1)

We have found that language models can reach a design space of proteins distant from that explored by natural evolution and generate proteins that would take evolution hundreds of millions of years to discover. Protein language models do not explicitly work within the physical constraints of evolution, but instead can construct a model of the multiple paths evolution could have followed. (10) Simulations are computational representations of reality. In that sense, a language model which can predict possible outcomes of evolution can be said to be a simulator of it. ESM3 is an emergent simulator that has been learned by solving a token prediction task on data generated by evolution. (11)

evolutionaryscale.ai is a developer of biology artificial intelligence models intended to design therapies. The company predicts protein structures by integrating biological data from deoxyribonucleic acid sequences, gene expression and epigenetic states, enabling researchers to apply large language models to design ribonucleic acid-based drug therapies.

Arc Institute Headquartered in Palo Alto, CA, Arc is a nonprofit research partnership with Stanford University, UCSF, and UC Berkeley. Arc gives scientists multi-year funding for the development of experimental, computational technological capabilities. Arc’s mission is to accelerate scientific progress, understand the causes of disease, and narrow the gap between discoveries and public benefits.

Knona, Mikail, et a. Global modules robustly emerge from local interactions and smooth gradients. Nature. February 19, 2025. MIT neuroscientists including Ila Fiete provide another, novel explanation for nature’s apparent spontaneous, oriented propensity to organize itself into ascendant entity/ensemble vitalities.

Modular structure and function are ubiquitous in biology from the organization of animal brains and bodies to the scale of ecosystems. However, the way modularity emerges from non-modular precursors remain unclear. Here we introduce the principle of peak selection, a process by which purely local interactions and smooth gradients can drive the self-organization of discrete global modules. The process combines the positional and Turing pattern-formation mechanisms into a model for morphogenesis. (Excerpt)

Nichele, Stefano, et al. Cellular Automata, Distributed Dynamical Systems, and Their Applications to Intelligence. Artificial Life. 31/1, 2025. SN, Hiroki Sayama and Chrystopher Nehaniv introduce a special issue on these title aspects with regard to how they serve and embellish nature’s vital, complex spontaneities across the ecomos. The four included papers are from a workshop at a 2023 Artificial Life conference in Sapporo, Japan, to bridge the gap between the ALife community and the artificial intelligence (AI) researchers interested in exploring concepts from nonlinear phenomena.

Distributed dynamical systems like cellular automata (CAs) and random boolean networks (RBNs) have long been used to understand computation and self-replication in biology, morphogenesis, gene regulation, life-as-it-could-be, and the Universe. Recent advances, such as continuous CAs, Lenia, and neural-based CAs have been proposed to study the emergence of a more general intelligence based on their support properties like self-organization, emergence, and open-endedness. (Abstract)

In “Cell-Cell Interactions: How Coupled Boolean Networks Tend to Criticality,” Braccini and coauthors investigate interacting RBNs as a theoretical model of multicellular biological systems with cell–cell interactions. They find not only that the interacting versions of RBNs show the same general trends of dynamical properties as their individual counterparts but also that the networks in ordered or chaotic regimes tend toward a critical regime when turned into interacting networks.

Sayama, Hiroki. Swarm systems as a platform for open-ended evolutionary dynamics. Philosophical Transactions A. January, 2025. The SUNY Binghamton director of the Center of Complex Systems (search) continues his contributions with an extensive proposal for going forward into an empowered, informed, creative futurity.

Artificial swarm systems have been extensively studied and used in computer science, robotics, engineering and other technological fields as a platform for distributed systems to achieve pre-defined objectives. In addition, heterogeneous versions can serve asl platforms for open-ended evolutionary dynamics that keep exploring diverse possibilities and generating novel outputs. In this article, I discuss my Swarm Chemistry to illustrate these beneficial characteristics including multi-scale structures and behaviours, robust self-organization, self-repair and ecological interactions of emergent patterns.

Wickelgren, Ingrid. The Poetry Fan Who Taught an LLM to Read and Write DNA. Quanta. February 5, 2025. A veteran science author and journalist (see her website) surveys the latest bioinformatic frontiers by way of a profile of Brian Hie, a lab leader at the Arc Institute (see his group page at arcinstitute.org). In a historic regard, since last year, a vibrant collaborative endeavor (see Thomas Hayes) has been facilitated by new generative, foundational and large language AI abilities which can then be tailored for genetic, protein and medical benefits. Dr. Hie notes that life’s long stochastic evolution has previously allowed harmful mutations or viruses which can just now begin to be carefully edited out.

DNA is often compared to a written language. The metaphor leaps out: Like letters of the alphabet, molecules (the nucleotide bases A, T, C and G, for adenine, thymine, cytosine and guanine) are arranged into sequences — words, paragraphs, chapters, perhaps — in every organism, from bacteria to humans. Like a language, they encode information.

The formula for Evo’s success is basic in principle. The model is large, bestowed with 7 billion variables, known in computer science as parameters, and trained on loads of data. Under that paradigm, Evo acquires an uncanny knack for divining what sequences are compatible with life and for spinning out useful variations of nature’s molecules. The affinity for scanning a sonnet or identifying structure in a well-composed English lyric is similar to wanting to develop models that make genomic or protein sequences more interpretable and reveal their hidden structure. It’s almost like literary criticism on biology sequences.

Barzon, Giacomo, et al. Excitation-Inhibition Balance Controls Information Encoding in Neural Populations. Physics Review Letters. 134/068403, 2025. University of Padova, MPI Physics of Complex Systems, and École Polytechnique de Lausanne contribute more evidential proof of life’s ubiquitous preference to balance beam these coincident opposites for best behaviors. See also Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics by Guillermo Morales, et al in PNAS (120/9, 2023).

Understanding how the complex connectivity structure of the brain shapes its information-processing capabilities is a work in process. Here we focus on a paradigmatic architecture to study how the neural activity of excitatory and inhibitory populations encodes information from external signals. We show that informative content is maximized at the edge of stability as inhibition balances excitation. Along with other recent findings, our results portend a deeper information-theoretic understanding of how the balance between excitation and inhibition controls optimal information-processing in neural populations. IAbstract)

Deco, Gustavo, et al. Complex harmonics reveal low-dimensional manifolds of critical brain dynamics.. Physical Review E. 111/014410, 2025. Universitat Pompeu Fabra, Barcelona and Oxford University open another window to view a neural nature which attains a twintelligence (herein a reciprocal poise) and effective cognizance by way of this complementarity and familiarity. See also Emergence of Power-Law Avalanches from Collective Stochastic Dynamics of Adaptive Neurons by Lik-Chun Chan, et al in PRX Life (3/013013, 2025).

The brain needs to perform time-critical computations to ensure survival, for which nonlocal, distributed computation at the whole-brain level make possible by self-organized criticality. These responses accord with Schrödinger's wave equation, so as to form a complex harmonics decomposition (CHARM) framework to express the complex network dynamics that are the key computational engines of critical brain dynamics. (Excerpt)

Hurtado-Gutiérrez, Hurtado-Gutiérrez. Programmable time crystals from higher-order packing fields. Physical Review E. 111/934119, 2025. We cite these findings by Electromagnetismo y Física de la Materia, Universidad de Granada researchers as still another window on the ubiquitous presence of critically poised, transitional phenomena in any manner of the curious geometric formations that an animated nature can take.

Time crystals are many-body systems that break time-translation symmetry, exhibit spatiotemporal order and periodic motion. Recent results have shown that coupling an external packing field to density fluctuations can trigger a transition to a time-crystal phase. Here, we exploit this mechanism to create on-demand programmable time crystals and elucidate the underlying critical point. Overall, these results demonstrate the versatility and broad possibilities of this promising route to time crystals. (Excerpt)
A scaling analysis of the results allows us to determine critical points which characterize this class of time-crystal phase transitions. Their exponents are compatible with the Kuramoto universality class that characterizes the synchronization of oscillators, independently of the packing order. We also define the condensates density profiles predicted for the higher-order shapes in terms of first-order ones. (10)

Gabrielli, Andrea, et al. Network Renormalization. arXiv:2412.12988. Enrico Fermi Research Center, Rome, IMT School for Advanced Studies, Lucca, University of Leiden and Universitat de Barcelona physicists including Ángeles Serrano begin to methodically scope out how this reliable physical approach can now be effectively applied to life’s many complex network vitalities, which has mostly eluded prior success. Their contribution so far involves a new informational content and the presence of chimeras and criticalities.

Renormalization group (RG) theories were developed to describe system configurations with many degrees of freedom, along with the associated model parameters and coupling constants. They also can identify critical points of phase transitions. Usually, the RG builds on the notions of homogeneity, symmetry, geometry and locality to define metric distances, scale transformations and self-similar coarse-graining. However, the strong heterogeneity of real-world networks complicates renormalization procedures. In this review, we discuss past attempts, the important advances, and the ochallenges on the road to network renormalization. (Excerpt)

Millan, Ann, et al. Topology shapes dynamics of higher-order networks. Nature Reviews Physics. February, 2025. System physicists in Spain, Sweden, Japan, the USA, UK, Belgium and Germany including Filippo Radicchi and Ginestra Bianconi add a further finesse to our Earthuman understandings of nature’s reticulate anatomy and metabolism which can apply to hyper intricate phases of world weather and deep neural learnings.

Higher-order networks capture the many-body interactions present in complex systems. The new theory of topological dynamics can enhance our understanding of such areas as climate phenomena and AI algorithms. It encodes the dynamics of a network through topological signals assigned not only to nodes but also to edges, triangles and cells. Recent findings show that topological signals lead to the emergence of distinct types of dynamical state and collective phenomena including pattern formation and percolation. These results offer insights into how topology shapes dynamics and how dynamics learns topology. (Excerpt)

Zhang, Zhang et al. Coarse-graining network flow through statistical physics and machine learning.. Nature Communications. 16/1605, 2025. We cite this entry by Beijing Normal University, Indiana University and University of Padua theorists including Manlio De Domenico as an example of new abilities to root complex system phenomena in deep physical substrates by way of an AI assistance.

Information dynamics plays a crucial role in complex systems from cells to societies. Recent advances in statistical physics have been able to find key network properties but large system sizes have computational issues. We use graph neural networks to identify coarse-graining groups to achieve a low computational complexity for practical applications. Our method offers multiscale compression perspective that preserves information flow in biological, social, and technological networks better than other methods mostly focused on network structure. (Excerpt)

, . Stepney, Susan. Physical reservoir computing: a tutorial. Natural Computing. November 2024.. Natural Computing.. November, 2024. The University of York computer scientist (search) provides a latest succinct explanation of this increasingly popular procedure especially as quantum versions become available. See, for example, A Reservoir-based Model for Human-like Perception of Complex Rhythm Pattern by Zhongju Yuan, et al at arXiv:2503.12509.

This tutorial covers physical reservoir computing which first defines what it means for a physical system to compute, rather than evolve under the laws of physics. It describes the underlying computational Echo State Network (ESN) model, and explains why the it is suitable for direct physical implementation. The entry then describes how to characterise a physical reservoir in terms of benchmark tasks, and task-independent measures, along with optimising configuration parameters, and exploring the space of potential configurations. (Excerpt)

Reservoir computing is derived from recurrent neural network theory that maps input signals into dimensional spaces through a non-linear system called a reservoir The first key benefit is that training is performed only at the readout stage. The second is that the computational power of natural systems, both classical and quantum, can reduce the relative cost.

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