
Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 1 through 15 of 63 found.
The Genesis Vision > Current Vistas
Jeffery, Kate and Carlo Rovelli.
Transitions in Brain Evolution: Space, Time and Entropy.
Trends in Neuroscience.
April,
2020.
University College London and University of AixMarseille physicists offer a way that might reconcile these seemingly disparate features of our standard scientific model by observing that cerebral cognition can be seen to mitigate thermodynamic costs. Their endeavor is set within the major transitions scale whose nested increase of mobility and memory rises along with entropic expenditures. A concern then becomes our human, linguistic mode in its Anthropocene phase that is capable of this retrospective view. But the emergence course is not seen as physically guaranteed – it could have come to a dead end at some point. In closing, it is noted that this may still happen to we peoples if nuclear armaments and other terminal perils are not resolved.
How did brains evolve to become so complex, and what is their future? Brains pose an explanatory challenge because entropy, which inexorably increases over time, is commonly associated with disorder and simplicity. Recently we showed how evolution is an entropic process, building structures – organisms – which themselves facilitate entropy growth. Here we suggest that key transitional points in evolution extended organisms’ reach into space and time, opening channels into new regions of a complex multidimensional state space that also allows entropy to increase. Brain evolution enabled representation of space and time, which vastly enhances this process. (Abstract)
The Genesis Vision > Current Vistas
Richardson, Ken.
In the Light of the Environment: Evolution through Biogrammars not Programmers.
Biological Theory.
June,
2020.
The emeritus Open University, UK psychologist has for some time (search) felt that current efforts to form a revised, updated evolutionary synthesis continue to miss what moves and informs living organism systems. As this site avers and cites, an array of selforganizing agencies are at generative work prior to selective effects. For this reason, it is necessary to move beyond a genetic basis only, even if expansive. From our late vantage, it would seem that some manner of retained, knowledgeable content which is vital for survival in changing environments is what actually evolves, grows and emerges. For a name, this corpora quality is dubbed a “biogrammar.” We add four quotes about this insightful view.
Biological understanding of human cognitive functions is incomplete because of failure to understand the evolution of complex functions and organisms in general. Here, that failure is attributed to an aspect of the standard neoDarwinian synthesis, namely commitment to evolution by natural selection of genetic programs in stable environments, a position that cannot easily explain the evolution of complexity. When we turn to consider more realistic, highly changeable environments, however, another possibility becomes clearer. An alternative to genetic programs—dubbed “biogrammars”—is proposed here to deal with complex, changing environments and explain evolving complexity from pregenetic life to human sociocognitive functions. (Abstract)
The purpose of this article is to show how these problems really stem from a failure to properly consider the complexity of environments of evolution, especially their changeability. Here it is suggested that what environmental changeability demands is not genetic programs, but inducible covariation grammars (“biogrammars”). By explaining evolving complexity, from primordial origins to human sociocognition, it is suggested that biogrammars constitute the most interesting aspect of “what has evolved.” (1)
It is in such global patterns that cognition emerges as a distinct biogrammatical level. Sensory stimuli are highly variable and “noisy.” Yet our cognitive experience of the environment is much more stable and consistent. Order is created by everupdating covariation patterns—cognitive biogrammars—rather than mere neural ones: a new level of selforganized regulations, creating new levels of environmental predictability. (8)
Evolution by biogrammars explains key transitions to complexity on the basis of a single, unitary but powerful principle. It also puts into clearer context the role of natural selection. Darwin admitted that natural selection might not be the only path to evolution. Already, by 1904, De Vries and others were pointing out that nothing can be selected until it already exists. Selforganizing biogrammars, working with informational structure, create variation and novel adaptations far more rapidly and fruitfully than random genetic mutations and natural selection. (9)
A Learning Planet > Original Wisdom > The Book of Nature
Lample, Guillaume and Francois Charton.
Deep Learning for Symbolic Mathematics.
arXiv:1912.01412.
We cite this entry by Facebook AI Research, Paris mathematicians here because at this frontier of computational studies, it refers to “Mathematics as a Natural Language.” The paper merited a news note Symbolic Mathematics Finally Yields to Neural Networks by Stephen Ornes in Quanta (May 20, 2020). While densely argued, the paper assumes a discernible legibility which resides deeply within natural creation. Some 400 years later, Galileo would be pleased.
Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper, we show that they can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations. We propose a syntax for representing mathematical problems, and methods for generating large datasets that can be used to train sequencetosequence models. We achieve results that outperform commercial Computer Algebra Systems such as Matlab or Mathematica. (Abstract)
A Learning Planet > The Spiral of Science
Hardwicke, Tom, et al.
Calibrating the Scientific Ecosystem through MetaResearch.
Annual Review of Statistics.
7/11,
2020.
As a big data tsunami engulfs quantum to genomic to astromic fields, MetaResearch Innovation Center Berlin and Stanford University scholars scope out ways to reorient and empower methods that can distill evidential patterns and findings. See also in this volume 21st Century Statistical and Computational Challenges in Astrophysics by Eric Feigelson, et al.
Modern astronomy has been rapidly increasing our ability to see deeper into the universe as it acquires enormous samples of cosmic populations. Gaining astrophysical insights from these datasets requires a wide range of sophisticated statistical and machine learning methods. Bayesian inference, central to linking astronomical data to nonlinear astrophysical models, addresses problems in solar physics, properties of star clusters, and exoplanet systems. The field of astrostatistics needs increased collaboration and joint development of new methodologies. Together, they will draw more astrophysical insights into astronomical populations and the cosmos itself.
While some scientists study insects, molecules, brains, or clouds, other scientists study science itself. Metaresearch, or researchonresearch, is an active discipline that investigates efficiency, quality, and bias in the scientific ecosystem, which is under some attack today. We introduce a translational framework that involves (a) identifying problems, (b) investigating problems, (c) developing solutions, and (d) evaluating solutions. In each of these areas, we review key metaresearch endeavors and discuss examples of prior and ongoing work. (Abstract excerpt)
A Learning Planet > Mindkind Knowledge
Global Brain Institute.
sites.google.com/site/gbialternative1..
The home page for this Free University of Brussels endeavor to engage and scope out into the 2010s the enveloping, vital presence of a worldwide cerebral faculty as it may gain an intelligence, knowledge and life of its own. The veteran director is Francis Heylighen, search for his comprehensive papers, and for members such as Clement Vidal, Marta Lenartowicz and Dirk Helbing.
We see people, machines and software systems as agents that communicate via complex network links. These agents contribute their own expertise to resolving problems and challenges. Thus the skills of different agents are pooled into a collective intelligence much greater than that of its individual members. This propagation across the global network is a complex process of selforganization. It is similar to the "spreading activation" that characterizes thinking in the human brain. This process will change the network by reinforcing useful links, while weakening less useful ones. So it can be said that the network learns and becomes more intelligent.
A Learning Planet > Mindkind Knowledge
Malone, Thomas.
Superminds.
Grand Haven, MI: Brilliance Publishing,
2019.
The author is founding director of the MIT Center for Collective Intelligence. Along with this volume and You Tube presentations he does advise that this enveloping noosphere with its infinity of web linkages should be much smarter than nodal online users and ought achieve its own coherent knowledge. Indeed this may be the only way we can save ourselves. Into 2020, a good example could be the intense, global proliferation of COVID19 data statistics, complex system analyses, and palliative proposals.
The MIT Center for Collective Intelligence explores how people and computers can be connected so that – collectively – they act more intelligently than any person, group, or computer has ever done before. (cci.mit.edu).
Animate Cosmos > Quantum Cosmology
Calcagni, Gianluca.
Classical and Quantum Cosmology.
Europe: Springer,
2017.
A Spanish National Research Council physicist provides an 800+ page, 3,500 reference text compendium for this 21st century unification of infinitesimal quantum phenomena with an inflationary and temporally dynamic universe of infinite expanse. See also Quantum Cosmology by Martin Bojowald (Springer, 2011) for another integral volume.
This comprehensive textbook is devoted to classical and quantum cosmology, with an emphasis on quantum gravity and string theory and their observational imprint. It covers major challenges in theoretical physics such as the big bang and the cosmological constant. An extensive review of standard cosmology, the cosmic microwave background, inflation and dark energy sets the scene for the application of main quantumgravity and stringtheory models of cosmology.
Animate Cosmos > Quantum Cosmology
Hartle, James.
Arrows of Time and Initial and Final Conditions in the Quantum Mechanics of Closed Systems Like the Universe.
arXiv:2002.07093.
We choose this recent entry by the UC Santa Barbara physicist to recognize his decadal flow of papers about the so mathematical matters of melding quantum depths with cosmic breadth. The third abstract is from a 1990 Jerusalem Winter School and well catches this human unification of depth and breadth. See also, for example, The Impact of Cosmology on Quantum Mechanics (1901.03933) and The Quantum Mechanics of Cosmology (1805.12246), abstracts below, for his oriented agenda.
In a quantum universe, arrows of time are described by the probabilities of appropriately coarse grained sets of histories of quantities like entropy that grow or decay. We show that the requirement of that these sets of histories decohere implies two things: (1) A time asymmetry between initial and final conditions that is a basis for arrows ot time. (2) How a final state of indifference that is represented by a final density matrix proportional to the unit density matrix is consistent with causality, and allows a finergrained description of the model universe in terms of decoherent histories than any other final state. (Abstract, 2002.07093)
When quantum mechanics was developed in the '20s of the last century another revolution in physics was just starting. It began with the discovery that the universe is expanding. For a long time quantum mechanics and cosmology developed independently of one another. Yet the very discovery of the expansion would eventually draw the two subjects together because it implied the big bang where quantum mechanics was important for cosmology and for understanding our observations of the universe today. (Abstract, 1901.03933)
This posting is 92 pages of from the lectures by the author at the 7th Jerusalem Winter School 1990 on Quantum Cosmology and Baby Universes. The lectures covered quantum mechanics for closed systems like the universe, generalized quantum mechanics, time in quantum mechanics, the quantum mechanics spacetime, and practical quantum cosmology. (Abstract, 1805.12246).
Animate Cosmos > Quantum Cosmology > quantum CS
Alon, Ofir and Axel Lode, eds.
Quantum ManyBody Dynamics in Physics, Chemistry and Mathematics.
Entropy.
May,
2020.
This is an introduction to a special collection issue by University of Haifa and AlbertLudwig University, Freiburg physicists across this intersect of these quantum and classical fields and endeavors, which presently joining up again in common understanding.
The Schrödinger equation is central to quantum mechanics and a cornerstone for the description of many fascinating phenomena in AMO, chemical, condensedmatter, and nuclear physics. Quantum manybody dynamics attract an enormous amount of interest in physics, chemistry, and mathematics alike. The purpose of this Special Issue is to amalgamate contributions from researchers actively working on solutions, applications, and theoretical methodologies for the timedependent Schrödinger equation for few and manyparticle systems. (Proposal)
Animate Cosmos > Quantum Cosmology > quantum CS
Berezutskii, Aleksandr, et al.
Probing Criticality in Quantum Spin Chains with Neural Networks.
arXiv:2005.02104.
A five person team based at the Deep Quantum Laboratory, Skolkovo Institute of Science, Moscow including Jacob Biamonte provide further insights into nature’s deep attraction to reside at an optimum critical poise even in the previously remote, fundamental depth.
The numerical emulation of quantum systems often requires an exponential number of degrees of freedom which translates to a computational bottleneck. Recent studies have revealed that neural networks are suitable for the determination of macroscopic phases of matter and associated phase transitions as well as efficient quantum state representation. In this work, we address quantum phase transitions in quantum spin chains and show that even neural networks with no hidden layers can be effectively trained to distinguish between magnetically ordered and disordered phases. Our results extend to a wide class of interacting quantum manybody systems and illustrate the wide applicability of neural networks to manybody quantum physics. (Abstract)
The concept of deep learning has attracted dramatic interest over the last decade. First applied in the domain of image and natural speech recognition, algorithms for machine learning have recently shown their utility in statistical mechanics of interacting classical and quantum systems. (2) The application of machine learning to quantum information problems has also received significant interest recently, promising to directly probe the entanglement entropy as well as other properties. (2)
Quantum spinchains are particular examples of exactly solvable or "quantum integrable" systems in 1+1 spacetime dimensions. Picture a ring of atoms (in order to have periodic boundary conditions) each of which possesses a quantum "degree of freedom", called a "spin", which can point in two directions, up or down. "Quantum" means that we allow for all complex linear superpositions of the different possible spin configurations of the ring, this set forms the physical state space. (Google)
Animate Cosmos > Quantum Cosmology > quantum CS
Carrasquilla, Juan.
Machine Learning for Quantum Matter.
arXiv:2003.11040.
This entry by a Vector Institute for Artificial Intelligence, Toronto mathematical physicist is a current example of the crossintegration of deep cerebral learning techniques with both classical physics and quantum domains.
Quantum matter, the research field studying material phases whose properties are intrinsically quantum mechanical, draws from areas as diverse as condensed matter physics, materials science, statistical mechanics, quantum information, quantum gravity, and largescale numerical simulations. Here we review the recent adaptation of machine learning ideas for quantum matter studies, ranging from algorithms that recognize conventional and topological states in synthetic and experimental data, to quantum states in terms of neural networks and quantum manybody physics. (Abstract excerpt)
Animate Cosmos > Quantum Cosmology > quantum CS
Giannozzi, Paolo, et al.
Quantum ESPRESSO toward the Exascale.
Journal of Chemical Physics.
152/154105,
2020.
We cite this entry by fifteen European Union physicists as a current example of how this once intractable, basic domain is now readily being availed for all manner of material, computational, linguistic and practical advantages. This project noted below began in 2002, and is here reviewed “at the turn of the twenties.”
Quantum ESPRESSO is an opensource distribution of computer codes for quantummechanical materials modeling based on densityfunctional theory, pseudopotentials, and plane waves, and renowned for its performance on a wide range of hardware. In this paper, we present a review of the ongoing effort to port Quantum ESPRESSO onto heterogeneous architectures based on hardware accelerators, which will overcome the energy constraints that are currently slowing exascale computing. (Abstract)
Quantum ESPRESSO Foundation: QEF is the home of this project for materials modeling at the nanoscale. We pledge ourselves to an open vision of science and software engineering. We foster the design, development, maintenance, and distribution of highquality opensource software for the quantum simulation of matter, and we are committed to the dissemination of the art and science of scientific computing, by promoting training courses worldwide.
Animate Cosmos > Quantum Cosmology > quantum CS
Kirchner, Stefan, et al.
Colloquium: Heavyelectron Quantum Criticality and Singleparticle Spectroscopy.
Reviews of Modern Physics.
92/011002,
2020.
A seven person international effort from Zhejiang University, Vienna University, MPI Chemical Physics, University of Science and Technology of China, Los Alamos National Laboratory, and Rice University, TX provides deeply technical excursion through these newly open frontiers where strong signatures of critically poised states can again be found. For specific case, they appear in ytterbium, rhenium, silicon compositions and other complex chemicals, that is to say, innately throughout material nature.
Angleresolved photoemission spectroscopy (ARPES) and scanning tunneling microscopy (STM) have become indispensable tools in the study of correlated quantum materials. Both probe complementary aspects of the singleparticle excitation spectrum. ARPES and STM can study the electronic Green’s function, a central object of manybody theory. This review focuses on heavyelectron quantum criticality, especially the role of Kondo destruction. Particular emphasis is placed on the question of how to distinguish between the signatures of the initial onset of hybridizationgap formation, which characterizes the lowenergy physics and, hence, the nature of quantum criticality. (Abstract excerpt)
I. QUANTUM CRITICALITY: Quantum phase transitions occur at zero temperature and like their finite temperature counterparts, they can be either first order or continuous. In contrast to the finite temperature case where thermal fluctuations drive the transition, quantum fluctuations, encoded already at the Hamiltonian level, are responsible for the occurrence of a quantum phase transition. If the transition is continuous, characteristic, critical scaling ensues in its vicinity which reflects the singular correlations of the ground state wave function. (3)
Animate Cosmos > Quantum Cosmology > exouniverse
Kartvelishvili, Guram, et al.
SelfOrganized Critical Multiverse.
arXiv:2003.12594.
As nature’s phenomenal propensity to seek and reside at an optimum, complementary balance between certain particle/wave, conserve/create, me/We states gains notice everywhere, University of Pennsylvania astrophysicists including Justin Khoury scope out ways to detect its effect on this vast expanse. After citations of its wide presence (see quotes), a review of deep parameters from an inflationary start to now are seen to express such a nonlinear poise. In wider regard, as human beings are lately assaulted is so many ways, at the same while a worldwise intelligence discovers a multiUniVerse to EarthVerse of a bipartite, bigender code. As the website documents, this source code seems to be genetic in actual kind as a vital endowment. See also Dynamical Criticality and Higgs Metastability by JK at 1912.06706 and Search Optimization, Funnel Tomography, and Dynamical Criticality on the String Landscape by JK and Onkar Farrikar at 1907.07693. We post several quotes in support
Recently a dynamical selection mechanism for vacua based on search optimization was proposed in the context of falsevacuum eternal inflation on the landscape. The search algorithm is optimal in regions of the landscape where the dynamics are tuned at criticality, with de Sitter vacua having an average lifetime of order their Page time. The purpose of this paper is to shed light on the nature of the dynamical phase transition at the Page lifetime. Through a change of variables the master equation governing the comoving volume of de Sitter vacua is mapped to a stochastic equation for coupled overdamped stochastic oscillators . We show that the displacement fluctuations for the oscillators exhibit a 1/f power spectrum over a broad range of frequencies. A 1/f power spectrum is a hallmark of nonequilibrium systems at criticality. In analogy with neuronal avalanches in the brain, de Sitter vacua at criticality can be thought of as undergoing scale invariant volume fluctuation avalanches. (Abstract excerpt)
The discovery that string theory admits a vast landscape of metastable vacua, together with the mechanism of eternal inflation for dynamically populating these vacua, has led to a paradigm shift in our understanding of fundamental physics. It entails that statistical physics, possibly in conjunction with selection (anthropic) effects, played a role in determining the physical parameters of our universe. Like many other statistical systems, it is natural to expect that the multiverse can exhibit phase transitions. Indeed, it has been shown recently that certain regions of the landscape display nonequilibrium critical phenomena, in the sense that their vacuum dynamics are tuned at dynamical criticality. (1)
Nonequilibrium systems exhibiting 1/f fluctuation spectra are ubiquitous in nature. Examples include neuronal dynamics, heart beat variability, linguistics (Zipf’s law), economic time series (stock market prices), music and art. Thus, complex behavior appears intimately related to dynamical criticality. This has motivated the tantalizing idea of selforganizing criticality. While the subject is not without controversy, it is worth noting that our framework satisfies what are believed to be necessary conditions for selforganized criticality — our landscape region is outofequilibrium, open/dissipative, and slowlydriven. (3)
Complex selforganized systems poised at criticality are ubiquitous in the natural world. This has led to the conjecture that dynamical criticality is evolutionarily favored because it offers an ideal tradeoff between robust response to external stimuli and flexibility of adaptation to a changing environment. In a forthcoming paper we will study another advantage of dynamical criticality, namely enhanced computational capabilities. Indeed, it has been argued that complex systems maximize their computational capabilities at the phase transition between stable and unstable dynamical behavior — the socalled “edge of chaos”. For instance, cellular automata with certain critical dynamical rules are capable of universal computation, exhibiting longlived and complex transient structures. (910)
Animate Cosmos > Organic > Biology Physics
Xue, Chi, et al.
Scaleinvariant Topology and Bursty Branching of Evolutionary Trees Emerge from Niche Construction.
Proceedings of the National Academy of Sciences.
117/7679,
2020.
University of Illinois genome biologists including Nigel Goldenfeld provide an exercise to show how, by way of statistical physics and network principles, that life’s circuitous, diverse, adaptive course can yet be found to have an intrinsic, self similar topology.
Phylogenetic trees describe both the evolutionary process and community diversity. Recent work has established that they exhibit scaleinvariant topology, which quantifies the fact that their branching lies in between balanced binary trees and maximally unbalanced ones. Here, we present a simple, coarsegrained statistical model of niche construction coupled to speciation. Finitesize scaling analysis of the dynamics shows that the resultant phylogenetic tree topology is scaleinvariant due to a singularity arising from large niche construction fluctuations that follow extinction events. The same model recapitulates the bursty pattern of diversification in time. (Abstract)
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