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Displaying entries 61 through 65 of 65 found.

Earth Earns: An Open Participatory Earthropocene to Astropocene CoCreative Future

Gianfrate, Antonio, et al. Reconfigurable quantum fluid molecules of bound states in the continuum. Nature Physics. 20/1, 2024. We enter this work by thirteen nanoscientists mainly at CNR Nanotechnology, Italy and Princeton University as another instance of mid 2020s Earthuman abilities to learn all about and delve into any depth of as an evolitionary project to begin an new intentional quantum phase cocreation.

Topological bound states are confined wave-mechanical objects that offer advantageous ways to enhance light–matter interactions in photonic devices. Here we show that polariton condensation into a negative-mass bound state in the continuum exhibits interactive confinement to attain optically reprogrammable molecular arrays of quantum fluids of light. We demonstrate the scalability of our technique by extended mono- and diatomic chains of bound-state-in-the-continuum polariton fluids. (Excerpt)

Mitra, Anupam, et al.. Macrostates vs. Microstates in the Classical Simulation of Critical Phenomena in Quench Dynamics of 1D Ising Models. arXiv:2310.08567.. This entry by Center for Quantum Information and Control, University of New Mexico physicists including Ivan Deutsch is posted as an example among many to show how readily human intellects can delve into these fundamental depths and then to take over and commence anew a second intentional, informed material cocreation. See also Ultracold field-linked tetratomic molecules by Chen, Xing-Yan Chen, et al in Nature (January 31, 2024) for a similar instance.

We study the tractability of classically simulating critical phenomena in the quench dynamics of one-dimensional transverse field Ising models (TFIMs) using highly truncated matrix product states (MPS). We focus on two paradigmatic examples: a dynamical quantum phase transition (DQPT) that occurs in nonintegrable long-range TFIMs, and the infinite-time correlation length of the integrable nearest-neighbor TFIM when quenched to the critical point. For the DQPT, we show that the order parameters can be efficiently simulated with surprisingly heavy truncation of the MPS bond dimension. This can be used to reliably extract critical properties of the phase transition, including critical exponents, even when the full many-body state is not simulated with high fidelity.

Terasa, Ivo, et al. Pathways towards truly brain-like computing primitives. MaterialsToday. October, 2023. In this Springer journal, twelve Institute of Materials Science, Kiel University, Germany researchers describe a novel application of AI neural large learning and an avail of animate complex system principles such as Distributed Plasticity: Operation near Criticality, Self-ordered Arrangement, Hierarchy, Modularity, Robustness, and Oscillatory Ensembles. In this regard, the team broaches an innovative synthesis of these methods and features by which to open a 2020s frontier of intentional cocreativity going forward.

Taking inspiration from biological and neural information processing, deep learning and artificial intelligence have made solutions to complex problems more feasible. To explore the capabilities of brain-like hardware computing, a platform with dynamic reconfigurable connections is mandatory. This work addresses this biological motivation and classifies them with respect to several fundamental principles of brain-like computing. The approaches range from interconnected nanogranular networks with dynamically reconfigurable connections and guided redox-wiring to the mimicking of neural action potentials by relaxation-type oscillators that are used as input stimuli. (Abstract excerpt)

The nervous systems of humans, mammals, and even simple living species like invertebrates are well adapted to changing environments. The remarkable interactions with their surroundings are a result of millions of years of evolution. Biological systems can perform cognitive tasks, such as pattern recognition, with low power consumption. Fundamental building blocks leading to such core abilities exploit neurons as central processing units, which are interconnected by synapses to form a complex dynamical three-dimensional network. It is therefore no surprise that attempts have been made to develop artificial information processing systems, to reach the performance and power efficiency of nervous systems. Machine Learning, Neuromorphic Engineering and in materia Computing can be identified as three main development avenues (1, 2)

Wytock, Thomas and Adison Motter. Cell reprogramming design by transfer learning of functional transcriptional networks. PNAS. 121/11, 2024. Northwestern University biophysicists (search AM) advance the latest mathematical insights into 3D genomics so to achieve better malady management and medicines.

The lack of genome-wide models for gene regulatory networks complicates the application of control theory to cell behavior. We address this by a transfer learning approach that leverages genome-wide transcriptomic profiles to characterize cell type attractors responses. These responses predict a combinatorial perturbation that minimizes the transcriptional difference between an initial and target cell type, bringing the regulatory network to the basin of attraction. This approach will enable the rapid identification of treatments for complex diseases, and how the dynamics of gene regulatory networks affect phenotypes. (Significance)

Scheffer, Marten, et al. A Dynamical Systems View of Psychiatric Disorders and Practical Implications. JAMA Psychiatry. April, 2024. In this dedicated AMA journal, thirteen theorists posted at Wageningen University, University of Amsterdam, University of Melbourne, Virginia Commonwealth University, and Johns Hopkins University describe a beneficial application of the latest complexity science to a wide range of mental maladies. The discussion involves both symptoms and their management course. The endeavor is now possible because a worldwise computational prowess and its daily public discourse has been able to realize a life-like dual reality with a mathematic, informative, programmic source and consequent phenotype phases. A vital aspect of this genotype code-script that its independent, self-familiar, presence is manifestly underlies every such instance. This novel avail is a harbinger of a more effective, natural health care approach, along with ecovillages and all else.

Importance: Dynamical systems theory is widely used to explain tipping points, cycles, and chaos in complex systems from the climate to ecosystems. It has been suggested that the same theory might be used to deal with psychiatric disorders, which come and go as symptoms change. Here we review evidence for the practical applicability of this theory in medical psychiatry. Observations: Emerging results suggest that the time series of mood and behavior do use similar generic nonlinear indicators as employed globally to monitor tropical rainforests and weather patterns. Conclusions: These observations evoke follow-up questions on how best to collect dynamic data, infer informative timescales, construct mechanistic models, and can give patients an active role in their lifelong challenges.

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