Condensed Matter Theory

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Optimal navigation of microswimmers in complex and noisy environments

(2022)

Authors:

Lorenzo Piro, Benoît Mahault, Ramin Golestanian
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Entanglement Negativity and Mutual Information after a Quantum Quench: Exact Link from Space-Time Duality

ArXiv 2203.17254 (2022)

Authors:

Bruno Bertini, Katja Klobas, Tsung-Cheng Lu
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Growth of Rényi Entropies in Interacting Integrable Models and the Breakdown of the Quasiparticle Picture

ArXiv 2203.17264 (2022)

Authors:

Bruno Bertini, Katja Klobas, Vincenzo Alba, Gianluca Lagnese, Pasquale Calabrese
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Emergent Organization and Polarization due to Active Fluctuations

(2022)

Authors:

Benoît Mahault, Prakhar Godara, Ramin Golestanian
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Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution

Proceedings of the National Academy of Sciences National Academy of Sciences 119:11 (2022) e2113883119

Authors:

Iain G Johnston, Kamaludin Dingle, Sam F Greenbury, Chico Q Camargo, Jonathan PK Doye, Sebastian E Ahnert, Ard A Louis

Abstract:

Significance Why does evolution favor symmetric structures when they only represent a minute subset of all possible forms? Just as monkeys randomly typing into a computer language will preferentially produce outputs that can be generated by shorter algorithms, so the coding theorem from algorithmic information theory predicts that random mutations, when decoded by the process of development, preferentially produce phenotypes with shorter algorithmic descriptions. Since symmetric structures need less information to encode, they are much more likely to appear as potential variation. Combined with an arrival-of-the-frequent mechanism, this algorithmic bias predicts a much higher prevalence of low-complexity (high-symmetry) phenotypes than follows from natural selection alone and also explains patterns observed in protein complexes, RNA secondary structures, and a gene regulatory network.
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