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Loop expansion around the Bethe solution for the random magnetic field Ising ferromagnets at zero temperature.

ABSTRACT: We apply to the random-field Ising model at zero temperature ([Formula: see text]) the perturbative loop expansion around the Bethe solution. A comparison with the standard ϵ expansion is made, highlighting the key differences that make the expansion around the Bethe solution much more appropriate to correctly describe strongly disordered systems, especially those controlled by a [Formula: see text] renormalization group (RG) fixed point. The latter loop expansion produces an effective theory with cubic vertices. We compute the one-loop corrections due to cubic vertices, finding additional terms that are absent in the ϵ expansion. However, these additional terms are subdominant with respect to the standard, supersymmetric ones; therefore, dimensional reduction is still valid at this order of the loop expansion.

SUBMITTER: Angelini MC

PROVIDER: S-EPMC7007560 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Loop expansion around the Bethe solution for the random magnetic field Ising ferromagnets at zero temperature.

Angelini Maria Chiara MC Lucibello Carlo C Parisi Giorgio G Ricci-Tersenghi Federico F Rizzo Tommaso T

Proceedings of the National Academy of Sciences of the United States of America 20200117 5

We apply to the random-field Ising model at zero temperature ([Formula: see text]) the perturbative loop expansion around the Bethe solution. A comparison with the standard ϵ expansion is made, highlighting the key differences that make the expansion around the Bethe solution much more appropriate to correctly describe strongly disordered systems, especially those controlled by a [Formula: see text] renormalization group (RG) fixed point. The latter loop expansion produces an effective theory wi ...[more]

PMID: 31953263

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Project description:BackgroundThroughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the conditional correlation between the microbes. In this high-dimensional setting, zero-inflation and unit-sum constraint for relative abundance data pose challenges to the reliable estimation of microbial interaction networks.Methods and resultsTo identify the microbial interaction network, the zero-inflated latent Ising (ZILI) model is proposed which assumes the distribution of relative abundance relies only on finite latent states and provides a novel way to solve issues induced by the unit-sum and zero-inflation constrains. A two-step algorithm is proposed for the model selection of ZILI. ZILI is evaluated through simulated data and subsequently applied to an infant gut microbiota dataset from New Hampshire Birth Cohort Study. The results are compared with results from Gaussian graphical model (GGM) and dichotomous Ising model (DIS). Providing ZILI is the true data-generating model, the simulation studies show that the two-step algorithm can identify the graphical structure effectively and is robust to a range of parameter settings. For the infant gut microbiota dataset, the final estimated networks from GGM and ZILI turn out to have significant overlap in which the ZILI tends to select the sparser network than those from GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature.ConclusionsConstrains induced by relative abundance of microbiota such as zero inflation and unit sum render the conditional correlation analysis unreliable for conventional methods such as GGM. The proposed optimal categoricalization based ZILI model provides an alternative yet elegant way to deal with these difficulties. The results from ZILI have reasonable biological interpretation. This model can also be used to study the microbial interaction in other body parts.

Dataset Information

Loop expansion around the Bethe solution for the random magnetic field Ising ferromagnets at zero temperature.

Publications

Loop expansion around the Bethe solution for the random magnetic field Ising ferromagnets at zero temperature.

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