Application of the variational autoencoder to detect the critical points of the anisotropic Ising model.

We generalize the previous study on the application of variational autoencoders to the two-dimensional Ising model to a system with anisotropy. Due to the self-duality property of the system, the critical points can be located exactly for the entire range of anisotropic coupling. This presents an excellent test bed for the validity of using a variational autoencoder to characterize an anisotropic classical model. We reproduce the phase diagram for a wide range of anisotropic couplings and temperatures via a variational autoencoder without the explicit construction of an order parameter. Considering that the partition function of (d+1)-dimensional anisotropic models can be mapped to that of the d-dimensional quantum spin models, the present study provides numerical evidence that a variational autoencoder can be applied to analyze quantum systems via the quantum Monte Carlo method.

Influence of state reopening policies in COVID-19 mortality.

By the end of May 2020, all states in the US have eased their COVID-19 mitigation measures. Different states adopted markedly different policies and timing for reopening. An important question remains in how the relaxation of mitigation measures is related to the number of casualties. To address this question, we compare the actual data to a hypothetical case in which the mitigation measures are left intact using a projection of the data from before mitigation measures were eased. We find that different states have shown significant differences between the actual number of deaths and the projected figures within the present model. We relate these differences to the states different policies and reopening schedules. Our study provides a gauge for the effectiveness of the approaches by different state governments and can serve as a guide for implementing best policies in the future. According to the Pearson correlation coefficients we obtained, the face mask mandate has the strongest correlation with the death count than any other policies we considered.

Effect of mitigation measures on the spreading of COVID-19 in hard-hit states in the U.S.

State government-mandated social distancing measures have helped to slow the growth of the COVID-19 pandemic in the United States. Many of the current predictive models of the development of COVID-19, especially after mitigation efforts, partially rely on extrapolations from data collected in other countries. Since most states enacted stay-at-home orders towards the end of March, the resulting effects of social distancing should be reflected in the death and infection counts by the end of April. Using the data available through April 25th, we investigate the change in the infection rate due to the mitigation efforts and project death and infection counts through September 2020 for some of the most heavily impacted states: New York, New Jersey, Michigan, Massachusetts, Illinois, and Louisiana. We find that with the current mitigation efforts, five of those six states have reduced their base reproduction number to a value less than one, stopping the exponential growth of the pandemic. We also project different scenarios after the mitigation is relaxed.

Deep learning on the 2-dimensional Ising model to extract the crossover region with a variational autoencoder.

The 2-dimensional Ising model on a square lattice is investigated with a variational autoencoder in the non-vanishing field case for the purpose of extracting the crossover region between the ferromagnetic and paramagnetic phases. The encoded latent variable space is found to provide suitable metrics for tracking the order and disorder in the Ising configurations that extends to the extraction of a crossover region in a way that is consistent with expectations. The extracted results achieve an exceptional prediction for the critical point as well as agreement with previously published results on the configurational magnetizations of the model. The performance of this method provides encouragement for the use of machine learning to extract meaningful structural information from complex physical systems where little a priori data is available.

Neuronal adaptor FE65 stimulates Rac1-mediated neurite outgrowth by recruiting and activating ELMO1.

Neurite outgrowth is a crucial process in developing neurons for neural network formation. Understanding the regulatory mechanisms of neurite outgrowth is essential for developing strategies to stimulate neurite regeneration after nerve injury and in neurodegenerative disorders. FE65 is a brain-enriched adaptor that stimulates Rac1-mediated neurite elongation. However, the precise mechanism by which FE65 promotes the process remains elusive. Here, we show that ELMO1, a subunit of ELMO1-DOCK180 bipartite Rac1 guanine nucleotide exchange factor (GEF), interacts with the FE65 N-terminal region. Overexpression of FE65 and/or ELMO1 enhances, whereas knockdown of FE65 or ELMO1 inhibits, neurite outgrowth and Rac1 activation. The effect of FE65 alone or together with ELMO1 is attenuated by an FE65 double mutation that disrupts FE65-ELMO1 interaction. Notably, FE65 is found to activate ELMO1 by diminishing ELMO1 intramolecular autoinhibitory interaction and to promote the targeting of ELMO1 to the plasma membrane, where Rac1 is activated. We also show that FE65, ELMO1, and DOCK180 form a tripartite complex. Knockdown of DOCK180 reduces the stimulatory effect of FE65-ELMO1 on Rac1 activation and neurite outgrowth. Thus, we identify a novel mechanism by which FE65 stimulates Rac1-mediated neurite outgrowth by recruiting and activating ELMO1.

Phosphorylation of FE65 Ser610 by serum- and glucocorticoid-induced kinase 1 modulates Alzheimer's disease amyloid precursor protein processing.

Alzheimer's disease (AD) is a fatal neurodegenerative disease affecting 36 million people worldwide. Genetic and biochemical research indicate that the excessive generation of amyloid-ß peptide (Aß) from amyloid precursor protein (APP), is a major part of AD pathogenesis. FE65 is a brain-enriched adaptor protein that binds to APP. However, the role of FE65 in APP processing and the mechanisms that regulate binding of FE65 to APP are not fully understood. In the present study, we show that serum- and glucocorticoid-induced kinase 1 (SGK1) phosphorylates FE65 on Ser(610) and that this phosphorylation attenuates FE65 binding to APP. We also show that FE65 promotes amyloidogenic processing of APP and that FE65 Ser(610) phosphorylation inhibits this effect. Furthermore, we found that the effect of FE65 Ser(610) phosphorylation on APP processing is linked to a role of FE65 in metabolic turnover of APP via the proteasome. Thus FE65 influences APP degradation via the proteasome and phosphorylation of FE65 Ser(610) by SGK1 regulates binding of FE65 to APP, APP turnover and processing.

Solving the parquet equations for the Hubbard model beyond weak coupling.

We find that imposing crossing symmetry in the iteration process considerably extends the range of convergence for solutions of the parquet equations for the Hubbard model. When crossing symmetry is not imposed, the convergence of both simple iteration and more complicated continuous loading (homotopy) methods is limited to high temperatures and weak interactions. We modify the algorithm to impose the crossing symmetry without increasing the computational complexity. We also imposed time reversal and a subset of the point group symmetries, but they did not further improve the convergence. We elaborate the details of the latency hiding scheme which can significantly improve the performance in the computational implementation. With these modifications, stable solutions for the parquet equations can be obtained by iteration more quickly even for values of the interaction that are a significant fraction of the bandwidth and for temperatures that are much smaller than the bandwidth. This may represent a crucial step towards the solution of two-particle field theories for correlated electron models.

Superglass phase of interacting bosons.

We introduce a Bose-Hubbard Hamiltonian with random disordered interactions as a model to study the interplay of superfluidity and glassiness in a system of three-dimensional hard-core bosons at half-filling. Solving the model using large-scale quantum Monte Carlo simulations, we show that these disordered interactions promote a stable superglass phase, where superflow and glassy density localization coexist in equilibrium without exhibiting phase separation. The robustness of the superglass phase is underlined by its existence in a replica mean-field calculation on the infinite-dimensional Hamiltonian.

Spin-glass transition at nonzero temperature in a disordered dipolar Ising system: the case of LiHoxY(1-x)F4.

The physics of the spin-glass (SG) state, with magnetic moments (spins) frozen in random orientations, is one of the most intriguing problems in condensed matter physics. In LiHoxY(1-x)F4, the Ho3+ moments, which are well described by Ising spins with only discrete "up or down" directions, interact predominantly via the inherently frustrated magnetostatic dipole-dipole interactions. The random frustration causing the SG behavior originates from the random substitution of dipole-coupled Ho3+ by nonmagnetic Y3+. In this Letter, we provide compelling evidence from extensive computer simulations that a SG transition at nonzero temperature occurs in a realistic microscopic model of LiHoxY(1-x)F4. This resolves the long-standing, and still ongoing, controversy about the existence of a SG transition in disordered dipolar Ising systems.

Functional renormalization group analysis of the half-filled one-dimensional extended Hubbard model.

We study the phase diagram of the half-filled one-dimensional extended Hubbard model at weak coupling using a novel functional renormalization group (FRG) approach. The FRG method includes in a systematic manner the effects of the scattering processes involving electrons away from the Fermi points. Our results confirm the existence of a finite region of bond charge density wave, also known as a "bond order wave" near U=2V and clarify why earlier g-ology calculations have not found this phase. We argue that this is an example in which formally irrelevant corrections change the topology of the phase diagram. Whenever marginal terms lead to an accidental symmetry, this generalized FRG method may be crucial to characterize the phase diagram accurately.