Geometrically protected triple-point crossings in an optical lattice

We show how to realize topologically protected crossings of three energy bands, integer-spin analogs of Weyl fermions, in three-dimensional optical lattices. Our proposal only involves ultracold atom techniques that have already been experimentally demonstrated and leads to isolated triple-point crossings (TPCs) which are required to exist by a novel combination of lattice symmetries. The symmetries also allow for a new type of topological object, the type-II, or tilted, TPC. Our Rapid Communication shows that spin-1 Weyl points, which have not yet been observed in the bandstructure of crystals, are within reach of ultracold atom experiments.Comment: 5 pages, 2 figures + 3 pages, 3 figures supplemental material. Added appendix on model symmetries, fixed typos and added references. This is the final, published versio

GUT MICROBIOTA CROSSTALK WITH CONVENTIONAL AND NON-CONVENTIONAL T CELLS: A GAME OF MANY PLAYERS.

The presence of microbial commensals in the gut requires the establishment of a complex network of reciprocal interactions between the microbiota and the host immune system to allow nutrient absorption while preventing undesired mucosal immune responses. Despite these homeostatic mechanisms, during intestinal inflammation alterations of the microbiota composition, namely dysbiosis, trigger abnormal immune responses. Here, we aimed at investigating the functional crosstalk between gut microbiota and the mucosal immune system during inflammation and upon induction of microbial dysbiosis. We observed that inflammation-induced and antibiotic-driven types of dysbiosis are phenotypically and functionally modifying CD4+ T and iNKT cells activity. Moreover, during intestinal inflammation, the experimental manipulation of the microbiota community through Faecal Microbiota Transplantation (FMT) reduces colonic inflammation and initiates the restoration of intestinal homeostasis through the induction of IL-10 production by immune cells. Further, we performed a comprehensive analysis on intestinal iNKT cells isolated from surgical specimens of active Inflammatory Bowel Disease (IBD) patients and non-IBD donors. We report that the exposure to mucosa-associated microbiota drives iNKT cell pro-inflammatory activation, inducing direct pathogenicity against the intestinal epithelium. Collectively, we provided solid evidence that a strict crosstalk between the gut microbiota and the intestinal conventional and non-conventional T cells exists. Antibiotic-associated dysbiosis has immunostimulatory functions. Moreover, FMT can therapeutically control intestinal experimental colitis and this poses FMT as a valuable therapeutic option in immune-related pathologies. In addition, we generated fundamental knowledge about the pathogenic functions exerted by human intestinal iNKT cells upon the interaction with mucosa-associated microbiota communities

Effects of disorder on Coulomb-assisted braiding of Majorana zero modes

Majorana zero modes in one-dimensional topological superconductors obey non-Abelian braiding statistics. Braiding manipulations can be realized by controlling Coulomb couplings in hybrid Majorana-transmon devices. However, strong disorder may induce accidental Majorana modes, which are expected to have detrimental effects on braiding statistics. Nevertheless, we show that the Coulomb-assisted braiding protocol is efficiently realized also in the presence of accidental modes. The errors occurring during the braiding cycle are small if the couplings of the computational Majorana modes to the accidental ones are much weaker than the maximum Coulomb coupling.Comment: 7 pages, 4 figures, this is the final, published versio

Machine learning applied to ambulatory blood pressure monitoring: a new tool to diagnose autonomic failure?

BACKGROUND: Autonomic failure (AF) complicates Parkinson’s disease (PD) in one-third of cases, resulting in complex blood pressure (BP) abnormalities. While autonomic testing represents the diagnostic gold standard for AF, accessibility to this examination remains limited to a few tertiary referral centers. OBJECTIVE: The present study sought to investigate the accuracy of a machine learning algorithm applied to 24-h ambulatory BP monitoring (ABPM) as a tool to facilitate the diagnosis of AF in patients with PD. METHODS: Consecutive PD patients naïve to vasoactive medications underwent 24 h-ABPM and autonomic testing. The diagnostic accuracy of a Linear Discriminant Analysis (LDA) model exploiting ABPM parameters was compared to autonomic testing (as per a modified version of the Composite Autonomic Symptom Score not including the sudomotor score) in the diagnosis of AF. RESULTS: The study population consisted of n = 80 PD patients (33% female) with a mean age of 64 ± 10 years old and disease duration of 6.2 ± 4 years. The prevalence of AF at the autonomic testing was 36%. The LDA model showed 91.3% accuracy (98.0% specificity, 79.3% sensitivity) in predicting AF, significantly higher than any of the ABPM variables considered individually (hypotensive episodes = 82%; reverse dipping = 79%; awakening hypotension = 74%). CONCLUSION: LDA model based on 24-h ABPM parameters can effectively predict AF, allowing greater accessibility to an accurate and easy to administer test for AF. Potential applications range from systematic AF screening to monitoring and treating blood pressure dysregulation caused by PD and other neurodegenerative disorders

Ultracold atoms in U(2) non-Abelian gauge potentials preserving the Landau levels

We study ultracold atoms subjected to U(2) non-Abelian potentials: we consider gauge potentials having, in the Abelian limit, degenerate Landau levels and we then investigate the effect of general homogeneous non-Abelian terms. The conditions under which the structure of degenerate Landau levels is preserved are classified and discussed. The typical gauge potentials preserving the Landau levels are characterized by a fictitious magnetic field and by an effective spin-orbit interaction, e.g. obtained through the rotation of two-dimensional atomic gases coupled with a tripod scheme. The single-particle energy spectrum can be exactly determined for a class of gauge potentials, whose physical implementation is explicitly discussed. The corresponding Landau levels are deformed by the non-Abelian contribution of the potential and their spin degeneracy is split. The related deformed quantum Hall states for fermions and bosons (in the presence of strong intra-species interaction) are determined far from and at the degeneracy points of the Landau levels. A discussion of the effect of the angular momentum is presented, as well as results for U(3) gauge potentials

Quantum hashing with the icosahedral group

We study an efficient algorithm to hash any single qubit gate (or unitary matrix) into a braid of Fibonacci anyons represented by a product of icosahedral group elements. By representing the group elements by braid segments of different lengths, we introduce a series of pseudo-groups. Joining these braid segments in a renormalization group fashion, we obtain a Gaussian unitary ensemble of random-matrix representations of braids. With braids of length O[log(1/epsilon)], we can approximate all SU(2) matrices to an average error epsilon with a cost of O[log(1/epsilon)] in time. The algorithm is applicable to generic quantum compiling.Comment: 5 pages, 4 figures; revised version, to appear in Phys. Rev. Lett

Ultra-compact binary neural networks for human activity recognition on RISC-V processors

Human Activity Recognition (HAR) is a relevant inference task in many mobile applications. State-of-the-art HAR at the edge is typically achieved with lightweight machine learning models such as decision trees and Random Forests (RFs), whereas deep learning is less common due to its high computational complexity. In this work, we propose a novel implementation of HAR based on deep neural networks, and precisely on Binary Neural Networks (BNNs), targeting low-power general purpose processors with a RISC-V instruction set. BNNs yield very small memory footprints and low inference complexity, thanks to the replacement of arithmetic operations with bit-wise ones. However, existing BNN implementations on general purpose processors impose constraints tailored to complex computer vision tasks, which result in over-parametrized models for simpler problems like HAR. Therefore, we also introduce a new BNN inference library, which targets ultra-compact models explicitly. With experiments on a single-core RISC-V processor, we show that BNNs trained on two HAR datasets obtain higher classification accuracy compared to a state-of-the-art baseline based on RFs. Furthermore, our BNN reaches the same accuracy of a RF with either less memory (up to 91%) or more energy-efficiency (up to 70%), depending on the complexity of the features extracted by the RF