Investigating ultrafast quantum magnetism with machine learning

We investigate the efficiency of the recently proposed Restricted Boltzmann Machine (RBM) representation of quantum many-body states to study both the static properties and quantum spin dynamics in the two-dimensional Heisenberg model on a square lattice. For static properties we find close agreement with numerically exact Quantum Monte Carlo results in the thermodynamical limit. For dynamics and small systems, we find excellent agreement with exact diagonalization, while for systems up to N=256 spins close consistency with interacting spin-wave theory is obtained. In all cases the accuracy converges fast with the number of network parameters, giving access to much bigger systems than feasible before. This suggests great potential to investigate the quantum many-body dynamics of large scale spin systems relevant for the description of magnetic materials strongly out of equilibrium.Comment: 18 pages, 5 figures, data up to N=256 spins added, minor change

Long-term prediction of adherence to continuous positive air pressure therapy for the treatment of moderate/severe obstructive sleep apnea syndrome

BACKGROUND: Continuous positive airway pressure (CPAP) therapy is a highly effective treatment for obstructive sleep apnea syndrome (OSAS). However, poor adherence is a limiting factor, and a significant proportion of patients are unable to tolerate CPAP. The aim of this study was to determine predictors of long-term non-compliance with CPAP. METHODS: CPAP treatment was prescribed to all consecutive patients with moderate or severe OSAS (AHI ≥15 events/h) (n = 295) who underwent a full-night CPAP titration study at home between February 1, 2002 and December 1, 2016. Adherence was defined as CPAP use for at least 4 h per night and five days per week. Subjects had periodical follow-up visits including clinical and biochemical evaluation and assessment of adherence to CPAP. RESULTS: Median follow-up observation was 74.8 (24.2/110.9) months. The percentage of OSAS patients adhering to CPAP was 41.4% (42.3% in males and 37.0% in females), and prevalence was significantly higher in severe OSAS than in moderate (51.8% vs. 22.1%; p < 0.001; respectively). At multivariate analysis, lower severity of OSAS (HR = 0.66; CI 95 0.46-0.94) p < 0.023), cigarette smoking (HR = 1.72; CI 95 1.13-2.61); p = 0.011), and previous cardiovascular events (HR = 1.95; CI 95 1.03-3.70; p = 0.04) were the only independent predictors of long-term non-adherence to CPAP after controlling for age, gender, and metabolic syndrome. CONCLUSIONS: In our cohort of patients with moderate/severe OSAS who were prescribed CPAP therapy, long-term compliance to treatment was present in less than half of the patients. Adherence was positively associated with OSAS severity and negatively associated with cigarette smoking and previous cardiovascular events at baseline

Parametrically driven THz magnon-pairs: predictions towards ultimately fast and minimally dissipative switching

Findings ways to achieve switching between magnetic states at the fastest possible time scale that simultaneously dissipates the least amount of energy is one of the main challenges in magnetism. Antiferromagnets exhibit intrinsic dynamics in the THz regime, the highest among all magnets and are therefore ideal candidates to address this energy-time dilemma. Here we study theoretically THz-driven parametric excitation of antiferromagnetic magnon-pairs at the edge of the Brillouin zone and explore the potential for switching between two stable oscillation states. Using a semi-classical theory, we predict that switching can occur at the femtosecond time scale with an energy dissipation down to a few zepto Joule. This result touches the thermodynamical bound of the Landauer principle, and approaches the quantum speed limit up to 5 orders of magnitude closer than demonstrated with magnetic systems so far.Comment: 8 pages, 4 figure

The effect of voltage distortion on ageing acceleration of insulation systems under partial discharge activity

The features of harmonic distortion which may affect significantly the reliability of typical ac-power network equipment, such as low-voltage self-healing capacitors used for reactive power and harmonic compensation are investigated. Moreover, the effect of high-frequency pulse-like voltage generated by adjustable speed drives (ASD) on electrical machine insulation is also investigated, resorting to life tests carried out on different insulating materials of the standard and "corona resistant" type, at electrical field levels able to incept partial discharges (PD)

Extreme learning machine collocation for the numerical solution of elliptic PDEs with sharp gradients

We address a new numerical method based on machine learning and in particular based on the concept of the so-called Extreme Learning Machines, to approximate the solution of linear elliptic partial differential equations with collocation. We show that a feedforward neural network with a single hidden layer and sigmoidal transfer functions and fixed, random, internal weights and biases can be used to compute accurately enough a collocated solution for such problems. We discuss how one can set the range of values for both the weights between the input and hidden layer and the biases of the hidden layer in order to obtain a good underlying approximating subspace, and we explore the required number of collocation points. We demonstrate the efficiency of the proposed method with several one-dimensional diffusion–advection–reaction benchmark problems that exhibit steep behaviors, such as boundary layers. We point out that there is no need of iterative training of the network, as the proposed numerical approach results to a linear problem that can be easily solved using least-squares and regularization. Numerical results show that the proposed machine learning method achieves a good numerical accuracy, outperforming central Finite Differences, thus bypassing the time-consuming training phase of other machine learning approaches

Corona poling for polarization of nanofibrous mats: advantages and open issues

This paper deals with the polarization process of piezoelectric nanofibrous mats of PVdF-TrFE by using a corona discharge process. With respect to traditional contact poling this process reduces the electrical breakdown risk which could easily occur when a highly porous mat is placed between two solid electrodes. Different set-up configurations were investigated by varying the applied voltage and the distance between the needle and the sample. The polarized nanofibers show a piezoelectric strain coefficients (mathrmd_33) comparable with the values of a commercial stiff film

Role of stochastic noise and generalization error in the time propagation of neural-network quantum states

Neural-network quantum states (NQS) have been shown to be a suitable variational ansatz to simulate out-of-equilibrium dynamics in two-dimensional systems using time-dependent variational Monte Carlo (t-VMC). In particular, stable and accurate time propagation over long time scales has been observed in the square-lattice Heisenberg model using the Restricted Boltzmann machine architecture. However, achieving similar performance in other systems has proven to be more challenging. In this article, we focus on the two-leg Heisenberg ladder driven out of equilibrium by a pulsed excitation as a benchmark system. We demonstrate that unmitigated noise is strongly amplified by the nonlinear equations of motion for the network parameters, which causes numerical instabilities in the time evolution. As a consequence, the achievable accuracy of the simulated dynamics is a result of the interplay between network expressiveness and measures required to remedy these instabilities. We show that stability can be greatly improved by appropriate choice of regularization. This is particularly useful as tuning of the regularization typically imposes no additional computational cost. Inspired by machine learning practice, we propose a validation-set based diagnostic tool to help determining optimal regularization hyperparameters for t-VMC based propagation schemes. For our benchmark, we show that stable and accurate time propagation can be achieved in regimes of sufficiently regularized variational dynamics

MAPPA. Methodologies applied to archaeological potential Predictivity

The fruitful cooperation over the years between the university teaching staff of Univerità di Pisa (Pisa University), the officials of the Soprintendenza per i Beni Archeologici della Toscana (Superintendency for Archaeological Heritage of Tuscany), the officials of the Soprintendenza per i Beni Architettonici, Paesaggistici, Artistici ed Etnoantropologici per le Province di Pisa e Livorno (Superintendency for Architectural, Landscape and Ethno-anthropological Heritage for the Provinces of Pisa and Livorno), and the Comune di Pisa (Municipality of Pisa) has favoured a great deal of research on issues regarding archaeological heritage and the reconstruction of the environmental and landscape context in which Pisa has evolved throughout the centuries of its history. The desire to merge this remarkable know-how into an organic framework and, above all, to make it easily accessible, not only to the scientific community and professional categories involved, but to everyone, together with the wish to provide Pisa with a Map of archaeological potential (the research, protection and urban planning tool capable of converging the heritage protection needs of the remains of the past with the development requirements of the future) led to the development of the MAPPA project – Methodologies applied to archaeological potential predictivity - funded by Regione Toscana in 2010. The two-year project started on 1 July 2011 and will end on 30 June 2013. The first year of research was dedicated to achieving the first objective, that is, to retrieving the results of archaeological investigations from the archives of Superintendencies and University and from the pages of scientific publications, and to making them easily accessible; these results have often never been published or have often been published incompletely and very slowly. For this reason, a webGIS (“MappaGIS” that may freely accessed at http://mappaproject.arch.unipi.it/?page_id=452) was created and will be followed by a MOD (Mappa Open Data archaeological archive), the first Italian archive of open archaeological data, in line with European directives regarding access to Public Administration data and recently implemented by the Italian government also (the beta version of the archive can be viewed at http://mappaproject.arch.unipi.it/?page_id=454). Details are given in this first volume about the operational decisions that led to the creation of the webGIS: the software used, the system architecture, the organisation of information and its structuring into various information layers. But not only. The creation of the webGIS also gave us the opportunity to focus on a series of considerations alongside the work carried out by the MAPPA Laboratory researchers. We took the decision to publish these considerations with a view to promoting debate within the scientific community and, more in general, within the professional categories involved (e.g. public administrators, university researchers, archaeology professionals). This allowed us to overcome the critical aspects that emerged, such as the need to update the archaeological excavation documentation and data archiving systems in order to adjust them to the new standards provided by IT development; most of all, the need for greater and more rapid spreading of information, without which research cannot truly progress. Indeed, it is by comparing and connecting new data in every possible and, at times, unexpected way that research can truly thrive

Numerical Bifurcation Analysis of PDEs From Lattice Boltzmann Model Simulations: a Parsimonious Machine Learning Approach

We address a three-tier data-driven approach for the numerical solution of the inverse problem in Partial Differential Equations (PDEs) and for their numerical bifurcation analysis from spatio-temporal data produced by Lattice Boltzmann model simulations using machine learning. In the first step, we exploit manifold learning and in particular parsimonious Diffusion Maps using leave-one-out cross-validation (LOOCV) to both identify the intrinsic dimension of the manifold where the emergent dynamics evolve and for feature selection over the parameter space. In the second step, based on the selected features, we learn the right-hand-side of the effective PDEs using two machine learning schemes, namely shallow Feedforward Neural Networks (FNNs) with two hidden layers and single-layer Random Projection Networks (RPNNs), which basis functions are constructed using an appropriate random sampling approach. Finally, based on the learned black-box PDE model, we construct the corresponding bifurcation diagram, thus exploiting the numerical bifurcation analysis toolkit. For our illustrations, we implemented the proposed method to perform numerical bifurcation analysis of the 1D FitzHugh-Nagumo PDEs from data generated by D1Q3 Lattice Boltzmann simulations. The proposed method was quite effective in terms of numerical accuracy regarding the construction of the coarse-scale bifurcation diagram. Furthermore, the proposed RPNN scheme was ∼ 20 to 30 times less costly regarding the training phase than the traditional shallow FNNs, thus arising as a promising alternative to deep learning for the data-driven numerical solution of the inverse problem for high-dimensional PDEs