64 research outputs found
Deep learning of spatial densities in inhomogeneous correlated quantum systems
Machine learning has made important headway in helping to improve the treatment of quantum many-body systems. A domain of particular relevance are correlated inhomogeneous systems. What has been missing so far is a general, scalable deep-learning approach that would enable the rapid prediction of spatial densities for strongly correlated systems in arbitrary potentials. In this work, we present a straightforward scheme, where we learn to predict densities using convolutional neural networks trained on random potentials. While we demonstrate this approach in 1D and 2D lattice models using data from numerical techniques like Quantum Monte Carlo, it is directly applicable as well to training data obtained from experimental quantum simulators. We train networks that can predict the densities of multiple observables simultaneously and that can predict for a whole class of many-body lattice models, for arbitrary system sizes. We show that our approach can handle well the interplay of interference and interactions and the behaviour of models with phase transitions in inhomogeneous situations, and we also illustrate the ability to solve inverse problems, finding a potential for a desired density
NetKet: A machine learning toolkit for many-body quantum systems
We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and flexible implementation of neural-network quantum states, which are used as a variational ansatz for quantum wavefunctions. NetKet provides algorithms for several key tasks in quantum many-body physics and quantum technology, namely quantum state tomography, supervised learning from wavefunction data, and ground state searches for a wide range of customizable lattice models. Our aim is to provide a common platform for open research and to stimulate the collaborative development of computational methods at the interface of machine learning and many-body physics
Modern applications of machine learning in quantum sciences
In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning
Deflection control for reinforced recycled aggregate concrete beams: Experimental database and extension of the fib Model Code 2010 model
Recycled aggregate concrete (RAC) has emerged as a viable solution for
solving some of the environmental problems of concrete production.
However, design guidelines for deflection control of reinforced RAC
members have not yet been proposed. This study presents a
comprehensive analysis of the applicability of the fib Model Code 2010
(MC2010) deflection control model to reinforced RAC beams. Three
databases of long-term studies on natural aggregate concrete (NAC) and
RAC beams were compiled and meta-analyses of deflection predictions
by MC2010 were performed. First, the MC2010 deflection control model
was tested against a large database of long-term tests on NAC beams.
Second, a database of RAC and companion NAC beams was compiled
and initial and long-term deflections were calculated using the MC2010
model. It was shown that deflections of RAC beams are significantly
underestimated relative to NAC beams. Previously proposed
modifications for MC2010 equations for shrinkage strain and creep
coefficient were used, and new modifications for the modulus of elasticity
and empirical coefficient β were proposed. The improved MC2010
deflection control model on RAC beams was shown to have equal
performance to that on companion NAC beams. The proposals presented
in this paper can help engineers to more reliably perform deflection
control of reinforced RAC members.This is the peer-reviewed version of the article:
N. Tošić, S. Marinković, and J. de Brito, ‘Deflection control for reinforced recycled aggregate concrete beams: Experimental database and extension of the fib Model Code 2010 model’, Structural Concrete, vol. 20, no. 6, pp. 2015–2029, 2019 [https://doi.org/10.1002/suco.201900035
Physical strain during activities of daily living of patients with coronary artery disease
The purpose of this study was to determine the physical strain of activities of daily living (ADL) in patients with coronary artery disease (CAD) compared with healthy controls. Seventeen patients with CAD and 15 controls performed a graded exercise bicycle test and 5 ADL tasks: walking with/without load, vacuum cleaning, undressing, and walking stairs. Peak heart rate (HRpeak) and peak oxygen uptake (VO(2)peak) were determined during the bicycle test. Heart rate (HR) and oxygen uptake (VO2) were continuously measured during all ADL tasks. Physical strain during ADL tasks was calculated using HR and VO2 response, expressed relative to individual HR and VO2 reserves (%HRR, %VO2R, respectively). Perceived strain was measured using the Rating of Perceived Exertion (RPE) scale. HRpeak and VO(2)peak were lower (
Durable concrete structures. Design Guide. Bulletin d'Information n 183 Comité Euro-International du Béton, Lausanne / Suisse, 1989, 112 pp., 134 figg., 16 tabb
DOMINO, doxycycline 40 mg vs. minocycline 100 mg in the treatment of rosacea: a randomized, single-blinded, noninferiority trial, comparing efficacy and safety
Background There is a lack of evidence for minocycline in the treatment of rosacea. Objectives To compare the efficacy and safety of doxycycline 40 mg vs. minocycline 100 mg in papulopustular rosacea. Methods In this randomized, single-centre, 1 : 1 allocation, assessor-blinded, noninferiority trial, patients with mild-to-severe papulopustular rosacea were randomly allocated to either oral doxycycline 40 mg or minocycline 100 mg for a 16-week period with 12 weeks of follow-up. Our primary outcomes were the change in lesion count and change in patient's health-related quality of life (using RosaQoL). Intention-to-treat and per protocol analyses were performed. Results Of the 80 patients randomized (40 minocycline, 40 doxycycline), 71 were treated for 16 weeks. Sixty-eight patients completed the study. At week 16, the median change in lesion count was comparable in both groups: doxycycline vs. minocycline, respectively 13 vs. 14 fewer lesions. The RosaQoL scores were decreased for both doxycycline and minocycline, respectively by 0.62 and 0.86. Secondary outcomes were comparable except for Investigator's Global Assessment success, which was seen significantly more often in the minocycline group than in the doxycycline group (60% vs. 18%, P <0.001). At week 28, outcomes were comparable, except for RosaQoL scores and PaGA, which were significantly different in favour of minocycline (P = 0.005 and P = 0.043, respectively), and fewer relapses were recorded in the minocycline group than in the doxycycline group (7% and 48%, respectively; P <0.001). No serious adverse reactions were reported. Conclusions Minocycline 100 mg is noninferior to doxycycline 40 mg in efficacy over a 16-week treatment period. At follow-up, RosaQoL and PaGA were statistically significantly more improved in the minocycline group than in the doxycycline group, and minocycline 100 mg gives longer remission. In this study there was no significant difference in safety between these treatments; however, based on previous literature minocycline has a lower risk-to-benefit ratio than doxycycline. Minocycline 100 mg may be a good alternative treatment for those patients who, for any reason, are unable or unwilling to take doxycycline 40 m
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