Nanoscale phase separation and pseudogap in the hole-doped cuprates from fluctuating Cu-O-Cu bonds

The pseudogap phenomenology is one of the enigmas of the physics of high-Tc superconductors. Many members of the cuprate family have now been experimentally characterized with high resolution in both real and momentum space, which revealed highly anisotropic Fermi arcs and local domains which break rotational symmetry in the CuO2 plane at the intraunit cell level. While most theoretical approaches to date have focused on the role of electronic correlations and dopinginduced disorder to explain these features, we show that many features of the pseudogap phase can be reproduced by considering the interplay between electronic and nonlinear electron-phonon interactions within a model of fluctuating Cu-O-Cu bonds. Remarkably, we find that electronic segregation arises naturally without the need to explicitly include disorder. Our approach points not only to the key role played by the oxygen bond in the pseudogap phase, but opens different directions to explore how nonequilibrium lattice excitations can be used to control the properties of the pseudogap phase.This work has been supported by the Spanish Ministry MINECO (National Plan 15 Grant: FISICATEAMO No. FIS2016-79508-P, SEVERO OCHOA No. SEV2015-0522, FPI), European Social Fund, Fundacio Cellex, Generalitat de Catalunya (AGAUR Grant No. 2017 SGR 1341 and CERCA/Program), EU FEDER, ERC AdG OSYRIS and NOQIA, ERC StG SEESUPER, EU FETPRO QUIC, and the National Science Centre, PolandSymfonia Grant No. 2016/20/W/ST4/00314. A.D. was financed by a Juan de la Cierva fellowship (IJCI-2017- 33180). R.W.C. acknowledges funding from the Polish National Center via Miniatura-2 Program Grant No. 2018/02/X/ST3/01718.Peer ReviewedPostprint (author's final draft

Efficient training of energy-based models via spin-glass control

[EN] We introduce a new family of energy-based probabilistic graphical models for efficient unsupervised learning. Its definition is motivated by the control of the spin-glass properties of the Ising model described by the weights of Boltzmann machines. We use it to learn the Bars and Stripes dataset of various sizes and the MNIST dataset, and show how they quickly achieve the performance offered by standard methods for unsupervised learning. Our results indicate that the standard initialization of Boltzmann machines with random weights equivalent to spin-glass models is an unnecessary bottleneck in the process of training. Furthermore, this new family allows for very easy access to low-energy configurations, which points to new, efficient training algorithms. The simplest variant of such algorithms approximates the negative phase of the log-likelihood gradient with no Markov chain Monte Carlo sampling costs at all, and with an accuracy sufficient to achieve good learning and generalization.ML and AA groups acknowledge the Spanish Ministry MINECO and State Research Agency AEI (FIDEUA PID2019-106901GBI00/10.13039/501100011033, Severo Ochoa Grant Nos. SEV-2015-0522 and CEX2019-000910-S, FPI), the European Social Fund, Fundacio Cellex, Fundacio Mir-Puig, Generalitat de Catalunya (AGAUR Grant Nos. 2017 SGR 1341 and SGR 1381, CERCA program, QuantumCAT U16-011424, co-funded by ERDF Operational Program of Catalonia 2014-2020), ERC AdG NOQIA and CERQUTE, EU FEDER, MINECO-EU QUANTERA MAQS (funded by the State Research Agency AEI PCI2019-111828-2/10.13039/501100011033), the National Science Centre, Poland-Symfonia Grant No. 2016/20/W/ST4/00314 and the AXA Chair in Quantum Information Science. A P-K acknowledges funding from Fundacio Obra Socialla Caixa' (LCF/BQ/ES15/10360001) and the European Union's Horizon 2020 research and innovation programme-Grant Agreement No. 648913. G M-G acknowledges funding from Fundacio Obra Social 'la Caixa' (LCF-ICFO grant). M A G-M acknowledges funding from the Spanish Ministry of Education and Vocational Training (MEFP) through the Beatriz Galindo program 2018 (BEAGAL18/00203).Pozas-Kerstjens, A.; Muñoz-Gil, G.; Piñol, E.; Garcia March, MA.; Acín, A.; Lewenstein, M.; Grzybowski, PR. (2021). Efficient training of energy-based models via spin-glass control. Machine Learning: Science and Technology. 2(2). https://doi.org/10.1088/2632-2153/abe8070250262

The Cardiomyopathy Registry of the EURObservational Research Programme of the European Society of Cardiology: Baseline data and contemporary management of adult patients with cardiomyopathies

AIMS: The Cardiomyopathy Registry of the EURObservational Research Programme is a prospective, observational, and multinational registry of consecutive patients with four cardiomyopathy subtypes: hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), and restrictive cardiomyopathy (RCM). We report the baseline characteristics and management of adults enrolled in the registry. METHODS AND RESULTS: A total of 3208 patients were enrolled by 69 centres in 18 countries [HCM (n\u2009=\u20091739); DCM (n\u2009=\u20091260); ARVC (n\u2009=\u2009143); and RCM (n\u2009=\u200966)]. Differences between cardiomyopathy subtypes (P\u2009<\u20090.001) were observed for age at diagnosis, history of familial disease, history of sustained ventricular arrhythmia, use of magnetic resonance imaging or genetic testing, and implantation of defibrillators. When compared with probands, relatives had a lower age at diagnosis (P\u2009<\u20090.001), but a similar rate of symptoms and defibrillators. When compared with the Long-Term phase, patients of the Pilot phase (enrolled in more expert centres) had a more frequent rate of familial disease (P\u2009<\u20090.001), were more frequently diagnosed with a rare underlying disease (P\u2009<\u20090.001), and more frequently implanted with a defibrillator (P\u2009=\u20090.023). Comparing four geographical areas, patients from Southern Europe had a familial disease more frequently (P\u2009<\u20090.001), were more frequently diagnosed in the context of a family screening (P\u2009<\u20090.001), and more frequently diagnosed with a rare underlying disease (P\u2009<\u20090.001). CONCLUSION: By providing contemporary observational data on characteristics and management of patients with cardiomyopathies, the registry provides a platform for the evaluation of guideline implementation. Potential gaps with existing recommendations are discussed as well as some suggestions for improvement of health care provision in Europe