Spectral origin of the colossal magnetodielectric effect in multiferroic DyMn2O5

The origin of the colossal magnetodielectric effect in DyMn2O5 [1] has been an outstanding question in multiferroics. Here, we report the activation of the electric dipole mode at 4-5 cm-1 in an applied magnetic field which fully accounts for the CMD effect. We examine two alternative explanations of this mode: an electromagnon and transitions between f-electron levels of Dy3+ ions. The experimental and theoretical evidence supports the electromagnon origin of the CMD effect.Comment: 5 pages, 4 figures, submitted to PR

Coexistence and competition of magnetism and superconductivity on the nanometer scale in underdoped BaFe1.89Co0.11As2

We report muon spin rotation (muSR) and infrared (IR) spectroscopy experiments on underdoped BaFe1.89Co0.11As2 which show that bulk magnetism and superconductivity (SC) coexist and compete on the nanometer length scale. Our combined data reveal a bulk magnetic order, likely due to an incommensurate spin density wave (SDW), which develops below Tmag \approx 32 K and becomes reduced in magnitude (but not in volume) below Tc = 21.7 K. A slowly fluctuating precursor of the SDW seems to develop alrady below the structural transition at Ts \approx 50 K. The bulk nature of SC is established by the muSR data which show a bulk SC vortex lattice and the IR data which reveal that the majority of low-energy states is gapped and participates in the condensate at T << Tc

Multi-Layer Cyber-Physical Security and Resilience for Smart Grid

The smart grid is a large-scale complex system that integrates communication technologies with the physical layer operation of the energy systems. Security and resilience mechanisms by design are important to provide guarantee operations for the system. This chapter provides a layered perspective of the smart grid security and discusses game and decision theory as a tool to model the interactions among system components and the interaction between attackers and the system. We discuss game-theoretic applications and challenges in the design of cross-layer robust and resilient controller, secure network routing protocol at the data communication and networking layers, and the challenges of the information security at the management layer of the grid. The chapter will discuss the future directions of using game-theoretic tools in addressing multi-layer security issues in the smart grid.Comment: 16 page

Anomalous Proximity Effect in Underdoped YBaCuO Josephson Junctions

Josephson junctions were photogenerated in underdoped thin films of the YBa$_2$Cu$_3$O$_{6+x}$ family using a near-field scanning optical microscope. The observation of the Josephson effect for separations as large as 100 nm between two wires indicates the existence of an anomalously large proximity effect and show that the underdoped insulating material in the gap of the junction is readily perturbed into the superconducting state. The critical current of the junctions was found to be consistent with the conventional Josephson relationship. This result constrains the applicability of SO(5) theory to explain the phase diagram of high critical temperature superconductors.Comment: 11 pages, 4 figure

Electrodynamics of a Clean Vortex Lattice

We report on a microscopic evaluation of electrodynamic response for the vortex lattice state of a model s-wave superconductor. Our calculation accounts self-consistently for both quasiparticle and order parameter response and establishes the collective nature of linear response in the clean limit. We discuss the effects of homogeneous and inhomogeneous pinning on the optical conductivity and the penetration depth, and comment on the relationship between macroscopic and local penetration depths. We find unexpected relationships between pinning arrangements and conductivity due to the strongly non-local response.Comment: 4 pages, 2 figure

Muon spin rotation study of magnetism and superconductivity in Ba(Fe1-xCox)2As2 single crystals

Using muon spin rotation (muSR) we investigated the magnetic and superconducting properties of a series of Ba(Fe1-xCox)2As2 single crystals with 0 =< x =< 0.15. Our study details how the antiferromagnetic order is suppressed upon Co substitution and how it coexists with superconductivity. In the non-superconducting samples at 0 < x < 0.04 the antiferromagnetic order parameter is only moderately suppressed. With the onset of superconductivity this suppression becomes faster and it is most rapid between x = 0.045 and 0.05. As was previously demonstrated by muSR at x = 0.055 [P. Marsik et al., Phys. Rev. Lett. 105, 57001 (2010)], the strongly weakened antiferromagnetic order is still a bulk phenomenon that competes with superconductivity. The comparison with neutron diffraction data suggests that the antiferromagnetic order remains commensurate whereas the amplitude exhibits a spatial variation that is likely caused by the randomly distributed Co atoms. A different kind of magnetic order that was also previously identified [C. Bernhard et al., New J. Phys. 11, 055050 (2009)] occurs at 0.055 < x < 0.075 where Tc approaches the maximum value. The magnetic order develops here only in parts of the sample volume and it seems to cooperate with superconductivity since its onset temperature coincides with Tc. Even in the strongly overdoped regime at x = 0.11, where the static magnetic order has disappeared, we find that the low energy spin fluctuations are anomalously enhanced below Tc. These findings point toward a drastic change in the relationship between the magnetic and superconducting orders from a competitive one in the strongly underdoped regime to a constructive one in near optimally and overdoped samples.Comment: 33 Pages, 9 Figure

The Meissner effect in a strongly underdoped cuprate above its critical temperature

The Meissner effect and the associated perfect "bulk" diamagnetism together with zero resistance and gap opening are characteristic features of the superconducting state. In the pseudogap state of cuprates unusual diamagnetic signals as well as anomalous proximity effects have been detected but a Meissner effect has never been observed. Here we have probed the local diamagnetic response in the normal state of an underdoped La1.94Sr0.06CuO4 layer (up to 46 nm thick, critical temperature Tc' < 5 K) which was brought into close contact with two nearly optimally doped La1.84Sr0.16CuO4 layers (Tc \approx 32 K). We show that the entire 'barrier' layer of thickness much larger than the typical c axis coherence lengths of cuprates exhibits a Meissner effect at temperatures well above Tc' but below Tc. The temperature dependence of the effective penetration depth and superfluid density in different layers indicates that superfluidity with long-range phase coherence is induced in the underdoped layer by the proximity to optimally doped layers; however, this induced order is very sensitive to thermal excitation.Comment: 7 pages, 7 figures + Erratu

Normal-State Spin Dynamics and Temperature-Dependent Spin Resonance Energy in an Optimally Doped Iron Arsenide Superconductor

The proximity of superconductivity and antiferromagnetism in the phase diagram of iron arsenides, the apparently weak electron-phonon coupling and the "resonance peak" in the superconducting spin excitation spectrum have fostered the hypothesis of magnetically mediated Cooper pairing. However, since most theories of superconductivity are based on a pairing boson of sufficient spectral weight in the normal state, detailed knowledge of the spin excitation spectrum above the superconducting transition temperature Tc is required to assess the viability of this hypothesis. Using inelastic neutron scattering we have studied the spin excitations in optimally doped BaFe1.85Co0.15As2 (Tc = 25 K) over a wide range of temperatures and energies. We present the results in absolute units and find that the normal state spectrum carries a weight comparable to underdoped cuprates. In contrast to cuprates, however, the spectrum agrees well with predictions of the theory of nearly antiferromagnetic metals, without complications arising from a pseudogap or competing incommensurate spin-modulated phases. We also show that the temperature evolution of the resonance energy follows the superconducting energy gap, as expected from conventional Fermi-liquid approaches. Our observations point to a surprisingly simple theoretical description of the spin dynamics in the iron arsenides and provide a solid foundation for models of magnetically mediated superconductivity.Comment: 8 pages, 4 figures, and an animatio

Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study

Background Self-reported symptoms during the COVID-19 pandemic have been used to train artificial intelligence models to identify possible infection foci. To date, these models have only considered the culmination or peak of symptoms, which is not suitable for the early detection of infection. We aimed to estimate the probability of an individual being infected with SARS-CoV-2 on the basis of early self-reported symptoms to enable timely self-isolation and urgent testing. Methods In this large-scale, prospective, epidemiological surveillance study, we used prospective, observational, longitudinal, self-reported data from participants in the UK on 19 symptoms over 3 days after symptoms onset and COVID-19 PCR test results extracted from the COVID-19 Symptom Study mobile phone app. We divided the study population into a training set (those who reported symptoms between April 29, 2020, and Oct 15, 2020) and a test set (those who reported symptoms between Oct 16, 2020, and Nov 30, 2020), and used three models to analyse the selfreported symptoms: the UK’s National Health Service (NHS) algorithm, logistic regression, and the hierarchical Gaussian process model we designed to account for several important variables (eg, specific COVID-19 symptoms, comorbidities, and clinical information). Model performance to predict COVID-19 positivity was compared in terms of sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in the test set. For the hierarchical Gaussian process model, we also evaluated the relevance of symptoms in the early detection of COVID-19 in population subgroups stratified according to occupation, sex, age, and body-mass index. Findings The training set comprised 182 991 participants and the test set comprised 15 049 participants. When trained on 3 days of self-reported symptoms, the hierarchical Gaussian process model had a higher prediction AUC (0·80 [95% CI 0·80–0·81]) than did the logistic regression model (0·74 [0·74–0·75]) and the NHS algorithm (0·67 [0·67–0·67]). AUCs for all models increased with the number of days of self-reported symptoms, but were still high for the hierarchical Gaussian process model at day 1 (0·73 [95% CI 0·73–0·74]) and day 2 (0·79 [0·78–0·79]). At day 3, the hierarchical Gaussian process model also had a significantly higher sensitivity, but a non-statistically lower specificity, than did the two other models. The hierarchical Gaussian process model also identified different sets of relevant features to detect COVID-19 between younger and older subgroups, and between health-care workers and non-health-care workers. When used during different pandemic periods, the model was robust to changes in populations. Interpretation Early detection of SARS-CoV-2 infection is feasible with our model. Such early detection is crucial to contain the spread of COVID-19 and efficiently allocate medical resources. Funding ZOE, the UK Government Department of Health and Social Care, the Wellcome Trust, the UK Engineering and Physical Sciences Research Council, the UK National Institute for Health Research, the UK Medical Research Council, the British Heart Foundation, the Alzheimer’s Society, the Chronic Disease Research Foundation, and the Massachusetts Consortium on Pathogen Readiness