Thermal Control of the Magnon-Photon Coupling in a Notch Filter coupled to a Yttrium-Iron-Garnet/Platinum System

We report thermal control of mode hybridization between the ferromagnetic resonance (FMR) and a planar resonator (notch filter) working at 4.74 GHz. The chosen magnetic material is a ferrimagnetic insulator (Yttrium Iron Garnet: YIG) covered by 6 nm of platinum (Pt). A current induced heating method has been used in order to enhance the temperature of the YIG/Pt system. The device permits us to control the transmission spectra and the magnon-photon coupling strength at room temperature. These experimental findings reveal potentially applicable tunable microwave filtering function.Comment: 5 pages, 4 figure

Heterogeneous dynamics of the three dimensional Coulomb glass out of equilibrium

The non-equilibrium relaxational properties of a three dimensional Coulomb glass model are investigated by kinetic Monte Carlo simulations. Our results suggest a transition from stationary to non-stationary dynamics at the equilibrium glass transition temperature of the system. Below the transition the dynamic correlation functions loose time translation invariance and electron diffusion is anomalous. Two groups of carriers can be identified at each time scale, electrons whose motion is diffusive within a selected time window and electrons that during the same time interval remain confined in small regions in space. During the relaxation that follows a temperature quench an exchange of electrons between these two groups takes place and the non-equilibrium excess of diffusive electrons initially present decreases logarithmically with time as the system relaxes. This bimodal dynamical heterogeneity persists at higher temperatures when time translation invariance is restored and electron diffusion is normal. The occupancy of the two dynamical modes is then stationary and its temperature dependence reflects a crossover between a low-temperature regime with a high concentration of electrons forming fluctuating dipoles and a high-temperature regime in which the concentration of diffusive electrons is high.Comment: 10 pages, 9 figure

Population Dynamics and Non-Hermitian Localization

We review localization with non-Hermitian time evolution as applied to simple models of population biology with spatially varying growth profiles and convection. Convection leads to a constant imaginary vector potential in the Schroedinger-like operator which appears in linearized growth models. We illustrate the basic ideas by reviewing how convection affects the evolution of a population influenced by a simple square well growth profile. Results from discrete lattice growth models in both one and two dimensions are presented. A set of similarity transformations which lead to exact results for the spectrum and winding numbers of eigenfunctions for random growth rates in one dimension is described in detail. We discuss the influence of boundary conditions, and argue that periodic boundary conditions lead to results which are in fact typical of a broad class of growth problems with convection.Comment: 19 pages, 11 figure

Dynamical ultrametricity in the critical trap model

We show that the trap model at its critical temperature presents dynamical ultrametricity in the sense of Cugliandolo and Kurchan [CuKu94]. We use the explicit analytic solution of this model to discuss several issues that arise in the context of mean-field glassy dynamics, such as the scaling form of the correlation function, and the finite time (or finite forcing) corrections to ultrametricity, that are found to decay only logarithmically with the associated time scale, as well as the fluctuation dissipation ratio. We also argue that in the multilevel trap model, the short time dynamics is dominated by the level which is at its critical temperature, so that dynamical ultrametricity should hold in the whole glassy temperature range. We revisit some experimental data on spin-glasses in light of these results.Comment: 7 pages, 4 .eps figures. submitted to J. Phys.

Crossover from stationary to aging regime in glassy dynamics

We study the non-equilibrium dynamics of the spherical p-spin models in the scaling regime near the plateau and derive the corresponding scaling functions for the correlators. Our main result is that the matching between different time regimes fixes the aging function in the aging regime to $h(t)=\exp(t^{1-\mu})$. The exponent $\mu$ is related to the one giving the length of the plateau. Interestingly $1-\mu$ is quickly very small when one goes away from the dynamic transition temperature in the glassy phase. This gives new light on the interpretation of experiments and simulations where simple aging was found to be a reasonable but not perfect approximation, which could be attributed to the existence of a small but non-zero stretching exponent.Comment: 7 pages+2 figure

1D Aging

We derive exact expressions for a number of aging functions that are scaling limits of non-equilibrium correlations, R(tw,tw+t) as tw --> infinity with t/tw --> theta, in the 1D homogenous q-state Potts model for all q with T=0 dynamics following a quench from infinite temperature. One such quantity is (the two-point, two-time correlation function) when n/sqrt(tw) --> z. Exact, closed-form expressions are also obtained when one or more interludes of infinite temperature dynamics occur. Our derivations express the scaling limit via coalescing Brownian paths and a ``Brownian space-time spanning tree,'' which also yields other aging functions, such as the persistence probability of no spin flip at 0 between tw and tw+t.Comment: 4 pages (RevTeX); 2 figures; submitted to Physical Review Letter

First-order indicators for the estimation of discrete fractures in porous media

International audienceFaults and geological barriers can drastically affect the flow patterns in porous media. Such fractures can be modeled as interfaces that interact with the surrounding matrix. We propose a new technique for the estimation of the location and hydrogeological properties of a small number of large fractures in a porous medium from given distributed pressure or flow data. At each iteration, the algorithm builds a short list of candidates by comparing fracture indicators. These indicators quantify at the first order the decrease of a data misfit function; they are cheap to compute. Then, the best candidate is picked up by minimization of the objective function for each candidate. Optimally driven by the fit to the data, the approach has the great advantage of not requiring remeshing, nor shape derivation. The stability of the algorithm is shown on a series of numerical examples representative of typical situations.Les écoulements dans les milieux poreux peuvent être radicalement modifiés par la présence de failles ou de barrières géologiques.De telles fractures peuvent être modélisées comme des interfaces qui interagissent avec la matrice environnante. Nous proposons une nouvelle technique pour l'estimation de l'emplacement et des propriétés hydrogéologiques d'un petit nombre de grandes fractures dans un milieu poreux à partir de mesures distribuées de pression ou de flux données. À chaque itération, l'algorithme construit une courte liste de candidats par comparaison d'indicateurs de fracture. Ces indicateurs quantifient au premier ordre la décroissance d'une fonctiond'écart aux données; ils sont peut coûteux à calculer. Le meilleur candidat est ensuite isolé par minimisation de la fonctionobjectif pour chaque candidat. Guidée de façon optimale par la reproduction des données, l'approche a le grand avantage de ne pas nécessiter de remaillage, ni de dérivation de forme. La stabilité de l'algorithme est montrée sur une série d'exemples numériquesreprésentatifs de situations typiques

Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics

Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments. However, high annotation and compute costs limit the field's efficacy. Generalizable pretrained networks can overcome these costs, but high-quality pretraining requires vast annotated libraries, limiting its current applicability primarily to bird taxa. Here, we identify the optimum pretraining strategy for a data-deficient domain using coral reef bioacoustics. We assemble ReefSet, a large annotated library of reef sounds, though modest compared to bird libraries at 2% of the sample count. Through testing few-shot transfer learning performance, we observe that pretraining on bird audio provides notably superior generalizability compared to pretraining on ReefSet or unrelated audio alone. However, our key findings show that cross-domain mixing which leverages bird, reef and unrelated audio during pretraining maximizes reef generalizability. SurfPerch, our pretrained network, provides a strong foundation for automated analysis of marine PAM data with minimal annotation and compute costs.Comment: 18 pages, 5 figure

Forecasting Seizures in Dogs with Naturally Occurring Epilepsy

Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low-gamma (30-70 Hz), and high-gamma (70-180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring