Universal Parametric Correlations of Eigenfunctions in Chaotic and Disordered Systems

This paper establishes the universality of parametric correlations of eigenfunctions in chaotic and weakly disordered systems. We demonstrate this universality in the framework of the gaussian random matrix process and obtain predictions for a number of parametric correlators, one of them analytically. We present numerical evidence from different models that verifies our predictions.Comment: 11 pages, RevTeX, 2 uuencoded Postscript figure

Learning a hierarchical belief network of independent factor analyzers

Abstract Many belief networks have been proposed that are composed of binary units. However, for tasks such as object and speech recognition which produce real-valued data, binary network models are usually inadequate. Independent component analysis (ICA) learns a model from real data, but the descriptive power of this model is severly limited. We begin by describing the independent factor analysis (IFA) technique, which overcomes some of the limitations of ICA. We then create a multilayer network by cascading singlelayer IFA models. At each level, the IFA network extracts realvalued latent variables that are non-linear functions of the input data with a highly adaptive functional form, resulting in a hierarchical distributed representation of these data. Whereas exact maximum-likelihood learning of the network is intractable, we derive an algorithm that maximizes a lower bound on the likelihood, based on a variational approach

Active inference, evidence accumulation, and the urn task

Deciding how much evidence to accumulate before making a decision is a problem we and other animals often face, but one that is not completely understood. This issue is particularly important because a tendency to sample less information (often known as reflection impulsivity) is a feature in several psychopathologies, such as psychosis. A formal understanding of information sampling may therefore clarify the computational anatomy of psychopathology. In this theoretical letter, we consider evidence accumulation in terms of active (Bayesian) inference using a generic model of Markov decision processes. Here, agents are equipped with beliefs about their own behavior--in this case, that they will make informed decisions. Normative decision making is then modeled using variational Bayes to minimize surprise about choice outcomes. Under this scheme, different facets of belief updating map naturally onto the functional anatomy of the brain (at least at a heuristic level). Of particular interest is the key role played by the expected precision of beliefs about control, which we have previously suggested may be encoded by dopaminergic neurons in the midbrain. We show that manipulating expected precision strongly affects how much information an agent characteristically samples, and thus provides a possible link between impulsivity and dopaminergic dysfunction. Our study therefore represents a step toward understanding evidence accumulation in terms of neurobiologically plausible Bayesian inference and may cast light on why this process is disordered in psychopathology

Spectroscopy of bulk and few-layer superconducting NbSe2_2 with van der Waals tunnel junctions

Tunnel junctions, a well-established platform for high-resolution spectroscopy of superconductors, require defect-free insulating barriers with clean engagement to metals on both sides. Extending the range of materials accessible to tunnel junction fabrication, beyond the limited selection which allows high-quality oxide formation, requires the development of alternative fabrication techniques. Here we show that van-der-Waals (vdW) tunnel barriers, fabricated by stacking layered semiconductors on top of the transition metal dichalcogenide (TMD) superconductor NbSe$_2$, sustain a stable, low noise tunneling current, and exhibit strong suppression of sub-gap tunneling. We utilize the technique to measure the spectra of bulk (20 nm) and ultrathin (3- and 4-layer) devices at 70 mK. The spectra exhibit two distinct energy gaps, the larger of which decreases monotonously with thickness and $T_C$, in agreement with BCS theory. The spectra are analyzed using a two-band model modified to account for depairing. We show that in the bulk, the smaller gap exhibits strong depairing in an in-plane magnetic field, consistent with a high Fermi velocity. In the few-layer devices, depairing of the large gap is negligible, consistent with out-of-plane spin-locking due to Ising spin-orbit coupling. Our results demonstrate the utility of vdW tunnel junctions in mapping the intricate spectral evolution of TMD superconductors over a range of magnetic fields.Comment: This submission contains the first part of arxiv:1703.07677 with the addition of spectra taken on this devices. The second part of 1703.07677 will be published separatel

The Perturbed Static Path Approximation at Finite Temperature: Observables and Strength Functions

We present an approximation scheme for calculating observables and strength functions of finite fermionic systems at finite temperature such as hot nuclei. The approach is formulated within the framework of the Hubbard-Stratonovich transformation and goes beyond the static path approximation and the RPA by taking into account small amplitude time-dependent fluctuations around each static value of the auxiliary fields. We show that this perturbed static path approach can be used systematically to obtain good approximations for observable expectation values and for low moments of the strength function. The approximation for the strength function itself, extracted by an analytic continuation from the imaginary-time response function, is not always reliable, and we discuss the origin of the discrepancies and possible improvements. Our results are tested in a solvable many-body model.Comment: 37 pages, 8 postscript figures included, RevTe

Variational approximation for mixtures of linear mixed models

Mixtures of linear mixed models (MLMMs) are useful for clustering grouped data and can be estimated by likelihood maximization through the EM algorithm. The conventional approach to determining a suitable number of components is to compare different mixture models using penalized log-likelihood criteria such as BIC.We propose fitting MLMMs with variational methods which can perform parameter estimation and model selection simultaneously. A variational approximation is described where the variational lower bound and parameter updates are in closed form, allowing fast evaluation. A new variational greedy algorithm is developed for model selection and learning of the mixture components. This approach allows an automatic initialization of the algorithm and returns a plausible number of mixture components automatically. In cases of weak identifiability of certain model parameters, we use hierarchical centering to reparametrize the model and show empirically that there is a gain in efficiency by variational algorithms similar to that in MCMC algorithms. Related to this, we prove that the approximate rate of convergence of variational algorithms by Gaussian approximation is equal to that of the corresponding Gibbs sampler which suggests that reparametrizations can lead to improved convergence in variational algorithms as well.Comment: 36 pages, 5 figures, 2 tables, submitted to JCG

Self-consistent quantal treatment of decay rates within the perturbed static path approximation

The framework of the Perturbed Static Path Approximation (PSPA) is used to calculate the partition function of a finite Fermi system from a Hamiltonian with a separable two body interaction. Therein, the collective degree of freedom is introduced in self-consistent fashion through a Hubbard-Stratonovich transformation. In this way all transport coefficients which dominate the decay of a meta-stable system are defined and calculated microscopically. Otherwise the same formalism is applied as in the Caldeira-Leggett model to deduce the decay rate from the free energy above the so called crossover temperature $T_0$.Comment: 17 pages, LaTex, no figures; final version, accepted for publication in PRE; e-mail: [email protected]