On lunar ''temperatures''

Thermal measurements on lunar surface at radio wavelength

Numerical Implementation of Gradient Algorithms

A numerical method for computational implementation of gradient dynamical systems is presented. The method is based upon the development of geometric integration numerical methods, which aim at preserving the dynamical properties of the original ordinary differential equation under discretization. In particular, the proposed method belongs to the class of discrete gradients methods, which substitute the gradient of the continuous equation with a discrete gradient, leading to a map that possesses the same Lyapunov function of the dynamical system, thus preserving the qualitative properties regardless of the step size. In this work, we apply a discrete gradient method to the implementation of Hopfield neural networks. Contrary to most geometric integration methods, the proposed algorithm can be rewritten in explicit form, which considerably improves its performance and stability. Simulation results show that the preservation of the Lyapunov function leads to an improved performance, compared to the conventional discretization.Spanish Government project no. TIN2010-16556 Junta de Andalucía project no. P08-TIC-04026 Agencia Española de Cooperación Internacional para el Desarrollo project no. A2/038418/1

Tropospheric range error parameters: Further studies

Improved parameters are presented for predicting the tropospheric effect on electromagnetic range measurements from surface meteorological data. More geographic locations have been added to the earlier list. Parameters are given for computing the dry component of the zenith radio range effect from surface pressure alone with an rms error of 1 to 2 mm, or the total range effect from the dry and wet components of the surface refractivity and a two-part quartic profile model. The new parameters are obtained, as before, from meteorological balloon data but with improved procedures, including the conversion of the geopotential heights of the balloon data to actual or geometric heights before using the data. The revised values of the parameter k (dry component of vertical radio range effect per unit pressure at the surface) show more latitude variation than is accounted for by the variation of g, the acceleration of gravity

Dense Associative Memory is Robust to Adversarial Inputs

Deep neural networks (DNN) trained in a supervised way suffer from two known problems. First, the minima of the objective function used in learning correspond to data points (also known as rubbish examples or fooling images) that lack semantic similarity with the training data. Second, a clean input can be changed by a small, and often imperceptible for human vision, perturbation, so that the resulting deformed input is misclassified by the network. These findings emphasize the differences between the ways DNN and humans classify patterns, and raise a question of designing learning algorithms that more accurately mimic human perception compared to the existing methods. Our paper examines these questions within the framework of Dense Associative Memory (DAM) models. These models are defined by the energy function, with higher order (higher than quadratic) interactions between the neurons. We show that in the limit when the power of the interaction vertex in the energy function is sufficiently large, these models have the following three properties. First, the minima of the objective function are free from rubbish images, so that each minimum is a semantically meaningful pattern. Second, artificial patterns poised precisely at the decision boundary look ambiguous to human subjects and share aspects of both classes that are separated by that decision boundary. Third, adversarial images constructed by models with small power of the interaction vertex, which are equivalent to DNN with rectified linear units (ReLU), fail to transfer to and fool the models with higher order interactions. This opens up a possibility to use higher order models for detecting and stopping malicious adversarial attacks. The presented results suggest that DAM with higher order energy functions are closer to human visual perception than DNN with ReLUs

An Information-Based Neural Approach to Constraint Satisfaction

A novel artificial neural network approach to constraint satisfaction problems is presented. Based on information-theoretical considerations, it differs from a conventional mean-field approach in the form of the resulting free energy. The method, implemented as an annealing algorithm, is numerically explored on a testbed of K-SAT problems. The performance shows a dramatic improvement to that of a conventional mean-field approach, and is comparable to that of a state-of-the-art dedicated heuristic (Gsat+Walk). The real strength of the method, however, lies in its generality -- with minor modifications it is applicable to arbitrary types of discrete constraint satisfaction problems.Comment: 13 pages, 3 figures,(to appear in Neural Computation

Earthquake cycles and neural reverberations

Driven systems of interconnected blocks with stick-slip friction capture main features of earthquake processes. The microscopic dynamics closely resemble those of spiking nerve cells. We analyze the differences in the collective behavior and introduce a class of solvable models. We prove that the models exhibit rapid phase locking, a phenomenon of particular interest to both geophysics and neurobiology. We study the dependence upon initial conditions and system parameters, and discuss implications for earthquake modeling and neural computation

Neural network computation by in vitro transcriptional circuits

The structural similarity of neural networks and genetic regulatory networks to digital circuits, and hence to each other, was noted from the very beginning of their study [1, 2]. In this work, we propose a simple biochemical system whose architecture mimics that of genetic regulation and whose components allow for in vitro implementation of arbitrary circuits. We use only two enzymes in addition to DNA and RNA molecules: RNA polymerase (RNAP) and ribonuclease (RNase). We develop a rate equation for in vitro transcriptional networks, and derive a correspondence with general neural network rate equations [3]. As proof-of-principle demonstrations, an associative memory task and a feedforward network computation are shown by simulation. A difference between the neural network and biochemical models is also highlighted: global coupling of rate equations through enzyme saturation can lead to global feedback regulation, thus allowing a simple network without explicit mutual inhibition to perform the winner-take-all computation. Thus, the full complexity of the cell is not necessary for biochemical computation: a wide range of functional behaviors can be achieved with a small set of biochemical components

Exciton-polariton behaviour in bulk and polycrystalline ZnO

We report detailed reflectance studies of the exciton–polariton structure of thin film polycrystalline ZnO and comparison with bulk crystal behaviour. Near-normal incidence reflectance spectra of these samples are fitted using a two-band dielectric response function. Our data show that the reflectance data in polycrystalline ZnO differ substantially from the bulk material, with Fabry–Perot oscillations at energies below the transverse A exciton and above the longitudinal B exciton in the films. In the strong interaction regime between these energies no evidence is seen of the normally rapid oscillations associated with the anomalous waves. We demonstrate that the strong interaction of the damped exciton with the photon leads to polaritons in this region with substantial damping such that the Fabry–Perot modes are eliminated. Good qualitative agreement is achieved between the model and data. The importance of the polariton model in understanding the reflectance data of polycrystalline material is clearly see