Optimal Capacity of the Blume-Emery-Griffiths perceptron

A Blume-Emery-Griffiths perceptron model is introduced and its optimal capacity is calculated within the replica-symmetric Gardner approach, as a function of the pattern activity and the imbedding stability parameter. The stability of the replica-symmetric approximation is studied via the analogue of the Almeida-Thouless line. A comparison is made with other three-state perceptrons.Comment: 10 pages, 8 figure

A spherical Hopfield model

We introduce a spherical Hopfield-type neural network involving neurons and patterns that are continuous variables. We study both the thermodynamics and dynamics of this model. In order to have a retrieval phase a quartic term is added to the Hamiltonian. The thermodynamics of the model is exactly solvable and the results are replica symmetric. A Langevin dynamics leads to a closed set of equations for the order parameters and effective correlation and response function typical for neural networks. The stationary limit corresponds to the thermodynamic results. Numerical calculations illustrate our findings.Comment: 9 pages Latex including 3 eps figures, Addition of an author in the HTML-abstract unintentionally forgotten, no changes to the manuscrip

Measurement of mesoscopic High-TcT_c superconductors using Si mechanical micro-oscillators

In a superconducting mesoscopic sample, with dimensions comparable to the London penetration depth, some properties are qualitatively different to those found in the bulk material. These properties include magnetization, vortex dynamics and ordering of the vortex lattice. In order to detect the small signals produced by this kind of samples, new instruments designed for the microscale are needed. In this work we use micromechanical oscillators to study the magnetic properties of a Bi$_2$Sr$_2$CaCu$_2$O$_{8 + \delta}$ disk with a diameter of 13.5 microns and a thickness of 2.5 microns. The discussion of our results is based on the existence and contribution of inter and intra layer currents.Comment: 4 pages, 6 figure

Minimalist AdaBoost for blemish identification in potatoes

We present a multi-class solution based on minimalist Ad- aBoost for identifying blemishes present in visual images of potatoes. Using training examples we use Real AdaBoost to rst reduce the fea- ture set by selecting ve features for each class, then train binary clas- siers for each class, classifying each testing example according to the binary classier with the highest certainty. Against hand-drawn ground truth data we achieve a pixel match of 83% accuracy in white potatoes and 82% in red potatoes. For the task of identifying which blemishes are present in each potato within typical industry dened criteria (10% coverage) we achieve accuracy rates of 93% and 94%, respectively