Magnetic probe array with high sensitivity for fluctuating field

A magnetic probe array is constructed to measure precisely the spatial structure of a small fluctuating field included in a strong confinement field that varies with time. To exclude the effect of the confinement field, the magnetic probes consisting of figure-eight-wound coils are prepared. The spatial structure of the fluctuating field is obtained from a Fourier analysis of the probe signal. It is found that the probe array is more sensitive to the fluctuating field with a high mode number than that with a low mode number. An experimental demonstration of the present method is attempted using a field-reversed configuration plasma, where the fluctuating field with 0.1% of the confinement field is successfully detected.     

Robust loss functions for boosting

Boosting is known as a gradient descent algorithm over loss functions. It is often pointed out that the typical boosting algorithm, Adaboost, is highly affected by outliers. In this letter, loss functions for robust boosting are studied. Based on the concept of robust statistics, we propose a transformation of loss functions that makes boosting algorithms robust against extreme outliers. Next, the truncation of loss functions is applied to contamination models that describe the occurrence of mislabels near decision boundaries. Numerical experiments illustrate that the proposed loss functions derived from the contamination models are useful for handling highly noisy data in comparison with other loss functions.     

Minimum cost source location problem with vertex-connectivity requirements in digraphs

Given a digraph (or an undirected graph) G=(V,E) with a set V of vertices v with nonnegative real costs w(v), and a set E of edges and a positive integer k, we deal with the problem of finding a minimum cost subset SV such that, for each vertex vV−S, there are k vertex-disjoint paths from S to v. In this paper, we show that the problem can be solved by a greedy algorithm in time in a digraph (or in time in an undirected graph), where n=|V| and m=|E|. Based on this, given a digraph and two integers k and ℓ, we also give a polynomial time algorithm for finding a minimum cost subset SV such that for each vertex vV−S, there are k vertex-disjoint paths from S to v as well as ℓ vertex-disjoint paths from v to S.     

A new learning method using prior information of neural networks

In this paper, we present a new learning method using prior information for three-layer neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering interrelated weights values. Thus, the training results are usually not good. The reason for this in that each parameter has its influence on others during learning. To overcome this problem, we first give an exact mathematical equation that describes the relation between weight values given a set of data conveying prior information. The we present a new learning method that trains part of the weights and calculates the others using these exact mathematical equations. This method often a priori keeps the given mathematical structure exactly the same during learning; in other words, training is done so that the network follows a predetermined trajectory. Numerical computer simulation results are provided to support this approach. This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999.     

Rapid reinforcement for fusion mass-spliced fibers using low-power induction heating

A rapid reinforcement method using low-power induction heating is developed. The reinforcement of a mass-spliced fiber ribbon unit comprising five graded-index multimode fibers was completed in 30 sec with good performance by supplying 30 W of electric power.     

Flow and deformation of fresh concrete

The results of studies made from the standpoint of rheology on methods of predicting flows and deformations of fresh concrete and mortar as basic research for rationalization of concrete construction are described. This paper contains as follows:

1. method of measuring viscosity of fresh concrete and mortar;
2. method of estimating deformation of fresh concrete due to its own weight;
3. proposal of the “Inclined Pipe Test Method” as a new method of testing consistency of grout mortar.

     

ORBIT: A parallel computing model of Prolog

This paper proposes a parallel processing model of the Prolog language. The model modifies Or-parallelism by introducing the “process bundle” as a candidate for simultaneous execution. The Process bundle is a subset of backtrack points stacked in depth-first execution. The process bundle includes one or more backtrack points, so it provides a longer process life cycle than the Or-parallel process. A process bundle is dispatched when an idle processor requests a job from an executing processor. The executing processor dispatches a message containing the full environment by which the idle processor can execute the process without any communication with other processors.     

Theory of quasi-two-dimensional quantum solids. II. An anisotropic reaction-matrix approach

A self-consistent theory is proposed for anisotropic quantum solids such as dense helium monolayers adsorbed onto graphite, based upon the reactionmatrix formalism. The effect of the excitations normal to the adsorption plane is fully included in the ground-state as well as in the vibrational properties. Dispersion relations are derived both for the in-plane and out-of-plane vibrational modes, and the coupling of the two modes is studied as well.     

Superconductivity in dilute Cu-Nb-Sn alloys containing Nb3Sn precipitates

The dilute Cu-Nb-Sn alloys containing small amounts of Nb and Sn less than 1 at % exhibited superconductivity after quenching from the liquid state and ageing. The best superconducting properties ( andJ _c=130 A cm^–2) in a Cu-0.30 at % Nb 0.15 at % Sn alloy were obtained when the sample was aged at 550° C for 384 h. This sample exhibited a structure of fine Nb₃Sn precipitates of 200 to 500Å diameter distributed homogeneously in the Cu matrix, and therefore it was concluded that superconductivity in these alloys resulted from the proximity effect of Nb₃Sn particles. In spite of the similar structure obtained by ageing at 800° C, the Cu-Nb-Sn alloys showed inferior superconducting properties compared to the Cu-0.4 at % Nb alloy and this would be explained qualitatively by the difference in the mean free path in the two alloys.     

Multiple kernel learning with gaussianity measures

Kernel methods are known to be effective for nonlinear multivariate analysis. One of the main issues in the practical use of kernel methods is the selection of kernel. There have been a lot of studies on kernel selection and kernel learning. Multiple kernel learning (MKL) is one of the promising kernel optimization approaches. Kernel methods are applied to various classifiers including Fisher discriminant analysis (FDA). FDA gives the Bayes optimal classification axis if the data distribution of each class in the feature space is a gaussian with a shared covariance structure. Based on this fact, an MKL framework based on the notion of gaussianity is proposed. As a concrete implementation, an empirical characteristic function is adopted to measure gaussianity in the feature space associated with a convex combination of kernel functions, and two MKL algorithms are derived. From experimental results on some data sets, we show that the proposed kernel learning followed by FDA offers strong classification power.