A geometric learning algorithm for elementary perceptron and its convergence analysis

In this paper, the geometric learning algorithm (GLA) is proposed for an elementary perceptron which includes a single output neuron. The GLA is a modified version of the affine projection algorithm (APA) for adaptive filters. The weights update vector is determined geometrically towards the intersection of the k hyperplanes which are perpendicular to patterns to be classified. k is the order of the GLA. In the case of the APA, the target of the coefficients update is a single point which corresponds to the best identification of the unknown system. On the other hand, in the case of the GLA, the target of the weights update is an area, in which all the given patterns are classified correctly. Thus, their convergence conditions are different. In this paper, the convergence condition of the 1st order GLA for 2 patterns is theoretically derived. The new concept `the angle of the solution area\u27 is introduced. The computer simulation results support that this new concept is a good estimation of the convergence properties

A recurrent neural network with serial delay elements for memorizing limit cycles

A recurrent neural network (RNN), in which each unit has serial delay elements, is proposed for memorizing limit cycles (LCs). This network is called DRNN in this paper. An LC consists of several basic patterns. The hysteresis information of LCs, realized on the connections from the delay elements to the units, is very efficient in the following reasons. First, the same basic patterns can be shared by different LCs. This make it possible to drastically increase the number of LCs, even though using a small number of the basic patterns. Second, noise performance, that is, probability of recalling the exact LC starting from the noisy LC, can be improved. The hysteresis information consists of two components, the order of the basic patterns included in an LC, and the cross-correlation among all the basic patterns. The former is highly dependent on the number of LCs, and the latter the number of all the basic patterns. In order to achieve good noise performance, a small number of the basic patterns is preferred. These properties of the DRNN are theoretically analyzed and confirmed through computer simulations. It is also confirmed that the DRNN is superior to the RNN without delay elements for memorizing LCs

Probabilistic memory capacity of recurrent neural networks

金沢大学理工研究域電子情報学系In this paper, probabilistic memory capacity of recurrent neural networks(RNNs) is investigated. This probabilistic capacity is determined uniquely if the network architecture and the number of patterns to be memorized are fixed. It is independent from a learning method and the network dynamics. It provides the upper bound of the memory capacity by any learning algorithms in memorizing random patterns. It is assumed that the network consists of N units, which take two states. Thus, the total number of patterns is the Nth power of 2. The probabilities are obtained by discriminations whether the connection weights, which can store random M patterns at equilibrium states, exist or not. A theoretical way for this purpose is derived, and actual calculation is executed by the Monte Carlo method. The probabilistic memory capacity is very important in applying the RNNs to real fields, and in evaluating goodness of learning algorithms. As an example of a learning algorithm, the improved error correction learning is investigated, and its convergence probabilities are compared with the upper bound. A linear programming method can be effectively applied to this numerical analysis

Dose responses of scattered- and direct-X-ray-irradiated CR-39 and methylviologen-encapsulated silica nanocapsule-doped CR-39 and their mechanisms

The photoexcited emissions of direct- and scattered-X-ray-irradiated CR-39 and methylviologen-encapsulated silica nanocapsule (MV2+@SiO2 NC)-doped CR-39 were observed, and they showed a dose response. The benzophenone radical was formed in a shallow trap in CR-39 upon X-ray irradiation from 10 to 30 Gy, and the fluorescence intensity increased with the dose. Methylviologen in SiO2 NCs competitively captured electrons generated by X-ray irradiation, and the captured electrons were reverse transferred to the shallow traps with time. A minimum dose rate of 300 µGy/s was observed between 1 and 5 Gy. Finally, a dose response of less than 2 mGy for scattered X-rays was obtained in this system

Contribution of fly ash to mortar strength development under steam and internal curing

The purpose of this study is to quantitatively evaluate the effects of steam curing and internal curing on contribution of fly ash to strength development of mortar by using cementing efficiency factor (k-value) that represents strength development performance as a binder of fly ash. In addition, the pozzolanic reaction of fly ash was evaluated from the viewpoint of calcium hydroxide consumption by using thermogravimetry and differential thermal analysis as well as the degree of fly ash reaction by using selective dissolution method. The result indicated that steam curing improved early compressive strength and internal curing improved compressive strength and k-value at all ages. Also, a linearrelationship between the degree of fly ash reaction and the k-value was shown regardless of the age and the replacement ratio of fly ash

Emission image of X-ray-irradiated CR-39 stick doped with methylviologen-encapsulated silica nanocapsules using LED light

Light-emitting diode (LED)-light-excited emission images of 6 MeV-X-ray (10 Gy)-irradiated CR-39 doped with methylviologen-encapsulated silica nanocapsules (MV @SiO2 NCs) were observed using an iPhone 5S for the first time. The excitation and fluorescence spectra were also observed, and the emission peak at 580 nm produced by the X-ray irradiation was confirmed. Emission intensities of 80 kV-X-ray (0.5, 1, 1.5, and 2 Gy)-irradiated CR-39 doped with MV @ SiO2 NCs could be measured using a portable fluorometer (FC-1), and a good linear relationship between their emission intensity and dose was clearly observed

Spindle and Giant Cell Type Undifferentiated Carcinoma of the Proximal Bile Duct

Undifferentiated spindle and giant cell carcinoma is an extremely rare malignant neoplasm arising in the extrahepatic bile duct. We herein present the case of a 67-year-old male who developed an undifferentiated spindle and giant cell carcinoma of the proximal bile duct. A nodular infiltrating tumor was located at the proximal bile duct, resulting in obstructive jaundice. Histologically, the tumor was composed of mainly spindle-shaped and giant cells and showed positive immunoreactivity for both cytokeratin and vimentin. Adjuvant chemotherapy was administered following extrahepatic bile duct resection, and he has been doing well for 16 months since the surgical treatment. The literature on this rare malignancy is also reviewed and discussed