Statistical Mechanics of Time Domain Ensemble Learning

Conventional ensemble learning combines students in the space domain. On the other hand, in this paper we combine students in the time domain and call it time domain ensemble learning. In this paper, we analyze the generalization performance of time domain ensemble learning in the framework of online learning using a statistical mechanical method. We treat a model in which both the teacher and the student are linear perceptrons with noises. Time domain ensemble learning is twice as effective as conventional space domain ensemble learning.Comment: 10 pages, 10 figure

Statistical Mechanics of Linear and Nonlinear Time-Domain Ensemble Learning

Conventional ensemble learning combines students in the space domain. In this paper, however, we combine students in the time domain and call it time-domain ensemble learning. We analyze, compare, and discuss the generalization performances regarding time-domain ensemble learning of both a linear model and a nonlinear model. Analyzing in the framework of online learning using a statistical mechanical method, we show the qualitatively different behaviors between the two models. In a linear model, the dynamical behaviors of the generalization error are monotonic. We analytically show that time-domain ensemble learning is twice as effective as conventional ensemble learning. Furthermore, the generalization error of a nonlinear model features nonmonotonic dynamical behaviors when the learning rate is small. We numerically show that the generalization performance can be improved remarkably by using this phenomenon and the divergence of students in the time domain.Comment: 11 pages, 7 figure

Statistical Mechanics of Nonlinear On-line Learning for Ensemble Teachers

We analyze the generalization performance of a student in a model composed of nonlinear perceptrons: a true teacher, ensemble teachers, and the student. We calculate the generalization error of the student analytically or numerically using statistical mechanics in the framework of on-line learning. We treat two well-known learning rules: Hebbian learning and perceptron learning. As a result, it is proven that the nonlinear model shows qualitatively different behaviors from the linear model. Moreover, it is clarified that Hebbian learning and perceptron learning show qualitatively different behaviors from each other. In Hebbian learning, we can analytically obtain the solutions. In this case, the generalization error monotonically decreases. The steady value of the generalization error is independent of the learning rate. The larger the number of teachers is and the more variety the ensemble teachers have, the smaller the generalization error is. In perceptron learning, we have to numerically obtain the solutions. In this case, the dynamical behaviors of the generalization error are non-monotonic. The smaller the learning rate is, the larger the number of teachers is; and the more variety the ensemble teachers have, the smaller the minimum value of the generalization error is.Comment: 13 pages, 9 figure

Oscillation Phenomena in the disk around the massive black hole Sagittarius A*

We report the detection of radio QPOs with structure changes using the Very Long Baseline Array (VLBA) at 43 GHz. We found conspicuous patterned changes of the structure with P = 16.8 +- 1.4, 22.2 +- 1.4, 31.2 +- 1.5, 56.4 +- 6 min, very roughly in a 3:4:6:10 ratio. The first two periods show a rotating one-arm structure, while the P = 31.4 min shows a rotating 3-arm structure, as if viewed edge-on. At the central 50 microasec the P = 56.4 min period shows a double amplitude variation of those in its surroundings. Spatial distributions of the oscillation periods suggest that the disk of SgrA* is roughly edge-on, rotating around an axis with PA = -10 degree. Presumably, the observed VLBI images of SgrA* at 43 GHz retain several features of the black hole accretion disk of SgrA* in spite of being obscured and broadened by scattering of surrounding plasma.Comment: 24 pages, 20 figures, revised version submitted to MN main journal (2010, Jan., 12th

LOX/LH2 propulsion system for launch vehicle upper stage, test results

The test results of small LOX/LH2 engines for two propulsion systems, a pump fed system and a pressure fed system are reported. The pump fed system has the advantages of higher performances and higher mass fraction. The pressure fed system has the advantages of higher reliability and relative simplicity. Adoption of these cryogenic propulsion systems for upper stage of launch vehicle increases the payload capability with low cost. The 1,000 kg thrust class engine was selected for this cryogenic stage. A thrust chamber assembly for the pressure fed propulsion system was tested. It is indicated that it has good performance to meet system requirements

Average profiles of the solar wind and outer radiation belt during the extreme flux enhancement of relativistic electrons at geosynchronous orbit

We report average profiles of the solar wind and outer radiation belt during the extreme flux enhancement of relativistic electrons at geosynchronous orbit (GEO). It is found that seven of top ten extreme events at GEO during solar cycle 23 are associated with the magnetosphere inflation during the storm recovery phase as caused by the large-scale solar wind structure of very low dynamic pressure (<1.0 nPa) during rapid speed decrease from very high (>650 km/s) to typical (400–500 km/s) in a few days. For the seven events, the solar wind parameters, geomagnetic activity indices, and relativistic electron flux and geomagnetic field at GEO are superposed at the local noon period of GOES satellites to investigate the physical cause. The average profiles support the "double inflation" mechanism that the rarefaction of the solar wind and subsequent magnetosphere inflation are one of the best conditions to produce the extreme flux enhancement at GEO because of the excellent magnetic confinement of relativistic electrons by reducing the drift loss of trapped electrons at dayside magnetopause