Improved zinc oxide thermal control coatings

Ferricyanide/ferrocyanide couple prevents zinc oxide pigment degradation in thermal control coatings. Chemical couple retards physical optical property changes

Effect of environment on thermal control coatings

Ferrocyanide and ferricyanide additives for prevention of optical degradation of coatings by ultraviolet radiation and vacuu

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

Spectral properties of coupled cavity arrays in one dimension

Spectral properties of coupled cavity arrays in one dimension are investigated by means of the variational cluster approach. Coupled cavity arrays consist of two distinct "particles," namely, photons and atomiclike excitations. Spectral functions are evaluated and discussed for both particle types. In addition, densities of states, momentum distributions and spatial correlation functions are presented. Based on this information, polariton "quasiparticles" are introduced as appropriate, wave vector and filling dependent linear combinations of photon and atomiclike particles. Spectral functions and densities of states are evaluated for the polariton quasiparticles, and the weights of their components are analyzed.Comment: 17 pages, 16 figures, version as publishe

Bi-simulation Between P Colonies and P Systems with Multi-stable Catalysts

International audienc

Are There Oscillations in the Baryon/Meson Ratio?

All available data indicate a surplus of baryon states over meson states for energies greater than about 1.5 GeV. Since hadron-scale string theory suggests that their numbers should become equal with increasing energy, it has recently been proposed that there must exist exotic mesons with masses just above 1.7 GeV in order to fill the deficit. We demonstrate that a string-like picture is actually consistent with the present numbers of baryon and meson states, and in fact predicts regular oscillations in their ratio. This suggests a different role for new hadronic states.Comment: 14 pages (RevTeX), McGill/92-0

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

Unconventional magnetic order on the hyperhoneycomb Kitaev lattice in β\beta-Li2IrO3: full solution via magnetic resonant x-ray diffraction

The recently-synthesized iridate $\beta$-Li$_2$IrO$_3$ has been proposed as a candidate to display novel magnetic behavior stabilized by frustration effects from bond-dependent, anisotropic interactions (Kitaev model) on a three-dimensional "hyperhoneycomb" lattice. Here we report a combined study using neutron powder diffraction and magnetic resonant x-ray diffraction to solve the complete magnetic structure. We find a complex, incommensurate magnetic order with non-coplanar and counter-rotating Ir moments, which surprisingly shares many of its features with the related structural polytype "stripyhoneycomb" $\gamma$-Li$_2$IrO$_3$, where dominant Kitaev interactions have been invoked to explain the stability of the observed magnetic structure. The similarities of behavior between those two structural polytypes, which have different global lattice topologies but the same local connectivity, is strongly suggestive that the same magnetic interactions and the same underlying mechanism governs the stability of the magnetic order in both materials, indicating that both $\beta$- and $\gamma$-Li$_2$IrO$_3$ are strong candidates to realize dominant Kitaev interactions in a solid state material.Comment: 14 pages, 9 figure

Optimization of the Asymptotic Property of Mutual Learning Involving an Integration Mechanism of Ensemble Learning

We propose an optimization method of mutual learning which converges into the identical state of optimum ensemble learning within the framework of on-line learning, and have analyzed its asymptotic property through the statistical mechanics method.The proposed model consists of two learning steps: two students independently learn from a teacher, and then the students learn from each other through the mutual learning. In mutual learning, students learn from each other and the generalization error is improved even if the teacher has not taken part in the mutual learning. However, in the case of different initial overlaps(direction cosine) between teacher and students, a student with a larger initial overlap tends to have a larger generalization error than that of before the mutual learning. To overcome this problem, our proposed optimization method of mutual learning optimizes the step sizes of two students to minimize the asymptotic property of the generalization error. Consequently, the optimized mutual learning converges to a generalization error identical to that of the optimal ensemble learning. In addition, we show the relationship between the optimum step size of the mutual learning and the integration mechanism of the ensemble learning.Comment: 13 pages, 3 figures, submitted to Journal of Physical Society of Japa