Multi-vector feature space based on pseudo-Euclidean space and oblique basis

The Earth Mover’s Distance (EMD) and the quadratic-form distance (QFD) are representative distances used in similarity searches of images. Although the QFD greatly outperforms the EMD in speed, the EMD outperform the QFD in performance. The EMD, however, has almost no theoretical justification and requires high computation costs. We propose a feature space model we call a “multi-vector feature space based on pseudo-Euclidean space and an oblique basis (MVPO).” In MVPO, an object such as an image is represented by a vector set (roughly speaking, a solid) and the EMD is reinterpreted as the distance between vector sets while the QFD is reinterpreted as the distance between the centroids of vector sets. Therefore MVPO gives a common geometrical view to these distances. We hypothesized that in MVPO the entity of an image is represented by a vector set (solid) and geometrical reasoning is applicable to MVPO. Our hypothesis explains well that the EMD outperforms the QFD in performance because the centroid of a solid is the simplest approximation of it. Our hypothesis implies that the performance of the QFD should be good when solids are far apart but bad when they are close together. We conjectured that discriminability would decline—that is, dissimilar images would be judged to be similar—when the centroids of solids are very close. Our experiment supported this conjecture. And from our hypothesis we conjectured that by making an original solid simpler, we can make an approximation method that has better performance than the QFD and faster than the EMD. The results of our experiment with this method supported our conjecture and consequently our hypothesis.

A family of subgradient-based methods for convex optimization problems in a unifying framework

We propose a new family of subgradient- and gradient-based methods which converges with optimal complexity for convex optimization problems whose feasible region is simple enough. This includes cases where the objective function is non-smooth, smooth, have composite/saddle structure, or are given by an inexact oracle model. We unified the way of constructing the subproblems which are necessary to be solved at each iteration of these methods. This permitted us to analyse the convergence of these methods in a unified way compared to previous results which required different approaches for each method/algorithm. Our contribution rely on two well-known methods in non-smooth convex optimization: the mirror-descent method (MDM) by Nemirovski-Yudin and the dual-averaging method by Nesterov. Therefore, our family of methods includes them and many other methods as particular cases. For instance, the proposed family of classical gradient methods and its accelerations generalize Devolder et al.'s, Nesterov's primal/dual gradient methods, and Tseng's accelerated proximal gradient methods. Also our family of methods can partially become special cases of other universal methods, too. As an additional contribution, the novel extended MDM removes the compactness assumption of the feasible region and the fixation of the total number of iterations which is required by the original MDM in order to attain the optimal complexity.     

Path-bounded three-dimensional finite automata

The comparative study of the computational powers of deterministic and nondeterministic computations is one of the central tasks in complexity theory. This paper investigates the computational power of nondeterministic computing devices with restricted nondeterminism. There are only few results measuring the computational power of restricted nondeterminism. In general, there are three possibilities to measure the amount of nondeterminism in computation. In this paper, we consider the possibility to count the number of different nondeterministic computation paths on any input. In particular, we deal with five-way three-dimensional finite automata with multiple input heads operating on three-dimensional input tapes. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Xenon diffusion behaviour in pyrolytic SiC

The fractional release of ¹³³Xe, produced in pyrolytic SiC by fission recoil, has been measured below 1753? C both in isochronal and isothermal annealings. The release behaviour is interpreted for three temperature ranges; below 1200? C, from 1200 to 1400? C and above 1400? C. The release in the highest temperature region (>1400? C) would be due to vacancy mechanism, and the apparent diffusion coefficient is expressed as $$D = 3.7 \times 10^6 {\text{ exp ( - 157}} \times {\text{10}}^{\text{3}} {\text{/}}RT{\text{) cm}}^{\text{2}} {\text{ sec}}^{{\text{ - 1}}} .$$ The release in the medium temperature region (1200 to 1400? C) is probably due to the grain-boundary diffusion coupled with the migration of C or Si atoms in the boundary, and the apparent diffusion coefficient is expressed as $$D = 8.6 \times 10^{ - 6} {\text{ exp ( - 78}} \times {\text{10}}^{\text{3}} {\text{/}}RT{\text{) cm}}^{\text{2}} {\text{ sec}}^{{\text{ - 1}}} .$$ The release in the lowest temperature region (<1200? C) is explained by assuming the interstitial diffusion of Xe ejected from the trapping sites.     

The self-assembled monolayer of saccharide via click chemistry: Formation and protein recognition

We prepared the saccharide-immobilized substrate via click chemistry. The azide-terminated saccharides were reacted by a facile metathesis reaction. The density of saccharides was controlled by the incubation time of SAM preparation. The saccharide-protein interaction was analyzed using the saccharide substrate. The interaction of the saccharide substrate with protein was strong and specific due to the glyco-cluster effects. The interaction with amyloid β was analyzed by the monosaccharide-immobilized substrate, and the sulfonated saccharides showed strong interaction.     

Tuning the mechanical toughness of the metal nanoparticle-implanted glass: The effect of nanoparticle growth conditions

Recently, a novel framework for toughening brittle oxide glass by implanting a soft secondary material using an electrofloat method was proposed. Softening of a glass surface owing to the presence of metal nanoparticles as a secondary material evidenced by experiments and partially supported by numerical simulations was found to be important in the increased toughness. However, only limited experimental conditions of the process of nanoparticle growth were explored in the aforementioned study. Thus, we further investigated the experimental conditions in this study to optimize the magnitude of the applied voltage, its duration time, and the temperature during the process. We found that the crack initiation load (CIL) and the indentation fracture toughness increase at the appropriate voltage and time duration, and at a temperature higher than 1000°C. From observational analysis, the size, density, and depth of the nanoparticle-implanted layer are related to the toughness. In particular, when metal nanoparticles in a glass matrix are larger and denser up to half the total volume fraction, the mechanical toughness of the hybrid glass seems to get higher. Additionally, implantation of a softer metal, such as silver, was conducted to consider the dependence of secondary materials, and the silver nanoparticle-implanted glass showed the highest toughness. In the course of the investigation of the toughening mechanism for the two different materials using nanoindentation technique, a clear evidence of increased plastic deformation was observed for Cu implantation, while a decrease in Young’s modulus was only seen for Ag implantation. Peridynamics simulation was performed to examine the effect of the toughening due to the composite glass surface layer with the different Young’s modulus. It is shown that the Young’s modulus of the composite has an optimal value to maximize the toughness.