Cardinality Estimation in Inner Product Space

This article addresses the problem of cardinality estimation in inner product spaces. Given a set of high-dimensional vectors, a query, and a threshold, this problem estimates the number of vectors such that their inner products with the query are not less than the threshold. This is an important problem for recent machine-learning applications that maintain objects, such as users and items, by using matrices. The important requirements for solutions of this problem are high efficiency and accuracy. To satisfy these requirements, we propose a sampling-based algorithm. We build trees of vectors via transformation to a Euclidean space and dimensionality reduction in a pre-processing phase. Then our algorithm samples vectors existing in the nodes that intersect with a search range on one of the trees. Our algorithm is surprisingly simple, but it is theoretically and practically fast and effective. We conduct extensive experiments on real datasets, and the results demonstrate that our algorithm shows superior performance compared with existing techniques.Hirata K., Amagata D., Hara T.. Cardinality Estimation in Inner Product Space. IEEE Open Journal of the Computer Society 3, 208 (2022); https://doi.org/10.1109/OJCS.2022.3215206

Sol–gel based structural designs of macropores and material shapes of metal–organic framework gels

We have developed a general synthetic strategy to control macroporous structures and material shapes of metal–organic framework (MOF) gels via a sol–gel based structural control process. A series of 1, 3, 5-benzene tricarboxylic acid (BTC) based MOF gels, Cr-BTC and Zr-BTC, have been chosen as a proof of concept

Multi-Metric Near-Optimal Image Denoising

It is necessary to optimize the parameters for each image input to achieve the maximum denoising performance because the performance of denoising algorithms depends largely on the selection of the associated parameters. The commonly used objective image quality measures in quantitatively evaluating a denoised image are PSNR, SSIM, and MS-SSIM, which assume that the original image exists and is fully available as a reference. However, we do not have access to such reference images in many practical applications. Most existing methods for no-reference denoising parameter optimization either use the estimated noise distribution or a unique no-reference image quality evaluation measure. In the chapter, for BM3D, which is a state-of-the-art denoising algorithm, we introduce a natural image statistics (NIS) based on the generalized Gaussian distribution (GGD) and the elastic net regularization (EN) regression method and propose its use to perform the BM3D parameter optimization for PSNR, SSIM, and MS-SSIM, respectively, which are the popular image quality evaluation measures, without reference image and knowledge of the noise distribution. Experimental results with several images demonstrate the effectiveness of the proposed approach

Legionnaires\u27 Disease, its Bacteriological Characteristics, Distribution and Clinical Features in Japan

DISTRIBUTION OF LEGIONELLA SPECIES FROM ENVIRONMENTAL SOURCE IN JAPAN Each 500 ml of 408 water samples including 343 coiling tower water from sites widely distributed over Japan, 25 samples of paddy field, 29 samples of river and 11 samples of lake, fountain, puddle and shower water in Nagasaki area, was obtained and centrifuged at 10,000 rpm for 20 min. The sediments were cultured in a selective medium1) for isolation of Legionella spp. after low pH treatment. In some negative samples, 3 ml of the sediments were inoculated to guinea pigs intraperitoneally and they were sacrificed four days later. The peritoneal swab and homogenate of spleen were inoculated to B-CYE agar, Each 200 g of soil was suspended in 400 ml of distilled water with 0.5% of Tween 60 far 20 min. The supernatant was centrifuged at 1,000 rpm for 10 min., and again the supernatant was centrifuged at 3,000 rpm for 10 min. The sediment were obtained and used for isolation of Legionella spp

Categorical Diversity-Aware Inner Product Search

The problem of maximum inner product search (MIPS) is one of the most important components in machine learning systems. However, this problem does not care about diversity, although result diversification can improve user satisfaction. This paper hence considers a new problem, namely the categorical diversity-aware IPS problem, in which users can select preferable categories. Exactly solving this problem needs O(n) time, where n is the number of vectors, and is not efficient for large n. We hence propose an approximation algorithm that has a probabilistic success guarantee and runs in sub-linear time to n. We conduct extensive experiments on real datasets, and the results demonstrate the superior performance of our algorithm to that of a baseline using an existing MIPS technique.Hirata K., Amagata D., Fujita S., et al. Categorical Diversity-Aware Inner Product Search. IEEE Access 11, 2586 (2023); https://doi.org/10.1109/ACCESS.2023.3234072

Refractive index measurement of hydrogen isotopologue mixture and applicability for homogeneity of hydrogen solid at cryogenic temperature in fusion fuel system

Deuterium (D)-Tritium (T) nuclear fusion reaction has potential as an energy source in the future. In both magnetic confinement and inertial confinement fusion reactors, solid D-T will generally be supplied as fusion fuel. The efficiency of the nuclear fusion reaction depends on the quality of solid D-T fuel, which is related to the composition, homogeneity, helium-3 (3He) content, and so on. However, there is no technique for in-situ examination of solid D-T fuel. In this study, we consider a simple and precise method for the characterization of solid hydrogen isotopologues at cryogenic temperature using refractive index measurement, and evaluate the distribution of hydrogen isotopologue composition and homogeneity. To evaluate without the effect of tritium decay, the homogeneity of the hydrogen (H2)-deuterium (D2) mixture is measured at first. By the in-situ refractive index measurement at cryogenic temperature, the homogeneity of solid H2-D2 mixture is roughly quantified. The phase diagram of the H2-D2 mixture shows a solid solution type. D2-rich crystal first appears from the liquid phase as a primary crystal. The composition of D2 in liquid phase ias homogeneous, whereas it reduces by obeying the liquidus line in the phase diagram with the crystallization. On the other hand, the composition of the H2-D2 mixture in solid phase is inhomogeneous because the mobility of H2 and D2 in solid phase was too slow to be homogeneous and solid. The compositions of H2-D2 mixture in liquid and solid phases could be evaluated by the in-situ refractive index measurement in time. Consequently, the refractive index measurement shows great potential as an inspection method of solid D-T fuel in fusion reactors.Zhang J., Iwamoto A., Shigemori K., et al. Refractive index measurement of hydrogen isotopologue mixture and applicability for homogeneity of hydrogen solid at cryogenic temperature in fusion fuel system. Nuclear Fusion 63, 076020 (2023); https://doi.org/10.1088/1741-4326/acd015