Locality-Sensitive Hashing with Margin Based Feature Selection

We propose a learning method with feature selection for Locality-Sensitive Hashing. Locality-Sensitive Hashing converts feature vectors into bit arrays. These bit arrays can be used to perform similarity searches and personal authentication. The proposed method uses bit arrays longer than those used in the end for similarity and other searches and by learning selects the bits that will be used. We demonstrated this method can effectively perform optimization for cases such as fingerprint images with a large number of labels and extremely few data that share the same labels, as well as verifying that it is also effective for natural images, handwritten digits, and speech features.Comment: 9 pages, 6 figures, 3 table

Hyperplane Arrangements and Locality-Sensitive Hashing with Lift

Locality-sensitive hashing converts high-dimensional feature vectors, such as image and speech, into bit arrays and allows high-speed similarity calculation with the Hamming distance. There is a hashing scheme that maps feature vectors to bit arrays depending on the signs of the inner products between feature vectors and the normal vectors of hyperplanes placed in the feature space. This hashing can be seen as a discretization of the feature space by hyperplanes. If labels for data are given, one can determine the hyperplanes by using learning algorithms. However, many proposed learning methods do not consider the hyperplanes' offsets. Not doing so decreases the number of partitioned regions, and the correlation between Hamming distances and Euclidean distances becomes small. In this paper, we propose a lift map that converts learning algorithms without the offsets to the ones that take into account the offsets. With this method, the learning methods without the offsets give the discretizations of spaces as if it takes into account the offsets. For the proposed method, we input several high-dimensional feature data sets and studied the relationship between the statistical characteristics of data, the number of hyperplanes, and the effect of the proposed method.Comment: 9 pages, 7 figure

Spatial Endogenous Fire Risk and Efficient Fuel Management and Timber Harvest

This paper integrates a spatial fire behavior model and a stochastic dynamic optimization model to determine the optimal spatial pattern of fuel management and timber harvest. Each years fire season causes the loss of forest values and lives in the western US. This paper uses a multi-plot analysis and incorporates uncertainty about fire ignition locations and weather conditions to inform policy by examining the role of spatial endogenous risk - where management actions on one stand affect fire risk in that and adjacent stands. The results support two current strategies, but question two other strategies, for managing forests with fire risk.Resource /Energy Economics and Policy,

Using information for problem solving

For more about the East-West Center, see http://www.eastwestcenter.org/</a

森林管理における最適化モデルの応用 －伐採計画問題－

要旨あり最適化技術に基づく統計的推論研究ノー

森林資源管理問題への数理モデルの応用

Open House, ISM in Tachikawa, 2012.6.15統計数理研究所オープンハウス（立川）、H24.6.15ポスター発

Inhibitory Potencies of Several Iridoids on Cyclooxygenase-1, Cyclooxygnase-2 Enzymes Activities, Tumor Necrosis factor-α and Nitric Oxide Production In Vitro

To verify the anti-inflammatory potency of iridoids, seven iridoid glucosides (aucubin, catalpol, gentiopicroside, swertiamarin, geniposide, geniposidic acid and loganin) and an iridoid aglycone (genipin) were investigated with in vitro testing model systems based on inhibition of cyclooxygenase (COX)-1/-2 enzymes, the tumor necrosis factor-α (TNF-α) formation and nitric oxide (NO) production. The hydrolyzed-iridoid products (H-iridoid) with β-gludosidase treatment only showed inhibitory activities, and revealed different potencies, depending on their chemical structures. Without the β-gludosidase treatment, no single iridoid glycoside exhibited any activities. The aglycone form (genipin) also did not show inhibitory activities. To compare anti-inflammatory potency, the inhibitory concentrations (IC50) in each testing system were measured. The hydrolyzed-aucubin product (H-aucubin) with β-gludosidase treatment showed a moderate inhibition on COX-2 with IC50 of 8.83 μM, but much less inhibition (IC50, 68.9 μM) on COX-1 was noted. Of the other H-iridoid products, the H-loganin and the H-geniposide exhibited higher inhibitory effects on COX-1, revealing IC50 values of 3.55 and 5.37 μM, respectively. In the case of TNF-α assay, four H-iridoid products: H-aucubin, H-catalpol, H-geniposide and H-loganin suppressed the TNF-α formation with IC50 values of 11.2, 33.3, 58.2 and 154.6 μM, respectively. But other H-iridoid products manifested no significant activity. Additional experiments on NO production were conducted. We observed that only the H-aucubin exhibited a significant suppression with IC50 value of 14.1 μM. Genipin, an agycone form, showed no inhibitory effects on all testing models, implying the hydrolysis of the glycosidic bond of iridoid glycoside is a pre-requisite step to produce various biological activities