University of Electro-Communications

About: University of Electro-Communications is a education organization based out in Tokyo, Japan. It is known for research contribution in the topics: Laser & Robot. The organization has 8041 authors who have published 16950 publications receiving 235832 citations. The organization is also known as: UEC & Denki-Tsūshin Daigaku.

Alternative Selective Oxidation Pathways for Aldehyde Oxidation and Alkene Epoxidation on a SiO2-Supported Ru−Monomer Complex Catalyst

Abstract: We have prepared a novel Ru-mononer complex supported on a SiO2 surface by using a Ru-monomer complex precursor with a p-cymene ligand, which was found to be highly active for the selective oxidation of aldehydes and the epoxidation of alkenes using O2 The structure of the supported Ru catalyst was characterized by means of FT-IR, solid-state NMR, diffuse-reflectance UV/vis, XPS, Ru K-edge EXAFS, and DFT calculations, which demonstrated the formation of isolatedly located, unsaturated Ru centers behind a p-cymene ligand of the Ru-complex precursor The site-isolated Ru-monomer complex on SiO2 achieved tremendous TONs (turnover numbers) for the selective oxidation of aldehydes and alkenes; eg TONs of 38,800,000 for selective isobutyraldehyde (IBA) oxidation and 2,100,000 for trans-stilbene epoxidation at ambient temperature, which are among the highest TONs in metal-complex catalyzes to our knowledge We also found that the IBA sole oxidation with an activation energy of 48 kJ mol−1 much more facile tha

Image-Based Food Calorie Estimation Using Knowledge on Food Categories, Ingredients and Cooking Directions

Abstract: Image-based food calorie estimation is crucial to diverse mobile applications for recording everyday meal. However, some of them need human help for calorie estimation, and even if it is automatic, food categories are often limited or images from multiple viewpoints are required. Then, it is not yet achieved to estimate food calorie with practical accuracy and estimating food calories from a food photo is an unsolved problem. Therefore, in this paper, we propose estimating food calorie from a food photo by simultaneous learning of food calories, categories, ingredients and cooking directions using deep learning. Since there exists a strong correlation between food calories and food categories, ingredients and cooking directions information in general, we expect that simultaneous training of them brings performance boosting compared to independent single training. To this end, we use a multi-task CNN [1]. In addition, in this research, we construct two kinds of datasets that is a dataset of calorie-annotated recipe collected from Japanese recipe sites on the Web and a dataset collected from an American recipe site. In this experiment, we trained multi-task and single-task CNNs. As a result, the multi-task CNN achieved the better performance on both food category estimation and food calorie estimation than single-task CNNs. For the Japanese recipe dataset, by introducing a multi-task CNN, 0.039 were improved on the correlation coefficient, while for the American recipe dataset, 0.090 were raised compared to the result by the single-task CNN.

Radical-copper wheels: structure and magnetism of hexanuclear hybrid arrays.

Abstract: Complexation of copper(II) bromide and chloride with 4-pyrimidinyl nitronyl nitroxide (4PMNN) as a bridging ligand gave discrete hexanuclear complexes carrying 12 spins, [CuX2·(4PMNN)]6 [X = Br (1), Cl (2)], which crystallize in a trigonal space group . The crystallographic parameters are C11H15Br2CuN4O2·0.3H2O, a = 28.172(2), c = 12.590(2) A, V = 8653(2) A3, and Z = 18 for 1, and C11H15Cl2CuN4O2·0.3H2O, a = 28.261(2), c = 12.378(1) A, and Z = 18 for 2. The hexanuclear arrays construct a perfect column perpendicular to the molecular plane. The diameter of the resultant honeycomblike channel is ca. 11.5 A defined by the interatomic distance of two opposing copper ions. Their magnetic behavior is interpreted as the simultaneous presence of ferro and antiferromagnetic couplings. The ferromagnetic couplings are attributed to the interactions between a copper spin and the axially coordinated nitronyl nitroxide spin and between nitronyl nitroxide groups through van der Waals contacts. The antiferromagnetic coup...

Category-Based Deep CCA for Fine-Grained Venue Discovery from Multimodal Data

Abstract: In this work, travel destination and business location are taken as venues. Discovering a venue by a photo is very important for context-aware applications. Unfortunately, few efforts paid attention to complicated real images such as venue photos generated by users. Our goal is fine-grained venue discovery from heterogeneous social multimodal data. To this end, we propose a novel deep learning model, Category-based Deep Canonical Correlation Analysis (C-DCCA). Given a photo as input, this model performs (i) exact venue search (find the venue where the photo was taken), and (ii) group venue search (find relevant venues with the same category as that of the photo), by the cross-modal correlation between the input photo and textual description of venues. In this model, data in different modalities are projected to a same space via deep networks. Pairwise correlation (between different modal data from the same venue) for exact venue search and category-based correlation (between different modal data from different venues with the same category) for group venue search are jointly optimized. Because a photo cannot fully reflect rich text description of a venue, the number of photos per venue in the training phase is increased to capture more aspects of a venue. We build a new venue-aware multimodal dataset by integrating Wikipedia featured articles and Foursquare venue photos. Experimental results on this dataset confirm the feasibility of the proposed method. Moreover, the evaluation over another publicly available dataset confirms that the proposed method outperforms state-of-the-arts for cross-modal retrieval between image and text.