Tracking topic evolution via salient keyword matching with consideration of semantic broadness for Web video discovery

A method to track topic evolution via salient keyword matching with consideration of semantic broadness for Web video discovery is presented in this paper. The proposed method enables users to understand the evolution of topics over time for discovering Web videos in which they are interested. A framework that enables extraction and tracking of the hierarchical structure, which contains Web video groups with various degrees of semantic broadness, is newly derived as follows: Based on network analysis using multimodal features, i.e., features of video contents and metadata, our method extracts the hierarchical structure and salient keywords that represent contents of each Web video group. Moreover, salient keyword matching, which is newly developed by considering salient keyword distribution, semantic broadness of each Web video group and initial topic relevance, is applied to each hierarchical structure obtained in different time stamps. Unlike methods in previous works, by considering the semantic broadness as well as the salient keyword distribution, our method can overcome the problem of the desired semantic broadness of topics being different depending on each user. Also, the initial topic relevance enables correction of the gap from an initial topic at the start of tracking. Consequently, it becomes feasible to track the evolution of topics over time for finding Web videos in which the users are interested. Experimental results for real-world datasets containing YouTube videos verify the effectiveness of the proposed method.     

Practical Design for Improving the Sensitivity to Search for Dark Matter Axions with Rydberg Atoms

A microwave single-photon detector was developed with highly-excited alkaline Rydberg-atoms in a cooled resonant cavity to search for dark matter axions. This detector belongs to a microwave single-photon counter, thus being free from the standard quantum limit (SQL). High sensitivity of the present detector system was demonstrated by measuring the thermal blackbody radiations in the cavity at temperatures as low as 70 mK where the sensitivity is below the SQL. The detection sensitivity of the present system is mainly limited by stray electric fields present in the detection region. Practical design of a new experimental scheme with a guiding electric field through the atomic-beam trajectory is here presented and discussed to avoid the effect of stray electric field and thus to improve the detection sensitivity.      

Inherent matrix identities on ARMA lattice filter realizationalgorithm and their application

Presents the matrix identities that are inherent in the solution of the normal equations for an ARMA lattice filter. This derivation also makes clear the relationship between the recursive least squares (RLS) method and the ARMA lattice filter realization algorithm. Further, as an application of the matrix identities, a new method for model identification with frequency weighting (MIFW) is presented     

A High-Sensitivity Microwave-Single-Photon Detector with Rydberg Atoms at Low Temperature

A high-sensitivity microwave-single-photon detector was developed in Kyoto, in which microwave photons in a resonant cavity cooled at very low temperatures are absorbed by highly excited Rydberg atoms and the Rydberg atoms thereby promoted to a higher excited state are then selectively field-ionized and detected. This scheme allows us to count microwave photons one by one, thus provide a single-photon counting without the limit of standard quantum limit (SQL). The apparatus “CARRACK” for the single-photon detector was constructed based on this scheme, where the cavity was cooled down to 10 mK range to reduce the background of thermal blackbody photons from the cavity wall. The apparatus has served for years to search for dark matter axions in the 10 μeV (∼2.4 GHz) mass region. Thermal blackbody photons in a microwave resonant cavity at temperatures as low as 70 mK have been measured, the sensitivity being below the SQL limit. A number of improvements in the detection efficiency and sensitivity have been planned and will be reported. Applications of the detector to fundamental physics are also discussed shortly.      

Convolutional sparse coding‐based deep random vector functional link network for distress classification of road structures

This paper presents a convolutional sparse coding (CSC)‐based deep random vector functional link network (CSDRN) for distress classification of road structures. The main contribution of this paper is the introduction of CSC into a feature extraction scheme in the distress classification. CSC can extract visual features representing characteristics of target images because it can successfully estimate optimal convolutional dictionary filters and sparse features as visual features by training from a small number of distress images. The optimal dictionaries trained from distress images have basic components of visual characteristics such as edge and line information of distress images. Furthermore, sparse feature maps estimated on the basis of the dictionaries represent both strength of the basic components and location information of regions having their components, and these maps can represent distress images. That is, sparse feature maps can extract key components from distress images that have diverse visual characteristics. Therefore, CSC‐based feature extraction is effective for training from a limited number of distress images that have diverse visual characteristics. The construction of a novel neural network, CSDRN, by the use of a combination of CSC‐based feature extraction and the DRN classifier, which can also be trained from a small dataset, is shown in this paper. Accurate distress classification is realized via the CSDRN.     

Trial Realization of Human-Centered Multimedia Navigation for Video Retrieval

A trial realization of human-centered navigation for video retrieval is presented in this article. This system consists of the following functions: (a) multimodal analysis for collaborative use of multimedia data, (b) preference extraction for the system to adapt to users' individual demands, and (c) adaptive visualization for users to be guided to their desired contents. By using these functions, users can find their desired video contents more quickly and accurately than with the conventional retrieval schemes since our system can provide new pathways to the desired contents. Experimental results verify the effectiveness of the proposed system.     

Dataset complexity assessment based on cumulative maximum scaled area under Laplacian spectrum

Multimedia Tools and Applications - Dataset complexity assessment aims to predict classification performance on a dataset with complexity calculation before training a classifier, which can also be...     

Magnetic Field-Map Measurement of a Superconducting Triplet Quadrupole and a Dipole Magnet for BigRIPS Separator at RIKEN

Measurements of the magnetic field distributions of the superconducting triplet quadrupoles (STQs) and the room-temperature dipoles for the BigRIPS separator are performed in order to use ion optics calculations of the BigRIPS. This report describes the results of magnetic field-map measurement of the superferric-typed STQ and dipole magnet including the field mapping device we employed for the measurements.