Supervised Hashing with End-to-End Binary Deep Neural Network

Image hashing is a popular technique applied to large scale content-based visual retrieval due to its compact and efficient binary codes. Our work proposes a new end-to-end deep network architecture for supervised hashing which directly learns binary codes from input images and maintains good properties over binary codes such as similarity preservation, independence, and balancing. Furthermore, we also propose a new learning scheme that can cope with the binary constrained loss function. The proposed algorithm not only is scalable for learning over large-scale datasets but also outperforms state-of-the-art supervised hashing methods, which are illustrated throughout extensive experiments from various image retrieval benchmarks.Comment: Accepted to IEEE ICIP 201

Selective Deep Convolutional Features for Image Retrieval

Convolutional Neural Network (CNN) is a very powerful approach to extract discriminative local descriptors for effective image search. Recent work adopts fine-tuned strategies to further improve the discriminative power of the descriptors. Taking a different approach, in this paper, we propose a novel framework to achieve competitive retrieval performance. Firstly, we propose various masking schemes, namely SIFT-mask, SUM-mask, and MAX-mask, to select a representative subset of local convolutional features and remove a large number of redundant features. We demonstrate that this can effectively address the burstiness issue and improve retrieval accuracy. Secondly, we propose to employ recent embedding and aggregating methods to further enhance feature discriminability. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art retrieval accuracy.Comment: Accepted to ACM MM 201

Prototyping Hexagonal Light Concentrators Using High-Reflectance Specular Films for the Large-Sized Telescopes of the Cherenkov Telescope Array

We have developed a prototype hexagonal light concentrator for the Large-Sized Telescopes of the Cherenkov Telescope Array. To maximize the photodetection efficiency of the focal-plane camera pixels for atmospheric Cherenkov photons and to lower the energy threshold, a specular film with a very high reflectance of 92-99% has been developed to cover the inner surfaces of the light concentrators. The prototype has a relative anode sensitivity (which can be roughly regarded as collection efficiency) of about 95 to 105% at the most important angles of incidence. The design, simulation, production procedure, and performance measurements of the light-concentrator prototype are reported.Comment: 21 pages, 14 figures, accepted for publication in JINS

Healthcare use for diarrhoea and dysentery in actual and hypothetical cases, Nha Trang, Viet Nam.

To better understand healthcare use for diarrhoea and dysentery in Nha Trang, Viet Nam, qualitative interviews with community residents and dysentery case studies were conducted. Findings were supplemented by a quantitative survey which asked respondents which healthcare provider their household members would use for diarrhoea or dysentery. A clear pattern of healthcare-seeking behaviours among 433 respondents emerged. More than half of the respondents self-treated initially. Medication for initial treatment was purchased from a pharmacy or with medication stored at home. Traditional home treatments were also widely used. If no improvement occurred or the symptoms were perceived to be severe, individuals would visit a healthcare facility. Private medical practitioners are playing a steadily increasing role in the Vietnamese healthcare system. Less than a quarter of diarrhoea patients initially used government healthcare providers at commune health centres, polyclinics, and hospitals, which are the only sources of data for routine public-health statistics. Given these healthcare-use patterns, reported rates could significantly underestimate the real disease burden of dysentery and diarrhoea

A Simple Methodology for Conversion of Mouse Monoclonal Antibody to Human-Mouse Chimeric Form

10.1155/2013/716961Clinical and Developmental Immunology2013Article number 716961,6 page