Unsupervised Deep Hashing for Large-scale Visual Search

Learning based hashing plays a pivotal role in large-scale visual search. However, most existing hashing algorithms tend to learn shallow models that do not seek representative binary codes. In this paper, we propose a novel hashing approach based on unsupervised deep learning to hierarchically transform features into hash codes. Within the heterogeneous deep hashing framework, the autoencoder layers with specific constraints are considered to model the nonlinear mapping between features and binary codes. Then, a Restricted Boltzmann Machine (RBM) layer with constraints is utilized to reduce the dimension in the hamming space. Extensive experiments on the problem of visual search demonstrate the competitiveness of our proposed approach compared to state-of-the-art

Multi-hierarchical Convolutional Network for Efficient Remote Photoplethysmograph Signal and Heart Rate Estimation from Face Video Clips

Heart beat rhythm and heart rate (HR) are important physiological parameters of the human body. This study presents an efficient multi-hierarchical spatio-temporal convolutional network that can quickly estimate remote physiological (rPPG) signal and HR from face video clips. First, the facial color distribution characteristics are extracted using a low-level face feature Generation (LFFG) module. Then, the three-dimensional (3D) spatio-temporal stack convolution module (STSC) and multi-hierarchical feature fusion module (MHFF) are used to strengthen the spatio-temporal correlation of multi-channel features. In the MHFF, sparse optical flow is used to capture the tiny motion information of faces between frames and generate a self-adaptive region of interest (ROI) skin mask. Finally, the signal prediction module (SP) is used to extract the estimated rPPG signal. The experimental results on the three datasets show that the proposed network outperforms the state-of-the-art methods.Comment: 33 pages,9 figure

Coverage Analysis of Single-swarm mmWave UAV Networks under Multiple Types of Blockages

Millimeter wave (mmWave)-based unmanned aerial vehicle (UAV) communication is susceptible to blockages, even from humans. Previous studies that primarily focused only on static blockage may not accurately characterize the system performance. This paper investigates the coverage performance of mmWave UAV networks by jointly considering multiple types of blockages under finite homogeneous Poisson point process and Binomial point process, which are commonly employed in finite area scenarios with random and fixed number of UAVs, respectively. Particularly, we derive the average line-of-sight probability and coverage probability under static, dynamic, and self blockages. Simulations verify our theoretical results, demonstrating that: the above system performance predominantly depends on self-blockage if UAVs are at high altitudes. Conversely, at relatively low altitudes, all three types of blockages impact them, with static blockage being the dominant factor. To avoid self-blockage, UAV height should satisfy h > hR+ri/tan φb, where hR is the height of the user equipment (UE), ri is the two-dimensional distance of the UAV-UE link, φb is the elevation angle between UE and UAV. The required height is proportional to ri and increases as distance d between the user and UE decreases, as φb is proportional to d. The findings help on designing the network parameters. To our best knowledge, this is the first work to analyze the coverage of mmWave UAV networks under multiple types of blockages

Clinical Characteristics of 26 Human Cases of Highly Pathogenic Avian Influenza A (H5N1) Virus Infection in China

BACKGROUND: While human cases of highly pathogenic avian influenza A (H5N1) virus infection continue to increase globally, available clinical data on H5N1 cases are limited. We conducted a retrospective study of 26 confirmed human H5N1 cases identified through surveillance in China from October 2005 through April 2008. METHODOLOGY/PRINCIPAL FINDINGS: Data were collected from hospital medical records of H5N1 cases and analyzed. The median age was 29 years (range 6-62) and 58% were female. Many H5N1 cases reported fever (92%) and cough (58%) at illness onset, and had lower respiratory findings of tachypnea and dyspnea at admission. All cases progressed rapidly to bilateral pneumonia. Clinical complications included acute respiratory distress syndrome (ARDS, 81%), cardiac failure (50%), elevated aminotransaminases (43%), and renal dysfunction (17%). Fatal cases had a lower median nadir platelet count (64.5 x 10(9) cells/L vs 93.0 x 10(9) cells/L, p = 0.02), higher median peak lactic dehydrogenase (LDH) level (1982.5 U/L vs 1230.0 U/L, p = 0.001), higher percentage of ARDS (94% [n = 16] vs 56% [n = 5], p = 0.034) and more frequent cardiac failure (71% [n = 12] vs 11% [n = 1], p = 0.011) than nonfatal cases. A higher proportion of patients who received antiviral drugs survived compared to untreated (67% [8/12] vs 7% [1/14], p = 0.003). CONCLUSIONS/SIGNIFICANCE: The clinical course of Chinese H5N1 cases is characterized by fever and cough initially, with rapid progression to lower respiratory disease. Decreased platelet count, elevated LDH level, ARDS and cardiac failure were associated with fatal outcomes. Clinical management of H5N1 cases should be standardized in China to include early antiviral treatment for suspected H5N1 cases