Optical monitoring of gamma-ray source fields

The three gamma-ray burst source fields GBS1028+46, GBS1205+24, and GBS2252-03 have been monitored for transient optical emission for a combined total of 52 hours. No optical events were seen. The limiting magnitude for the search was M sub V = 15.8 longer and M sub V = 17.0 for 6.0 s or longer

Discovery of near-Earth asteroids by CCD scanning

The found near-Earth asteroid are different objects with peculiar orbits. With the new technique of CCD scanning we entered the domain of the smallest, the fastest, and the closest near-Earth asteroids. The results are presented

A Novel Multi-Color Feature Selection Method for Person Re-identification

In this paper, a novel multi-color feature selection method is proposed for person re-identification. Firstly, multi-color features, which consisting of HSV, LAB, RGB and nRnG color features, were extracted and concatenated into a whole feature vector. Then the D-optimal Partial Least Squares feature selection method was adopted to select an optimal feature subset that could minimize the variance of the regression model. Finally, an asymmetric distance model for similarity matching was utilized to observe distinctive features from a different perspective. Experimental results show that rank 1 performance of the proposed method were 48.67%, 63.12% and 65.04% respectively on the VIPeR, Prid_450s and CUHK01 databases, which have achieved state-of-art performances

A mesocosm concept for the simulation of near-natural shallow underwater climates: The Kiel Outdoor Benthocosms (KOB)

Biogenic, seasonal, and stochastic fluctuations at various scales characterize coastal marine habitats and modulate environmental stress. The relevance of most past studies into climate change impacts is weakened by the usually intentional exclusion of fluctuations from the experimental design. We describe a new outdoor mesocosm system for benthic research (“benthocosms”) which permit the control and manipulation of several environmental variables while admitting all natural in situ fluctuations. This is achieved by continuously measuring the relevant variables (e.g., temperature, pH, O2, CO2) in situ, defining these in real time as reference values in the control software and simulating target climates by delta treatments. The latter constitute the manipulative addition of predefined changes (e.g., “warming”, “acidification”) to the reference values. We illustrate the performance of the system by presenting the environmental data of four seasonal experiments which together represent an entire year. The “Kiel Outdoor Benthocosms” allow realizing near-natural climate change experiments on complex benthic communities under controlled scenarios

Mobile communication base station antenna measurement using unmanned aerial vehicle

Traditional base station antenna measurement methods conducted with professional worker climbing towers tend to raise safety and inefficiency concerns in practical application. Designed to address the above problems, this paper proposes an intelligent and fully automatic antenna measurement unmanned aerial vehicle (UAV) system for mobile communication base station. Firstly, an antenna database, containing 19,715 images, named UAV-Antenna is constructed by image capturing with the help of UAVs flying around various base stations. Secondly, Mask R-CNN is adopted to train an optimal instance segmentation model on UAV-Antenna. Then, pixel coordinates and threshold are utilized for measuring antenna quantity and separate all antenna data for further measuring. Finally, a least squares method is employed for measuring antenna parameters. Experimental results show that the proposed method can not only satisfy the industry application standards, but also guarantee safety of labors and efficiency of performance

Intermittency and structure functions in channel flow turbulence

We present a study of intermittency in a turbulent channel flow. Scaling exponents of longitudinal streamwise structure functions, $\zeta_p /\zeta_3$, are used as quantitative indicators of intermittency. We find that, near the center of the channel the values of $\zeta_p /\zeta_3$ up to $p=7$ are consistent with the assumption of homogeneous/isotropic turbulence. Moving towards the boundaries, we observe a growth of intermittency which appears to be related to an intensified presence of ordered vortical structures. In fact, the behaviour along the normal-to-wall direction of suitably normalized scaling exponents shows a remarkable correlation with the local strength of the Reynolds stress and with the \rms value of helicity density fluctuations. We argue that the clear transition in the nature of intermittency appearing in the region close to the wall, is related to a new length scale which becomes the relevant one for scaling in high shear flows.Comment: 4 pages, 6 eps figure

Asian female facial beauty prediction using deep neural networks via transfer learning and multi-channel feature fusion

Facial beauty plays an important role in many fields today, such as digital entertainment, facial beautification surgery and etc. However, the facial beauty prediction task has the challenges of insufficient training datasets, low performance of traditional methods, and rarely takes advantage of the feature learning of Convolutional Neural Networks. In this paper, a transfer learning based CNN method that integrates multiple channel features is utilized for Asian female facial beauty prediction tasks. Firstly, a Large-Scale Asian Female Beauty Dataset (LSAFBD) with a more reasonable distribution has been established. Secondly, in order to improve CNN's self-learning ability of facial beauty prediction task, an effective CNN using a novel Softmax-MSE loss function and a double activation layer has been proposed. Then, a data augmentation method and transfer learning strategy were also utilized to mitigate the impact of insufficient data on proposed CNN performance. Finally, a multi-channel feature fusion method was explored to further optimize the proposed CNN model. Experimental results show that the proposed method is superior to traditional learning method combating the Asian female FBP task. Compared with other state-of-the-art CNN models, the proposed CNN model can improve the rank-1 recognition rate from 60.40% to 64.85%, and the pearson correlation coefficient from 0.8594 to 0.8829 on the LSAFBD and obtained 0.9200 regression prediction results on the SCUT dataset

WISE/NEOWISE Observations of Comet 103P/Hartley 2

We report results based on mid-infrared photometry of comet 103P/Hartley 2 taken during 2010 May 4-13 (when the comet was at a heliocentric distance of 2.3 AU, and an observer distance of 2.0 AU) by the Wide-field Infrared Survey Explorer. Photometry of the coma at 22 μm and data from the University of Hawaii 2.2 m telescope obtained on 2010 May 22 provide constraints on the dust particle size distribution, d log n/d log m, yielding power-law slope values of alpha = –0.97 ± 0.10, steeper than that found for the inbound particle fluence during the Stardust encounter of comet 81P/Wild 2. The extracted nucleus signal at 12 μm is consistent with a body of average spherical radius of 0.6 ± 0.2 km (one standard deviation), assuming a beaming parameter of 1.2. The 4.6 μm band signal in excess of dust and nucleus reflected and thermal contributions may be attributed to carbon monoxide or carbon dioxide emission lines and provides limits and estimates of species production. Derived carbon dioxide coma production rates are 3.5(± 0.9) × 10^(24) molecules per second. Analyses of the trail signal present in the stacked image with an effective exposure time of 158.4 s yields optical-depth values near 9 × 10^(–10) at a delta mean anomaly of 0.2 deg trailing the comet nucleus, in both 12 and 22 μm bands. A minimum chi-squared analysis of the dust trail position yields a beta-parameter value of 1.0 × 10^(–4), consistent with a derived mean trail-grain diameter of 1.1/ρ cm for grains of ρ g cm^(–3) density. This leads to a total detected trail mass of at least 4 × 10^(10) ρ kg

TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model\u2019s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets