Tracking the l_2 Norm with Constant Update Time

The l_2 tracking problem is the task of obtaining a streaming algorithm that, given access to a stream of items a_1,a_2,a_3,... from a universe [n], outputs at each time t an estimate to the l_2 norm of the frequency vector f^{(t)}in R^n (where f^{(t)}_i is the number of occurrences of item i in the stream up to time t). The previous work [Braverman-Chestnut-Ivkin-Nelson-Wang-Woodruff, PODS 2017] gave a streaming algorithm with (the optimal) space using O(epsilon^{-2}log(1/delta)) words and O(epsilon^{-2}log(1/delta)) update time to obtain an epsilon-accurate estimate with probability at least 1-delta. We give the first algorithm that achieves update time of O(log 1/delta) which is independent of the accuracy parameter epsilon, together with the nearly optimal space using O(epsilon^{-2}log(1/delta)) words. Our algorithm is obtained using the Count Sketch of [Charilkar-Chen-Farach-Colton, ICALP 2002]

X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM

The study on point sources in astronomical images is of special importance, since most energetic celestial objects in the Universe exhibit a point-like appearance. An approach to recognize the point sources (PS) in the X-ray astronomical images using our newly designed granular binary-tree support vector machine (GBT-SVM) classifier is proposed. First, all potential point sources are located by peak detection on the image. The image and spectral features of these potential point sources are then extracted. Finally, a classifier to recognize the true point sources is build through the extracted features. Experiments and applications of our approach on real X-ray astronomical images are demonstrated. comparisons between our approach and other SVM-based classifiers are also carried out by evaluating the precision and recall rates, which prove that our approach is better and achieves a higher accuracy of around 89%.Comment: Accepted by ICSP201

Efficient Online Model Adaptation by Incremental Simplex Tableau

Online multi-kernel learning is promising in the era of mobile computing, in which a combined classifier with multiple kernels are offline trained, and online adapts to personalized features for serving the end user precisely and smartly. The online adaptation is mainly carried out at the end-devices, which requires the adaptation algorithms to be light, efficient and accurate. Previous results focused mainly on efficiency. This paper proposes an novel online model adaptation framework for not only efficiency but also optimal online adaptation. At first, an online optimal incremental simplex tableau (IST)algorithm is proposed, which approaches the model adaption by linear programming and produces the optimized model update in each step when a personalized training data is collected.But keeping online optimal in each step is expensive and may cause over-fitting especially when the online data is noisy. A Fast-IST approach is therefore proposed, which measures the deviation between the training data and the current model. It schedules updating only when enough deviation is detected. The efficiency of each update is further enhanced by running IST only limited iterations, which bounds the computation complexity. Theoretical analysis and extensive evaluations show that Fast-IST saves computation cost greatly, while achieving speedy and accurate model adaptation.It provides better model adaptation speed and accuracy while using even lower computing cost than the state-of-the art

Measles vaccination among children in border areas of Yunnan Province, Southwest China.

BackgroundBorder areas are at high risk of measles epidemics. This study aimed to evaluate the effectiveness of the implementation of the routine two-dose measles containing vaccine (MCV) program in border counties of Southwest China.MethodsData used in the study were derived from a cross-sectional survey among 1,467 children aged 8 to 84 months from five border counties of Yunnan Province, Southwest China in 2016. The participants were recruited using a multistage sampling method. Primary guardians of the children were interviewed to collect information on vaccination history, socio-economic status, and knowledge about immunization. Both coverage and timely coverage for the first (MCV1) and the second (MCV2) dose of MCV were calculated. The Kaplan-Meier method was performed to estimate the cumulative coverage of MCV, and Log-rank tests were adopted to compare the differences across counties and birth cohorts. Univariate and multivariate logistic regression models were used to investigate the predictors of delayed MCV1 vaccination.ResultsThe coverage for MCV1 and MCV2 were 97.5% and 93.4%, respectively. However, only 63.8% and 84.0% of the children received MCV1 or MCV2 on time. Significant differences in the cumulative coverage were detected across counties and birth cohorts. Results of the multivariate logistic regression analysis indicated that children whose primary guardian knew the schedule of MCV were less likely to receive MCV1 late (OR = 0.63, PConclusionsAlthough the coverage for MCV is high in border areas of Southwest China, the timeliness of MCV vaccination seems suboptimal. Tailored information from local health professionals may help to reduce untimely vaccination

Discovering areas of interest with geo-tagged images and check-ins

Geo-tagged image is an ideal source for the discovery of popular travel places. However, the aspects of popular venues for daily-life purposes like dining and shopping are often missing in the mined locations from geo-tagged images. Fortunately check-in websites provide us a unique opportunity of analyzing people's preferences in their daily lives to complement the knowledge mined from geo-tagged images. This paper presents a novel approach for the discovery of Areas of Interest (AoI). By analyzing both geo-tagged images and check-ins, the approach exploits travelers' flavors as well as the preferences of daily-life activities of local residents to find AoI in a city. The proposed approach consists of two major steps. Firstly, we devise a density-based clustering method to discover AoI, mainly based on the image densities but also reinforced by the secondary densities from the images' neighboring venues. Then we propose a novel joint authority analysis framework to rank AoI. The framework simultaneously considers both the location-location transitions, and the user-location relations. An interactive presentation interface for visualizing AoI is also presented. The approach is tested with very large datasets for Shanghai city. They consist of 49,460 geo-tagged images from Panoramio.com, and 1,361,547 check-ins from the check-in website Qieke.com. By evaluating the ranking accuracy and quality of AoI, we demonstrate great improvements of our method over compared methods

Research on the Electrodeposition of Graphene Quantum Dots under Supercritical Conditions to Enhance Nickel-Based Composite Coatings

A graphene quantum dots (GQDs)-reinforced nickel-based composite coating was electrodeposited on the surface of a copper plate with a supercritical carbon dioxide fluid (SC-CO2)-assisted DC power supply. The effect of the current density on surface morphology, microstructure, average grain size, hardness, and corrosion resistance of the resulting coatings was investigated in detail. It was found that the GQDs composite coating showed a more compact surface, a smaller grain size, higher microhardness, and stronger corrosion resistance than the pure Ni coating produced in SC-CO2 and a texture coefficient indicative of a (111) preferred orientation. When the current density was 8 A/dm2, the surface morphology of the GQDs composite coating showed a high density, and the grain size was about 23 nm. In addition, the micro-hardness and corrosion resistance of the GQDs composite coating was greatly improved compared with those of the pure nickel coating; at the same time, its wear rate, friction coefficient, and self-corrosion current density were decreased by 73.2%, 17.5%, and 9.2%, respectively