A Universal Update-pacing Framework For Visual Tracking

This paper proposes a novel framework to alleviate the model drift problem in visual tracking, which is based on paced updates and trajectory selection. Given a base tracker, an ensemble of trackers is generated, in which each tracker's update behavior will be paced and then traces the target object forward and backward to generate a pair of trajectories in an interval. Then, we implicitly perform self-examination based on trajectory pair of each tracker and select the most robust tracker. The proposed framework can effectively leverage temporal context of sequential frames and avoid to learn corrupted information. Extensive experiments on the standard benchmark suggest that the proposed framework achieves superior performance against state-of-the-art trackers.Comment: Submitted to ICIP 201

Improving the Performance of OTDOA based Positioning in NB-IoT Systems

In this paper, we consider positioning with observed-time-difference-of-arrival (OTDOA) for a device deployed in long-term-evolution (LTE) based narrow-band Internet-of-things (NB-IoT) systems. We propose an iterative expectation-maximization based successive interference cancellation (EM-SIC) algorithm to jointly consider estimations of residual frequency-offset (FO), fading-channel taps and time-of-arrival (ToA) of the first arrival-path for each of the detected cells. In order to design a low complexity ToA detector and also due to the limits of low-cost analog circuits, we assume an NB-IoT device working at a low-sampling rate such as 1.92 MHz or lower. The proposed EM-SIC algorithm comprises two stages to detect ToA, based on which OTDOA can be calculated. In a first stage, after running the EM-SIC block a predefined number of iterations, a coarse ToA is estimated for each of the detected cells. Then in a second stage, to improve the ToA resolution, a low-pass filter is utilized to interpolate the correlations of time-domain PRS signal evaluated at a low sampling-rate to a high sampling-rate such as 30.72 MHz. To keep low-complexity, only the correlations inside a small search window centered at the coarse ToA estimates are upsampled. Then, the refined ToAs are estimated based on upsampled correlations. If at least three cells are detected, with OTDOA and the locations of detected cell sites, the position of the NB-IoT device can be estimated. We show through numerical simulations that, the proposed EM-SIC based ToA detector is robust against impairments introduced by inter-cell interference, fading-channel and residual FO. Thus significant signal-to-noise (SNR) gains are obtained over traditional ToA detectors that do not consider these impairments when positioning a device.Comment: Accepted in GlobeCom 2017, 7 pages, 11 figure

Pattern formation in oscillatory complex networks consisting of excitable nodes

Oscillatory dynamics of complex networks has recently attracted great attention. In this paper we study pattern formation in oscillatory complex networks consisting of excitable nodes. We find that there exist a few center nodes and small skeletons for most oscillations. Complicated and seemingly random oscillatory patterns can be viewed as well-organized target waves propagating from center nodes along the shortest paths, and the shortest loops passing through both the center nodes and their driver nodes play the role of oscillation sources. Analyzing simple skeletons we are able to understand and predict various essential properties of the oscillations and effectively modulate the oscillations. These methods and results will give insights into pattern formation in complex networks, and provide suggestive ideas for studying and controlling oscillations in neural networks.Comment: 15 pages, 7 figures, to appear in Phys. Rev.

Structure and control of self-sustained target waves in excitable small-world networks

Small-world networks describe many important practical systems among which neural networks consisting of excitable nodes are the most typical ones. In this paper we study self-sustained oscillations of target waves in excitable small-world networks. A novel dominant phase-advanced driving (DPAD) method, which is generally applicable for analyzing all oscillatory complex networks consisting of nonoscillatory nodes, is proposed to reveal the self-organized structures supporting this type of oscillations. The DPAD method explicitly explores the oscillation sources and wave propagation paths of the systems, which are otherwise deeply hidden in the complicated patterns of randomly distributed target groups. Based on the understanding of the self-organized structure, the oscillatory patterns can be controlled with extremely high efficiency.Comment: 16 pages 5 figure

Ten-year changes in the prevalence of overweight, obesity and central obesity among the Chinese adults in urban Shanghai, 1998–2007 — comparison of two cross-sectional surveys

BACKGROUND: In China, obesity is expected to increase rapidly in both urban and rural areas. However, there have been no comprehensive reports on secular trends in obesity prevalence among Chinese adults in urban Shanghai, which is the largest city in southern China. METHODS: In 1998–2001 and again in 2007–2008, two independent population-based cross-sectional surveys were conducted in Shanghai to investigate the prevalence of metabolic disorders. These surveys obtained height, waist circumference (WC), and weight measurements for Chinese adults aged between 20 and 74 years who lived in urban communities. From the 1998–2001 survey, 4,894 participants (2,081 men and 2,813 women, mean age: 48.9 years) were recruited, and 4,395 participants (1,599 men and 2,796 women, mean age: 49.8 years) were recruited from the 2007–2008 survey. Using the World Health Organization criteria, overweight was defined as 25 kg/m(2) ≤ BMI < 30 kg/m(2) and obesity as BMI ≥ 30 kg/m(2). Central obesity was defined as WC ≥ 90 cm in men or ≥85 cm in women. The differences in prevalence of obesity, central obesity and overweight between the two surveys were tested using multivariable logistic regression analyses. RESULTS: Compared to the 1998–2001 survey, in the 2007–2008 survey the BMI distribution for men and the WC distribution for both genders is shifted significantly to the right along the x-axis (all p < 0.001). Over the ten years, the prevalence of combined overweight and obesity increased 24% (from 31.5% to 39.1%, p < 0.001) in men, but decreased 8% (from 27.3% to 25.0%; p < 0.01) in women. The prevalence of central obesity increased 40% in men (from 19.5% to 27.3%; p < 0.01), but the increase was not significant in women (15.0% to 17.1%; p = 0.051). In the total population, only central obesity showed a significant change between the populations in the two surveys, increasing 29% (from 17.3% to 22.4%; p < 0.001). CONCLUSIONS: Over this 10 year period, central obesity increased significantly in the Shanghai adult population. However, the prevalence of combined overweight and obesity was significantly increased in men but not in women

IR Design for Application-Specific Natural Language: A Case Study on Traffic Data

In the realm of software applications in the transportation industry, Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their ease of use and various other benefits. With the ceaseless progress in computer performance and the rapid development of large-scale models, the possibility of programming using natural language in specified applications - referred to as Application-Specific Natural Language (ASNL) - has emerged. ASNL exhibits greater flexibility and freedom, which, in turn, leads to an increase in computational complexity for parsing and a decrease in processing performance. To tackle this issue, our paper advances a design for an intermediate representation (IR) that caters to ASNL and can uniformly process transportation data into graph data format, improving data processing performance. Experimental comparisons reveal that in standard data query operations, our proposed IR design can achieve a speed improvement of over forty times compared to direct usage of standard XML format data

Low-Loss High Power RF Switching Using Multifinger AlGaN/GaN MOSHFETs

We demonstrate a novel RF switch based on a multifinger AlGan/GaN MOSHFET. Record high saturation current and breakdown voltage, extremely low gate leakage current and low gate capacitance of the III-N MOSHFETs make them excellent active elements for RF switching. Using a single element test circuit with 1-mm wide multifinger MOSHFET we achieved 0.27 dB insertion loss and more than 40 dB isolation. These parameters can be further improved by impedance matching and by using submicron gate devices. The maximum switching power extrapolated from the results for 1a/mm 100 mum wide device exceeds 40 W for a 1-mm wide 2-a/mm MOSHFET

PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 Are Associated with Type 2 Diabetes in a Chinese Population

Recent advance in genetic studies added the confirmed susceptible loci for type 2 diabetes to eighteen. In this study, we attempt to analyze the independent and joint effect of variants from these loci on type 2 diabetes and clinical phenotypes related to glucose metabolism.Twenty-one single nucleotide polymorphisms (SNPs) from fourteen loci were successfully genotyped in 1,849 subjects with type 2 diabetes and 1,785 subjects with normal glucose regulation. We analyzed the allele and genotype distribution between the cases and controls of these SNPs as well as the joint effects of the susceptible loci on type 2 diabetes risk. The associations between SNPs and type 2 diabetes were examined by logistic regression. The associations between SNPs and quantitative traits were examined by linear regression. The discriminative accuracy of the prediction models was assessed by area under the receiver operating characteristic curves. We confirmed the effects of SNPs from PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 on risk for type 2 diabetes, with odds ratios ranging from 1.114 to 1.406 (P value range from 0.0335 to 1.37E-12). But no significant association was detected between SNPs from WFS1, FTO, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30 and type 2 diabetes. Analyses on the quantitative traits in the control subjects showed that THADA SNP rs7578597 was association with 2-h insulin during oral glucose tolerance tests (P = 0.0005, empirical P = 0.0090). The joint effect analysis of SNPs from eleven loci showed the individual carrying more risk alleles had a significantly higher risk for type 2 diabetes. And the type 2 diabetes patients with more risk allele tended to have earlier diagnostic ages (P = 0.0006).The current study confirmed the association between PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 and type 2 diabetes. These type 2 diabetes risk loci contributed to the disease additively