Instantaneous Spectral Analysis: Time-Frequency Mapping via Wavelet Matching with Application to Contaminated-Site Characterization by 3D GPR

Spectral decomposition, by which a time series is transformed from the 1D time/amplitude domain to the 2D time/spectrum domain, has become a popular and useful tool in seismic exploration for hydrocarbons. The windowed, or short-time Fourier transform (STFT) was one early approach to computing the time-frequency (t-f) distribution. This method relies on the user selecting a fixed time window, then computing the Fourier spectrum within the time window while sliding the window along the length of the trace. The primary limitation of the STFT is the fixed window which prevents either time localization of high frequency components (if a long window is used) or spectral resolution of the low-frequency components (if a short window is used)

Polymorphisms of STAT-6, STAT-4 and IFN-γ genes and the risk of asthma in Chinese population

SummaryBackgroundAsthma is a complex disease resulting from multiple gene–gene and gene–environment interactions. Study on gene–gene interactions could provide insight into the pathophysiologic mechanisms of the disease.ObjectivesWe investigated the single nucleotide polymorphisms and interactions among three different loci in three candidate genes (STAT-6 G2964A, STAT-4 T90089C and IFN-γ T874A) in 95 Chinese asthmatic subjects and 95 matched controls to determine the possible associations with asthma.MethodsGenotyping of the gene polymorphisms was performed by means of PCR-SSCP analysis. Genotype–phenotype associations were examined in dominant and recessive genetic models using logistic regression. The method of multifactor dimensionality reduction was used to analyze gene–gene interactions.ResultsNo statistically significant difference was found in the distribution of the STAT-6 G2964A polymorphisms between asthmatic patients and controls in this case–control study. The STAT-4 T90089C polymorphisms were significantly associated with asthma in the dominant model (p=0.007). As for the IFN-γ T874A, the significant associations were found in both dominant model (p=0.004) and recessive model (p=0.006). A significant gene–gene interaction was found among STAT-6, STAT-4 and IFN-γ on the risk of asthma. In the best 3-locus model, the odds ratio for the high-risk to the low-risk group was 6.9 (95% CI, 3.5–13.7; p<0.0001).ConclusionsOur findings suggest that STAT-4 T90089C and IFN-γ T874A polymorphisms might be the genetic factors for the risk of asthma in the Chinese population. In addition, the significant interactions among STAT-6 G2964A, STAT-4 T90089C and IFN-γ T874A may increase an individual's susceptibility and contribute to the pathogenesis of asthma

CryptoEval: Evaluating the Risk of Cryptographic Misuses in Android Apps with Data-Flow Analysis

The misunderstanding and incorrect configurations of cryptographic primitives have exposed severe security vulnerabilities to attackers. Due to the pervasiveness and diversity of cryptographic misuses, a comprehensive and accurate understanding of how cryptographic misuses can undermine the security of an Android app is critical to the subsequent mitigation strategies but also challenging. Although various approaches have been proposed to detect cryptographic misuses in Android apps, seldom studies have focused on estimating the security risks introduced by cryptographic misuses. To address this problem, we present an extensible framework for deciding the threat level of cryptographic misuses in Android apps. Firstly, we propose a unified specification for representing cryptographic misuses to make our framework extensible and develop adapters to unify the detection results of the state-of-the-art cryptographic misuse detectors, resulting in an adapter-based detection toolchain for a more comprehensive list of cryptographic misuses. Secondly, we employ a misuse-originating data-flow analysis to connect each cryptographic misuse to a set of data-flow sinks in an app, based on which we propose a quantitative data-flow-driven metric for assessing the overall risk of the app introduced by cryptographic misuses. To make the per-app assessment more useful in the app vetting at the app-store level, we apply unsupervised learning to predict and classify the top risky threats, to guide more efficient subsequent mitigations. In the experiments on an instantiated implementation of the framework, we evaluate the accuracy of our detection and the effect of data-flow-driven risk assessment of our framework. Our empirical study on over 40,000 apps as well as the analysis of popular apps reveals important security observations on the real threats of cryptographic misuses in Android apps