Ubiquity of Internet-connected and sensor-equipped portable devices sparked a new set of mobile computing applications that leverage the proliferating sensing capabilities of smartphones. For many of these applications, accurate estimation of the user heading, as compared to the phone heading, is of paramount importance. This is of special importance for many crowd-sensing applications, where the phone can be carried in arbitrary positions and orientations relative to the user body. Current state-of-the-art focus mainly on estimating the phone orientation, require the phone to be placed in a particular position, require user intervention, and/or do not work accurately indoors; which limits their ubiquitous usability in different applications.In this paper we present Humaine, a novel system to reliably and accurately estimate the user orientation relative to the Earth coordinate system. Humaine requires no priorconfiguration nor user intervention and works accurately indoors and outdoors for arbitrary cell phone positions and orientations relative to the user body. The system applies statistical analysis techniques to the inertial sensors widely available on today's cell phones to estimate both the phone and user orientation. Implementation of the system on different Android devices with 170 experiments performed at different indoor and outdoor testbeds shows that Humaine significantly outperforms the state-of-the-art in diverse scenarios, achieving a median accuracy of 15 • averaged over a wide variety of phone positions. This is 558% better than the-state-of-the-art. The accuracy is bounded by the error in the inertial sensors readings and can be enhanced with more accurate sensors and sensor fusion.
Integrative approaches that combine multiple forms of data can more accurately capture pathway associations and so provide a comprehensive understanding of the molecular mechanisms that cause complex diseases. Association analyses based on single nucleotide polymorphism (SNP) genotypes, copy number variant (CNV) genotypes, and gene expression profiles are the 3 most common paradigms used for gene set/pathway enrichment analyses. Many work has been done to leverage information from 2 types of data from these 3 paradigms. However, to the best of our knowledge, there is no work done before to integrate the 3 paradigms all together. In this article, we present an integrated analysis that combine SNP, CNV, and gene expression data to generate a single gene list. We present different methods to compare this gene list with the other 3 possible lists that result from the combinations of the following pairs of data: SNP genotype with gene expression, CNV genotype with gene expression, and SNP genotype with CNV genotype. The comparison is done using 3 different cancer datasets and 2 different methods of comparison. Our results show that integrating SNP, CNV, and gene expression data give better association results than integrating any pair of 3 data.
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