3,424 research outputs found
Power of selective genotyping in genome-wide association studies of quantitative traits
The selective genotyping approach in quantitative genetics means genotyping only individuals with extreme phenotypes. This approach is considered an efficient way to perform gene mapping, and can be applied in both linkage and association studies. Selective genotyping in association mapping of quantitative trait loci was proposed to increase the power of detecting rare alleles of large effect. However, using this approach, only common variants have been detected. Studies on selective genotyping have been limited to single-locus scenarios. In this study we aim to investigate the power of selective genotyping in a genome-wide association study scenario, and we specifically study the impact of minor allele frequency of variants on the power of this approach. We use the Genetic Analysis Workshop 16 rheumatoid arthritis whole-genome data from the North American Rheumatoid Arthritis Consortium. Two quantitative traits, anti-cyclic citrullinated peptide and rheumatoid factor immunoglobulin M, and one binary trait, rheumatoid arthritis affection status, are used in the analysis. The power of selective genotyping is explored as a function of three parameters: sampling proportion, minor allele frequency of single-nucleotide polymorphism, and test level. The results show that the selective genotyping approach is more efficient in detecting common variants than detecting rare variants, and it is efficient only when the level of declaring significance is not stringent. In summary, the selective genotyping approach is most suitable for detecting common variants in candidate gene-based studies
Strong consistency of estimators in partially linear models for longitudinal data with mixing-dependent structure
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection
Multimodal information (e.g., visible and thermal) can generate robust
pedestrian detections to facilitate around-the-clock computer vision
applications, such as autonomous driving and video surveillance. However, it
still remains a crucial challenge to train a reliable detector working well in
different multispectral pedestrian datasets without manual annotations. In this
paper, we propose a novel unsupervised domain adaptation framework for
multispectral pedestrian detection, by iteratively generating pseudo
annotations and updating the parameters of our designed multispectral
pedestrian detector on target domain. Pseudo annotations are generated using
the detector trained on source domain, and then updated by fixing the
parameters of detector and minimizing the cross entropy loss without
back-propagation. Training labels are generated using the pseudo annotations by
considering the characteristics of similarity and complementarity between
well-aligned visible and infrared image pairs. The parameters of detector are
updated using the generated labels by minimizing our defined multi-detection
loss function with back-propagation. The optimal parameters of detector can be
obtained after iteratively updating the pseudo annotations and parameters.
Experimental results show that our proposed unsupervised multimodal domain
adaptation method achieves significantly higher detection performance than the
approach without domain adaptation, and is competitive with the supervised
multispectral pedestrian detectors
Miriam: Exploiting Elastic Kernels for Real-time Multi-DNN Inference on Edge GPU
Many applications such as autonomous driving and augmented reality, require
the concurrent running of multiple deep neural networks (DNN) that poses
different levels of real-time performance requirements. However, coordinating
multiple DNN tasks with varying levels of criticality on edge GPUs remains an
area of limited study. Unlike server-level GPUs, edge GPUs are resource-limited
and lack hardware-level resource management mechanisms for avoiding resource
contention. Therefore, we propose Miriam, a contention-aware task coordination
framework for multi-DNN inference on edge GPU. Miriam consolidates two main
components, an elastic-kernel generator, and a runtime dynamic kernel
coordinator, to support mixed critical DNN inference. To evaluate Miriam, we
build a new DNN inference benchmark based on CUDA with diverse representative
DNN workloads. Experiments on two edge GPU platforms show that Miriam can
increase system throughput by 92% while only incurring less than 10\% latency
overhead for critical tasks, compared to state of art baselines
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