Sensor drift is an intractable obstacle to practical temperature measurement in smart building. In this article, we propose a sensor spatial correlation model. Given prior knowledge, maximum a posteriori (MAP) estimation is performed to calibrate drifts. MAP is formulated as a nonconvex problem with three hyper-parameters. An alternating-based method is proposed to solve this nonconvex formulation. Cross-validation, Gibbs expectation-maximization (EM) and variational Bayesian EM (VB-EM) are further exploited to determine hyper-parameters. Experimental results on widely used benchmarks from the simulator EnergyPlus demonstrate that compared with state-of-the-art methods, the proposed framework can achieve a robust drift calibration and a better tradeoff between accuracy and runtime. On average, compared with state-of-the-art, the proposed framework can achieve about 3× accuracy improvement. In order to attain the same drift calibration accuracy with VB-EM, Gibbs EM needs 10 000 samples, which will incur a 30× runtime overhead.

Layout hotpot detection is one of the main steps in modern very-large-scale-integration (VLSI) chip design. A typical hotspot detection flow is extremely time consuming due to the computationally expensive mask optimization and lithographic simulation. Recent researches try to facilitate the procedure with a reduced flow, including feature extraction, training set generation, and hotspot detection, where feature extraction methods and hotspot detection engines are deeply studied. However, the performance of hotspot detectors relies highly on the quality of reference layout libraries which are costly to obtain and usually predetermined or randomly sampled in previous works. In this article, we propose an active learning-based layout pattern sampling and hotspot detection flow, which simultaneously optimizes the machine-learning model and the training set that aims to achieve similar or better hotspot detection performance with much smaller number of training instances. Experimental results show that our proposed method can significantly reduce lithography simulation overhead while attaining satisfactory detection accuracy on designs under both DUV and EUV lithography technologies.

Sensor drift is an intractable obstacle to practical temperature measurement in smart building. In this paper, we propose a sensor spatial correlation model. Given prior knowledge, Maximum-aposteriori (MAP) estimation is performed to calibrate drifts. MAP is formulated as a non-convex problem with three hyper-parameters. An alternating-based method is proposed to solve this non-convex formulation. Cross-validation and Expectation-maximum with Gibbs sampling are further to determine hyper-parameters. Experimental results show that on benchmarks from simulator EnergyPlus, compared with state-of-the-art method, the proposed framework can achieve a robust drift calibration and a better trade-off between accuracy and runtime.

BACKGROUND: High-throughput techniques bring novel tools and also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, normalization serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects. RESULTS: In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and by using the hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors. CONCLUSIONS: Simulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named "SCBN", which is freely available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html .