Variable Selection in Linear Models with Grouped Variables

Linear mixed models have been widely used for repeated measurements, longitudinal studies, or multilevel data. The selection of random effects in linear mixed models has received much attention recently in the literature. Random effects consider dependent structure between repeatedly measured data. Due to computational challenges, the selection of grouped random effects has yet to be studied. Grouped random effects, including genetics data or categorical variables, are commonly seen in practice. We present an efficient method for selecting random effects at group levels in linear mixed models. Specifically, the proposed method employs a restricted maximum likelihood function to estimate the covariance matrix of random effects. To achieve sparse estimation and grouped random effects selection, we then introduce a new shrinkage penalty term. In addition, we extend the idea of grouped variable selection onto the latent regression model. By incorporating regression onto latent traits, latent regression models provide a way to uncover hidden influential factors from the data and make more accurate predictions. Specifically, we develop a variable selection approach for latent regression item response theory models by introducing the group LASSO penalty into the marginal log-likelihood function of observed test responses. We derive the explicit forms of updating steps for model parameters in a modified Newton-Raphson method. Our approach selects significant covariates and estimates model parameters simultaneously. For both variable selection frameworks, we perform simulation studies to evaluate the variable selection performance of the proposed methods. We then compare them to existing or naive selection methods. Additionally, we apply the proposed methods on real data sets

A case study of the approaches used and accuracy of performance modelling for non-domestic buildings in the UK

The UK's goal of transitioning to net zero carbon buildings has led to an increasing focus on the reliability of modelling results for energy consumption. Detailed modelling of HVAC systems and controls is considered a breakthrough in improving model accuracy. This paper uses a school building as a case study. Two dynamic simulation approaches, template and detailed component level HVAC modelling, are used in the IES VE software to predict energy consumption and compare the results with measured data. The root causes of the performance gap are analysed based on the calibration of the models. At the same time, this study trades off the complexity of the performance modelling input parameters against the accuracy of the output results. Then explore the interoperability of input parameters in these two approaches to avoid additional uncertainties introduced by detailed modelling. Some insights are provided into the modelling of operational energy use for non-domestic buildings in the UK

High-Resolution ADCs Design in Image Sensors

This paper presents design considerations for high-resolution and high-linearity ADCs for biomedical imaging ap-plications. The work discusses how to improve dynamic spec-ifications such as Spurious Free Dynamic Range (SFDR) and Signal-to-Noise-and-Distortion Ratio (SNDR) in ultra-low power and high-resolution analog-to-digital converters (ADCs) including successive approximation register (SAR) for biomedical imaging application. The results show that with broad range of mismatch error, the SFDR is enhanced by about 10 dB with the proposed performance enhancement technique, which makes it suitable for high resolution image sensors sensing systems

High Linearity SAR ADC for Smart Sensor Applications

This paper presents capacitive array optimization technique to improve the Spurious Free Dynamic Range (SFDR) and Signal-to-Noise-and-Distortion Ratio (SNDR) of Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) for smart sensor application. Monte Carlo simulation results show that capacitive array optimization technique proposed can make the SFDR, SNDR and (Signal-to-Noise Ratio) SNR more concentrated, which means the differences between maximum value and minimum value of SFDR, SNDR and SNR are much smaller than the conventional calibration techniques, more stable performance enhancement can be achieved, and the averaged SFDR is improved from 72.9 dB to 91.1 dB by using the capacitive array optimization method, 18.2 dB improvement of SFDR is obtained with only little expense of digital logic circuits, which makes it good choice for high resolution and high linearity smart sensing systems

Testing 2D temperature models in Bayesian retrievals of atmospheric properties from hot Jupiter phase curves

Spectroscopic phase curves of transiting hot Jupiters are spectral measurements at multiple orbital phases, giving a set of disc-averaged spectra that probe multiple hemispheres. By fitting model phase curves to observations, we can constrain the atmospheric properties of hot Jupiters such as molecular abundance, aerosol distribution and thermal structure, which offer insights into their dynamics, chemistry, and formation. In this work, we propose a novel 2D temperature scheme consisting of a dayside and a nightside to retrieve information from near-infrared phase curves, and apply the scheme to phase curves of WASP-43b observed by HST/WFC3 and Spitzer/IRAC. In our scheme, temperature is constant on isobars on the nightside and varies with cos$^n$(longitude/$\epsilon$) on isobars on the dayside, where $n$ and $\epsilon$ are free parameters. We fit all orbital phases simultaneously using the radiative transfer package NEMESISPY coupled to a Bayesian inference code. We first validate the performance of our retrieval scheme with synthetic phase curves generated from a GCM, and find our 2D scheme can accurately retrieve the latitudinally-averaged thermal structure and constrain the abundance of H$_2$O and CH$_4$. We then apply our 2D scheme to the observed phase curves of WASP-43b and find: (1) the dayside temperature-pressure profiles do not vary strongly with longitude and are non-inverted; (2) the retrieved nightside temperatures are extremely low, suggesting significant nightside cloud coverage; (3) the H$_2$O volume mixing ratio is constrained to $5.6\times10^{-5}$--$4.0\times10^{-4}$, and we retrieve an upper bound for CH$_4$ at $\sim$10$^{-6}$.Comment: 23 pages, 20 figures, 3 tables, accepted for publication in MNRA

Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios

Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs). Due to the black-box property and various types of CAVs, how to test and evaluate CAVs adaptively remains a major challenge. Many approaches have been proposed to adaptively generate testing scenarios during the testing process. However, most existing approaches cannot be applied to complex scenarios, where the variables needed to define such scenarios are high dimensional. Towards filling this gap, the adaptive testing with sparse control variates method is proposed in this paper. Instead of adaptively generating testing scenarios, our approach evaluates CAVs' performances by adaptively utilizing the testing results. Specifically, each testing result is adjusted using multiple linear regression techniques based on control variates. As the regression coefficients can be adaptively optimized for the CAV under test, using the adjusted results can reduce the estimation variance, compared with using the testing results directly. To overcome the high dimensionality challenge, sparse control variates are utilized only for the critical variables of testing scenarios. To validate the proposed method, the high-dimensional overtaking scenarios are investigated, and the results demonstrate that our approach can further accelerate the evaluation process by about 30 times