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Accurately detecting lane lines in 3D space is crucial for autonomous driving. Existing methods usually first transform image-view features into bird-eye-view (BEV) by aid of inverse perspective mapping (IPM), and then detect lane lines based on the BEV features. However, IPM ignores the changes in road height, leading to inaccurate view transformations. Additionally, the two separate stages of the process can cause cumulative errors and increased complexity. To address these limitations, we propose an efficient transformer for 3D lane detection. Different from the vanilla transformer, our model contains a decomposed cross-attention mechanism to simultaneously learn lane and BEV representations. The mechanism decomposes the cross-attention between image-view and BEV features into the one between image-view and lane features, and the one between lane and BEV features, both of which are supervised with ground-truth lane lines. Our method obtains 2D and 3D lane predictions by applying the lane features to the image-view and BEV features, respectively. This allows for a more accurate view transformation than IPM-based methods, as the view transformation is learned from data with a supervised cross-attention. Additionally, the cross-attention between lane and BEV features enables them to adjust to each other, resulting in more accurate lane detection than the two separate stages. Finally, the decomposed cross-attention is more efficient than the original one. Experimental results on OpenLane and ONCE-3DLanes demonstrate the state-of-the-art performance of our method.

Genomic data arising from a genome-wide association study (GWAS) are often not only of large-scale, but also incomplete. A specific form of their incompleteness is missing values with non-ignorable missingness mechanism. The intrinsic complications of genomic data present significant challenges in developing an unbiased and informative procedure of phenotype-genotype association analysis by a statistical variable selection approach. In this paper we develop a coherent procedure of categorical phenotype-genotype association analysis, in the presence of missing values with non-ignorable missingness mechanism in GWAS data, by integrating the state-of-the-art methods of random forest for variable selection, weighted ridge regression with EM algorithm for missing data imputation, and linear statistical hypothesis testing for determining the missingness mechanism. Two simulated GWAS are used to validate the performance of the proposed procedure. The procedure is then applied to analyze a real data set from breast cancer GWAS.

The goal of this study is to establish a general framework for predicting the so-called critical Turning Period in an infectious disease epidemic such as the COVID-19 outbreak in China early this year. This framework enabled a timely prediction of the turning period when applied to Wuhan COVID-19 epidemic and informed the relevant authority for taking appropriate and timely actions to control the epidemic. It is expected to provide insightful information on turning period for the world's current battle against the COVID-19 pandemic. The underlying mathematical model in our framework is the individual Susceptible-Exposed- Infective-Removed (iSEIR) model, which is a set of differential equations extending the classic SEIR model. We used the observed daily cases of COVID-19 in Wuhan from February 6 to 10, 2020 as the input to the iSEIR model and were able to generate the trajectory of COVID-19 cases dynamics for the following days at midnight of February 10 based on the updated model, from which we predicted that the turning period of CIVID-19 outbreak in Wuhan would arrive within one week after February 14. This prediction turned to be timely and accurate, providing adequate time for the government, hospitals, essential industry sectors and services to meet peak demands and to prepare aftermath planning. Our study also supports the observed effectiveness on flatting the epidemic curve by decisively imposing the Lockdown and Isolation Control Program in Wuhan since January 23, 2020. The Wuhan experience provides an exemplary lesson for the whole world to learn in combating COVID-19., Comment: 24 paages, 9 figures, 10 tables

This paper focuses on studying and understanding of stochastic dynamics in population composition when the population is subject to rumor spreading. We undertake the study by first developing an individual Susceptible-Exposed-Infectious-Removed (iSEIR) model, an extension of the SEIR model, for summarizing rumor-spreading behaviors of interacting groups in the population. With this iSEIR model, the interacting groups may be regarded as nodes in a multiplex network. Then various properties of the dynamic behaviors of the interacting groups in rumor spreading can be drawn from samples of the multiplex network. The samples are simulated based on the iSEIR model with different settings in terms of population scale, population distribution and transfer rate. Results from the simulation study show that effective control of rumor spreading in the multiplex network entails an efficient management on information flow, which may be achieved by setting appropriate immunization and spreading thresholds in individual behavior dynamics. Under the proposed iSEIR model we also have derived a steady-state result, named the "supersaturation phenomenon", when the rumor spreading process becomes equilibrium, which may help us to make the optimal or better control of information flow in the practice., Comment: 21 pages, 12 figures

Most estimates for penalised linear regression can be viewed as posterior modes for an appropriate choice of prior distribution. Bayesian shrinkage methods, particularly the horseshoe estimator, have recently attracted a great deal of attention in the problem of estimating sparse, high-dimensional linear models. This paper extends these ideas, and presents a Bayesian grouped model with continuous global-local shrinkage priors to handle complex group hierarchies that include overlapping and multilevel group structures. As the posterior mean is never a sparse estimate of the linear model coefficients, we extend the recently proposed decoupled shrinkage and selection (DSS) technique to the problem of selecting groups of variables from posterior samples. To choose a final, sparse model, we also adapt generalised information criteria approaches to the DSS framework. To ensure that sparse groups, in which only a few predictors are active, can be effectively identified, we provide an alternative degrees of freedom estimator for sparse Bayesian linear models that takes into account the effects of shrinkage on the model coefficients. Simulations and real data analysis using our proposed method show promising performance in terms of correct identification of active and inactive groups, and prediction, in comparison with a Bayesian grouped slab-and-spike approach.

Testing the association between a phenotype and many genetic variants from case-control data is essential in genome-wide association study (GWAS). This is a challenging task as many such variants are correlated or non-informative. Similarities exist in testing the population difference between two groups of high dimensional data with intractable full likelihood function. Testing may be tackled by a maximum composite likelihood (MCL) not entailing the full likelihood, but current MCL tests are subject to power loss for involving non-informative or redundant sub-likelihoods. In this paper, we develop a forward search and test method for simultaneous powerful group difference testing and informative sub-likelihoods composition. Our method constructs a sequence of Wald-type test statistics by including only informative sub-likelihoods progressively so as to improve the test power under local sparsity alternatives. Numerical studies show that it achieves considerable improvement over the available tests as the modeling complexity grows. Our method is further validated by testing the motivating GWAS data on breast cancer with interesting results obtained., Comment: 31 pages, 5 figures

The traditional maximum likelihood estimator (MLE) is often of limited use in complex high-dimensional data due to the intractability of the underlying likelihood function. Maximum composite likelihood estimation (McLE) avoids full likelihood specification by combining a number of partial likelihood objects depending on small data subsets, thus enabling inference for complex data. A fundamental difficulty in making the McLE approach practicable is the selection from numerous candidate likelihood objects for constructing the composite likelihood function. In this paper, we propose a flexible Gibbs sampling scheme for optimal selection of sub-likelihood components. The sampled composite likelihood functions are shown to converge to the one maximally informative on the unknown parameters in equilibrium, since sub-likelihood objects are chosen with probability depending on the variance of the corresponding McLE. A penalized version of our method generates sparse likelihoods with a relatively small number of components when the data complexity is intense. Our algorithms are illustrated through numerical examples on simulated data as well as real genotype SNP data from a case-control study., Comment: 29 pages, 2 figures