SR-SqueezeNet: A lightweight hyperspectral identification model for oil spills based on smoothed activation functions.

The demand for real-time identification of oil spills in disaster emergency response is urgent, Unmanned Aerial Vehicles (UAVs) are important monitoring means for oil spills by advantage of their flexible, fast and low-cost, so it's crucial of developing lightweight model for UAVs. This paper proposed a lightweight hyperspectral identification model called SR-SqueezeNet, which based on SqueezeNet model and used the designed smooth-type activation function Smooth-ReLU. And this research conducted a series of experiments based on the multi-dimensional airborne images of the oil spills.

Constructing Hierarchical Zeolites with Highly Complete Framework via Controlled Desilication.

Desilication in alkaline medium has been widely used in construction of hierarchical zeolites for industrially relevant catalytic processes. The built of hierarchy in zeolites, especially with low aluminum stability or high Si/Al ratio, often suffers from uncontrolled destruction of zeolitic framework, accompanied by a significant loss of microporous domains and intrinsic acidity after desilication. Here, we report a novel and simple methodology for preparation of hierarchical zeolites with highly complete framework and minimum sacrifice of microporosity and acidity.

Shrinkage estimation of fixed and random effects in linear quantile mixed models.

This paper presents a Bayesian analysis of linear mixed models for quantile regression using a modified Cholesky decomposition for the covariance matrix of random effects and an asymmetric Laplace distribution for the error distribution. We consider several novel Bayesian shrinkage approaches for both fixed and random effects in a linear mixed quantile model using extended penalties. To improve mixing of the Markov chains, a simple and efficient partially collapsed Gibbs sampling algorithm is developed for posterior inference.

Bayesian variable selection in linear quantile mixed models for longitudinal data with application to macular degeneration.

This paper presents a Bayesian analysis of linear mixed models for quantile regression based on a Cholesky decomposition for the covariance matrix of random effects. We develop a Bayesian shrinkage approach to quantile mixed regression models using a Bayesian adaptive lasso and an extended Bayesian adaptive group lasso. We also consider variable selection procedures for both fixed and random effects in a linear quantile mixed model via the Bayesian adaptive lasso and extended Bayesian adaptive group lasso with spike and slab priors.

A Performance Evaluation Scheme for Multiple Object Tracking with HFSWR.

High-frequency surface wave radar (HFSWR) can detect and continuously track ship objects in real time and beyond the horizon. When ships navigate in a sea area, their motions in a time period form a scenario. The diversity and complexity of the motion scenarios make it difficult to accurately track ships, in which failures such as track fragmentation (TF) are frequently observed.