For many years, subset analysis has been a popular topic for the biostatistics and clinical trials literature. In more recent years, the discussion has focused on finding subsets of genomes which play a role in the effect of treatment, often referred to as stratified or personalized medicine. Though highly sought after, methods for detecting subsets with altering treatment effects are limited a...

At least since Gabriel (1969), a broad class of multiple comparison procedures, socalled simultaneous test procedures (STPs), is established in the statistical literature. Elements of an STP are a testing family (H, T ), consisting of a set of null hypotheses and corresponding test statistics, and a common critical constant cα. The threshold cα with which each of the test statistics has to be c...

Recent studies have proven that synthetic aperture radar (SAR) automatic target recognition (ATR) models based on deep neural networks (DNN) are vulnerable to adversarial examples. However, existing attacks easily fail in the case where perturbations cannot be fully fed victim models. We call this situation perturbation offset. Moreover, since background clutter takes up most of area SAR images...

In the constrained computing environments such as mobile device or satellite on-board system, various computational factors of hardware resource can restrict processing deep learning (DL) services. Recent DL models image analysis mainly require larger memory occupation for intermediate feature map footprint than given specification and overhead (in FLOP) to meet service-level objective in sense...

This work aims to calculate interlaminar stress distribution through the thickness of multilayered composite shell structures by employing a novel nonlinear layer-wise finite element formulation. Adapting Mindlin– Reissner theory in each layer, shear-deformable presents shear distributions increasing number layers. The normal is then determined using difference solution general form equilibrium...

Recently, convolutional neural networks (CNNs) have set latest state-of-the-art on various human activity recognition (HAR) datasets. However, deep CNNs often require more computing resources, which limits their applications in embedded HAR. Although many successful methods been proposed to reduce memory and FLOPs of CNNs, they involve special network architectures designed for visual tasks, ar...