GASL: Guided Attention for Sparsity Learning in Deep Neural Networks

The main goal of network pruning is imposing sparsity on the neural network by increasing the number of parameters with zero value in order to reduce the architecture size and the computational speedup. In most of the previous research works, sparsity is imposed stochastically without considering any prior knowledge of the weights distribution or other internal network characteristics. Enforcing too much sparsity may induce accuracy drop due to the fact that a lot of important elements might have been eliminated. In this paper, we propose Guided Attention for Sparsity Learning (GASL) to achieve (1) model compression by having less number of elements and speed-up; (2) prevent the accuracy drop by supervising the sparsity operation via a guided attention mechanism and (3) introduce a generic mechanism that can be adapted for any type of architecture; Our work is aimed at providing a framework based on interpretable attention mechanisms for imposing structured and non-structured sparsity in deep neural networks. For Cifar-100 experiments, we achieved the state-of-the-art sparsity level and 2.91x speedup with competitive accuracy compared to the best method. For MNIST and LeNet architecture we also achieved the highest sparsity and speedup level

Implantable Medical Devices; Networking Security Survey

Abstract The industry of implantable medical devices (IMDs) is constantly evolving, which is dictated by the pressing need to comprehensively address new challenges in the healthcare field. Accordingly, IMDs are becoming more and more sophisticated. Not long ago, the range of IMDs' technical capacities was expanded, making it possible to establish Internet connection in case of necessity and/or emergency situation for the patient. At the same time, while the web connectivity of today's implantable devices is rather advanced, the issue of equipping the IMDs with sufficiently strong security system remains unresolved. In fact, IMDs have relatively weak security mechanisms which render them vulnerable to cyber-attacks that compromise the quality of IMDs' functionalities. This study revolves around the security deficiencies inherent to three types of sensor-based medical devices; biosensors, insulin pump systems and implantable cardioverter defibrillators. Manufacturers of these devices should take into consideration that security and effectiveness of the functionality of implants is highly dependent on the design. In this paper, we present a comprehensive study of IMDs' architecture and specifically investigate their vulnerabilities at networking interface