Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU

In this paper, we present that security threats coming with existing GPU memory management strategy are overlooked, which opens a back door for adversaries to freely break the memory isolation: they enable adversaries without any privilege in a computer to recover the raw memory data left by previous processes directly. More importantly, such attacks can work on not only normal multi-user operating systems, but also cloud computing platforms. To demonstrate the seriousness of such attacks, we recovered original data directly from GPU memory residues left by exited commodity applications, including Google Chrome, Adobe Reader, GIMP, Matlab. The results show that, because of the vulnerable memory management strategy, commodity applications in our experiments are all affected

A review of optimal planning active distribution system:Models, methods, and future researches

Due to the widespread deployment of distributed energy resources (DERs) and the liberalization of electricity market, traditional distribution networks are undergoing a transition to active distribution systems (ADSs), and the traditional deterministic planning methods have become unsuitable under the high penetration of DERs. Aiming to develop appropriate models and methodologies for the planning of ADSs, the key features of ADS planning problem are analyzed from the different perspectives, such as the allocation of DGs and ESS, coupling of operation and planning, and high-level uncertainties. Based on these analyses, this comprehensive literature review summarizes the latest research and development associated with ADS planning. The planning models and methods proposed in these research works are analyzed and categorized from different perspectives including objectives, decision variables, constraint conditions, and solving algorithms. The key theoretical issues and challenges of ADS planning are extracted and discussed. Meanwhile, emphasis is also given to the suitable suggestions to deal with these abovementioned issues based on the available literature and comparisons between them. Finally, several important research prospects are recommended for further research in ADS planning field, such as planning with multiple micro-grids (MGs), collaborative planning between ADSs and information communication system (ICS), and planning from different perspectives of multi-stakeholders

Global stability of a three-species food-chain model with diffusion and nonlocal delays

In this paper, a three species reaction-diffusion food-chain system with nonlocal delays is investigated. Sufficient conditions are derived for the global stability of a positive steady state and boundary steady states of the system by using the energy function method. Numerical simulations are carried out to illustrate the theoretical results

Traditional Wooden Buildings in China

Chinese ancient architecture, with its long history, unique systematic features and wide-spread employment as well as its abundant heritages, is a valuable legacy of the whole world. Due to the particularity of the material and structure of Chinese ancient architecture, relatively research results are mostly published in Chinese, which limits international communication. On account of the studies carried out in Nanjing Forestry University and many other universities and teams, this chapter emphatically introduces the development, structural evolution and preservation of traditional Chinese wooden structure; research status focuses on material properties, decay pattern, anti-seismic performance and corresponding conservation and reinforcement technologies of the main load-bearing members in traditional Chinese wooden structure

Insight into the effect of hospital-based prehabilitation on postoperative outcomes in patients with total knee arthroplasty: A retrospective comparative study

Background: Osteoarthritis (OA) has become one of the most prevalent joint diseases worldwide, leading to a growing burden of pain and disability as populations age. Although there is consistent evidence to support postoperative rehabilitation and high-intensity prehabilitation for total knee arthroplasty (TKA), the clinical outcomes of hospital-based prehabilitation remain unclear. We aimed to evaluate the effect of a hospital-based prehabilitation program on knee score (KS), function score (FS), and length of stay (LOS) among patients with knee OA after TKA. Methods: A retrospective comparative study was conducted at Renmin Hospital of Wuhan University among patients with primary knee OA. Seventy-two postopearative patients who did not undergo the prehabilitation program were included as the control group, while 68 postoperative patients who underwent the prehabilitation program were assigned to the intervention group. All patients went through the same care after TKA. The KS, FS, and pain levels were measured 5 days before surgery, immediately preceding surgery, immediately after the surgery, and at 1 week and 1 month postoperatively. LOS for each patient was recorded. Results: The new prehabilitation training program significantly improved the KS over time in the intervention group. However, no significant between-group difference was identified in the change of FS. The prehabilitation program also provided shorter LOS. Conclusions: The hospital-based prehabilitation program leads to improved recovery, as indicated by higher KS postoperatively, which may result in improved clinical outcomes of TKA

ALL IN ONE NETWORK FOR DRIVER ATTENTION MONITORING

Nowadays, driver drowsiness and driver distraction is considered as a major risk for fatal road accidents around the world. As a result, driver monitoring identifying is emerging as an essential function of automotive safety systems. Its basic features include head pose, gaze direction, yawning and eye state analysis. However, existing work has investigated algorithms to detect these tasks separately and was usually conducted under laboratory environments. To address this problem, we propose a multi-task learning CNN framework which simultaneously solve these tasks. The network is implemented by sharing common features and parameters of highly related tasks. Moreover, we propose Dual-Loss Block to decompose the pose estimation task into pose classification and coarse-to-fine regression and Objectcentric Aware Block to reduce orientation estimation errors. Thus, with such novel designs, our model not only achieves SOA results but also reduces the complexity of integrating into automotive safety systems. It runs at 10 fps on vehicle embedded systems which marks a momentous step for this field. More importantly, to facilitate other researchers, we publish our dataset FDUDrivers which contains 20000 images of 100 different drivers and covers various real driving environments. FDUDrivers might be the first comprehensive dataset regarding driver attention monitorin