Energy Efficiency Optimization in Active Reconfigurable Intelligent Surface-Aided Integrated Sensing and Communication Systems

Energy efficiency (EE) is a challenging task in integrated sensing and communication (ISAC) systems, where high spectral efficiency and low energy consumption appear as conflicting requirements. Although passive reconfigurable intelligent surface (RIS) has emerged as a promising technology for enhancing the EE of the ISAC system, the multiplicative fading feature hinders its effectiveness. This paper proposes the use of active RIS with its amplification gains to assist the ISAC system for EE improvement. Specifically, we formulate an EE optimization problem in an active RIS-aided ISAC system under system power budgets, considering constraints on user communication quality of service and sensing signal-to-noise ratio (SNR). A novel alternating optimization algorithm is developed to address the highly non-convex problem by leveraging a combination of the generalized Rayleigh quotient optimization approach, semidefinite relaxation (SDR), and the majorization-minimization (MM) framework. Furthermore, to accelerate the algorithm and reduce computational complexity, we derive a semi-closed form for eigenvalue determination. Numerical results demonstrate the effectiveness of the proposed approach, showcasing significant improvements in EE compared to both passive RIS and spectrum efficiency optimization cases

Initiatives to improve prescribing efficiency in China and their influence : cardio-cerebral vascular medicines as a case history to provide future direction

Background: Pharmaceutical expenditure has grown by 16% per annum in China during the past decade. This has been enhanced by increased coverage as well as incentives to physicians and hospitals. Hospital pharmacies dispense 80% of medicines in China, with medicines currently accounting for 46% of total hospital expenditure. Hospitals currently rely on revenues from procurement of medicines for their sustainability. Principal measures to moderate growth in drug expenditure include initiatives to reduce procured drug prices. However, to date no formal pricing policies for generics in China similar to Europe and limited demand-side measures to enhance appropriate prescribing, e.g. no universal measures to monitor the quality of prescribing. Current incentives have led to irrationality in prescribing; e.g. high use of injectable drugs, antibiotics and traditional Chinese medicines (TCMs) despite the development of essential medicine lists. Objective: Assess current utilisation and expenditure of CV medicines including TCMs between 2006 and 2012 and compare the findings with Western European countries. Methods: Uncontrolled retrospective study of prescriptions to treat cardio-vascular disease in one of the largest hospitals in Southwest China. Results: Utilisation of CV drugs increased 3.3 fold during the study period, greatest for TCMs. Procured expenditure increased 4.85 fold. Variable utilisation of generics at 29% to 31% of the total for each molecule in recent years among high volume pharmacotherapeutic products. However, low prices for generics have been achieved through multiple supply-side measures, matching those achieved among some European countries. Continued irrationality in prescribing is seen with high use TCMs despite limited evidence and the utilisation of drugs dropping significantly once low prices procured. Conclusion: Prices still have appreciable impact on the subsequent utilization of different CV drugs in China. Consequently, there is appreciable potential to introduce measures similar to Western Europe to improve future rationality and reduce overall drug costs. This could include robust formularies, quality targets and financial incentives. We are beginning to see improved rationality in the use of medicines with a reduction in TCMs. This will be monitored along with other suggestions to further enhance accessibility to medicines in China without prohibitive increases in pharmaceutical and overall expenditure

Analysis of the influence of recent reforms in China : cardiovascular and cerebrovascular medicines as a case history to provide future direction

Background: Pharmaceutical expenditure has grown by 16% per annum in China, enhanced by incentives for physicians and hospitals. Hospital pharmacies dispense 80% of medicines in China, accounting for 46% of total hospital expenditure. Principal measures to moderate drug expenditure growth include pricing initiatives as limited demand-side measures. Objective: Assess current utilization and expenditure including traditional Chinese medicines (TCMs) between 2006 and 2012. Methods: Uncontrolled retrospective study of medicines to treat cardiovascular and cerebrovascular diseases in one of the largest hospitals in southwest China. Results: Utilization increased 3.3-fold for cerebrovascular medicines, greatest for TCMs, with expenditure increasing 4.85-fold. Low prices for generics were seen, similar to Europe. However, there was variable utilization of generics at 29-31% of total product volumes in recent years. There continued to be irrationality in prescribing with high use of TCMs, and the utilization of different medicines dropping significantly once they achieved low prices. Conclusion: Prices still have an appreciable impact on utilization in China. Potential measures similar to those implemented among western European countries could improve prescribing rationality and conserve resources

A Survey on Automated Software Vulnerability Detection Using Machine Learning and Deep Learning

Software vulnerability detection is critical in software security because it identifies potential bugs in software systems, enabling immediate remediation and mitigation measures to be implemented before they may be exploited. Automatic vulnerability identification is important because it can evaluate large codebases more efficiently than manual code auditing. Many Machine Learning (ML) and Deep Learning (DL) based models for detecting vulnerabilities in source code have been presented in recent years. However, a survey that summarises, classifies, and analyses the application of ML/DL models for vulnerability detection is missing. It may be difficult to discover gaps in existing research and potential for future improvement without a comprehensive survey. This could result in essential areas of research being overlooked or under-represented, leading to a skewed understanding of the state of the art in vulnerability detection. This work address that gap by presenting a systematic survey to characterize various features of ML/DL-based source code level software vulnerability detection approaches via five primary research questions (RQs). Specifically, our RQ1 examines the trend of publications that leverage ML/DL for vulnerability detection, including the evolution of research and the distribution of publication venues. RQ2 describes vulnerability datasets used by existing ML/DL-based models, including their sources, types, and representations, as well as analyses of the embedding techniques used by these approaches. RQ3 explores the model architectures and design assumptions of ML/DL-based vulnerability detection approaches. RQ4 summarises the type and frequency of vulnerabilities that are covered by existing studies. Lastly, RQ5 presents a list of current challenges to be researched and an outline of a potential research roadmap that highlights crucial opportunities for future work

The Fastest Deformable Part Model for Object Detection

This paper solves the speed bottleneck of deformable part model (DPM), while maintaining the accuracy in de-tection on challenging datasets. Three prohibitive steps in cascade version of DPM are accelerated, including 2D cor-relation between root filter and feature map, cascade part pruning and HOG feature extraction. For 2D correlation, the root filter is constrained to be low rank, so that 2D cor-relation can be calculated by more efficient linear combi-nation of 1D correlations. A proximal gradient algorithm is adopted to progressively learn the low rank filter in a dis-criminative manner. For cascade part pruning, neighbor-hood aware cascade is proposed to capture the dependence in neighborhood regions for aggressive pruning. Instead of explicit computation of part scores, hypotheses can be pruned by scores of neighborhoods under the first order ap-proximation. For HOG feature extraction, look-up tables are constructed to replace expensive calculations of orien-tation partition and magnitude with simpler matrix index operations. Extensive experiments show that (a) the pro-posed method is 4 times faster than the current fastest DPM method with similar accuracy on Pascal VOC, (b) the pro-posed method achieves state-of-the-art accuracy on pedes-trian and face detection task with frame-rate speed. 1