Quantum Relaxation Method for Linear Systems in Finite Element Problems

Quantum linear system algorithms (QLSAs) for gate-based quantum computing can provide exponential speedups for linear systems of equations. The growth of the condition number with problem size for a system of equations arising from a finite element discretization inhibits the direct application of QLSAs for a speedup. Furthermore, QLSAs cannot use an approximate solution or initial guess to output an improved solution. Here, we present Quantum Relaxation for Linear System (qRLS), as an iterative approach for gate-based quantum computers by embedding linear stationary iterations into a larger block linear system. The block linear system is positive-definite and its condition number scales linearly with the number of iterations independent of the size and condition number of the original system, effectively managing the condition number of the finite element problem. The well-conditioned system enables a practical iterative solution of finite element problems using the state-of-the-art Quantum Signal Processing (QSP) variant of QLSAs. Using positive-definite QLSAs l iterations can be performed in O(\sqrt{l}) time, which is unattainable on classical computers. The complexity of the iterations scales favorably compared to classical architectures due to solution time scaling independent of system size with O(\log(N)) qubits, an exponential improvement opening a new paradigm for iterative finite element solutions on quantum hardware

Revisiting Fine-Tuning Strategies for Self-supervised Medical Imaging Analysis

Despite the rapid progress in self-supervised learning (SSL), end-to-end fine-tuning still remains the dominant fine-tuning strategy for medical imaging analysis. However, it remains unclear whether this approach is truly optimal for effectively utilizing the pre-trained knowledge, especially considering the diverse categories of SSL that capture different types of features. In this paper, we first establish strong contrastive and restorative SSL baselines that outperform SOTA methods across four diverse downstream tasks. Building upon these strong baselines, we conduct an extensive fine-tuning analysis across multiple pre-training and fine-tuning datasets, as well as various fine-tuning dataset sizes. Contrary to the conventional wisdom of fine-tuning only the last few layers of a pre-trained network, we show that fine-tuning intermediate layers is more effective, with fine-tuning the second quarter (25-50%) of the network being optimal for contrastive SSL whereas fine-tuning the third quarter (50-75%) of the network being optimal for restorative SSL. Compared to the de-facto standard of end-to-end fine-tuning, our best fine-tuning strategy, which fine-tunes a shallower network consisting of the first three quarters (0-75%) of the pre-trained network, yields improvements of as much as 5.48%. Additionally, using these insights, we propose a simple yet effective method to leverage the complementary strengths of multiple SSL models, resulting in enhancements of up to 3.57% compared to using the best model alone. Hence, our fine-tuning strategies not only enhance the performance of individual SSL models, but also enable effective utilization of the complementary strengths offered by multiple SSL models, leading to significant improvements in self-supervised medical imaging analysis

Improving spam email classification accuracy using ensemble techniques: a stacking approach

Spam emails pose a substantial cybersecurity danger, necessitating accurate classification to reduce unwanted messages and mitigate risks. This study focuses on enhancing spam email classification accuracy using stacking ensemble machine learning techniques.We trained and tested five classifiers: logistic regression, decision tree, K-nearest neighbors (KNN), Gaussian naive Bayes and AdaBoost. To address overfitting, two distinct datasets of spam emails were aggregated and balanced. Evaluating individual classifiers based on recall, precision and F1 score metrics revealed AdaBoost as the top performer. Considering evolving spam technology and new message types challenging traditional approaches, we propose a stacking method. By combining predictions from multiple base models, the stacking method aims to improve classification accuracy. The results demonstrate superior performance of the stacking method with the highest accuracy (98.8%), recall (98.8%) and F1 score (98.9%) among tested methods. Additional experiments validated our approach by varying dataset sizes and testing different classifier combinations. Our study presents an innovative combination of classifiers that significantly improves accuracy, contributing to the growing body of research on stacking techniques. Moreover, we compare classifier performances using a unique combination of two datasets, highlighting the potential of ensemble techniques, specifically stacking, in enhancing spam email classification accuracy. The implications extend beyond spam classification systems, offering insights applicable to other classification tasks. Continued research on emerging spam techniques is vital to ensure long-term effectiveness

Analyzing The Influence of Students’ Personal Traits and Perceived Course Characteristics On Online Engagement: An Evidence from a Developing Economy

With time, world is shifting towards online approach more and more. Students are used to gain education traditionally by personally going to learning institutes but now they are shifting towards online education. The primary aim of the research is to investigate those factors that influence engagement of student in online education. Most importantly, online learning is a novel concept in developing economies because of lack of resources and awareness, people do not prefer online classes. However, developing countries are gradually moving towards some progressive aspects. Hence, it is necessary to evaluate students’ online engagement. For this purpose, Social Cognitive Theory (SCT) and Technological Acceptance Model (TAM) are used. In this research, the dependent variable is Engagement and independent variables are Communication Competencies, Self-regulation, Attitude towards online education. Sense of Identity and Sense of Presence. In this research, quantitative method is used to investigate concepts to find relationships between variables and forecast results. The correlation research approach is used in this research. A survey was conducted with local students via questionnaire (n=152). For data analysis, SPSS and smart PLS-SEM is used in this research. According to the findings of the study, Communication Competencies, Attitude towards online education and Sense of presence impacts significantly on engagement, while Self-regulation and Sense of Identity impact insignificantly on engagement. we recommend taking quizzes during or at the end of the session would be very helpful

Catheter Related Infections in Medical Intensive Care Units

To determine the frequency of different isolates from samples taken from catheter tips of tracheal suction catheters, endotracheal tubes and central venous pressure line catheters among the patients of medical intensive care unitsMethods: In this descriptive cross sectional study a total of 200 patients were checked for bacterial or fungal growth. Included samples were 140 from suction catheters, 51 from endotracheal tubes and 9 from CVP catheters cultured for bacterial or fungal growth. Different organisms were identified on the basis of colony morphology, Colony staining and Biochemical reactions.Results: Out of 200 patients, majority (72.5%) patients were found to be positive for bacterial or fungal growth. Out of which 89(62.2%) were male and 54(37.8%) were females. One hundred and one (69.7%), 38(26.2%), 6(4.1%) growth cultures were obtained from samples of tracheal suction catheter tips, ETT tips and CVP catheter tips respectively. Microorganisms isolated were Acinetobacter species 62(42.8%), Klebsiella species 43(29.7%), Pseudomonas species 19(13.1%), E.coli 8(5.5%), MRSA 5(3.4%), Candida albicans 4(2.8%), Proteus 2(1.4%) and Staphylococcus aureus 2(1.4%).Conclusion: Acinetobacter, Klebsiella and Pseudomonas were the most frequent infectious agents isolated from catheter tips in settings of medical intensive care units