STUDENTS' INTELLECTUAL-HUMILITY AND COGNITIVE-FLEXIBILITY: THE ROLE OF MINDFULNESS AS A MEDIATOR

The primary objective of this study was to explore the inter-connections among mindfulness, cognitive flexibility, and intellectual humility in adult’s population. Additionally, the research aimed to investigate the mediating role of mindfulness in this association between cognitive flexibility and intellectual humility with different demographic differences (age, gender, and culture). Sample of 800 students selected from Hazara University, Mansehra (HU), and the University of AJK. The study employed a convenient sampling method. The results indicated a significant association between the study as well as demographics differences exists among study variables. In conclusion, this study contributes to our understanding of the connections between mindfulness, cognitive flexibility, and intellectual humility in a student context. The findings suggest that cultivating mindfulness may enhance cognitive flexibility and intellectual humility, with demographic factors playing a role in shaping these psychological attributes. This study has practical implications for enhanced academic performance, effective stress management, improved decision-making, life-long learning, personal well-being, critical thinking skills, adaptability in changing environments and positive interpersonal relationships

TurboViT: Generating Fast Vision Transformers via Generative Architecture Search

Vision transformers have shown unprecedented levels of performance in tackling various visual perception tasks in recent years. However, the architectural and computational complexity of such network architectures have made them challenging to deploy in real-world applications with high-throughput, low-memory requirements. As such, there has been significant research recently on the design of efficient vision transformer architectures. In this study, we explore the generation of fast vision transformer architecture designs via generative architecture search (GAS) to achieve a strong balance between accuracy and architectural and computational efficiency. Through this generative architecture search process, we create TurboViT, a highly efficient hierarchical vision transformer architecture design that is generated around mask unit attention and Q-pooling design patterns. The resulting TurboViT architecture design achieves significantly lower architectural computational complexity (>2.47$\times$ smaller than FasterViT-0 while achieving same accuracy) and computational complexity (>3.4$\times$ fewer FLOPs and 0.9% higher accuracy than MobileViT2-2.0) when compared to 10 other state-of-the-art efficient vision transformer network architecture designs within a similar range of accuracy on the ImageNet-1K dataset. Furthermore, TurboViT demonstrated strong inference latency and throughput in both low-latency and batch processing scenarios (>3.21$\times$ lower latency and >3.18$\times$ higher throughput compared to FasterViT-0 for low-latency scenario). These promising results demonstrate the efficacy of leveraging generative architecture search for generating efficient transformer architecture designs for high-throughput scenarios.Comment: 5 page

MAPLE: Microprocessor A Priori for Latency Estimation

Modern deep neural networks must demonstrate state-of-the-art accuracy while exhibiting low latency and energy consumption. As such, neural architecture search (NAS) algorithms take these two constraints into account when generating a new architecture. However, efficiency metrics such as latency are typically hardware dependent requiring the NAS algorithm to either measure or predict the architecture latency. Measuring the latency of every evaluated architecture adds a significant amount of time to the NAS process. Here we propose Microprocessor A Priori for Latency Estimation MAPLE that does not rely on transfer learning or domain adaptation but instead generalizes to new hardware by incorporating a prior hardware characteristics during training. MAPLE takes advantage of a novel quantitative strategy to characterize the underlying microprocessor by measuring relevant hardware performance metrics, yielding a fine-grained and expressive hardware descriptor. Moreover, the proposed MAPLE benefits from the tightly coupled I/O between the CPU and GPU and their dependency to predict DNN latency on GPUs while measuring microprocessor performance hardware counters from the CPU feeding the GPU hardware. Through this quantitative strategy as the hardware descriptor, MAPLE can generalize to new hardware via a few shot adaptation strategy where with as few as 3 samples it exhibits a 6% improvement over state-of-the-art methods requiring as much as 10 samples. Experimental results showed that, increasing the few shot adaptation samples to 10 improves the accuracy significantly over the state-of-the-art methods by 12%. Furthermore, it was demonstrated that MAPLE exhibiting 8-10% better accuracy, on average, compared to relevant baselines at any number of adaptation samples.Comment: 13 pages, 4 figure

Virtual Histology with Photoacoustic Remote Sensing

Histopathology plays a central role in cancer screening, surgical margin analysis, cancer classification, and understanding disease progression. The vast majority of biopsies or surgical excisions are examined via transmission-mode bright-field microscopy. However, bright-field microscopy requires thin stained tissue samples as it is unable to visualize contrast on thick tissues. Consequently, biopsies and surgically excised specimens undergo extensive tissue processing to prepare histology slides. This tissue processing can take up to two weeks for complex cases before a diagnosis can be presented, potentially resulting in poorer patient outcomes. Surgical margins are commonly analyzed intraoperatively using frozen sectional analysis. While this technique has improved patient outcomes, the quality of frozen sections is often lower than post-operative histologic analysis. This lower quality leads to significant variability in diagnosis. Ultimately, both frozen section analysis and standard histologic analysis are limiting because of the need to process tissues to cater to bright-field microscopy. It would be desirable to forego creating thin tissue sections and instead visualize tissue morphology directly on biopsies and surgical specimens or even directly on the patient’s body (in-situ). Photoacoustic remote sensing (PARSTM) is an emerging non-contact imaging technique. PARS microscopy is an all-optical photoacoustic imaging modality that takes advantage of endogenous optical absorption present within tissues to provide contrast to enable non- contact label-free imaging. PARS has demonstrated excellent resolution and contrast in various applications, such as in-vivo imaging, functional imaging, and deep imaging, while operating in a reflection-mode architecture. This non-contact label-free reflection-mode design lends itself well to imaging unprocessed tissue specimens or in-situ morphological assessment. Using PARS microscopy, this thesis takes preliminary steps towards an in-situ surgical microscope. These steps take the form of developing a PARS system that can recover contrast from DNA and visualize the resulting nuclear morphology in real-time and on arbitrarily sized specimens. Later, this system was expanded to image additional contrasts from hemoglobin to approach the diagnostic information provided by standard histopathology. This research imaged a variety of human tissue types, including breast, gastrointestinal, and skin. These specimens were in the form of thin unstained slides and thick tissue blocks. The tissue blocks serve as an analog to visualization of contrast fresh tissues and in-situ imaging. Adjacent sections of each tissue type were prepared using standard histopathology and compared against the PARS images for experimental validation. These results represent the first reports of imaging human tissues with a non-contact label-free reflection-mode modality. The author believes this research takes vital steps towards an imaging technique that may one day reveal cancer in-situ

Antimicrobial Susceptibility Patterns of Leading Uropathogens and an Empirical Therapy at a Tertiary Care Hospital, Muzaffarabad

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COVID-Net Assistant: A Deep Learning-Driven Virtual Assistant for COVID-19 Symptom Prediction and Recommendation

As the COVID-19 pandemic continues to put a significant burden on healthcare systems worldwide, there has been growing interest in finding inexpensive symptom pre-screening and recommendation methods to assist in efficiently using available medical resources such as PCR tests. In this study, we introduce the design of COVID-Net Assistant, an efficient virtual assistant designed to provide symptom prediction and recommendations for COVID-19 by analyzing users' cough recordings through deep convolutional neural networks. We explore a variety of highly customized, lightweight convolutional neural network architectures generated via machine-driven design exploration (which we refer to as COVID-Net Assistant neural networks) on the Covid19-Cough benchmark dataset. The Covid19-Cough dataset comprises 682 cough recordings from a COVID-19 positive cohort and 642 from a COVID-19 negative cohort. Among the 682 cough recordings labeled positive, 382 recordings were verified by PCR test. Our experimental results show promising, with the COVID-Net Assistant neural networks demonstrating robust predictive performance, achieving AUC scores of over 0.93, with the best score over 0.95 while being fast and efficient in inference. The COVID-Net Assistant models are made available in an open source manner through the COVID-Net open initiative and, while not a production-ready solution, we hope their availability acts as a good resource for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative solutions

PCBDet: An Efficient Deep Neural Network Object Detection Architecture for Automatic PCB Component Detection on the Edge

There can be numerous electronic components on a given PCB, making the task of visual inspection to detect defects very time-consuming and prone to error, especially at scale. There has thus been significant interest in automatic PCB component detection, particularly leveraging deep learning. However, deep neural networks typically require high computational resources, possibly limiting their feasibility in real-world use cases in manufacturing, which often involve high-volume and high-throughput detection with constrained edge computing resource availability. As a result of an exploration of efficient deep neural network architectures for this use case, we introduce PCBDet, an attention condenser network design that provides state-of-the-art inference throughput while achieving superior PCB component detection performance compared to other state-of-the-art efficient architecture designs. Experimental results show that PCBDet can achieve up to 2$\times$ inference speed-up on an ARM Cortex A72 processor when compared to an EfficientNet-based design while achieving $\sim$2-4\% higher mAP on the FICS-PCB benchmark dataset.Comment: 7 pages, 6 figure

Knowledge, attitude, and practice towards COVID-19 among Syrian people resident in Turkey

Background: Measuring knowledge, attitude, and practice towards COVID-19 helps policymakers observe knowledge gaps and provide key messages to people to act better against the pandemic. This study aims to assess the knowledge, attitude, and practice towards COVID-19 among Syrian people resident in Turkey.  Methods: A cross-sectional study designed to assess the knowledge, attitude, and practice towards COVID-19 among the Syrian people resident in Turkey. The data were collected via a web-based and self-administered questionnaire of 313 participants from 17-31 July 2020. SPSS version 16.0 was recruited to analyze the data using univariate and multivariable regression data analyses. Results: Our finding as the first study among Syrian people resident in Turkey found a high rate of good knowledge, attitude, and practice towards COVID-19 accordingly with 83.0%, 72.0%, 84.0%. Regression analysis showed that age-group of 45 years and more years, marital status of being married, female gender, living in urban area were significantly associated with upper knowledge score. Age-group of 45 years and more significantly associated with positive attitude score but inversely being married and unemployed statues significantly associated with a negative attitude. Regarding practice score, married and female people had better practice, but poor-rated health status was significantly associated with the weak practice. Conclusion: Although our finding showed a good rate for knowledge, attitude, and practice towards COVID-19, but it needs to improve cause of many barriers on Syrian people resident in Turkey, such as living in a crowded place, distant from health care services, losing whole or part of their income due to COVID-19 as an economic crisis, different language barriers. Some groups like men, people living in a rural area, and those unemployed or lost their job should be exposed by timely and accurate knowledge