Competitive Facility Location under Random Utilities and Routing Constraints

In this paper, we study a facility location problem within a competitive market context, where customer demand is predicted by a random utility choice model. Unlike prior research, which primarily focuses on simple constraints such as a cardinality constraint on the number of selected locations, we introduce routing constraints that necessitate the selection of locations in a manner that guarantees the existence of a tour visiting all chosen locations while adhering to a specified tour length upper bound. Such routing constraints find crucial applications in various real-world scenarios. The problem at hand features a non-linear objective function, resulting from the utilization of random utilities, together with complex routing constraints, making it computationally challenging. To tackle this problem, we explore three types of valid cuts, namely, outer-approximation and submodular cuts to handle the nonlinear objective function, as well as sub-tour elimination cuts to address the complex routing constraints. These lead to the development of two exact solution methods: a nested cutting plane and nested branch-and-cut algorithms, where these valid cuts are iteratively added to a master problem through two nested loops. We also prove that our nested cutting plane method always converges to optimality after a finite number of iterations. Furthermore, we develop a local search-based metaheuristic tailored for solving large-scale instances and show its pros and cons compared to exact methods. Extensive experiments are conducted on problem instances of varying sizes, demonstrating that our approach excels in terms of solution quality and computation time when compared to other baseline approaches

Federated Few-shot Learning for Cough Classification with Edge Devices

Automatically classifying cough sounds is one of the most critical tasks for the diagnosis and treatment of respiratory diseases. However, collecting a huge amount of labeled cough dataset is challenging mainly due to high laborious expenses, data scarcity, and privacy concerns. In this work, our aim is to develop a framework that can effectively perform cough classification even in situations when enormous cough data is not available, while also addressing privacy concerns. Specifically, we formulate a new problem to tackle these challenges and adopt few-shot learning and federated learning to design a novel framework, termed F2LCough, for solving the newly formulated problem. We illustrate the superiority of our method compared with other approaches on COVID-19 Thermal Face & Cough dataset, in which F2LCough achieves an average F1-Score of 86%. Our results show the feasibility of few-shot learning combined with federated learning to build a classification model of cough sounds. This new methodology is able to classify cough sounds in data-scarce situations and maintain privacy properties. The outcomes of this work can be a fundamental framework for building support systems for the detection and diagnosis of cough-related diseases.Comment: 21 pages, 5 figure

The psychological impact of the COVID-19 epidemic among healthcare workers at the grassroots level in Vietnam

The COVID-19 pandemic has significantly affected the mental health of healthcare workers (HCWs). Therefore, an immediate priority is to monitor rates of mental health issues to understand related factors and inform interventions. The main purpose of this study was to evaluate the psychological and mental health impact of COVID-19 and some related factors among HCWs at the grassroots level in Vietnam. A cross-sectional study was conducted on 675 HCWs working at health facilities through questionnaires. The finding showed the rate of HCWs who were psychologically affected by the COVID-19 pandemic was 37.2%, of which 64.1% of HCWs were afraid to tell their families about the risk of exposure to COVID-19 at work. The 31.0% of HCWs had difficulty sleeping deeply/insomnia due to the COVID-19 epidemic. No statistically significant differences were found in the psychological impact of COVID-19 between HCW groups by age, gender, seniority, and professional qualifications. Some groups of HCWs were found to have poor psychological health. Our research suggests that during the COVID-19 pandemic, HCWs working in healthcare facilities experienced an increased psychological burden; psychological interventions for those at high risk and with common mental disorders should be included to reduce this burden and protect HCWs' mental health

Knowledge of tuberculosis among physicians working at community health stations in Vietnam

Tuberculosis (TB) remains a significant health problem worldwide, including in Vietnam, where around 174,000 newly diagnosed cases and 13,200 patients died in 2018. There are challenges in the diagnostic process, treatment, and follow-up. The physicians with knowledge of TB working at commune health stations play an essential role in this struggle. The primary purpose of this study was to evaluate knowledge of TB and related factors among physicians working at community health stations in the Northern provinces of Vietnam. A cross-sectional survey was implemented on 335 physicians working at community health stations in 5 Northern provinces in Vietnam from September 2019 to October 2020. The result showed that the TB knowledge of physicians was not good. There were some severe knowledge gaps concerning at-risk groups, the main symptoms of TB, sputum tests for both diagnosis and follow-up and management of attack therapy. The TB training participants had better TB knowledge than others (aOR=1.68; 95%CI: 1.047-2.712). This study underlines the importance of clinical experience and TB training to TB knowledge. We suggest that a TB training plan is required for physicians. Our survey results could inform the process of defining the physicians who work at community health stations' role in TB management in the future

A Multiple Choices Reading Comprehension Corpus for Vietnamese Language Education

Machine reading comprehension has been an interesting and challenging task in recent years, with the purpose of extracting useful information from texts. To attain the computer ability to understand the reading text and answer relevant information, we introduce ViMMRC 2.0 - an extension of the previous ViMMRC for the task of multiple-choice reading comprehension in Vietnamese Textbooks which contain the reading articles for students from Grade 1 to Grade 12. This dataset has 699 reading passages which are prose and poems, and 5,273 questions. The questions in the new dataset are not fixed with four options as in the previous version. Moreover, the difficulty of questions is increased, which challenges the models to find the correct choice. The computer must understand the whole context of the reading passage, the question, and the content of each choice to extract the right answers. Hence, we propose the multi-stage approach that combines the multi-step attention network (MAN) with the natural language inference (NLI) task to enhance the performance of the reading comprehension model. Then, we compare the proposed methodology with the baseline BERTology models on the new dataset and the ViMMRC 1.0. Our multi-stage models achieved 58.81% by Accuracy on the test set, which is 5.34% better than the highest BERTology models. From the results of the error analysis, we found the challenge of the reading comprehension models is understanding the implicit context in texts and linking them together in order to find the correct answers. Finally, we hope our new dataset will motivate further research in enhancing the language understanding ability of computers in the Vietnamese language

On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation

Constructing a robust model that can effectively generalize to test samples under distribution shifts remains a significant challenge in the field of medical imaging. The foundational models for vision and language, pre-trained on extensive sets of natural image and text data, have emerged as a promising approach. It showcases impressive learning abilities across different tasks with the need for only a limited amount of annotated samples. While numerous techniques have focused on developing better fine-tuning strategies to adapt these models for specific domains, we instead examine their robustness to domain shifts in the medical image segmentation task. To this end, we compare the generalization performance to unseen domains of various pre-trained models after being fine-tuned on the same in-distribution dataset and show that foundation-based models enjoy better robustness than other architectures. From here, we further developed a new Bayesian uncertainty estimation for frozen models and used them as an indicator to characterize the model's performance on out-of-distribution (OOD) data, proving particularly beneficial for real-world applications. Our experiments not only reveal the limitations of current indicators like accuracy on the line or agreement on the line commonly used in natural image applications but also emphasize the promise of the introduced Bayesian uncertainty. Specifically, lower uncertainty predictions usually tend to higher out-of-distribution (OOD) performance.Comment: Advances in Neural Information Processing Systems (NeurIPS) 2023, Workshop on robustness of zero/few-shot learning in foundation model

When Intervention Becomes Imperative: A Case Report of Spontaneous Vulvar Edema During Pregnancy

Spontaneous idiopathic vulvar edema during the second trimester is a rare condition. The approach to managing this condition involves relieving symptoms, identifying underlying causes, and implementing appropriate treatment. Managing such cases during pregnancy is challenging because of concerns for potential adverse fetal outcomes. Conservative management expects the condition to be relieved spontaneously postpartum, whereas invasive treatment offers a more rapid resolution. Treatment choices are controversial because each method has its pros and cons and influences the delivery process to a certain extent. Surgical drainage becomes a viable option when patients are not responsive to medications. We report a case of spontaneous massive vulvar edema in a 22-year-old primigravida in her 23rd week of pregnancy. After ruling out other notable causes of vulvar edema, we decided to intervene using an invasive procedure because she complained of progressive symptoms and discomfort. Subsequently, the edema subsided postprocedure, and the patient experienced successful labor with no complications. This report aims to alert clinicians that drainage attempts should be considered in pregnant patients with worsening symptoms