Waves, damped wave and observation

We consider the wave equation in a bounded domain (eventually convex). Two kinds of inequality are described when occurs trapped ray. Applications to control theory are given. First, we link such kind of estimate with the damped wave equation and its decay rate. Next, we describe the design of an approximate control function by an iterative time reversal method.Comment: 6 figures, French-Chinese Summer Institute on Applied Mathematics, references are update

Trustworthy machine learning through the lens of privacy and security

Nowadays, machine learning (ML) becomes ubiquitous and it is transforming society. However, there are still many incidents caused by ML-based systems when ML is deployed in real-world scenarios. Therefore, to allow wide adoption of ML in the real world, especially in critical applications such as healthcare, finance, etc., it is crucial to develop ML models that are not only accurate but also trustworthy (e.g., explainable, privacy-preserving, secure, and robust). Achieving trustworthy ML with different machine learning paradigms (e.g., deep learning, centralized learning, federated learning, etc.), and application domains (e.g., computer vision, natural language, human study, malware systems, etc.) is challenging, given the complicated trade-off among utility, scalability, privacy, explainability, and security. To bring trustworthy ML to real-world adoption with the trust of communities, this study makes a contribution of introducing a series of novel privacy-preserving mechanisms in which the trade-off between model utility and trustworthiness is optimized in different application domains, including natural language models, federated learning with human and mobile sensing applications, image classification, and explainable AI. The proposed mechanisms reach deployment levels of commercialized systems in real-world trials while providing trustworthiness with marginal utility drops and rigorous theoretical guarantees. The developed solutions enable safe, efficient, and practical analyses of rich and diverse user-generated data in many application domains

Effectiveness of Teaching About Preventing and Detecting Complications of Hand, Foot, and Mouth Disease at My Phuoc Hospital

Hand, foot, and mouth disease (HFMD) is an infectious illness that affects millions of people, especially children under five years of age. The social impact of severe HFMD to the health community, family members, and children is also of big concern. In the south of Vietnam, Binh Duong province is located in the area affected by HFMD. Unfortunately, caregivers have little knowledge to prevent and detect HFMD severity in children. The research question asked how effective teaching about HFMD was to improve knowledge of caregivers for detecting HFMD severity in children under five years of age at My Phuoc Hospital in Binh Duong Province, Vietnam. Therefore, the purpose of this quasi-experimental study was to determine the effectiveness of HFMD teaching to improve knowledge for preventing and detecting severe HFMD in children less than five years of age. In the analysis section, data were extracted from 52 caregivers caring for children under five years of age before and after a health education session. The results showed the intervention measures improved knowledge about HFMD in participants after the intervention. In the pretest, 34.6% caregiver had levels of poor and very poor understanding, 38.5% had moderate levels, and 26.9% had good and very good knowledge levels. This finding compared with 86.5% caregivers who achieved very good knowledge levels after intervention