The report describes the discussions from the Workshop on Mathematical Opportunities in Digital Twins (MATH-DT) from December 11-13, 2023, George Mason University. It illustrates that foundational Mathematical advances are required for Digital Twins (DTs) that are different from traditional approaches. A traditional model, in biology, physics, engineering or medicine, starts with a generic physical law (e.g., equations) and is often a simplification of reality. A DT starts with a specific ecosystem, object or person (e.g., personalized care) representing reality, requiring multi -scale, -physics modeling and coupling. Thus, these processes begin at opposite ends of the simulation and modeling pipeline, requiring different reliability criteria and uncertainty assessments. Additionally, unlike existing approaches, a DT assists humans to make decisions for the physical system, which (via sensors) in turn feeds data into the DT, and operates for the life of the physical system. While some of the foundational mathematical research can be done without a specific application context, one must also keep specific applications in mind for DTs. E.g., modeling a bridge or a biological system (a patient), or a socio-technical system (a city) is very different. The models range from differential equations (deterministic/uncertain) in engineering, to stochastic in biology, including agent-based. These are multi-scale hybrid models or large scale (multi-objective) optimization problems under uncertainty. There are no universal models or approaches. For e.g., Kalman filters for forecasting might work in engineering, but can fail in biomedical domain. Ad hoc studies, with limited systematic work, have shown that AI/ML methods can fail for simple engineering systems and can work well for biomedical problems. A list of `Mathematical Opportunities and Challenges' concludes the report.

Trustworthiness is a core tenet of medicine. The patient-physician relationship is evolving from a dyad to a broader ecosystem of healthcare. With the emergence of artificial intelligence (AI) in medicine, the elements of trust must be revisited. We envision a roadmap for the establishment of trustworthy AI ecosystems in nuclear medicine. In this report, AI is contextualized in the history of technological revolutions. Opportunities for AI applications in nuclear medicine related to diagnosis, therapy and workflow efficiency, as well as emerging challenges and critical responsibilities are discussed. Establishing and maintaining leadership in AI requires a concerted effort to promote the rational and safe deployment of this innovative technology by engaging patients, nuclear medicine physicians, scientists, technologists, referring providers, among other stakeholders, while protecting our patients and society. This strategic plan is prepared by the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging (SNMMI).

South Korea 13.12 Sri Lanka 13.13 Thailand 13.14 Taiwan 13.15 Rest of Asia-Pacific 14 Competitive Landscape 14.1 Competitive Quadrant 14.2 Market Share Analysis 14.3 Strategic Initiatives 14.3.1 M&A and Investments 14.3.2

Machine Learning: It is the process by which a machine can become intelligent and behave like a human for the assigned tasks.

Wearable sensor-based human activity recognition (HAR) has emerged as a principal research area and is utilized in a variety of applications. Recently, deep learning-based methods have achieved significant improvement in the HAR field with the development of human-computer interaction applications. However, they are limited to operating in a local neighborhood in the process of a standard convolution neural network, and correlations between different sensors on body positions are ignored. In addition, they still face significant challenging problems with performance degradation due to large gaps in the distribution of training and test data, and behavioral differences between subjects. In this work, we propose a novel Transformer-based Adversarial learning framework for human activity recognition using wearable sensors via Self-KnowledgE Distillation (TASKED), that accounts for individual sensor orientations and spatial and temporal features. The proposed method is capable of learning cross-domain embedding feature representations from multiple subjects datasets using adversarial learning and the maximum mean discrepancy (MMD) regularization to align the data distribution over multiple domains. In the proposed method, we adopt the teacher-free self-knowledge distillation to improve the stability of the training procedure and the performance of human activity recognition. Experimental results show that TASKED not only outperforms state-of-the-art methods on the four real-world public HAR datasets (alone or combined) but also improves the subject generalization effectively.

While there have been a number of remarkable breakthroughs in machine learning (ML), much of the focus has been placed on model development. However, to truly realize the potential of machine learning in real-world settings, additional aspects must be considered across the ML pipeline. Data-centric AI is emerging as a unifying paradigm that could enable such reliable end-to-end pipelines. However, this remains a nascent area with no standardized framework to guide practitioners to the necessary data-centric considerations or to communicate the design of data-centric driven ML systems. To address this gap, we propose DC-Check, an actionable checklist-style framework to elicit data-centric considerations at different stages of the ML pipeline: Data, Training, Testing, and Deployment. This data-centric lens on development aims to promote thoughtfulness and transparency prior to system development. Additionally, we highlight specific data-centric AI challenges and research opportunities. DC-Check is aimed at both practitioners and researchers to guide day-to-day development. As such, to easily engage with and use DC-Check and associated resources, we provide a DC-Check companion website (https://www.vanderschaar-lab.com/dc-check/). The website will also serve as an updated resource as methods and tooling evolve over time.

We have the vision of a world where each day is safer and more secure than the one before. These aren't easy goals to accomplish – but we're not here for easy. We are a company built on the foundation of challenging and disrupting the way things are done, and we're looking for innovators who are as committed to shaping the future of cybersecurity as we are. Disruption is at the core of our technology and on our way of work to meet the needs of our employees now and in the future through FLEXWORK, our approach to how we work. And because it FLEXes around each individual employee based on their individual choices, employees are empowered to push boundaries and help us all evolve, together.

The concept of artificial intelligence is still being accepted by the vast majority of our society. In fact, not many people really understand the depths of this technology that is altering so many things. AI is a reactive way of putting the intelligence of the human mind into a machine, in hopes of having these machines to hold the same capabilities that the human brain has. This technology has been helping the world grow in so many different ways. Advancements in sectors such as health care, retail shops, crime investigations, and data analysis in businesses and science have shaped the 21st century into the tech world it has become.

There is increasing awareness that the greatest problems with artificial intelligence are not primarily technical, but rather how to achieve value from the technology. This was a growing problem even in the booming economy of the last several years, but a much more important issue in the current pandemic-driven recessionary economic climate. Older AI technologies like natural language processing, and newer ones like deep learning, work well for the most part and are capable of providing considerable value to organizations that implement them. The challenges are with large-scale implementation and deployment of AI, which are necessary to achieve value. There is substantial evidence of this in surveys.

Market Drivers: Rising complexity and diversity of mobile networks is driving the market of deep learning. These increasing complexity has made the managing of the network difficult.