Coffee Time: 15:30 - 16:00. Kernel matrices can be found in computational physics, chemistry, statistics, and machine learning. Fast algorithms for matrix-vector multiplication for kernel matrices have been developed, and is a subject of continuing interest, including here at KAUST. One also often needs fast algorithms to solve systems of equations involving large kernel matrices. Fast direct methods can sometimes be used, for example, when the physical problem is 2-dimensional. In this talk, we address preconditioning for the iterative solution of kernel matrix systems. The spectrum of a kernel matrix significantly depends on the parameters of the kernel function used to define the kernel matrix, e.g., a length scale.

Opportunities and Challenges in Free Space Optics:

• Inter-Satellite Communications,

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Flash floods are one of the most common natural disasters worldwide, causing thousands of

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The rampage of incessant cyber attacks have caused the disclosure of billions of users’ p

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 Deep Neural Networks (DNNs) are increasingly being used to control physical/mechani

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Cyber-physical system design involves heterogeneous components for sensing, control, actu

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Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon,

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We will discuss Integrated Circuit (IC) security and trust threats and corresponding coun

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Mobile autonomous devices use sophisticated algorithms to automatically navigate diverse

The workshop aims to bring together experts to present their latest research efforts related to Embedded and Cyber Connected Systems architectures and platforms that can scale efficiently, as well as operate securely and resiliently to provide the necessary resources demanded by current and future network applications.

Available as dedicated hardware components into several mobile and server-grade processors, and recently included in infrastructure-as-a-service commercial offerings by several cloud providers, TEEs allow applications with high privacy and confidentiality demands to be deployed and executed over untrusted environments, shielding data and code from compromised systems or powerful attackers. After an  introduction to basic concepts for TEEs, I will survey some of our most recent contributions exploiting TEEs, including as defensive tools in the context of Federated Learning, as support to build secure cache systems for edge networks, as protection mechanisms in a med-tech/e-health context,  shielding novel environments (ie, WebAssembly), and more. Finally, I will highlight some of the lessons learned and offer open perspectives, hopefully useful and inspirational to future researchers and practitioners entering this exciting area of research.