The Domain Name System (DNS) is a core protocol for the Internet. It resolves mappings between Internet Protocol (IP) addresses and their corresponding Fully Qualified Domain Names (FQDNs). Since all Internet communications rely on it, DNS structuring should therefore be resilient and robust against failure to avoid any service interruption. While the research community and experienced practitioners have established best practices to this end, many worldwide DNS implementations are still prone to many types of configuration errors. In this talk, I discuss the adoption of these approaches in some countries. Also, a case study is presented considering domains in Saudi Arabia (.sa) that illustrates the value of measuring the DNS at this scale. The results are valuable to improve the DNS infrastructure in the kingdom. Lastly, I provide recommendations to improve DNS service resilience and robustness.

This talk provides an overview of current research efforts on the CF-mMIMO systems and their promising future application scenarios.

This work is geared toward the design and analysis of reliable Hyperloop communication systems. Hyperloops pose several challenges for the communication system for providing a reliable communication system while dealing with a complex structure, moving at tremendous speeds inside an evacuated tube. The adopted sealed steel-structured tube in the Hyperloop prevents signal penetration, isolating the inner world from the outside and creating a highly scattering environment for electromagnetic (EM) waves. Furthermore, the exceptionally high speed of the traveling pod results in severe Doppler shifts and frequent handovers, leading to increased transmission errors and delays. We propose a novel system configuration where wireless transmitters or access points (APs) are placed inside the tube to communicate with the moving receiver using optical fiber as a backhaul link.

In this talk, I will talk about general approaches for multiscale modeling (closely related to porous media applications). I will mainly focus on numerical approaches, where multiscale finite element basis functions are constructed and used in approximating the solution. In these approaches, macroscopic equations are formed via some variational formulations of the problem. I will discuss how these approaches are used in deriving, so called upscaling techniques and the relation to well known upscaling methods. The concepts discussed in the talk are used for linear and nonlinear problems. I will discuss some applications.

Dear Kaustians, We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023, taking place at KAUST, Building 9, from May 21 to June 1, 2023. Following the highly successful 2022 edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2023 workshop aims to build on the achievements of last year's event, which featured 28 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, and Imperial College London, among others.

My research at the University of Toronto involves designing and fabricating CMOS-compatible nano-plasmonic waveguide structures and devices for on-chip communication. By taking advantage of the absence of diffraction limits, strong modal confinement is made possible using plasmonic device architectures, laying the foundations for improved optical processes and photonic circuit integration. All-oxide structures have emerged as promising plasmonic materials that can serve as relatively low carrier density Drude metals and dielectric spacers by their electro-optic tunability and versatility. I demonstrated the facile integration of all-oxide heterointerfaces into metal–insulator–semiconductor (MIS) electro-optic structures. From there, I fabricated MIS devices that incorporated semiconductive oxide layers with oxide dielectric spacers on silicon-on-insulator (SOI) platforms, constituting graded-index coupled hybrid plasmonic waveguide (CHPW) optical modulators.

This joint workshop between KAUST and Fudan University covers a variety of topics related to optical communication and devices for visible light communication. Theoretical modeling, device engine/materials investigation and sub-system development will be discussed. These research are essential to eventually realize the goals of connecting all the unconnected, SMART systems and optical internet-of-things. The workshop will be running in hybrid mode, and presenters will be from both KAUST and Fudan University.

The theory of mean field games (MFG for short) aims to study limiting behavior of Nash equilibria of (stochastic) differential games when the number of agents tends to infinity. While in general existence of MFG Nash equilibria can be established under fairly general assumptions, uniqueness is the exception rather than the rule.

How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary.

Edge devices refer to compact hardware that performs data processing and analysis close to the data source, eliminating the need for data transmission to centralized systems for analysis. These devices are typically integrated into other equipment, such as sensors or smart appliances, and can collect and process data in real time.

I consider the vector counterpart of the classical p-Laplace and p-heat equations which are some of the building blocks for the mathematical description of non-linear plasticity, non-Newtonian fluids, and turbulent eddy viscosity models. I will discuss results of “natural regularity” and their role in the study of the well-posedness of the nonlinear pdes as well as in the theory of convergence for space-time discretisation methods. In particular, I will present the “A-approximation" method which generalises results by Necas and which reduces the problem to a family of linear ones. Coffee Time: 15:30 - 16:00

Classification is a major tool of statistics and machine learning. Several classifiers have interesting visualizations of their inner workings. Here we pursue a different goal, which is to visualize the cases being classified, either in training data or in test data. An important aspect is whether a case has been classified to its given class (label) or whether the classifier wants to assign it to a different class. This is reflected in the probability of the alternative class (PAC). A high PAC indicates label bias, i.e. the possibility that the case was mislabeled. The PAC is used to construct a silhouette plot which is similar in spirit to the silhouette plot for cluster analysis. The average silhouette width can be used to compare different classifications of the same dataset. We will also draw quasi residual plots of the PAC versus a data feature, which may lead to more insight in the data. One of these data features is how far each case lies from its given class, yielding so-called class maps. The proposed displays are constructed for discriminant analysis, k-nearest neighbors, support vector machines, CART, random forests, and neural networks. The graphical displays are illustrated and interpreted on data sets containing images, mixed features, and texts.

A multivariate dataset consists of n cases in d dimensions, and is often stored in an n by d data matrix. It is well-known that real data may contain outliers. Depending on the situation, outliers may be (a) undesirable errors which can adversely affect the data analysis, or (b) valuable nuggets of unexpected information. In statistics and data analysis the word outlier usually refers to a row of the data matrix, and the methods to detect such outliers only work when at least half the rows are clean. But often many rows have a few contaminated cell values, which may not be visible by looking at each variable (column) separately. We describe a method to detect deviating data cells in a multivariate sample which takes the correlations between the variables into account. It has no restriction on the number of clean rows, and can deal with high dimensions. Other advantages are that it provides predicted values of the outlying cells, while imputing missing values at the same time. We illustrate the method on several real data sets, where it uncovers more structure than found by purely columnwise methods or purely rowwise methods. The proposed method can help to diagnose why a certain row is outlying, e.g. in process control. It also serves as an initial step for estimating multivariate location and scatter matrices, and for cellwise robust principal component analysis.

The theory of mean field games (MFG for short) aims to study limiting behavior of Nash equilibria of (stochastic) differential games when the number of agents tends to infinity. While in general existence of MFG Nash equilibria can be established under fairly general assumptions, uniqueness is the exception rather than the rule.

How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary.

The success of deep learning in many application domains has been nothing short of dramatic. The success has brought the spotlight onto security and privacy concerns with deep learning. One of them is the threat of "model extraction": when a machine learning model is made available to customers via an inference interface, a malicious customer can use repeated queries to this interface and use the information gained to construct a surrogate model. In this talk, I will describe our work in exploring whether model extraction constitutes a realistic threat. I will also discuss possible countermeasures, focussing on deterrence mechanisms that allow for the verification of ownership of ML models.

Abstract

Effective process monitoring is crucial to ensure engineering

The theory of mean field games (MFG for short) aims to study limiting behavior of Nash equilibria of (stochastic) differential games when the number of agents tends to infinity. While in general existence of MFG Nash equilibria can be established under fairly general assumptions, uniqueness is the exception rather than the rule. For finite horizon mean field games uniqueness typically holds if the time horizon is small (or the data satisfies other smallness conditions).

Abstract

How does an elastic membrane lie on a given obstacle?

How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary.

In this talk, I will be talking about β-Ga2O3 based monolithically integrated logic circuits and flash memory devices toward extreme environment operations. Moreover, the important challenges in material and device fabrication for realizing this technology will be discussed. Finally, I will introduce the promises of β-Ga2O3 at low-temperature (~ 4 K) operation for space electronics applications.

In recent years we are witnessing the advent of service computing and cloud technologies in industrial applications, with intriguing innovations and novel compelling challenges. For instance, in the automotive, there are initiatives for consolidating Electronic Control Units (ECUs) as virtual machines on the same board. Or in the Industry 4.0 (I4.0), researchers and practitioners are dealing with the challenge of making the factory floor programmable by softwarizing hardware elements with edge-cloud native components. The talk will delve into this novel trend, discussing enabling virtualization technologies for industrial systems, including hypervisors, real-time container-based solutions, and software orchestration approaches.

The growth of digital cameras and data communication has led to an exponential increase in video production and dissemination. As a result, automatic video analysis and understanding has become a crucial research topic in the computer vision community. However, the localization problem, which involves identifying a specific event in a large volume of data, particularly in long-form videos, remains a significant challenge.

This thesis focuses on Global Navigation Satellite Systems (GNSS)-based localization and attitude determination, essential for navigation and control systems in various platforms. Carrier-phase observations from GNSS signals are more accurate than pseudo-range, but resolving integer ambiguities in carrier-phase data is challenging. The thesis proposes three attitude determination methods based on an optimized GNSS attitude model with nonlinear constraints. Additionally, a joint solution for real-time kinematic positioning and attitude determination is proposed, leveraging the correlation between GNSS data in these two problems. Riemannian optimization is applied to improve the accuracy and ambiguity resolution in both localization and attitude determination.

Abstract

The inclusion of boron in III-Nitride semiconductors is an exc