The main goal of this mini course is to discuss the state-of-the-art in understanding the phenomena of long time asympotitcs and phase transitions for a range of nonlinear Fokker-Planck equations with linear and nonlinear diffusion. They appear as natural macroscopic PDE descriptions of the collective behavior of particles such as Cucker-Smale models for consensus, the Keller Segel model for chemotaxis, and the Kuramoto model for synchronization. We will discuss the existence of phase transitions in a variety of these models using the natural free energy of the system and their interpretation as natural gradient flow structure with respect to the Wasserstein distance in probability measures. We will discuss both theoretical aspects as well as numerical schemes and simulations keeping those properties at the discrete level.

The implementation of robotic solutions to accomplish automated tasks in industrial sectors has tremendously increased the human performance capacity and enhanced the cost, quality and delivery time for different tasks and products. Having already proven their utility at the macroscale, robots also show their usefulness when we approach the infinitely small dimensions, for applications such as inspection, cell manipulation, assembly, biopsy and drug delivery. The way to the microworld is not that simple; reducing the size of robots from macro scale to micro scale cannot be accomplished by merely miniaturizing the different parts of an existing robot. The variation in the scale of physical effects with the size scale and the difficulties of fabrication and assembly at the micro scale make the macro-solutions for actuation and sensing unsuitable for microdevices. Other actuation mechanisms with deformable structures and integrated sensing solutions are more efficient at microscale and compatible with microfabrication limitations. This talk focuses on different aspects related to mobile microrobots, including limitations and challenges, actuation mechanisms, power delivery, and current works in our team for the development of mobile microrobots.

The main goal of this mini course is to discuss the state-of-the-art in understanding the phenomena of long time asympotitcs and phase transitions for a range of nonlinear Fokker-Planck equations with linear and nonlinear diffusion. They appear as natural macroscopic PDE descriptions of the collective behavior of particles such as Cucker-Smale models for consensus, the Keller Segel model for chemotaxis, and the Kuramoto model for synchronization. We will discuss the existence of phase transitions in a variety of these models using the natural free energy of the system and their interpretation as natural gradient flow structure with respect to the Wasserstein distance in probability measures. We will discuss both theoretical aspects as well as numerical schemes and simulations keeping those properties at the discrete level.

Modular robotics deals with robots that are an assemblage of smaller sized and often identical robots. The benefits of modular robots are many, chief among them being how easily they can be transported from one location to another. Moreover, their size can be adjusted according to the task at hand without requiring extensive redesign or specialization, therefore making them the object of significant research efforts.

To suit massive connectivity of machine-type communications in smart cities, this talk discusses the energy-efficient resource allocation for layered-division multiplexing (LDM) based non-orthogonal multicast and unicast transmission in cell-free massive multiple-input multiple-output (MIMO) systems, where each user equipment (UE) performs wireless information and power transfer simultaneously. Non-orthogonal multiple access (NOMA) is an emerging paradigm for the enabling of massive connectivity in 5G networks and beyond.

In this talk, I will first give an overview of the research activities in Structural and Functional Bioinformatics Group (http://sfb.kaust.edu.sa). I will then focus on our efforts on developing computational methods to tackle key open problems in Nanopore sequencing. In particular, I will introduce our recent works on developing a collection of computational methods to decode raw electrical current signal sequences into DNA sequences, to simulate raw signals of Nanopore, and to efficiently and accurately align electrical current signal sequences with DNA sequences. I will further introduce their applications in biomedicine and healthcare.

Intrinsic connectivity networks (ICNs) refer to brain functional networks that are consistently found under various conditions, during tasks or at rest. Some studies demonstrated that while some stimuli do not impact intrinsic connectivity, other stimuli actually activate intrinsic connectivity through suppression, excitation, moderation or modification. Most analyses of fMRI data use ad-hoc methods to estimate the latent structure of ICNs. Modeling the effects on ICNs has also not been fully investigated. We propose a Bayesian Intrinsic Connectivity Network (BICNet) model, an extended Bayesian dynamic sparse latent factor model, to identify the ICNs and quantify task-related effects on the ICNs. BICNet has the following advantages: (1) It simultaneously identifies the individual and group-level ICNs; (2) It robustly identifies ICNs by jointly modeling rfMRI and tfMRI; (3) Compared to ICA-based methods, it can quantify the difference of ICN amplitudes across different states; (4) The sparsity of ICNs automatically performs feature selection, instead of ad-hoc thresholding. We apply BICNet to the rfMRI and language tfMRI data from the HCP and identify several ICNs related to distinct language processing functions.

The main goal of this mini course is to discuss the state-of-the-art in understanding the phenomena of long time asympotitcs and phase transitions for a range of nonlinear Fokker-Planck equations with linear and nonlinear diffusion. They appear as natural macroscopic PDE descriptions of the collective behavior of particles such as Cucker-Smale models for consensus, the Keller Segel model for chemotaxis, and the Kuramoto model for synchronization. We will discuss the existence of phase transitions in a variety of these models using the natural free energy of the system and their interpretation as natural gradient flow structure with respect to the Wasserstein distance in probability measures. We will discuss both theoretical aspects as well as numerical schemes and simulations keeping those properties at the discrete level.

The talk will present our decade-long efforts to build an integrated data-driven modeling system to study and predict the circulation and climate of the Arabian Peninsula at all scales. Starting from a general description of the Virtual Red Sea Initiative at its achievements so far, I will then outline our ongoing research under the KAUST Centre of Excellence for NEOM to develop new tools to seamlessly project and study the environment at the urban scales of NEOM. I will in particular discuss the involved scientific opportunities and challenges in terms of computational Sciences, including our extreme computational requirements, and the handling, analysis and visualization of very large datasets.

Electromagnetic devices and systems are at the heart of technological advances revolutionizing many fields of science and engineering including energy management, biomedical electronics, communications and computing, and even environmental monitoring and civil design. Many of these systems are electrically large, their frequency of operation has a wide dynamic range, their device components are geometrically intricate with dimensions varying by orders of magnitude, and finally their optimal design requires many repetitions of characterizations with different parameters.

In this talk we consider the problem of estimating the score function (or gradient of the log-likelihood) associated to a class of partially observed diffusion processes, with discretely observed, fixed length, data and finite dimensional parameters. We construct an estimator that is unbiased with no time-discretization bias. Using a simple Girsanov change of measure method to represent the score function, our methodology can be used for a wide class of diffusion processes and requires only access to a time-discretization method such as Euler-Maruyama. Our approach is based upon a novel adaptation of the randomization schemes developed by Glynn and co-authors along with a new coupled Markov chain simulation scheme. The latter methodology is an original type of coupling of the coupled conditional particle filter. We prove that our estimator is unbiased and of finite variance. We then illustrate our methodology on several challenging statistical examples. This is a joint work with Jeremy Heng (ESSEC, Singapore) and Jeremie Houssineau (Warwick, UK)

We propose a new optimization formulation for training federated learning models. The standard formulation has the form of an empirical risk minimization problem constructed to find a single global model trained from the private data stored across all participating devices. In contrast, our formulation seeks an explicit trade-off between this traditional global model and the local models, which can be learned by each device from its own private data without any communication. Further, we develop several efficient variants of SGD (with and without partial participation and with and without variance reduction) for solving the new formulation and prove communication complexity guarantees.

Wave functional materials are artificial materials that can control wave propagation as wish. In this talk, I will give a brief review on the progress of wave functional materials and reveal the secret behind the engineering of these materials to achieve desired properties. In particular, I will focus on our contributions on metamaterials and metasurfaces. I will introduce the development of effective medium, a powerful tool in modeling wave functional materials, followed by some illustrative examples demonstrating the intriguing properties, such as redirection, emission rate enhancement, wave steering and cloaking.

The mini-course is an introduction to intrinsic scaling, a powerful method in the analysis of degenerate and singular parabolic PDEs. The local Hölder continuity of bounded weak solutions will be derived from scratch for the model case of the degenerate p-Laplace equation. Our approach is entirely self-contained and focused on the essence of the method, leaving aside technical refinements needed to deal with more general equations.

In this talk, we start by introducing optimization and interesting optimization applications. We review some optimization formulations and focus on applications studied in our research, such as energy systems, and trajectory planning of autonomous underwater vehicles. After the introduction, we address the self-scheduling and market involvement of a virtual power plant using adaptive robust optimization under uncertainty in the wind speed and electricity prices.

Abstract

We develop a computational framework for risk mitigation in high population density event

Living in the booming age of information, we have to rely on powerful information retrieval tools to seek the unique piece of desired knowledge from such a big data world, like using personalized search engines and recommendation systems. In this thesis, we aim at advancing the development of the methodologies and principles of mining heterogeneous information networks through learning entity relations from a pairwise learning to rank optimization perspective. More specifically we first show the connections of different relation learning objectives modified from different ranking metrics including both pairwise and list-wise objectives. We prove that most of popular ranking metrics can be optimized in the same lower bound. Secondly, we propose the class-imbalance problem imposed by entity relation comparison in ranking objectives, and prove that class-imbalance problems can lead to frequency clustering and gradient vanishment problems. As a response, we indicate out that developing a fast adaptive sampling method is very essential to boost the pairwise ranking model. To model the entity dynamic dependency, we propose to unify the individual-level interaction and union-level interactions, and result in a multi-order attentive ranking model to improve the preference inference from multiple views.

The mini-course is an introduction to intrinsic scaling, a powerful method in the analysis of degenerate and singular parabolic PDEs. The local Hölder continuity of bounded weak solutions will be derived from scratch for the model case of the degenerate p-Laplace equation. Our approach is entirely self-contained and focused on the essence of the method, leaving aside technical refinements needed to deal with more general equations.

In this webinar, we describe the common terminology for these two communities. First, the traffic management system architecture, requirements, terminology, and services are discussed. A quick overview of existing technologies that can be useful for aerial deconfliction. Next, the focus will shift to wireless technologies used for the tactical (while flying) deconfliction: ADS-B, p2p LoRa, WiFi, FLARM.

Advances in power electronics have enabled many renewable energy applications. Wind energy harnessing is very promising and offshore farm installations have grown considerably in the past years. In this thesis, we introduce a novel power converter solution for the parallel connection of high-power offshore wind turbines, suitable for HVDC collection and transmission. For the parallel operation of energy sources in an HVDC grid, DC link voltage control is required. The proposed system is based on a full-power rated uncontrolled diode bridge rectifier in series with a partially-rated fully-controlled thyristor bridge rectifier. The thyristor bridge acts as a voltage regulator to ensure the flow of the desired current through each branch, where a reactor is placed in series for filtering of the DC current. AC filters are installed on the machine side to mitigate harmonic content. The mathematical modeling of the system is derived and the control design procedure is discussed. Guidelines for equipment and device specifications are presented. The concept is validated through simulation, and an experimental framework for testing of the system is suggested.

In my thesis, I consider the system of thermoelasticity endowed with polyconvex energy. After presenting the equations in their mathematical and physical context, I embed the equations of polyconvex thermoviscoelasticity into an augmented, symmetrizable, hyperbolic system which possesses a convex entropy. This allows to prove many important stability results, such as convergence from thermoviscoelasticity (with Newtonian viscosity and Fourier heat conduction) to smooth solutions of the system of adiabatic thermoelasticity, and convergence from thermoviscoelasticity to smooth solutions of thermoelasticity in the zero-viscosity limit. In addition, I establish a weak-strong uniqueness result in the class of entropy weak solutions and in a suitable class of measure-valued solutions, defined by means of generalized Young measures that describe both oscillatory and concentration effects. Also, I construct a variational scheme for isentropic processes of adiabatic polyconvex thermoelasticity: I establish existence of minimizers which converge to a measure-valued solution that dissipates the total energy, while the scheme converges when the limiting solution is smooth.

This thesis presents a set of quantile analysis methods for multivariate data and multivariate functional data, with an emphasis on environmental applications, and consists of four significant contributions.

Visualization and visual computing use computer-supported, interactive, visual representations of (abstract) data to amplify cognition. In recent years data complexity concerning volume, veracity, velocity, and variety has increased considerably. This is due to new data sources as well as the availability of uncertainty, error, and tolerance information.

The Internet of Things (IoT) is a foundational building block for the upcoming information revolution and imminent smart-world era. Particularly, the IoT bridges the cyber domain to everything and anything within our physical world which enables unprecedented ubiquitous monitoring, connectivity, and smart control. In this Ph.D. defense, we present Unmanned Aerial Vehicles (UAVs) enabled IoT network designs for enhanced estimation, detection, and connectivity. The utilization of UAVs can offer an extra level of flexibility which results in more advanced and efficient connectivity and data aggregation for the IoT devices.