Our talk will consider a cloud-enabled system, and will investigate ways of managing its performance through two particular interference mitigation techniques. In the first part of the talk, inspired by the classical information theoretical results on characterizing the interference channel achievable rate region, we will consider rate-splitting (RS) and common message decoding (CMD) schemes. The talk will shed light on ways of tackling two of the underlying optimization problems in this realm.

In this talk we present the derivation of a new Boussinesq-type system to describe the propagation of long waves of small amplitude in a basin with mildly varying bottom topography. We prove the existence and uniqueness of weak solutions for maximal times that do not depend on the amplitude of the waves. We then present the numerical solution of the new system using Galerkin finite element methods and prove the convergence of the semidiscrete solution to the exact solution. The system appears to describe well water waves even in benchmark experiments that involve also general bathymetries.

The Machine Learning Hub Seminar Series presents “Optimization and Learning in Computational Imaging” by Dr. Wolfgang Heidrich, Professor in Computer Science at KAUST. He leads the AI Initiative and is the Director of the KAUST Visual Computing Center. Computational imaging systems are based on the joint design of optics and associated image reconstruction algorithms. Historically, many such systems have employed simple transform-based reconstruction methods. Modern optimization methods and priors can drastically improve the reconstruction quality in computational imaging systems. Furthermore, learning-based methods can be used to design the optics along with the reconstruction method, yielding truly end-to-end learned imaging systems, blurring the boundary between imaging hardware and software.

In this talk, I will give an overview of my research which focuses on the development of innovative statistical methods and interactive visualization applications for geospatial data analysis and health surveillance. I will illustrate some of my projects in the following areas: 1. Development of new statistical methodology; 2. Development of open-source statistical software such as the R packages; 3. Health surveillance projects. Finally, I will describe my future research on innovation in data acquisition and visualization, precision disease mapping, and digital health surveillance, and how it can inform policymaking and improve population health globally.

In this talk, we review some recent advances in the analysis and design of algebraic flux correction (AFC) schemes for hyperbolic problems. In contrast to most variational stabilization techniques, AFC approaches modify the standard Galerkin discretization in a way which provably guarantees the validity of discrete maximum principles for scalar conservation laws and invariant domain preservation for hyperbolic systems. The corresponding inequality constraints are enforced by adding diffusive fluxes, and bound-preserving antidiffusive corrections are performed to obtain nonlinear high-order approximations. After introducing the AFC methodology and the underlying theoretical framework in the context of continuous piecewise-linear finite element discretizations, we present some of the limiting techniques that we use in high-resolution AFC schemes. This presentation is based on joint work with Dr. Manuel Quezada de Luna (KAUST) and other collaborators.

In this talk, I will introduce several ongoing projects at the networking lab in KAUST. I will start by highlighting our research profile along with our mission. Then, I will walk you through our projects and contributions in the domains of the internet of things, visible light communication, underwater communication, and future 6G networks. I will focus on the challenges facing each project, highlight our solution methodology, and discuss some performance evaluation results. I will focus on our work on Aqua-Fi, which aims at bringing the Internet into the underwater environment. I will also focus on our recent project on the communication via breath.

The potential of indirect execution attacks – e.g. Return-Oriented-Programming and Transient-Execution based Side-channels – threaten all modern computing platforms. Standard security policies are unable to eliminate these threats. What is the role of hardware in mitigating these threats? Why are the latest processor designs no longer proactively eliminating threats? In this talk, we will explore these questions and reconsider the role of hardware in securing systems. I will present Record-and-Replay as a fundamental solution that can enable a hardware-software co-design that can strengthen the security of modern computing platforms.

In this talk, I will address these questions with a focus on smart and multifunctional nanomaterial-based memory devices than can sense different physical qualities of the environment (Memsors) in addition to pressure-driven microfluidic logic gates which can process and analyze non-electronic media in an attempt to explore, enable and empower a wider range of IoT applications.

The emerging need to improve the quality of life for elderly and disabled individuals who rely on wheelchairs for mobility is our motivation for this work. Research on robotics wheelchair covers broad range from motion control, how to control the wheelchair movement, to complete autonomy.

We present a result on the convergence of weight normalized training of artificial neural networks. In the analysis, we consider over-parameterized 2-layer networks with rectified linear units (ReLUs) initialized at random and trained with batch gradient descent and a fixed step size. The proof builds on recent theoretical works that bound the trajectory of parameters from their initialization and monitor the network predictions via the evolution of a ''neural tangent kernel'' (Jacot et al. 2018). We discover that training with weight normalization decomposes such a kernel via the so called ''length-direction decoupling''. This in turn leads to two convergence regimes. From the modified convergence we make a few curious observations including a natural form of ''lazy training'' where the direction of each weight vector remains stationary.

Abstract

Our lives and society are digitized from every perspective by computers, smart devices an

In this talk we will  see patterned ferromagnetic films control of film aspect ratio which changing film demagnetizing fields increasing the ferromagnetic resonance (FMR) frequencies, Physical separation of two domain dynamics viz. domain wall motion and magnetization rotation incorporated with spiral inductors. Measurement results achieved showing 70% boost in inductance at frequencies between 2 GHz - 6 GHz.

In this thesis, a variety of applications in computer vision and graphics of inverse problems using tomographic imaging modalities will be presented: (i) The first application focuses on the CT reconstruction with a specific emphasis on recovering thin 1D and 2D manifolds embedded in 3D volumes. (ii) The second application is about space-time tomography (iii) Base on the second application, the third one is aiming to improve the tomographic reconstruction of time-varying geometries undergoing faster, non-periodic deformations, by a warp-and-project strategy. Finally, with a physically plausible divergence-free prior for motion estimation, as  well as a novel  view synthesis technique,  we present applications to dynamic fluid imaging which further demonstrates the flexibility of our optimization frameworks

The emergence of “fake news” along with sophisticated techniques using machine learning to create realistic looking media such as deepfakes, has led to a renewed interest in digital media forensics. In this talk, Professor Nasir Memon will broadly discuss how media is generated and manipulations have been traditionally detected. He will then look at new approaches using machine learning for creating media that are leading us to a world where images and video cannot be believed any more as they can evade traditional detection techniques. Professor Memon will end by discussing approaches that are being developed to return integrity and trust in digital media.

Contrary to the prevailing belief, we show that user authentication based on biometrics is vulnerable to dictionary attacks. We show the problem is particularly significant for partial prints used in smartphones and increasingly adopted for authentication tasks ranging from unlocking the devices screen up to payment authorization. We also show that speaker verification systems are also vulnerable to dictionary attacks. We then discuss ways to mitigate such attacks.

In this talk we discuss approaches to low power design for advanced communication and computing platforms. Specifically, we present the concept of cognitive power management, where contrary to common approaches that assume a 100% error free hardware, the algorithm is made aware of the statistical error performance of the underlying hardware platform. By accounting for hardware errors at the system level, the explorable power management design space is significantly expanded, leading to novel power saving schemes that deliver expected application performance at much lower power consumption.  Sample case studies including LTE system design and in-memory computing platforms will be presented and discussed.

Africa has one of the highest inequality factors in the world, reflecting its developed and developing nature. As such it suffers from the traditional “digital divide”, with low internet connectivity reach in rural areas, which is both economic and geographic in nature. In this talk we will summarize recent proposals to bridge the digital divide and offer a South African perspective on the problem. We will cover active research in South Africa on the topic and speculate what the network future in Africa might be.

Mini Course Part 4 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

Mini Course Part 3 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020.

Mini Course Part 2 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

Abstract

Nowadays, the devices in the Internet of Things have been widely used.

Mini Course Part 1 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

Abstract

The model under consideration in this work describes a vibrating structure of an interfac

The proposed method, called PREMA, leverages low-rank tensor factorization tools to provide recovery guarantees under certain conditions. PREMA is flexible in the sense that it can perform the disaggregation task on data that have missing entries, i.e., partially observed. The proposed method considers challenging scenarios: i) the available views of the data are aggregated in two dimensions, i.e., double aggregation, and ii) the aggregation patterns are unknown. Experiments on real data from different domains, i.e., sales data from retail companies, crime counts, and weather observations, are presented to showcase the effectiveness of PREMA.