This lecture will review research trends of III-V compound semiconductor ultra-high efficiency photovoltaic (PV) cells based on multiple junctions and quantum well/dot approaches. It will also introduce field trials of highly efficient green hydrogen production through the direct connection of electrolyzers and concentrator PV modules with the above-mentioned compound semiconductor solar cells built-in.

The Internet of Bodies (IoBs) is an imminent extension to the vast Internet of Things domain, where wearable, ingestible, injectable, and implantable smart objects form a network in, on, and around the human body.

The classical obstacle problem consists of finding the equilibrium position of an elastic membrane whose boundary is held fixed and which is constrained to lie above a given obstacle. By classical results of Caffarelli, the free boundary is smooth outside a set of singular points. Explicit examples show that the singular set could be, in general, as large as the regular set. In a recent paper with Ros-Oton and Serra we show that, generically, the singular set has codimension 3 inside the free boundary, solving a conjecture of Schaeffer in dimension n ≤ 4. The aim of this talk is to give an overview of these results.

In this paper, we propose a new methodological framework for performing extreme quantile regression using artificial neural networks, which are able to capture complex non-linear relationships and scale well to high-dimensional data.

Opportunities and Challenges in Free Space Optics:

• Inter-Satellite Communications,

Abstract

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

Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modelling and computational simulation of those systems is very difficult, in particular thinking of detailed models which are based on first principles. The difficulties start with geometric modelling which needs to extract basic structures from highly complex and variable phenotypes, on the other hand also has to take the statistic variability into account.

This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.

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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,

The Wave Map system describes the evolution of waves constrained on a (Riemannian)  manifold. For the 2 + 1 dimensional problem, when the target manifold is a sphere, the solution collapses in finite time. The Analysis is due to the pioneering work of Merle, Paphael and Rodnianski. Motivated by their work I will present a somewhat novel approach of the collapsing mechanism which is based on a view of the equations as a nonlinear gauge system. This is joint work with Dan Geba.

Complexity studies physical processes that are generally unpredictable or difficult to predict and depend on many degrees of freedom. In this talk, I will summarize my group's recent research, discussing present results and future challenges of Applied complexity both as a science and engineering.

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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.

In this presentation, we will review past, present, and future eras of quantum computing - such as Near Intermediate Scale Quantum (NISQ) and Utility Scale Quantum (USQ) eras – and cover their respective overarching aspirations and limitations. Once properly situated, we can identify the different roles that academic and industrial players are taking to drive this emerging technology and circumvent current systems' limitations.

In this thesis, we introduce a novel concept of metasurface optical accelerators for machine learning with the corresponding end-to-end optimization framework that is robust to fabrication intolerance and can simultaneously optimize in tens of millions of degrees of freedom. The core of this technology is universal approximators, a single surface of optical nanoresonators mathematically equivalent to a single layer of an artificial neural network (ANN).

Sobolev spaces are centrally important objects in PDE theory. Consequently, to understand how deep neural networks can be used to numerically solve PDEs a necessary first step is to determine now efficiently they can approximate Sobolev functions. In this talk we consider this problem for deep ReLU neural networks, which are the most important class of neural networks in practical applications.

Infinity-harmonic functions have recently found application in Semi-Supervised Learning, in the context of the so-called Lipschitz Learning. With this application in mind, we will discuss the Lipschitz extension problem, its solution via MacShane-Whitney extensions and its several drawbacks, leading to the notion of AMLE (Absolutely Minimising Lipschitz Extension).

In this talk, I will first give a convergence analysis of gradient descent (GD) method for training neural networks by relating them with finite element method. I will then present some acceleration techniques for GD method and also give some alternative training algorithms

A multi-agent system consists of individual agents sharing information and coordinating for collective decision making. The study of multi-agent decision making has important implications in conceiving networked engineering systems - a team of mobile robots or a fleet of drones - that can effectively coordinate to carry out assigned missions. Modeling such system as feedback interconnections of many smaller units allows us to examine its long-term behavior using analytical tools from feedback control theory, such as Lyapunov stability and bifurcation analysis. In this presentation, we discuss how such tools can be used to predict asymptotic behavior of the agents' decision making process and also to design computational models of the decision making process.

Statistical analysis for the purpose of prediction is preferably accompanied by uncertainty quantification, often in the form of prediction intervals. Deep learning approaches have been extensively shown to provide accurate point predictions in many applications.

The talk will give an overview of recent results for models of collective behavior governed by functional differential equations. It will focus on models of interacting agents with applications in biology (flocking, swarming), social sciences (opinion formation) and engineering (swarm robotics), where latency (delay) plays a significant role.