We consider the widely used continuous $Q_{k+1}-Q_k$ quadrilateral or hexahedral Taylor-Hood elements for the finite element discretization of the Stokes and generalized Stokes systems in two and three spatial dimensions.

Wave propagation and scattering problems are of huge importance in many applications in science and engineering - e.g., in seismic and medical imaging and more generally in acoustics and electromagnetics.

A subtle difference in a diffusion model can lead to an opposite conclusion. We need to understand how each component involved in the diffusion phenomenon contributes to the diffusion model. In this talk, we will discuss how nonconstant persistence and permeability play a role in the diffusion phenomenon.

Liquid metal(LM)-based electronics have the potential to shape the future of intelligent systems, soft robotics, and wearable technologies by leveraging their sensing, actuation, and computational capabilities. This talk will discuss methods to harness the unique properties of liquid metal for applications in wearable sensors, soft actuators, and reconfigurable electronic platforms.

The term doubly nonlinear refers to the fact that the diffusion part depends nonlinearly both on the gradient and the solution itself. Such equations describe several physical phenomena and were introduced by Lions and Kalashnikov. These equations have an intrinsic mathematical interest because they represent a natural bridge between the more natural generalizations of the heat equation: the p-Laplacian and the porous medium equation.

As many types of IoT devices worm their way into numerous settings and many aspects of our daily lives, awareness of their presence and functionality becomes a source of major concern. Hidden IoT devices can snoop (via sensing) on nearby unsuspecting users, and impact the environment where unaware users are present, via actuation.

In the first part of the talk, we introduce Ordered Dropout, a mechanism that achieves an ordered, nested representation of knowledge in deep neural networks (DNNs).

Recently, InGaN-based blue, green, and red (RGB) micro-LEDs have garnered significant attention and interest due to their exceptional features such as high contrast, intense brightness, excellent energy efficiency, and long device lifetimes, positioning them as strong contenders as the next-generation display technology.

Abstract

The Finite Element Tearing and Interconnecting (FETI) methodology is a family of domain d

Power system resilience is defined as the ability of power grids to anticipate, withstand, adapt and recover from high-impact, low-probability (HILP) events. There are long-term and short-term measures that system operators can apply to enhance resilience. Long-term measures include infrastructure hardening and resilient planning, while short-term operational measures are applied in the pre-event, during-event and post-event phases. Microgrids (MGs) can effectively enhance resilience for both transmission and distribution systems due to their ability to operate in a controlled, coordinated way when connected to the primary power grid and in islanded mode. In this presentation, MG-based operational measures for resilience enhancement will be presented, including MG-based resilient operational planning, the systematic formation of MGs after natural disasters hit the system and the role of MGs in power system restoration.

In reaction to the waning benefit of transistor scaling and the increasing demands on computing power, specialized accelerators have drawn significant attention from both academics and industry because of their orders-of-magnitude performance improvement and energy efficiency.

The rapid growth in mobile users and high-bandwidth applications is creating unprecedented challenges and opportunities in telecommunications. As we address the need for higher data rates, the mm-Wave 5G bands have been expanded to accommodate this demand.

Partial differential equations are a mathematical tool widely used to model phenomena in several different fields. A stochastic partial differential equation (SPDE) introduces random forcing to take the nature of real-world observations.

In this short course, we will introduce some elements in deriving the hp a posteriori error estimate for a high-order unfitted finite element method for elliptic interface problems. The key ingredient is an hp domain inverse estimate, which allows us to prove a sharp lower bound of the hp a posteriori error estimator.

Coffee time: 15:30–16:00. We consider high-order unfitted finite element methods on Cartesian meshes with hanging nodes for elliptic interface problems, which release the work of body-fitted mesh generation and allow us to design adaptive finite element methods for solving curved geometric singularities.

In the last decade reinforcement learning has demonstrated tremendous progress in terms of being a model-free control paradigm for decision making in complex systems with uncertainty and partial observability, thus making it a candidate technology for demand response with a promise of true scalability.

We study theoretical problems of fault diagnosis in circuits and switching networks, which are among the most fundamental models for computing Boolean functions.

Ultrawide bandgap (UWBG) semiconductors including AlN, Ga2O3, c-BN, diamond have attracted enormous interests. They offer markedly larger figures of merits for power and RF applications than other known semiconductors.

The talk will introduce the 5G advanced standard vision for vertical industries and relate it to the latest prototyping effort released by the Qualcomm Research teams.

Deep neural networks (DNNs) are revolutionizing computing, necessitating an integrated approach across the computing stack to optimize efficiency. In this talk, I will explore the frontier of DNN optimization, spanning algorithms, software, and hardware. We'll start with hardware-aware neural architecture search, demonstrating how tailoring DNN architectures to specific hardware can drastically enhance performance.

In this talk, I will present an innovative framework we developed to address these limitations and accurately predict pedestrian crossing intentions. At the core of the framework, we implement an image enhancement pipeline to enable the detection and rectification of various defects that may arise during unfavorable weather conditions. Following this, we employ a transformer-based network with a self-attention mechanism to predict the crossing intentions of pedestrians. This pipeline enhances the model's robustness and accuracy in classification tasks. We assessed our framework using the famous JAAD dataset. Performance metrics indicate that our model achieves state-of-the-art results while ensuring significantly low inference times.

In my lecture, I will share personal insights on transitioning from academia to management consulting and creating startups. I will discuss how to assess if a career in management consulting suits your goals and skills, and provide practical advice on interviewing successfully with consulting firms.

The rapid growth of Artificial Intelligence (AI) and Deep Learning mirrors an infectious phenomenon. While AI systems promise diverse applications and benefits, they bear substantial security and privacy risks. Indeed, AI represents a goldmine for the security and privacy research domain.

In this talk, we consider Bayesian parameter inference associated to a class of partially observed stochastic differential equations (SDE) driven by jump processes. Such type of models can be routinely found in applications, of which we focus on the case of neuroscience.