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With the growing need to process large volumes of data, edge computing near data collection sources has become increasingly important. However, the resource constraints of edge devices require more efficient data processing techniques. Near-memory computing (NMC) presents an efficient solution, especially for data-intensive applications, by enabling processing that is both energy-efficient and har
A multi-variable adaptive controller is derived as the explicit solution to a minimax dynamic game. The minimizing player selects the control action as a function of past state measurements and inputs. The maximizing player selects disturbances and model parameters for the underlying linear time-invariant dynamics. This leads to a Bellman equation that can be solved explicitly for the case with un
We introduce Thalassa, a framework designed to convert nonlinear systems of partial differential equations (PDEs) with a time-like component into tensor programs that solve these equations. These programs can run on GPUs as well as machine learning (ML) acceleration hardware, enabling scientific computing fields such as computational fluid dynamics, astrophysics, mechanics and biology to utilize a
In order to avoid extensive machine learning models selection and optimizations, Automated Machine Learning (AutoML) has arisen as a practical and efficient way to apply machine learning to many different application areas. Data poisoning is a real threat to the accuracy of machine learning models in different settings, and it has in recent research studies been shown that the usage of AutoML can
Characteristic Mode analysis is a widely used technique in antenna design, providing insight into the fundamental electromagnetic behavior of radiating structures. In this paper, we establish fundamental bounds on the slope of characteristic mode eigenvalues and angles, demonstrating that their rate of change is subject to fundamental constraints for all possible realizations within a given design
We study dynamical optimal transport of discrete time systems (dDOT) with Lagrangian cost. The problem is approached by combining optimal control and Kantorovich duality theory. Based on the derived solution, a first order splitting algorithm is proposed for numerical implementation. While solving partial differential equations is often required in the continuous time case, a salient feature of ou
The growing use of 5G networks for critical services makes them vulnerable to Distributed Denial of Service (DDoS) attacks. While numerous Machine Learning (ML)-based approaches have been proposed, the real-world deployability of these models remains understudied. This work presents what is, based on existing literature, the first simulation-driven methodology to evaluate both the transferability
Code Language Model (code-LM)-based vulnerability detection for C/C++ faces a substantial challenge. Previous research has shown that even though it is better than any prior machine learning approach, it still struggles to generalize well, as shown by the low F1 score. Prior works treated the problem as a binary classification: either vulnerable or non-vulnerable. Looking deeper at the various vul
Histatins are a family of multifunctional, cationic histidine-rich saliva peptides. The most prominently represented are Histatin 1, Histatin 3, and Histatin 5. Despite considerable similarities in primary structure, the three members are known to display varied antimicrobial properties and healing abilities. This study aims to provide a detailed structural comparison of Histatin 1, Histatin 3, an
Extended Reality (XR) serves as a broad term that encompasses several immersive technologies, including Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR). Currently, most XR devices are connected by cables, which restrict user mobility and negatively impact the overall Quality of Experience (QoE) for users. XR devices face limitations not only in terms of connectivity but also i
Introduction: Extrauterine pregnancy (EP), represents a significant challenge in reproductive medicine, manifesting in approximately 2% of all pregnancies, primarily implanting within the fallopian tubes (95%). Surgery remains a cornerstone in the therapeutic options for ectopic pregnancies. The most common surgical approach at the moment is laparoscopy. However a relatively new surgical technique
Beijing is particularly prone to frequent wintertime haze episodes. In this study, we introduce the FLEXPART (FLEXible PARTicle dispersion model) and SOSAA (the model to Simulate the concentration of Organic vapors, Sulfuric Acid, and Aerosols) modeling system for air quality analysis and applied the newly developed modeling system to a severe pollution episode during a case study in Beijing in No
Remembering everyday events involves noticing what different experiences share and preserving the details that set them apart, yet the neural processes supporting this balance remain unclear. Here, we record EEG while participants view naturalistic movie scenes that introduce episodic events with overlapping elements. Using time-resolved representational similarity analysis, we find that these eve
We study dynamical optimal transport of discrete time systems (dDOT) with Lagrangian cost. The problem is approached by combining optimal control and Kantorovich duality theory. Based on the derived solution, a first order splitting algorithm is proposed for numerical implementation. While solving partial differential equations is often required in the continuous time case, a salient feature of ou
We investigate optimal mass transport problem of affine-nonlinear dynamical systems with input and density constraints. Three algorithms are proposed to tackle this problem, including two Uzawa-type methods and a splitting algorithm based on the Douglas-Rachford algorithm. Some preliminary simulation results are presented to demonstrate the effectiveness of our approaches.
This paper investigates the use of decentralised control architectures with heterogeneous dynamics for improving performance in large-scale systems. Our focus is on two well-known decentralised approaches; the 'predecessor following' and 'bidirectional' architectures for vehicle platooning. The former, utilising homogeneous control dynamics, is known to face exponential growth in disturbance ampli
This paper concerns control of a class of systems where multiple dynamically stable agents share a nonlinear and bounded control-interconnection. The agents are subject to a disturbance which is too large to reject with the available control action, making it impossible to stabilize all agents in their desired states. In this nonlinear setting, we consider two different anti-windup equipped propor
This letter presents and analyzes an adaptive data-driven controller that learns the optimal processing rate in a multi-unit processing network in the presence of disturbances. We formulate an optimization problem of linear cost, linear dynamics for the processing network model and an affine constraint on the dispatcher policy. A data-driven linear equation is constructed, based on which the onlin
The scattering formulation of characteristic mode decomposition is utilized to extend modal analysis to lossless scatterers breaking time-reversal symmetry. This enables characteristic modes analysis on devices containing gyrotropic or moving media. The resulting nonreciprocity introduces features not observed in reciprocal scenarios, such as asymmetric phase progression in characteristic far fiel
