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In this paper, we unify two already published results on state feedback H-infinity optimality. Previously, optimality has been shown for a particular controller structure in the case that the open-loop state matrix is symmetric, as well as in the case that the closed-loop system is internally positive. By contrast, the main result of the present paper gives optimality based on neither of these two

Balcinrenone (AZD9977) is a selective mineralocorticoid receptor modulator in development in combination with dapagliflozin for treatment of heart failure with impaired kidney function and chronic kidney disease. A prespecified concentration–QT analysis was performed based on data from a phase I single ascending dose study prospectively designed as a thorough QT study substitute. Oral single doses

Exceptional are the instances where explicit solutions to optimal control problems are obtainable. Of particular interest are the explicit solutions derived for minimax problems, as they provide a framework for addressing challenges involving adversarial conditions and uncertainties. This thesis presents explicit solutions to a novel class of minimax optimal control problems for positive linear sy

This paper gives an overview of the approach of working methods at the Aedas R&D Computational Design and Research [CDR] Group. It first contextualizes research in architectural practice and tries to propose an explanation for the difficulties in implementing it; then explains the evolution of the groups' computing approach from bespoke to heuristic sets of lightweight applications. It conclud

When it comes to innovation, economic growth, affluence and international attractiveness, there are currently few places in the world that can compare with the San Francisco Bay Area. However, new megatrends such as Sustainability, can challenge its attractiveness. Scholars talk about the “Nordic approach”.In the geographical area of Southern Scandinavia, ‘The Strait Area’, the focus is on Sustain

We study an information design problem to reduce congestion in transportation networks. In presence of an uncertain network state, the central planner may sends private signals to the users, with the goal of steering the user equilibrium towards the system optimum flow. We consider private signals and provide sufficient conditions under which optimality may be achieved by information provision in

We deal with a control problem for a complex social network in which each agent has an action and an opinion, evolving according to a coevolutionary model. In particular, we consider a scenario in which a committed minority - a set of stubborn nodes - aims to steer a population, initially at a consensus, to a different consensus state. Our study focuses on determining the conditions under which su

In this technical note, we introduce a novel approach to studying consensus of continuous-time nonlinear systems with varying topology based on Hilbert metric. We demonstrate that this metric offers significant flexibility in analyzing consensus properties, while effectively handling nonlinearities and time dependencies. Notably, our approach relaxes key technical assumptions from some standard re

With the shift toward de-escalating surgery in breast cancer, prediction models incorporating imaging can reassess the need for surgical axillary staging. This study employed advancements in deep learning to comprehensively evaluate routine mammograms for preoperative lymph node metastasis prediction. Mammograms and clinicopathological data from 1265 cN0 T1-T2 breast cancer patients (primary surge

The formation of platoons, where groups of vehicles follow each other at close distances, has the potential to increase road capacity. In this paper, a decentralized control approach is presented that extends the well-known constant headway vehicle following approach to the two-dimensional case, i.e., lateral control is included in addition to the longitudinal control. The presented control scheme

Variance-reduced stochastic gradient methods have gained popularity in recent times. Several variants exist with different strategies for storing and sampling gradients, and this work concerns the interactions between these two aspects. We present a general proximal variance-reduced gradient method and analyze it under strong convexity assumptions. Special cases of the algorithm include SAGA, L-SV

Understanding the evolution of multipath components (MPCs) in real radio channels is crucial to enhancing channel modeling and multipath-assisted positioning. This paper provides an experimental analysis of the behavior of MPCs originating from a standard building facade at millimeter wave (mmWave) frequencies. Utilizing a high-resolution channel parameter estimation method alongside a joint clust

We study dynamics in a network of interacting agents updating their binary states according to a time-varying threshold rule. Specifically, agents revise their state asynchronously by comparing the weighted average of the current states of their neighbors in the interaction network with possibly heterogeneous time-varying threshold values. Such thresholds are determined by an exogenous signal repr

This thesis embarks on a journey in the advancement of urban traffic management, centering around the innovative integration of Autonomous Intersection Management (AIM) systems. The research encompasses a comprehensive exploration of various facets of AIM implementation, significantly contributing to the evolution of a more efficient and safer urban transport system.The research investigates the d

In the last couple of decades, developments in SAT-based optimization have led to highly efficient maximum satisfiability (MaxSAT) solvers, but in contrast to the SAT solvers on which MaxSAT solving rests, there has been little parallel development of techniques to prove the correctness of MaxSAT results. We show how pseudo-Boolean proof logging can be used to certify state-of-the-art core-guided

Safety measures need to be systemically investigated to what extent they evaluate the intended performance of Deep Neural Networks (DNNs) for critical applications. Due to a lack of verification methods for high-dimensional DNNs, a trade-off is needed between accepted performance and handling of out-of-distribution (OOD) samples.This work evaluates rejecting outputs from semantic segmentation DNNs

There are many pedagogical methods to engage students in active learning. This paper presents Discussion Seminars, which is one such method that is much appreciated by students. This paper explains how to structure seminars and discusses their benefits and what requirements they put on the teacher. The paper aims to inspire other teachers to try something similar.

A Brain-Computer Interface (BCI) is a system that, in real-time, translates the user's brain activity into commands that can be used to control applications, such as moving a cursor on the screen. The translation is made possible by machine learning methods and other algorithms. The thesis focuses on EEG-based BCIs which are the most common type of BCIs due to EEG measurements being non-invasive,

A recent singleshot multiplane image (MPI) generation enables to copy an observed reality within a camera frame into other reality domains via view synthesis. While the scene scale is unknown due to the nature of singleshot MPI processing, camera tracking algorithms can estimate depth within the application world coordinate system. Given such depth information, we propose to adjust the scale of si

We propose a variation of the forward–backward splitting method for solving structured monotone inclusions. Our method integrates past iterates and two deviation vectors into the update equations. These deviation vectors bring flexibility to the algorithm and can be chosen arbitrarily as long as they together satisfy a norm condition. We present special cases where the deviation vectors, selected