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We prove near-optimal tradeoffs for quantifier depth (also called quantifier rank) versus number of variables in first-order logic by exhibiting pairs of n-element structures that can be distinguished by a k-variable first-order sentence but where every such sentence requires quantifier depth at least nω (k/log k). Our tradeoffs also apply to first-order counting logic and, by the known connection
Conflict analysis has been successfully generalized from Boolean satisfiability (SAT) solving to mixed integer programming (MIP) solvers, but although MIP solvers operate with general linear inequalities, the conflict analysis in MIP has been limited to reasoning with the more restricted class of clausal constraint. This is in contrast to how conflict analysis is performed in so-called pseudo-Bool
Mutation testing is the state-of-the-art technique for assessing the fault detection capacity of a test suite. Unfortunately, a full mutation analysis is often prohibitively expensive. The CppCheck project for instance, demands a build time of 5.8 min and a test execution time of 17 s on our desktop computer. An unoptimised mutation analysis, for 55,000 generated mutants took 11.8 days in total, o
We present a methodology for establishing the existence of quadratic Lyapunov inequalities for a wide range of first-order methods used to solve convex optimization problems. In particular, we consider (i) classes of optimization problems of finite-sum form with (possibly strongly) convex and possibly smooth functional components, (ii) first-order methods that can be written as a linear system on
By measuring brain activity, through techniques such as electroencephalography (EEG), it is possible to decode which sound source a person is listening to, called auditory attention decoding (AAD). This can either be done investigating the relation between speech sources and corresponding brain responses over time, or by discrimi-natively estimating directions to which auditory attention is focuse
This paper serves as a first identification step in a two-step model-based control synthesis problem of switched linear systems (SLSs). More precisely, we present an algorithm that addresses the realization of the multi-input/multi-output MIMO-SLSs from Markov parameters under mild assumptions on the dwell-times and the submodels. A key point of the proposed approach is the introduction of the for
In this paper, we propose a framework to iden-tify discrete-time, multi-input/multi-output (MIMO), switched-linear systems (SLSs) from input-output data. The key step is an observer-based transformation to a switched auto-regressive with exogenous input (SARX) model. This transformation has a nontrivial kernel complicating identification, but converts the state-space identification problem to SARX
Dataflow is a Model of Computation (MoC) that describes applications as networks of actors. The CAL Actor Language (CAL) is one of the programming languages for describing such actors. A downside to CAL is that the actors and their networks are rigidly defined - it is not possible to have a parametric number of ports or actions in an actor. This makes it difficult to define flexible applications o
Reconfigurable intelligent surfaces (RISs) are promising to improve energy efficiency and coverage for 6G [1]. In this paper, we aim to optimize the deployment of BSs and RISs for enhanced coverage in terms of received power. Specifically, an active RIS structure [2] with tuneable power amplification is applied, and a framework of mixed integer linear programming (MILP) is proposed for the optimiz
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
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
We study optimal seeding problems for binary super-modular network games. The system planner's objective is to design a minimal cost seeding guaranteeing that at least a predefined fraction of the players adopt a certain action in every Nash equilibrium. Since the problem is known to be NP-hard and its exact solution would require full knowledge of the network structure, we focus on approximate so
This article considers state estimation of rigid-body dynamics where the positions q are available to measurement but where the angular velocities ̇or accelerations are not available to measurement. Using a stability-oriented approach to model-based design of state estimation for Euler–Lagrange systems and rigid-body dynamics, state estimation based on position measurement is shown to guarantee se
After the COVID-19 pandemic, during which teachers developed a lot of online course material, teachers worldwide are challenged with how to combine online teaching with classroom teaching in an efficient way. This paper presents Discussion seminars, a teaching activity that structures discussions clearly, making it easy for students and teachers to follow the discussion. The paper puts Discussion
Effective preprocessing of electroencephalography (EEG) data is fundamental for deriving meaningful insights. Independent component analysis (ICA) serves as an important step in this process by aiming to eliminate undesirable artifacts from EEG data. However, the decision on which and how many components to be removed remains somewhat arbitrary, despite the availability of both automatic and manua
Formal models for the safety validation of autonomous vehicles have become increasingly important. To this end, we present a safety framework for longitudinal automated driving. This framework enables the calculation of minimum safe inter-vehicular distances for arbitrary ego vehicle control policies in a computationally efficient manner. We use this framework to enhance and generalize the Respons
