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The 7th International Workshop on Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) seeks to bring together researchers from human-robot interaction (HRI), robotics, and mixed reality (MR) to address the challenges related to mixed reality interactions between humans and robots. Key topics include the development of robots capable of interacting with humans in mixed reali
Electric power systems are undergoing huge changes due to the shift from conventional power production to more renewable-based generation like solar and wind. This is primarily driven by the need to mitigate climate change by reducing CO2 emissions. The shift to more generation from solar and wind will affect the dynamical behaviour of power systems, and consequently how they should be controlled.
In this paper, a novel decentralized leader-follower control scheme for multi–agent systems is devised, where each agent communicates only with a subset of neighboring mates. The goal is to track assigned trajectories for the centroid and the formation of the system. The desired trajectories are known only by a subset of agents, named leaders: the other agents, the followers, are required to estim
This paper proposes a motion control scheme for robots operating in a dynamic environment with concave obstacles. A Model Predictive Controller (MPC) is constructed to drive the robot towards a goal position while ensuring collision avoidance without direct use of obstacle information in the optimization problem. This is achieved by guaranteeing tracking performance of an appropriately designed re
This paper gives a global solution to an H-infinity control problem for systems with symmetric state matrix and state-dependent input matrix. A simple, closed-form expression for the minimizing controller is obtained. This is in contrast to already established theory in which nonlinear H-infinity problems are solved locally. The result is then illustrated through an example, and the potential for
A well-balanced exploration-exploitation trade-off is crucial for successful acquisition functions in Bayesian optimization. However, there is a lack of quantitative measures for exploration, making it difficult to analyze and compare different acquisition functions. This work introduces two novel approaches - observation traveling salesman distance and observation entropy - to quantify the explor
Bayesian optimization (BO ) is an effective method for optimizing expensive-to-evaluate black-box functions. While high-dimensional problems can be particularly challenging, due to the multitude of parameter choices and the potentially high number of data points required to fit the model, this limitation can be addressed if the problem satisfies simplifying assumptions. Axis-aligned subspace appro
This thesis concerns control of capacity-constrained networks. These systems involve many agents interconnected by a resource distribution network. The capacity to generate and distribute this resource is constrained. This applies, for instance, to power grids, communication networks, smart surveillance camera networks, and district heating networks. District heating networks in particular are the
High-dimensional problems have long been considered the Achilles' heel of Bayesian optimization. Spurred by the curse of dimensionality, a large collection of algorithms aim to make it more performant in this setting, commonly by imposing various simplifying assumptions on the objective. In this paper, we identify the degeneracies that make vanilla Bayesian optimization poorly suited to high-dimen
Phase modulation and phase cycling schemes have been commonly used in electronic two-dimensional (2D) spectroscopy where the observables are incoherent signals such as fluorescence or photocurrent. Although the methods have distinct advantages compared to the coherent signal-detected 2D spectroscopy in sensitivity, possibility to measure spectra from isolated quantum systems and direct visualizati
Many problems in computer vision can be formulated as geometric estimation problems, i.e. given a collection of measurements (e.g. point correspondences) we wish to fit a model (e.g. an essential matrix) that agrees with our observations. This necessitates some measure of how much an observation 'agrees' with a given model. A natural choice is to consider the smallest perturbation that makes the o
We present property probes, a mechanism for helping a developer explore partial program analysis results in terms of the source program interactively while the program is edited. A node locator data structure is introduced that maps between source code spans and program representation nodes, and that helps identify probed nodes in a robust way, after modifications to the source code. We have devel
Objective: Obstetric anal sphincter injuries (OASIS) are severe complications to vaginal births with potential long-term consequences. Maternal origin has been proposed to affect the overall risk, but the association and underlying explanation are uncertain. The objective was to assess the association between maternal country of birth and OASIS. Methods: A Swedish nationwide cohort study including
Normalizing flows (NFs) have been shown to be advantageous in modeling complex distributions and improving sampling efficiency for unbiased sampling. In this work, we propose a new class of continuous NFs, ascent continuous normalizing flows (ACNFs), that makes a base distribution converge faster to a target distribution. As solving such a flow is non-trivial and barely possible, we propose a prac
We investigate the problem of low-complexity, high-dimensional channel estimation with beamspace observations, for the purpose of localization. Existing work on beamspace ESPRIT (estimation of signal parameters via rotational invariance technique) approaches requires either a shift-invariance structure of the transformation matrix, or a full-column rank condition. We extend these beamspace ESPRIT
Co-speech head and eyebrow gestures often align with pitch accents in spoken language production, thereby likely instantiating what we could refer to as multimodal prominence. Indeed, when speech is perceived audio-visually, controlled experimental studies have shown that both pitch accents and head and eyebrow gestures, when independently manipulated, have sizable effects on perceived prominence.
In this paper, we present a new generic Structure-From-Motion pipeline, GenSfM, that uses a non-parametric camera projection model. The model is self-calibrated during the reconstruction process and can fit a wide variety of cameras, ranging from simple low-distortion pinhole cameras to more extreme optical systems such as fisheye or catadioptric cameras. The key component in our framework is an a
To broaden our understanding of bradyarrhythmias and conduction disease, we performed common variant genome-wide association analyses in up to 1.3 million individuals and rare variant burden testing in 460,000 individuals for sinus node dysfunction (SND), distal conduction disease (DCD) and pacemaker (PM) implantation. We identified 13, 31 and 21 common variant loci for SND, DCD and PM, respective
This study examines the effectiveness of LU-PZE, a web-based interactive tool designed to help students visualize and interact with fundamental concepts in automatic control. A total of 200 students enrolled in an introductory course on automatic control had the option to use LU-PZE as an additional resource for learning. LU-PZE offers users randomized quizzes, structured assignments, and real-tim
Background: Obstetrical anal sphincter injuries are complications of vaginal birth that have the potential to cause substantial maternal morbidity. Predicting these injuries might help to improve maternal care as well as antenatal counseling and patient education. Previous attempts to create prediction models have in many cases involved variables only known postpartum, which limits their use in an
