Tracking passive magnetic markers plays a vital role in advancing healthcare and robotics, offering the potential to significantly improve the precision and efficiency of systems. This technology is key to developing smarter, more responsive tools and devices, such as enhanced surgical instruments, precise diagnostic tools, and robots with improved environmental interaction capabilities. However, traditionally, the tracking of magnetic markers is computationally expensive due to the requirement for iterative optimization procedures. Moreover, these methods depend on the magnetic dipole model for their optimization function, which can yield imprecise outcomes due to the model's significant inaccuracies when dealing with short distances between non-spherical magnet and sensor. Our article introduces a novel approach that leverages neural networks (NNs) to bypass these limitations, directly inferring the marker's position and orientation to accurately determine the magnet's five degrees of freedom (5 DoFs) in a single step without initial estimation. Although our method demands an extensive supervised training phase, we mitigate this by introducing a computationally more efficient method to generate synthetic, yet realistic data using Finite Element Methods simulations. Our novel method uses the rotational symmetry of axis-symmetric magnetic markers to transform the 3-D simulations into 2-D. The benefits of fast and accurate inference significantly outweigh the offline training preparation. In our evaluation, we use different cylindrical magnets, tracked with a square array of 16 sensors. We perform the sensors' reading and position inference on a portable, NN-oriented single-board computer, ensuring a compact setup. We benchmark our prototype against vision-based ground-truth data, achieving a mean positional error of 4 mm and an orientation error of 8 degrees within a $0.2\times 0$ . $2\times 0$ .15 m working volume. These results showcase our prototype's ability to balance accuracy and compactness effectively in tracking 5 DoFs.

Adaptive user interfaces (UIs) automatically change an interface to better support users' tasks. Recently, machine learning techniques have enabled the transition to more powerful and complex adaptive UIs. However, a core challenge for adaptive user interfaces is the reliance on high-quality user data that has to be collected offline for each task. We formulate UI adaptation as a multi-agent reinforcement learning problem to overcome this challenge. In our formulation, a user agent mimics a real user and learns to interact with a UI. Simultaneously, an interface agent learns UI adaptations to maximize the user agent's performance. The interface agent learns the task structure from the user agent's behavior and, based on that, can support the user agent in completing its task. Our method produces adaptation policies that are learned in simulation only and, therefore, does not need real user data. Our experiments show that learned policies generalize to real users and achieve on par performance with data-driven supervised learning baselines.

Tracking passive magnetic markers plays a vital role in advancing healthcare and robotics, offering the potential to significantly improve the precision and efficiency of systems. This technology is key to developing smarter, more responsive tools and devices, such as enhanced surgical instruments, precise diagnostic tools, and robots with improved environmental interaction capabilities. However, traditionally, the tracking of magnetic markers is computationally expensive due to the requirement for iterative optimization procedures. Moreover, these methods depend on the magnetic dipole model for their optimization function, which can yield imprecise outcomes due to the model's significant inaccuracies when dealing with short distances between non-spherical magnet and sensor.Our paper introduces a novel approach that leverages neural networks to bypass these limitations, directly inferring the marker's position and orientation to accurately determine the magnet's 5 DoF in a single step without initial estimation. Although our method demands an extensive supervised training phase, we mitigate this by introducing a computationally more efficient method to generate synthetic, yet realistic data using Finite Element Methods simulations. The benefits of fast and accurate inference significantly outweigh the offline training preparation. In our evaluation, we use different cylindrical magnets, tracked with a square array of 16 sensors. We perform the sensors' reading and position inference on a portable, neural networks-oriented single-board computer, ensuring a compact setup. We benchmark our prototype against vision-based ground truth data, achieving a mean positional error of 4 mm and an orientation error of 8 degrees within a 0.2x0.2x0.15 m working volume. These results showcase our prototype's ability to balance accuracy and compactness effectively in tracking 5 DoF.

We present Hedgehog, a single-actuator spherical pin-array device that produces cutaneous haptic sensations to the user's palms.Hedgehog can enrich digital experiences by providing dynamic haptic patterns over a spherical surface using a simple, hand-held device.The key to our design is that it uses a single central actuator, a spherical omnidirectional electromagnet, to control the extension of all the 86 movable pins.This keeps our design simple to fabricate and scalable.A core challenge with this type of design is that the pins in the array, made out of permanent magnets, need to have a stable position when retracted.We present a method to compute such an arrays' spatial stability, evaluate our hardware implementation in terms of its output force and pin's extension and compare it against our method's predictions.We also report our findings from three user studies investigating the perceived force and speed of traveling patterns.Finally, we present insights on the possible applications of Hedgehog.

Reinforcement Learning has two main challenges in the field of Human-Computer Interaction. The first challenge is generalization across tasks and environments. The second challenge is to achieve human-likeness. We propose a Hybrid Hierarchical Control framework for pointing tasks to address both challenges simultaneously. In our framework, we separate high-level decision-making from low-level motor and gaze control. This hierarchical structure promotes generalizability. By constraining the low-level control to human-like capabilities we aim to achieve human-like results. Finally, we present some applications that our framework could be used for.

We introduce an optimal control method for electromagnetic haptic guidance systems. Our real-time approach assists users in pen-based tasks such as drawing, sketching or designing. The key to our control method is that it guides users, yet does not take away agency. Existing approaches force the stylus to a continuously advancing setpoint on a target trajectory, leading to undesirable behavior such as loss of haptic guidance or unintended snapping. Our control approach, in contrast, gently pulls users towards the target trajectory, allowing them to always easily override the system to adapt their input spontaneously and draw at their own speed. To achieve this flexible guidance, our optimization iteratively predicts the motion of an input device such as a pen, and adjusts the position and strength of an underlying dynamic electromagnetic actuator accordingly. To enable real-time computation, we additionally introduce a novel and fast approximate model of an electromagnet. We demonstrate the applicability of our approach by implementing it on a prototypical hardware platform based on an electromagnet moving on a bi-axial linear stage, as well as a set of applications. Experimental results show that our approach is more accurate and preferred by users compared to open-loop and time-dependent closed-loop approaches.

In this paper we introduce a novel contact-free volumetric haptic feedback device. A symmetric electromagnet is used in combination with a dipole magnet model and a simple control law to deliver dynamically adjustable forces onto a hand-held tool. The tool only requires an embedded permanent magnet and thus can be entirely untethered. The force, however, while contact-free, remains grounded via the spherical electromagnet and relatively large forces (1N at contact) can be felt by the user. The device is capable of rendering both attracting and repulsive forces in a thin shell around the electromagnet. We report findings from a user experiment with 6 participants, characterizing force delivery aspects and perceived precision of our system. We found that users can discern at least 25 locations for repulsive forces.

We present Omni, a self-contained 3D haptic feedback system that is capable of sensing and actuating an untethered, passive tool containing only a small embedded permanent magnet. Omni enriches AR, VR and desktop applications by providing an active haptic experience using a simple apparatus centered around an electromagnetic base. The spatial haptic capabilities of Omni are enabled by a novel gradient-based method to reconstruct the 3D position of the permanent magnet in midair using the measurements from eight off-the-shelf hall sensors that are integrated into the base. Omni's 3 DoF spherical electromagnet simultaneously exerts dynamic and precise radial and tangential forces in a volumetric space around the device. Since our system is fully integrated, contains no moving parts and requires no external tracking, it is easy and affordable to fabricate. We describe Omni's hardware implementation, our 3D reconstruction algorithm, and evaluate the tracking and actuation performance in depth. Finally, we demonstrate its capabilities via a set of interactive usage scenarios.

We demonstrate a system to deliver dynamic guidance in drawing, sketching and handwriting tasks via an electromagnet moving underneath a high refresh rate pressure sensitive tablet presented in \citelangerak2019dynamic. The system allows the user to move the pen at their own pace and style and does not take away control. Using a closed-loop time-free approach allows for error-correcting behavior. The user will experience to be smoothly and natural pulled back to the desired trajectory rather than pushing or pulling the pen to a continuously advancing setpoint. The optimization of the setpoint with regard to the user is unique in our approach.

Aalto Interface Metrics (AIM) pools several empirically validated models and metrics of user perception and attention into an easy-to-use online service for the evaluation of graphical user interface (GUI) designs. Users input a GUI design via URL, and select from a list of 17 different metrics covering aspects ranging from visual clutter to visual learnability. AIM presents detailed breakdowns, visualizations, and statistical comparisons, enabling designers and practitioners to detect shortcomings and possible improvements. The web service and code repository are available at interfacemetrics.aalto.fi.

Artikel over het collectief vertalen van poezie; ervaringen van het Gents Collectief van Poezievertalers; Russischtalige poezie uit Oekraine.

Introductie op het werk van de Russische dichteres Svetlana Kekova (*1951); bespreking van drie gedichten van Kekova die in vertaling zijn gepubliceerd in hetzelfde tijdschrift (blz. 24-26)

Artikel over het collectief vertalen van poezie; ervaringen van het Gents Collectief van Poezievertalers.

Inleiding op de publicatie vijf gedichten uit de bundel Familiearchief van de dichter Boris Chersonski in Nederlandse vertaling.

The Bachelor Project assignment of internet startup Magnet.me, fulfilled by Tiddo Langerak and Alex Walterbos, consisted of the replacement of the IT infrastructure in the company. Before designing the new system, the old system was analyzed. Based on this analysis, a list of requirements was formed. The system has been designed so that it fulfills a significant amount of the requirements per definition: Using modern techniques like the Node.js platform, the AngularJS framework, Redis caching and an Nginx webserver, a high performance RESTful IT infrastructure was built. This design includes a server side service for business logic calculation and a Content Management System for internal usage. Using this system, Magnet.me hopes to grow substantially without being held back by technology. (Performance) tests have shown a significant improvement over the old system. The system is set up to be flexible, maintainable and reliable. Its performance is of a grade that, according to early estimations, should even be able to support international traffic. The Magnet.me management has already expressed their satisfaction with the systems performance, even before the system has been implemented completely.

Bespreking van de eerste Russische uitgave van gedichten en proza van Guido Gezelle: Esli serdce slysit. Stichi i proza. perevod s gollandskogo, predislovie i primecanija Iriny Michajlovoj i Alekseja Purina. Posleslovie Kejsa Verchejla. Sankt-Peterburg: Filologiceskij fakul'tet SPbGu 2006