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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.

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.

Overcoming society's complex problems requires novel solutions. Applying different cognitive styles can promote novelty when designing software aimed at these problems. Through an experiment with 80 software design practitioners, we found that female practitioners who had a preference for more than one cognitive style (intuition and rationality) produced the most novel software features of all participants., Comment: IEEE Software

Due to the rise of machine learning, Python is an increasingly popular programming language. Python, however, is dynamically typed. Dynamic typing has shown to have drawbacks when a project grows, while at the same time it improves developer productivity. To have the benefits of static typing, combined with high developer productivity, types need to be inferred. In this paper, we present DLTPy: a deep learning type inference solution for the prediction of types in function signatures based on the natural language context (identifier names, comments and return expressions) of a function. We found that DLTPy is effective and has a top-3 F1-score of 91.6%. This means that in most of the cases the correct type is within the top-3 predictions. We conclude that natural language contained in comments and return expressions are beneficial to predicting types more accurately. DLTPy does not significantly outperform or underperform the previous work NL2Type for Javascript, but does show that similar prediction is possible for Python., Comment: 10 pages, 8 figures The source code of DLTPy is publicly available at https://github.com/casperboone/dltpy/

We propose a system to deliver dynamic guidance in drawing, sketching and handwriting tasks via an electromagnet moving underneath a high refresh rate pressure sensitive tablet. The system allows the user to move the pen at their own pace and style and does not take away control. The system continously and iteratively measures the pen motion and adjusts magnet position and power according to the user input in real-time via a receding horizon optimal control formulation. The optimization is based on a novel approximate electromagnet model that is fast enough for use in real-time methods, yet provides very good fit to experimental data. Using a closed-loop time-free approach allows for error-correcting behavior, gently pulling the user back to the desired trajectory rather than pushing or pulling the pen to a continuously advancing setpoint. Our experimental results show that the system can control the pen position with a very low dispersion of 2.8mm (+/-0.8mm). An initial user study indicates that it significantly increases accuracy of users drawing a variety of shapes and that this improvement increases with complexity of the shape.

ANIMO (Analysis of Networks with Interactive MOdeling) is a software for modeling biological networks, such as e.g. signaling, metabolic or gene networks. An ANIMO model is essentially the sum of a network topology and a number of interaction parameters. The topology describes the interactions between biological entities in form of a graph, while the parameters determine the speed of occurrence of such interactions. When a mismatch is observed between the behavior of an ANIMO model and experimental data, we want to update the model so that it explains the new data. In general, the topology of a model can be expanded with new (known or hypothetical) nodes, and enables it to match experimental data. However, the unrestrained addition of new parts to a model causes two problems: models can become too complex too fast, to the point of being intractable, and too many parts marked as "hypothetical" or "not known" make a model unrealistic. Even if changing the topology is normally the easier task, these problems push us to try a better parameter fit as a first step, and resort to modifying the model topology only as a last resource. In this paper we show the support added in ANIMO to ease the task of expanding the knowledge on biological networks, concentrating in particular on the parameter settings.

The timed automata formalism is an important model for specifying and analysing real-time systems. Robustness is the correctness of the model in the presence of small drifts on clocks or imprecision in testing guards. A symbolic algorithm for the analysis of the robustness of timed automata has been implemented. In this paper, we re-analyse an industrial case lip synchronization protocol using the new robust reachability algorithm. This lip synchronization protocol is an interesting case because timing aspects are crucial for the correctness of the protocol. Several versions of the model are considered: with an ideal video stream, with anchored jitter, and with non-anchored jitter.

Three-dimensional (3D) chromatin structure is closely related to genome function, in particular transcription. However, the folding path of the chromatin fiber in the interphase nucleus is unknown. Here, we systematically measured the 3D physical distance between pairwise labeled genomic positions in gene-dense, highly transcribed domains and gene-poor less active areas on chromosomes 1 and 11 in G1 nuclei of human primary fibroblasts, using fluorescence in situ hybridization. Interpretation of our results and those published by others, based on polymer physics, shows that the folding of the chromatin fiber can be described as a polymer in a globular state (GS), maintained by intra-polymer attractive interactions that counteract self-avoidance forces. The GS polymer model is able to describe chromatin folding in as well the highly expressed domains as the lowly expressed ones, indicating that they differ in Kuhn length and chromatin compaction. Each type of genomic domain constitutes an ensemble of relatively compact globular folding states, resulting in a considerable cellto- cell variation between otherwise identical cells. We present evidence for different polymer folding regimes of the chromatin fiber on the length scale of a few mega base pairs and on that of complete chromosome arms (several tens of Mb). Our results present a novel view on the folding of the chromatin fiber in interphase and open the possibility to explore the nature of the intra-chromatin fiber interactions.