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This paper presents a data-driven min-max model predictive control (MPC) scheme for linear parameter-varying (LPV) systems. Contrary to existing data-driven LPV control approaches, we assume that the scheduling signal is unknown during offline data collection and online system operation. Assuming a quadratic matrix inequality (QMI) description for the scheduling signal, we develop a novel data-driven characterization of the consistent system matrices using only input-state data. The proposed data-driven min-max MPC minimizes a tractable upper bound on the worst-case cost over the consistent system matrices set and over all scheduling signals satisfying the QMI. The proposed approach guarantees recursive feasibility, closed-loop exponential stability and constraint satisfaction if it is feasible at the initial time. We demonstrate the effectiveness of the proposed method in simulation.

Data-driven inference of the generative dynamics underlying a set of observed time series is of growing interest in machine learning and the natural sciences. In neuroscience, such methods promise to alleviate the need to handcraft models based on biophysical principles and allow to automatize the inference of inter-individual differences in brain dynamics. Recent breakthroughs in training techniques for state space models (SSMs) specifically geared toward dynamical systems (DS) reconstruction (DSR) enable to recover the underlying system including its geometrical (attractor) and long-term statistical invariants from even short time series. These techniques are based on control-theoretic ideas, like modern variants of teacher forcing (TF), to ensure stable loss gradient propagation while training. However, as it currently stands, these techniques are not directly applicable to data modalities where current observations depend on an entire history of previous states due to a signal's filtering properties, as common in neuroscience (and physiology more generally). Prominent examples are the blood oxygenation level dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) or Ca$^{2+}$ imaging data. Such types of signals render the SSM's decoder model non-invertible, a requirement for previous TF-based methods. Here, exploiting the recent success of control techniques for training SSMs, we propose a novel algorithm that solves this problem and scales exceptionally well with model dimensionality and filter length. We demonstrate its efficiency in reconstructing dynamical systems, including their state space geometry and long-term temporal properties, from just short BOLD time series., Comment: 38th Conference on Neural Information Processing Systems (NeurIPS 2024)

Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational models, we currently do not have one model that captures the human mind in its entirety. Here we introduce Centaur, a computational model that can predict and simulate human behavior in any experiment expressible in natural language. We derived Centaur by finetuning a state-of-the-art language model on a novel, large-scale data set called Psych-101. Psych-101 reaches an unprecedented scale, covering trial-by-trial data from over 60,000 participants performing over 10,000,000 choices in 160 experiments. Centaur not only captures the behavior of held-out participants better than existing cognitive models, but also generalizes to new cover stories, structural task modifications, and entirely new domains. Furthermore, we find that the model's internal representations become more aligned with human neural activity after finetuning. Taken together, Centaur is the first real candidate for a unified model of human cognition. We anticipate that it will have a disruptive impact on the cognitive sciences, challenging the existing paradigm for developing computational models.

This paper introduces a novel parameterization to characterize unknown linear time-invariant systems using noisy data. The presented parameterization describes exactly the set of all systems consistent with the available data. We then derive verifiable conditions, when the consistency constraint reduces the set to the true system and when it does not have any impact. Furthermore, we demonstrate how to use this parameterization to perform a direct data-driven estimator synthesis with guarantees on the H_{\infty}-norm. Lastly, we conduct numerical experiments to compare our approach to existing methods., Comment: This work has been submitted to the American Control Conference 2025

In this work we consider three species competing with each other in the same habitat. One of the species lives in the entire habitat, competing with the other two species, while the other two inhabit two disjoint regions of the habitat. These two populations just interact on a region/interface which acts as a geographical barrier. This barrier condition causes a drastic change in species behaviour compared to the classical Lotka-Volterra competitive model, showing very rich and new different situations depending on the several parameters involved in the system., Comment: 28 pages, 8 figures

In this paper, we propose a collision avoidance safety filter for autonomous electric scooters to enable safe operation of such vehicles in pedestrian areas. In particular, we employ multiple low-cost ultrasonic sensors to detect a wide range of possible obstacles in front of the e-scooter. Based on possibly faulty distance measurements, we design a filter to mitigate measurement noise and missing values as well as a gain-scheduled controller to limit the velocity commanded to the e-scooter when required due to imminent collisions. The proposed controller structure is able to prevent collisions with unknown obstacles by deploying a reduced safe velocity ensuring a sufficiently large safety distance. The collision avoidance approach is designed such that it may be easily deployed in similar applications of general micromobility vehicles. The effectiveness of our proposed safety filter is demonstrated in real-world experiments., Comment: Accepted for presentation at the 17th IFAC Symposium on Control in Transportation Systems (CTS 2024)

In this work we consider an interface logistic problem where two populations live in two different regions, separated by a membrane or interface where it happens an interchange of flux. Thus, the two populations only interact or are coupled through such a membrane where we impose the so-called Kedem-Katchalsky boundary conditions. For this particular scenario we analyze the existence and uniqueness of positive solutions depending on the parameters involve in the system, obtaining interesting results where one can see for the first time the effect of the membrane under such boundary conditions. To do so, we first ascertain the asymptotic behaviour of several linear and nonlinear problems for which we include a diffusion coefficient and analyse the behaviour of the solutions when such a diffusion parameter goes to zero or infinity. Despite their own interest, since these asymptotic results have never been studied before, they will be crucial in analyzing the existence and uniqueness for the main interface logistic problems under analysis. Finally, we apply such an asymptotic analysis to characterize the existence of solutions in terms of the growth rate of the populations, when both populations possess the same growth rate and, also, when they depend on different parameters., Comment: 2 figures

We propose a stationary system that might be regarded as a migration model of some population abandoning their original place of abode and becoming part of another population, once they reach the interface boundary. To do so, we show a model where each population follows a logistic equation in their own environment while assuming spatial heterogeneities. Moreover, both populations are coupled through the common boundary, which acts as a permeable membrane on which their flow moves in and out. The main goal we face in this work will be to describe the precise interplay between the stationary solutions with respect to the parameters involved in the problem, in particular the growth rate of the populations and the coupling parameter involved on the boundary where the interchange of flux is taking place., Comment: 28 pages, 1 figure

We establish the existence of positive solutions for a system of coupled fourth-order partial differential equations on a bounded domain $\Omega \subset \mathbb{R}^n$\begin{align*} \left\{\begin{array}{l} \Delta^2u_1 +\beta_1 \Delta u_1-\alpha_1 u_1=f_1({ x},u_1,u_2),\\\Delta^2 u_2+\beta_2\Delta u_2-\alpha_2 u_2=f_2({ x},u_1,u_2), \end{array} \quad \quad x\in\Omega, \right. \end{align*}subject to homogeneous Navier boundary conditions, where the functions $f_1,f_2 : \Omega\times [0,\infty)\times [0,\infty) \rightarrow [0,\infty)$ are continuous, and $\alpha_1,\alpha_2,\beta_1$ and $\beta_2$ are real parameters satisfying certain constraints related to the eigenvalues of the associated Laplace operator.

Automated Machine Learning (AutoML) has gained increasing success on tabular data in recent years. However, processing unstructured data like text is a challenge and not widely supported by open-source AutoML tools. This work compares three manually created text representations and text embeddings automatically created by AutoML tools. Our benchmark includes four popular open-source AutoML tools and eight datasets for text classification purposes. The results show that straightforward text representations perform better than AutoML tools with automatically created text embeddings., Comment: Accecpted for Future Technologies Conference 2021

Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools. Ultrasound, in contrast to CT or X-Ray, has many practical advantages and can serve as a globally-applicable first-line examination technique. We provide the largest publicly available lung ultrasound (US) dataset for COVID-19 consisting of 106 videos from three classes (COVID-19, bacterial pneumonia, and healthy controls); curated and approved by medical experts. On this dataset, we perform an in-depth study of the value of deep learning methods for differential diagnosis of COVID-19. We propose a frame-based convolutional neural network that correctly classifies COVID-19 US videos with a sensitivity of 0.98+-0.04 and a specificity of 0.91+-08 (frame-based sensitivity 0.93+-0.05, specificity 0.87+-0.07). We further employ class activation maps for the spatio-temporal localization of pulmonary biomarkers, which we subsequently validate for human-in-the-loop scenarios in a blindfolded study with medical experts. Aiming for scalability and robustness, we perform ablation studies comparing mobile-friendly, frame- and video-based architectures and show reliability of the best model by aleatoric and epistemic uncertainty estimates. We hope to pave the road for a community effort toward an accessible, efficient and interpretable screening method and we have started to work on a clinical validation of the proposed method. Data and code are publicly available., Comment: 8 pages, 4 figures

With the rapid development of COVID-19 into a global pandemic, there is an ever more urgent need for cheap, fast and reliable tools that can assist physicians in diagnosing COVID-19. Medical imaging such as CT can take a key role in complementing conventional diagnostic tools from molecular biology, and, using deep learning techniques, several automatic systems were demonstrated promising performances using CT or X-ray data. Here, we advocate a more prominent role of point-of-care ultrasound imaging to guide COVID-19 detection. Ultrasound is non-invasive and ubiquitous in medical facilities around the globe. Our contribution is threefold. First, we gather a lung ultrasound (POCUS) dataset consisting of 1103 images (654 COVID-19, 277 bacterial pneumonia and 172 healthy controls), sampled from 64 videos. This dataset was assembled from various online sources, processed specifically for deep learning models and is intended to serve as a starting point for an open-access initiative. Second, we train a deep convolutional neural network (POCOVID-Net) on this 3-class dataset and achieve an accuracy of 89% and, by a majority vote, a video accuracy of 92% . For detecting COVID-19 in particular, the model performs with a sensitivity of 0.96, a specificity of 0.79 and F1-score of 0.92 in a 5-fold cross validation. Third, we provide an open-access web service (POCOVIDScreen) that is available at: https://pocovidscreen.org. The website deploys the predictive model, allowing to perform predictions on ultrasound lung images. In addition, it grants medical staff the option to (bulk) upload their own screenings in order to contribute to the growing public database of pathological lung ultrasound images. Dataset and code are available from: https://github.com/jannisborn/covid19_pocus_ultrasound. NOTE: This preprint is superseded by our paper in Applied Sciences: https://doi.org/10.3390/app11020672, Comment: 7 pages, 4 figures

In the first part of this study, we compared the performances of two categories of no-slip boundary treatments, i.e., the interpolated bounce-back schemes and the immersed boundary methods in a series of laminar flow simulations within the lattice Boltzmann method. In this second part, these boundary treatments are further compared in the simulations of turbulent flows with complex geometry to provide a next-level assessment of these schemes. Two non-trivial turbulent flow problems, a fully developed turbulent pipe flow at a low Reynolds number, and a decaying homogeneous isotropic turbulent flow laden with a large number of resolved spherical particles are considered. The major problem of the immersed boundary method revealed by the present study is its incapability in computing the local velocity gradients inside the diffused interface, which can result in significantly underestimated dissipation rate and viscous diffusion locally near the particle surfaces. Otherwise, both categories of the no-slip boundary treatments are able to provide accurate results for most of turbulent statistics in both the carrier and dispersed phases, provided that sufficient grid resolutions are used.

The ongoing integration of fine-grained power management features already established in CPU-driven Systems-on-Chip (SoCs) enables both traditional Field Programmable Gate Arrays (FPGAs) and, more recently, hybrid Programmable SoCs (pSoCs) to reach more energy-sensitive application domains (such as, e.g., automotive and robotics). By combining a fixed-function multi-core SoC with flexible, configurable FPGA fabric, the latter can be used to realize heterogeneous Real-time Systems (RTSs) commonly implementing complex application-specific architectures with high computation and communication (I/O) densities. Their dynamic changes in workload, currently active power saving features and thus power consumption require precise voltage and current sensing on all relevant supply rails to enable dependable evaluation of the various power management techniques. In this paper, we propose a low-cost 18-channel 16-bit-resolution measurement (sub-)system capable of 200 kSPS (kilo-samples per second) for instrumentation of current pSoC development boards. To this end, we join simultaneously sampling analog-to-digital converters (ADCs) and analog voltage/current sensing circuitry with a Cortex M7 microcontroller using an SD card for storage. In addition, we propose to include crucial I/O components such as Ethernet PHYs into the power monitoring to gain a holistic view on the RTS's temporal behavior covering not only computation on FPGA and CPUs, but also communication in terms of, e.g., reception of sensor values and transmission of actuation signals. We present an FMC-sized implementation of our measurement system combined with two Gigabit Ethernet PHYs and one HDMI input. Paired with Xilinx' ZC702 development board, we are able to synchronously acquire power traces of a Zynq pSoC and the two PHYs precise enough to identify individual Ethernet frames., Comment: 4 pages, 4 figures

We study an ergodic problem associated to a non-local Hamilton-Jacobi equation defined on the whole space $\lambda-\mathcal{L}[u](x)+|Du(x)|^m=f(x)$ and determine whether (unbounded) solutions exist or not. We prove that there is a threshold growth of the function $f$, that separates existence and non-existence of solutions, a {phenomenon} that does not appear in the local version of the problem. Moreover, we show that there exists a critical ergodic constant, $\lambda_*$, such that the ergodic problem has solutions for $\lambda\leq \lambda_*$ and such that the only solution bounded from below, which is unique up to an additive constant, is the one associated to $\lambda_*$.

In the context of a comprehensive Microsoft Academic (MA) study, we explored in an initial step the quality of linked references data in MA in comparison with Web of Science (WoS). Linked references are the backbone of bibliometrics, because they are the basis of the times cited information in citation indexes. We found that the concordance of linked references between MA and WoS ranges from weak to non-existent for the full sample (publications of the University of Zurich with less than 50 linked references in MA). An analysis with a sample restricted to less than 50 linked references in WoS showed a strong agreement between linked references in MA and WoS., Comment: 6 pages

This is the first detailed study on the coverage of Microsoft Academic (MA). Based on the complete and verified publication list of a university, the coverage of MA was assessed and compared with two benchmark databases, Scopus and Web of Science (WoS), on the level of individual publications. Citation counts were analyzed, and issues related to data retrieval and data quality were examined. A Perl script was written to retrieve metadata from MA based on publication titles. The script is freely available on GitHub. We find that MA covers journal articles, working papers, and conference items to a substantial extent and indexes more document types than the benchmark databases (e.g., working papers, dissertations). MA clearly surpasses Scopus and WoS in covering book-related document types and conference items but falls slightly behind Scopus in journal articles. The coverage of MA is favorable for evaluative bibliometrics in most research fields, including economics/business, computer/information sciences, and mathematics. However, MA shows biases similar to Scopus and WoS with regard to the coverage of the humanities, non-English publications, and open-access publications. Rank correlations of citation counts are high between MA and the benchmark databases. We find that the publication year is correct for 89.5% of all publications and the number of authors is correct for 95.1% of the journal articles. Given the fast and ongoing development of MA, we conclude that MA is on the verge of becoming a bibliometric superpower. However, comprehensive studies on the quality of MA metadata are still lacking.