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This paper reports the compatibility of heterostructure-based spin qubit devices with industrial CMOS technology. It features Si/Si-Ge quantum dot devices fabricated using Infineon's 200 mm production line within a restricted thermal budget. The devices exhibit state-of-the-art charge sensing, charge noise and valley splitting characteristics, showing that industrial fabrication is not harming the heterostructure quality. These measured parameters are all correlated to spin qubit coherence and qubit gate fidelity. We describe the single electron device layout, design and its fabrication process using electron beam lithography. The incorporated standard 90 nm back-end of line flow for gate-layer independent contacting and wiring can be scaled up to multiple wiring layers for scalable quantum computing architectures. In addition, we present millikelvin characterization results. Our work exemplifies the potential of industrial fabrication methods to harness the inherent CMOS-compatibility of the Si/Si-Ge material system, despite being restricted to a reduced thermal budget. It paves the way for advanced quantum processor architectures with high yield and device quality., Comment: This work has been submitted to the IEEE for possible publication. 4 pages, 3 figures

We describe an inexpensive and accessible instructional setup which explores ion trapping with a planar linear ion trap. The planar trap is constructed using standard printed circuit board manufacturing and is designed to trap macroscopic charged particles in air. Trapping, shuttling, and splitting is demonstrated to students using these particles, visible to the naked eye. Students have control over trap voltages and can compare properties of particle motion to an analytic model of the trap using a computer vision program for particle tracking. Learning outcomes include understanding the design considerations for planar RF traps, mechanisms underpinning ion ejection, the physics of micromotion, and methods of data analysis using standard computer vision libraries., Comment: 22 pages, 5 figures

We introduce a novel, general-purpose audio generation framework specifically designed for anomaly detection and localization. Unlike existing datasets that predominantly focus on industrial and machine-related sounds, our framework focuses a broader range of environments, particularly useful in real-world scenarios where only audio data are available, such as in video-derived or telephonic audio. To generate such data, we propose a new method inspired by the LLM-Modulo framework, which leverages large language models(LLMs) as world models to simulate such real-world scenarios. This tool is modular allowing a plug-and-play approach. It operates by first using LLMs to predict plausible real-world scenarios. An LLM further extracts the constituent sounds, the order and the way in which these should be merged to create coherent wholes. Much like the LLM-Modulo framework, we include rigorous verification of each output stage, ensuring the reliability of the generated data. The data produced using the framework serves as a benchmark for anomaly detection applications, potentially enhancing the performance of models trained on audio data, particularly in handling out-of-distribution cases. Our contributions thus fill a critical void in audio anomaly detection resources and provide a scalable tool for generating diverse, realistic audio data., Comment: 9 pages, under review

Nowadays, unmanned aerial vehicles (UAVs) are commonly used in search and rescue scenarios to gather information in the search area. The automatic identification of the person searched for in aerial footage could increase the autonomy of such systems, reduce the search time, and thus increase the missed person's chances of survival. In this paper, we present a novel approach to perform semantically conditioned open vocabulary object tracking that is specifically designed to cope with the limitations of UAV hardware. Our approach has several advantages. It can run with verbal descriptions of the missing person, e.g., the color of the shirt, it does not require dedicated training to execute the mission and can efficiently track a potentially moving person. Our experimental results demonstrate the versatility and efficacy of our approach., Comment: 7 pages, 3 figures

Inhalers spray over 100 million drug particles into the mouth, where a significant portion of the drug may deposit. Understanding how the complex interplay between particle and solid phases influence deposition is crucial for optimising treatments. Existing modelling studies neglect any effect of particle momentum on the fluid (one-way coupling), which may cause poor prediction of forces acting on particles. In this study, we simulate a realistic number of particles (up to 160 million) in a patient-specific geometry. We study the effect of momentum transfer from particles to the fluid (two-way coupling) and particle-particle interactions (four-way coupling) on deposition. We also explore the effect of tracking groups of particles (`parcels') to lower computational cost. Upper airway deposition fraction increased from 0.33 (one-way coupled) to 0.87 with two-way coupling and $10 \mu m$ particle diameter. Four-way coupling lowers upper airway deposition by approximately 10% at $100 \mu g$ dosages. We use parcel modelling to study deposition of $4 - 20 \mu m$ particles, observing significant influence of two-way coupling in each simulation. These results show that future studies should model realistic dosages for accurate prediction of deposition which may inform clinical decision-making., Comment: 31 pages, 13 figures

In this paper, we propose VLM-Vac, a novel framework designed to enhance the autonomy of smart robot vacuum cleaners. Our approach integrates the zero-shot object detection capabilities of a Vision-Language Model (VLM) with a Knowledge Distillation (KD) strategy. By leveraging the VLM, the robot can categorize objects into actionable classes -- either to avoid or to suck -- across diverse backgrounds. However, frequently querying the VLM is computationally expensive and impractical for real-world deployment. To address this issue, we implement a KD process that gradually transfers the essential knowledge of the VLM to a smaller, more efficient model. Our real-world experiments demonstrate that this smaller model progressively learns from the VLM and requires significantly fewer queries over time. Additionally, we tackle the challenge of continual learning in dynamic home environments by exploiting a novel experience replay method based on language-guided sampling. Our results show that this approach is not only energy-efficient but also surpasses conventional vision-based clustering methods, particularly in detecting small objects across diverse backgrounds.

It is generally accepted that all massive galaxies host supermassive black holes (BHs) in their center and that mergers of two galaxies lead to the formation of BH binaries. The most interesting among them comprise the mergers in their final state, that is to say with parsec (3.2 light years) or sub-parsec orbital separations. It is possible to detect these systems with binary self-lensing. Here we report the potential detection of a central supermassive BH binary in the active galaxy (AGN) NGC1566 based on a microlensing outburst. The light curve of the outburst - based on observations with the All Sky Automated Survey for SuperNovae - lasted from the beginning of 2017 until the beginning of 2020. The steep symmetric light curve as well as its shape look very different with respect to normal random variations in AGN. However, the observations could be easily reproduced with a best-fit standard microlensing light curve. Based on the light curve, we derived a characteristic timescale of 155 days. During the outburst, the continuum as well as the broad line intensities varied; however, the narrow emission lines did not. This is an indication that the lensing object orbits the AGN nucleus between the broad line region (BLR) and the narrow line region (NLR), that is, at a distance on the order of 250 light days. The light curve can be reproduced by a lens with a BH mass of 5*10^{5} M_solar. This implies a mass ratio to the central AGN on the order of 1 to 10., Comment: 7 pages, 4 figures, Astronomy & Astrophysics Letters in press

Multi-stage decision-making under uncertainty, where decisions are taken under sequentially revealing uncertain problem parameters, is often essential to faithfully model managerial problems. Given the significant computational challenges involved, these problems are typically solved approximately. This short note introduces an algorithmic framework that revisits a popular approximation scheme for multi-stage stochastic programs by Georghiou et al. (2015) and improves upon it to deliver superior policies in the stochastic setting, as well as extend its applicability to robust optimization and a contemporary Wasserstein-based data-driven setting. We demonstrate how the policies of our framework can be computed efficiently, and we present numerical experiments that highlight the benefits of our method.

Global trade is shaped by a complex mix of factors beyond supply and demand, including tangible variables like transport costs and tariffs, as well as less quantifiable influences such as political and economic relations. Traditionally, economists model trade using gravity models, which rely on explicit covariates but often struggle to capture these subtler drivers of trade. In this work, we employ optimal transport and a deep neural network to learn a time-dependent cost function from data, without imposing a specific functional form. This approach consistently outperforms traditional gravity models in accuracy while providing natural uncertainty quantification. Applying our framework to global food and agricultural trade, we show that the global South suffered disproportionately from the war in Ukraine's impact on wheat markets. We also analyze the effects of free-trade agreements and trade disputes with China, as well as Brexit's impact on British trade with Europe, uncovering hidden patterns that trade volumes alone cannot reveal.

The Stark effect provides a powerful method to shift the spectra of molecules, atoms and electronic transitions in general, becoming one of the simplest and most straightforward way to tune the frequency of quantum emitters by means of a static electric field. At the same time, in order to reduce the emitter sensitivity to charge noise, inversion symmetric systems are typically designed, providing a stable emission frequency, with a quadratic-only dependence on the applied field. However, such nonlinear behaviour might reflect in correlations between the tuning ability and unwanted spectral fluctuations. Here, we provide experimental evidence of this trend, using molecular quantum emitters in the solid state cooled down to liquid helium temperatures. We finally combine the electric field generated by electrodes, which results parallel to the molecule induced dipole, to optically excite long-lived charge states, acting in the perpendicular direction. Based on the anisotropy of the molecule's polarizability, our two-dimensional control of the local electric field allows not only to tune the emitter's frequency but also to sensibly suppress the spectral instabilities associated to field fluctuations.

This work demonstrates a fast thermo-optic switching mechanism on silicon nitride on insulator platform leveraging parity-time symmetry breaking. The cladding-free design enables low-loss optical propagation in a partially metal-covered waveguide, with the same metal layer serving as an integrated heater for rapid phase tuning. The fabricated device exhibits an 8.5 {\mu}s rise time for a {\pi} phase shift, despite the weak thermo-optic coefficient in silicon nitride. Additionally, the impact of thermal cross-talk is investigated and an insertion loss as low as 0.39 dB for a 100-{\mu}m-long heater-waveguide section is demonstrated., Comment: 15 pages, 6 figures

Attosecond X-ray pulses are the key to studying electron dynamics at their natural time scale involving specific electronic states. They are promising to build the conceptual bridge between physical and chemical photo-reaction processes. Free-electron lasers have demonstrated their capability of generating intense attosecond X-ray pulses. However, harnessing them for time-resolving experiments and investigations of nonlinear X-ray absorption mechanisms remains a cutting-edge challenge. We have characterised X-ray pulses with durations of down to 700$\,$attoseconds and peak powers up to 200$\,$GW at $\sim$ 1$\,$keV photon energy via angular streaking at the SQS instrument of the European XFEL. As direct application, we present results of nonlinear X-ray-matter interaction via state-specific spectroscopy on a transient system. Using the derived spectral and temporal information of each pulse, we deliberately steer the probability for formation of double-core vacancies in neon gas atoms through excitation or ionisation of the second inner-shell electron after K-shell ionisation. Our results advance the field of attosecond science with highly intense and fully characterised X-ray pulses to the site-specific investigation of electronic motion in transient media.

Embodied AI has made significant progress acting in unexplored environments. However, tasks such as object search have largely focused on efficient policy learning. In this work, we identify several gaps in current search methods: They largely focus on dated perception models, neglect temporal aggregation, and transfer from ground truth directly to noisy perception at test time, without accounting for the resulting overconfidence in the perceived state. We address the identified problems through calibrated perception probabilities and uncertainty across aggregation and found decisions, thereby adapting the models for sequential tasks. The resulting methods can be directly integrated with pretrained models across a wide family of existing search approaches at no additional training cost. We perform extensive evaluations of aggregation methods across both different semantic perception models and policies, confirming the importance of calibrated uncertainties in both the aggregation and found decisions. We make the code and trained models available at http://semantic-search.cs.uni-freiburg.de.

The IEA's International Civic and Citizenship Education Study (ICCS) investigates the ways in which young people around the world are prepared to undertake their roles as citizens. This report presents the European results from the third cycle of the study (ICCS 2022). Eighteen countries and two benchmarking participants (the German states of North Rhine-Westphalia and Schleswig-Holstein) administered the European student questionnaire to target grade students in this study cycle. ICCS 2022 studied contexts for and learning outcomes of civic and citizenship education in a wide range of national contexts at the beginning of the third decade of the 21st Century. The general purpose of the European student questionnaire is to explore specific European-related civic and citizenship issues derived from the overarching ICCS 2022 assessment framework, supplementing the data obtained from the international survey with a specific European perspective. The ICCS 2022 European student questionnaire included 12 questions aimed at examining students' interest and their opinions regarding European-related civic and citizenship issues such as students' sense of European identity; students' opportunities for learning about Europe provided by schools; and students' attitudes toward free movement of European citizens within Europe, toward the European Union, and toward cooperation among European countries. It also encompasses questions on students' perceptions of discrimination in their country, of the future of Europe, and of their life in the future, as well as on students' sustainable behaviors and those related to political and ethical consumerism. Over the past 50 years, the IEA has conducted comparative research studies in a range of domains focusing on educational policies, practices, and outcomes in many countries around the world. Prior to ICCS 2022, the IEA had conducted four international comparative studies of civic and citizenship education, with a first survey implemented in 1971, a second in 1999, a third in 2009 and a fourth in 2016. ICCS 2022 data will allow education systems to evaluate the strengths of educational policies, both internationally and in the European regional context, and to measure progress in achieving critical social objectives of their educational policy. [This report was jointly prepared by Libera Università Maria Santissima Assunta (LUMSA Università -- Rome, Italy).]

Java applications include third-party dependencies as bytecode. To keep these applications secure, researchers have proposed tools to re-identify dependencies that contain known vulnerabilities. Yet, to allow such re-identification, one must obtain, for each vulnerability patch, the bytecode fixing the respective vulnerability at first. Such patches for dependencies are curated in databases in the form of fix-commits. But fixcommits are in source code, and automatically compiling whole Java projects to bytecode is notoriously hard, particularly for non-current versions of the code. In this paper, we thus propose JESS, an approach that largely avoids this problem by compiling solely the relevant code that was modified within a given commit. JESS reduces the code, retaining only those parts that the committed change references. To avoid name-resolution errors, JESS automatically infers stubs for references to entities that are unavailable to the compiler. A challenge is here that, to facilitate the above mentioned reidentification, JESS must seek to produce bytecode that is almost identical to the bytecode which one would obtain by a successful compilation of the full project. An evaluation on 347 GitHub projects shows that JESS is able to compile, in isolation, 72% of methods and constructors, of which 89% have bytecode equal to the original one. Furthermore, on the Project KB database of fix-commits, in which only 8% of files modified within the commits can be compiled with the provided build scripts, JESS is able to compile 73% of all files that these commits modify., Comment: To be published in: ICSME 2024 Proceedings

The recent laser excitation of the low-lying Th-229 isomer transition is starting a revolution in ultralight dark matter searches. The enhanced sensitivity of this transition to the large class of dark matter models dominantly coupling to quarks and gluons will ultimately allow us to probe coupling strengths eight orders of magnitude smaller than the current bounds from optical atomic clocks, which are mainly sensitive to dark matter couplings to electrons and photons. We argue that, with increasing precision, observations of the Th-229 excitation spectrum will soon give world-leading constraints. Using data from the pioneering laser excitation of Th-229 by Tiedau et al. [Phys. Rev. Lett. 132, 182501 (2024)], we present a first dark matter search in the excitation spectrum. While the exclusion limits of our detailed study of the lineshape are still below the sensitivity of currently operating clock experiments, we project the measurement of Zhang et al. [arXiv:2406.18719 [physics.atom-ph]] to surpass it., Comment: 14 pages, 6 figures

Robustness against Out-of-Distribution (OoD) samples is a key performance indicator of a trajectory prediction model. However, the development and ranking of state-of-the-art (SotA) models are driven by their In-Distribution (ID) performance on individual competition datasets. We present an OoD testing protocol that homogenizes datasets and prediction tasks across two large-scale motion datasets. We introduce a novel prediction algorithm based on polynomial representations for agent trajectory and road geometry on both the input and output sides of the model. With a much smaller model size, training effort, and inference time, we reach near SotA performance for ID testing and significantly improve robustness in OoD testing. Within our OoD testing protocol, we further study two augmentation strategies of SotA models and their effects on model generalization. Highlighting the contrast between ID and OoD performance, we suggest adding OoD testing to the evaluation criteria of trajectory prediction models.

The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, addressing material challenges, exploring novel computing concepts, and finally examining the maturity level of emerging technologies while determining the next essential steps for their advancement., Comment: 90 pages, 22 figures, roadmap, neuromorphic

Building on tools that have been successfully used in the study of rational billiards, such as induced maps and interval exchange transformations, we provide a construction of a one-parameter family of isosceles triangles exhibiting non-periodic trajectories that are not everywhere dense. This provides, by elementary means, a definitive negative answer to a long-standing open question on the density of non-periodic trajectories in triangular billiards., Comment: 15 pages, 2 figures

Metabolic models condense biochemical knowledge about organisms in a structured and standardised way. As large-scale network reconstructions are readily available for many organisms of interest, genome-scale models are being widely used among modellers and engineers. However, these large models can be difficult to analyse and visualise, and occasionally generate hard-to-interpret or even biologically unrealistic predictions. Out of the thousands of enzymatic reactions in a typical bacterial metabolism, only a few hundred comprise the metabolic pathways essential to produce energy carriers and biosynthetic precursors. These pathways carry relatively high flux, are central to maintaining and reproducing the cell, and provide precursors and energy to engineered metabolic pathways. Here, focusing on these central metabolic subsystems, we present a manually-curated medium-scale model of energy and biosynthesis metabolism for the well-studied prokaryote Escherichia coli K-12 MG1655. The model is a sub-network of the most recent genome-scale reconstruction, iML1515, and comes with an updated layer of database annotations, as well as a range of metabolic maps for visualisation. We enriched the stoichiometric network with extensive biological information and quantitative data, enhancing the scope and applicability of the model. In addition, here we assess the properties of this model in relation to its genome-scale parent and demonstrate the use of the network and supporting data in various scenarios, including enzyme-constrained flux balance analysis, elementary flux mode analysis, and thermodynamic analysis. Overall, we believe this model holds the potential to become a reference medium-scale metabolic model for E. coli., Comment: Minor stylistic changes. Extended discussion in Supplementary Information A.5

Squeezed states are essential for continuous variable (CV) quantum information processing, with wide-ranging applications in computing, sensing and communications. Integrated photonic circuits provide a scalable, convenient platform for building large CV circuits. Thin-film Lithium Niobate (TFLN) is particularly promising due to its low propagation loss, efficient parametric down conversion, and fast electro-optical modulation. In this work, we demonstrate a squeezed light source on an integrated TFLN platform, achieving a measured shot noise reduction of 0.46 dB using modal phase matching and grating couplers with an efficiency of up to -2.2 dB. The achieved squeezing is comparable to what has been observed using more complex circuitry based on periodic poling. The simpler design allows for compact, efficient and reproducible sources of squeezed light., Comment: 6 pages, 6 figures

We carried out a parameter study of interacting disk galaxies with impact parameters ranging from central collisions to weakly interacting scenarios. The orientations of the disks were also varied. In particular, we investigated how magnetic field amplification depends on these parameters. We used magnetohydrodynamics for gas disks in combination with live dark-matter halos in adaptive mesh refinement simulations. The disks were initialized using a setup for isolated disks in hydrostatic equilibrium. Small-scale filtering of the velocity and magnetic field allowed us to estimate the turbulent electromotive force (EMF) and kinetic helicity. Time series of the average magnetic field in central and outer disk regions show pronounced peaks during close encounters and mergers. This agrees with observed magnetic fields at different interaction stages. The central field strength exceeds 10 microgauss (corresponding to an amplification factor of 2 to 3) for small impact parameters. As the disks are increasingly disrupted and turbulence is produced by tidal forces, the small-scale EMF reaches a significant fraction of the total EMF. The small-scale kinetic helicity is initially antisymmetric across the disk plane. Though its evolution is sensitive to both the impact parameter and inclinations of the rotation axes with respect to the relative motion of the disks, antisymmetry is generally broken through interactions and the merger remnant loses most of the initial helicity. The EMF and the magnetic field also decay rapidly after coalescence. The strong amplification during close encounters of the interacting galaxies is mostly driven by helical flows and a mean-field dynamo. The small-scale dynamo contributes significantly in post-interaction phases. However, the amplification of the magnetic field cannot be sustained., Comment: 14 pages, 9 figures, accepted for publication by Astronomy & Astrophysics

In an era of rapidly advancing data-driven applications, there is a growing demand for data in both research and practice. Synthetic data have emerged as an alternative when no real data is available (e.g., due to privacy regulations). Synthesizing tabular data presents unique and complex challenges, especially handling (i) missing values, (ii) dataset imbalance, (iii) diverse column types, and (iv) complex data distributions, as well as preserving (i) column correlations, (ii) temporal dependencies, and (iii) integrity constraints (e.g., functional dependencies) present in the original dataset. While substantial progress has been made recently in the context of generational models, there is no one-size-fits-all solution for tabular data today, and choosing the right tool for a given task is therefore no trivial task. In this paper, we survey the state of the art in Tabular Data Synthesis (TDS), examine the needs of users by defining a set of functional and non-functional requirements, and compile the challenges associated with meeting those needs. In addition, we evaluate the reported performance of 36 popular research TDS tools about these requirements and develop a decision guide to help users find suitable TDS tools for their applications. The resulting decision guide also identifies significant research gaps., Comment: 14 pages, 3 figures

We consider various classes of bounded operators on the Fock space $F^2$ of Gaussian square integrable entire functions over the complex plane. These include Toeplitz (type) operators, weighted composition operators, singular integral operators, Volterra-type operators and Hausdorff operators and range from classical objects in harmonic analysis to more recently introduced classes. As a leading problem and closely linked to well-known compactness characterizations we pursue the question of when these operators are contained in the Toeplitz algebra. This paper combines a (certainly in-complete) survey of the classical and more recent literature including new ideas for proofs from the perspective of quantum harmonic analysis (QHA). Moreover, we have added a number of new theorems and links between known results., Comment: 36 pages; comments are welcome

A key challenge for the widespread application of learning-based models for robotic perception is to significantly reduce the required amount of annotated training data while achieving accurate predictions. This is essential not only to decrease operating costs but also to speed up deployment time. In this work, we address this challenge for PAnoptic SegmenTation with fEw Labels (PASTEL) by exploiting the groundwork paved by visual foundation models. We leverage descriptive image features from such a model to train two lightweight network heads for semantic segmentation and object boundary detection, using very few annotated training samples. We then merge their predictions via a novel fusion module that yields panoptic maps based on normalized cut. To further enhance the performance, we utilize self-training on unlabeled images selected by a feature-driven similarity scheme. We underline the relevance of our approach by employing PASTEL to important robot perception use cases from autonomous driving and agricultural robotics. In extensive experiments, we demonstrate that PASTEL significantly outperforms previous methods for label-efficient segmentation even when using fewer annotations. The code of our work is publicly available at http://pastel.cs.uni-freiburg.de.

Semantic Bird's Eye View (BEV) maps offer a rich representation with strong occlusion reasoning for various decision making tasks in autonomous driving. However, most BEV mapping approaches employ a fully supervised learning paradigm that relies on large amounts of human-annotated BEV ground truth data. In this work, we address this limitation by proposing the first unsupervised representation learning approach to generate semantic BEV maps from a monocular frontal view (FV) image in a label-efficient manner. Our approach pretrains the network to independently reason about scene geometry and scene semantics using two disjoint neural pathways in an unsupervised manner and then finetunes it for the task of semantic BEV mapping using only a small fraction of labels in the BEV. We achieve label-free pretraining by exploiting spatial and temporal consistency of FV images to learn scene geometry while relying on a novel temporal masked autoencoder formulation to encode the scene representation. Extensive evaluations on the KITTI-360 and nuScenes datasets demonstrate that our approach performs on par with the existing state-of-the-art approaches while using only 1% of BEV labels and no additional labeled data., Comment: 23 pages, 5 figures

Crystal plasticity finite element simulations are frequently employed to predict the plastic anisotropy of polycrystalline metals based on their crystallographic texture. However, precipitation in age-hardenable aluminium alloys is known to affect the texture-induced plastic anisotropy. A new modelling approach that incorporates this effect into crystal plasticity constitutive models is presented to address this issue. The approach is based on the hypothesis that precipitation occurs in a global preferred direction, resulting in direction-dependent hardening behaviour for the slip systems. The direction dependency is implemented into a conventional crystal plasticity constitutive model by modification of the hardening law using only one additional parameter. To demonstrate the applicability of the new modelling approach, it is applied to an AA6014-T4 age-hardenable aluminium alloy and compared against the current state-of-the-art crystal plasticity modelling approach, taking only the crystallographic texture into account. The results demonstrate that the predicted accuracy of the plastic anisotropy for the AA6014-T4 aluminium alloy studied is significantly improved by the proposed new modelling approach for incorporating precipitation-related effects., Comment: as submitted

We show that, in the Nelson-Barr solution to the strong CP-problem, a naturally light scalar can arise. It gives rise to a completely new phenomenology beyond that of the celebrated QCD axion, if this field constitutes dark matter, as the CKM elements vary periodically in time. We also discuss how the model can be tested using quantum sensors, in particular using nuclear clocks, which leads to an interesting synergy between different frontiers of physics., Comment: 8 pages

Sensor setups of robotic platforms commonly include both camera and LiDAR as they provide complementary information. However, fusing these two modalities typically requires a highly accurate calibration between them. In this paper, we propose MDPCalib which is a novel method for camera-LiDAR calibration that requires neither human supervision nor any specific target objects. Instead, we utilize sensor motion estimates from visual and LiDAR odometry as well as deep learning-based 2D-pixel-to-3D-point correspondences that are obtained without in-domain retraining. We represent camera-LiDAR calibration as an optimization problem and minimize the costs induced by constraints from sensor motion and point correspondences. In extensive experiments, we demonstrate that our approach yields highly accurate extrinsic calibration parameters and is robust to random initialization. Additionally, our approach generalizes to a wide range of sensor setups, which we demonstrate by employing it on various robotic platforms including a self-driving perception car, a quadruped robot, and a UAV. To make our calibration method publicly accessible, we release the code on our project website at http://calibration.cs.uni-freiburg.de.

We give illustrative examples of how the computer algebra system OSCAR can support research in commutative algebra and algebraic geometry. We start with a thorough introduction to Groebner basis techniques, with particular emphasis on the computation of syzygies, then apply these techniques to deal with ideal and ring theoretic concepts such as primary decomposition and normalization, and finally use them for geometric case studies which concern curves and surfaces, both from a local and global point of view., Comment: 50 pages, 10 figures

Evidences are updated and strengthened for the two-scales picture of low-energy nucleon structure as a compact `hard' valence quark core surrounded by a `soft' cloud of quark-antiquark pairs (the meson cloud). These considerations are quantified by a spectral analysis of the mean-squared radii associated with the isoscalar and isovector electric form factors of the nucleon. Further supporting arguments come from corresponding studies of the axial and mass form factors and their inferred radii. Separating low-mass (mesonic) and high-mass (short-range) contributions in the spectral representations of each of these form factors, we conclude that a central core with an r.m.s. radius of about 1/2 fm results consistently as the common feature in all cases. Implications are discussed for baryonic matter at densities beyond that of equilibrium nuclear matter., Comment: 9 pages, 6 figures

In this paper we propose a novel control approach for opinion dynamics on evolving networks. The controls modify the strength of connections in the network, rather than influencing opinions directly, with the overall goal of steering the population towards a target opinion. This requires that the social network remains sufficiently connected, the population does not break into separate opinion clusters, and that the target opinion remains accessible. We present several approaches to addressing these challenges, considering questions of controllability, instantaneous control and optimal control. Each of these approaches provides a different view on the complex relationship between opinion and network dynamics and raises interesting questions for future research.

In an external electric or magnetic field, a gravitational wave (GW) may be converted into electromagnetic radiation. We present a coordinate-invariant framework to describe the GW signal in a detector that is based on this effect, such as cavities for axion searches. In this framework, we pay special attention to the definition of manifestly coordinate-independent expressions for the electromagnetic fields that an external observer would detect. A careful assessment of the detector's perceived motion allows us to treat both its mechanical and its electromagnetic response to the GW consistently. We further introduce well-defined approximations for which this motion may be neglected, and hence provide suggestions on which coordinate frame is suitable to characterise the GW signal in practice. We illustrate our findings in two examples, an infinitesimally thin rod and a spherical electromagnetic cavity., Comment: 22 pages plus appendix, 4 figures. v2: minor modifications, references added

Extended dynamic mode decomposition (EDMD) is a data-driven algorithm for approximating spectral data of the Koopman operator associated to a dynamical system, combining a Galerkin method of order N and collocation method of order M. Spectral convergence of this method subtly depends on appropriate choice of the space of observables. For chaotic analytic full branch maps of the interval, we derive a constraint between M and N guaranteeing spectral convergence of EDMD., Comment: 24 pages

Factored Markov decision processes (MDPs) are a prominent paradigm within the artificial intelligence community for modeling and solving large-scale MDPs whose rewards and dynamics decompose into smaller, loosely interacting components. Through the use of dynamic Bayesian networks and context-specific independence, factored MDPs can achieve an exponential reduction in the state space of an MDP and thus scale to problem sizes that are beyond the reach of classical MDP algorithms. However, factored MDPs are typically solved using custom-designed algorithms that can require meticulous implementations and considerable fine-tuning. In this paper, we propose a mathematical programming approach to solving factored MDPs. In contrast to existing solution schemes, our approach leverages off-the-shelf solvers, which allows for a streamlined implementation and maintenance; it effectively capitalizes on the factored structure present in both state and action spaces; and it readily extends to the largely unexplored class of robust factored MDPs, whose transition kernels are only known to reside in a pre-specified ambiguity set. Our numerical experiments demonstrate the potential of our approach.

We report the discovery of large ionized, [O II] emitting circumgalactic nebulae around the majority of thirty UV luminous quasars at $z=0.4-1.4$ observed with deep, wide-field integral field spectroscopy (IFS) with the Multi-Unit Spectroscopy Explorer (MUSE) by the Cosmic Ultraviolet Baryon Survey (CUBS) and MUSE Quasar Blind Emitters Survey (MUSEQuBES). Among the 30 quasars, seven (23%) exhibit [O II] emitting nebulae with major axis sizes greater than 100 kpc, twenty greater than 50 kpc (67%), and 27 (90%) greater than 20 kpc. Such large, optically emitting nebulae indicate that cool, dense, and metal-enriched circumgalactic gas is common in the halos of luminous quasars at intermediate redshift. Several of the largest nebulae exhibit morphologies that suggest interaction-related origins. We detect no correlation between the sizes and cosmological dimming corrected surface brightnesses of the nebulae and quasar redshift, luminosity, black hole mass, or radio-loudness, but find a tentative correlation between the nebulae and rest-frame [O II] equivalent width in the quasar spectra. This potential trend suggests a relationship between ISM content and gas reservoirs on CGM scales. The [O II]-emitting nebulae around the $z\approx1$ quasars are smaller and less common than Ly$\alpha$ nebulae around $z\approx3$ quasars. These smaller sizes can be explained if the outer regions of the Ly$\alpha$ halos arise from scattering in more neutral gas, by evolution in the cool CGM content of quasar host halos, by lower-than-expected metallicities on $\gtrsim50$ kpc scales around $z\approx1$ quasars, or by changes in quasar episodic lifetimes between $z=3$ and $1$., Comment: 18 pages, 5 figures, 2 tables. Accepted for publication in the Astrophysical Journal

Recent open-vocabulary robot mapping methods enrich dense geometric maps with pre-trained visual-language features. While these maps allow for the prediction of point-wise saliency maps when queried for a certain language concept, large-scale environments and abstract queries beyond the object level still pose a considerable hurdle, ultimately limiting language-grounded robotic navigation. In this work, we present HOV-SG, a hierarchical open-vocabulary 3D scene graph mapping approach for language-grounded robot navigation. Leveraging open-vocabulary vision foundation models, we first obtain state-of-the-art open-vocabulary segment-level maps in 3D and subsequently construct a 3D scene graph hierarchy consisting of floor, room, and object concepts, each enriched with open-vocabulary features. Our approach is able to represent multi-story buildings and allows robotic traversal of those using a cross-floor Voronoi graph. HOV-SG is evaluated on three distinct datasets and surpasses previous baselines in open-vocabulary semantic accuracy on the object, room, and floor level while producing a 75% reduction in representation size compared to dense open-vocabulary maps. In order to prove the efficacy and generalization capabilities of HOV-SG, we showcase successful long-horizon language-conditioned robot navigation within real-world multi-storage environments. We provide code and trial video data at http://hovsg.github.io/., Comment: Code and video are available at http://hovsg.github.io/

In this work we propose and investigate a strategy to prevent consensus in kinetic models for opinion formation. We consider a large interacting agent system, and assume that agent interactions are driven by compromise as well as self-thinking dynamics and also modulated by an underlying static social network. This network structure is included using so-called graphons, which modulate the interaction frequency in the corresponding kinetic formulation. We then derive the corresponding limiting Fokker Planck equation, and analyze its large time behavior. This microscopic setting serves as a starting point for the proposed control strategy, which steers agents away from mean opinion and is characterised by a suitable penalization depending on the properties of the graphon. We show that this minimalist approach is very effective by analyzing the quasi-stationary solutions mean-field model in a plurality of graphon structures. Several numerical experiments are also provided to show the effectiveness of the approach in preventing the formation of consensus steering the system towards a declustered state., Comment: Changed to align to the in-print version

Sample efficient learning of manipulation skills poses a major challenge in robotics. While recent approaches demonstrate impressive advances in the type of task that can be addressed and the sensing modalities that can be incorporated, they still require large amounts of training data. Especially with regard to learning actions on robots in the real world, this poses a major problem due to the high costs associated with both demonstrations and real-world robot interactions. To address this challenge, we introduce BOpt-GMM, a hybrid approach that combines imitation learning with own experience collection. We first learn a skill model as a dynamical system encoded in a Gaussian Mixture Model from a few demonstrations. We then improve this model with Bayesian optimization building on a small number of autonomous skill executions in a sparse reward setting. We demonstrate the sample efficiency of our approach on multiple complex manipulation skills in both simulations and real-world experiments. Furthermore, we make the code and pre-trained models publicly available at http://bopt-gmm. cs.uni-freiburg.de., Comment: 8 pages, 5 figures, 2 tables, Accepted at the 2024 IEEE International Conference on Intelligent Robots and Systems (IROS)

Active traffic management with autonomous vehicles offers the potential for reduced congestion and improved traffic flow. However, developing effective algorithms for real-world scenarios requires overcoming challenges related to infinite-horizon traffic flow and partial observability. To address these issues and further decentralize traffic management, we propose an asymmetric actor-critic model that learns decentralized cooperative driving policies for autonomous vehicles using single-agent reinforcement learning. By employing attention neural networks with masking, our approach efficiently manages real-world traffic dynamics and partial observability, eliminating the need for predefined agents or agent-specific experience buffers in multi-agent reinforcement learning. Extensive evaluations across various traffic scenarios demonstrate our method's significant potential in improving traffic flow at critical bottleneck points. Moreover, we address the challenges posed by conservative autonomous vehicle driving behaviors that adhere strictly to traffic rules, showing that our cooperative policy effectively alleviates potential slowdowns without compromising safety., Comment: accepted by IROS 2024

Semantic scene segmentation from a bird's-eye-view (BEV) perspective plays a crucial role in facilitating planning and decision-making for mobile robots. Although recent vision-only methods have demonstrated notable advancements in performance, they often struggle under adverse illumination conditions such as rain or nighttime. While active sensors offer a solution to this challenge, the prohibitively high cost of LiDARs remains a limiting factor. Fusing camera data with automotive radars poses a more inexpensive alternative but has received less attention in prior research. In this work, we aim to advance this promising avenue by introducing BEVCar, a novel approach for joint BEV object and map segmentation. The core novelty of our approach lies in first learning a point-based encoding of raw radar data, which is then leveraged to efficiently initialize the lifting of image features into the BEV space. We perform extensive experiments on the nuScenes dataset and demonstrate that BEVCar outperforms the current state of the art. Moreover, we show that incorporating radar information significantly enhances robustness in challenging environmental conditions and improves segmentation performance for distant objects. To foster future research, we provide the weather split of the nuScenes dataset used in our experiments, along with our code and trained models at http://bevcar.cs.uni-freiburg.de., Comment: Accepted for the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024

Four subriemannian (SR) structures over the Euclidean sphere $\mathbb{S}^7$ are considered in accordance to the previous literature. The defining bracket generating distribution is chosen as the horizontal space in the Hopf fibration, the quaternionic Hopf fibration or spanned by a suitable number of canonical vector fields. In all cases the induced SR geodesic flow on $T^*\mathbb{S}^7$ is studied. Adapting a method by A. Thimm, a maximal set of functionally independent and Poisson commuting first integrals are constructed, including the corresponding SR Hamiltonian. As a result, the complete integrability in the sense of Liouville is proved for the SR geodesic flow. It is observed that these first integrals arise as the symbols of commuting second order differential operators one of them being a (not necessarily intrinsic) sublaplacian. On the way one explicitly derives the Lie algebras of all SR isometry groups intersected with $O(8)$.

We study electric field induced two-dimensional coherent nonlinear optical spectroscopy (2DCS) in a Kitaev magnet at finite temperature. We show that 2DCS is susceptible to both types of fractional quasiparticles of this quantum spin-liquid, i.e., fermions and flux visons. Focusing on the second order response, we find a strong antidiagonal feature in the two-dimensional frequency plane, related to the galvanoelectric effect of the fractional fermions. Perpendicular to the antidiagonal, the width of this feature is set by quasiparticle relaxation rates beyond the bare Kitaev magnet, thereby providing access to single-particle characteristics within the multi-particle 2DCS continuum. While the structure of the 2DCS susceptibility stems from the fermionic quasiparticles and displays Fermi blocking versus temperature, the emergent bond randomness which arises due to thermally populated visons strongly modifies the fermionic spectrum. Therefore also the presence of gauge excitations is manifest in the 2DCS susceptibility as the temperature is increased beyond the flux proliferation crossover. Our results are consistent with and extend previous findings on second harmonic generation in Kitaev magnets., Comment: 10 pages, 9 figures

Automated and autonomous industrial inspection is a longstanding research field, driven by the necessity to enhance safety and efficiency within industrial settings. In addressing this need, we introduce an autonomously navigating robotic system designed for comprehensive plant inspection. This innovative system comprises a robotic platform equipped with a diverse array of sensors integrated to facilitate the detection of various process and infrastructure parameters. These sensors encompass optical (LiDAR, Stereo, UV/IR/RGB cameras), olfactory (electronic nose), and acoustic (microphone array) capabilities, enabling the identification of factors such as methane leaks, flow rates, and infrastructural anomalies. The proposed system underwent individual evaluation at a wastewater treatment site within a chemical plant, providing a practical and challenging environment for testing. The evaluation process encompassed key aspects such as object detection, 3D localization, and path planning. Furthermore, specific evaluations were conducted for optical methane leak detection and localization, as well as acoustic assessments focusing on pump equipment and gas leak localization., Comment: Submitted for publication in IEEE Sensors Journal

The availability of a robust map-based localization system is essential for the operation of many autonomously navigating vehicles. Since uncertainty is an inevitable part of perception, it is beneficial for the robustness of the robot to consider it in typical downstream tasks of navigation stacks. In particular localization and mapping methods, which in modern systems often employ convolutional neural networks (CNNs) for perception tasks, require proper uncertainty estimates. In this work, we present uncertainty-aware Panoptic Localization and Mapping (uPLAM), which employs pixel-wise uncertainty estimates for panoptic CNNs as a bridge to fuse modern perception with classical probabilistic localization and mapping approaches. Beyond the perception, we introduce an uncertainty-based map aggregation technique to create accurate panoptic maps, containing surface semantics and landmark instances. Moreover, we provide cell-wise map uncertainties, and present a particle filter-based localization method that employs perception uncertainties. Extensive evaluations show that our proposed incorporation of uncertainties leads to more accurate maps with reliable uncertainty estimates and improved localization accuracy. Additionally, we present the Freiburg Panoptic Driving dataset for evaluating panoptic mapping and localization methods. We make our code and dataset available at: \url{http://uplam.cs.uni-freiburg.de}