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In today's world, marked by social distancing and lockdowns, the development of digital financial services is becoming increasingly important, but there is little empirical work documenting the most important factors that contribute to the process of financial and digital inclusion. Because the speed with which states adapt to digital financial services is critical, we must ask how prepared states are for this transition and how far they have progressed in terms of financial and digital inclusion. In this context, the goal of this article is, on the one hand, to propose a financial responsibility process framework capable of raising awareness of the most important harmonized key levels of financial and digital inclusion process that, when properly managed, can lead to achieving an optimal level of financial responsibility, and, on the other hand, to assess the financial and digital inclusion process of two different age groups of individuals who are active in the financial environment (15-34 and 35-59 age groups). The Analytical Hierarchy Process AHP and Fuzzy AHP approaches are proposed as a framework for assessing the mechanism of financial and digital inclusion in five East Central European countries. The findings reflect differences between the analyzed countries in terms of the key levels of financial and digital inclusion (where digital and financial education are the most important levels), with Croatia, Czech Republic, and Poland being the most integrated and Romania being the least. According to the findings, as a country or region's level of financial and digital inclusion increases, so does its level of financial responsibility. This research can be a useful tool in raising awareness about the importance of directed behavior for financial responsibility, particularly for policymakers.

The torus in Active Galactic Nuclei (AGN) is a complex dynamical structure of gas and dust. It is thought to be composed of an equatorial dusty disk and a polar dusty wind launched by radiation pressure. However, this picture is based on studies of moderately accreting AGN. Models suggest that the disk/wind structure will change with specific accretion rate. Here we examine the wind launching region in two high accretion rate objects, I Zw 1 (super-Eddington) and H0557-385 (high-Eddington), using high spatial resolution interferometric observations in the $K$-band from VLTI/GRAVITY and $LM$-bands VLTI/MATISSE. We recover wavelength-dependent sizes of the dust emission using a Gaussian and power law fit to the visibilities. Both objects are partially resolved and have radial sizes in the $KLM$-bands between 0.3 - 1.5 mas, with no signs of elongation. Combining our measurements with VLTI/MIDI $N$-band data gives a full multi-wavelength picture of the dust structure. We find that in H0557-385, the dust sizes between $3.5-8\:\mu\mathrm{m}$ are independent of the wavelength, roughly constant at $3-10$ sublimation radii. We argue that this indicates a direct view of the wind launching region and, together with an absence of polar elongation, this implies that any wind would be launched in a preferentially equatorial direction or blown out by strong radiation pressure. The size-wavelength relation for both objects shows a preferentially disky equatorial dust distribution. We conclude that there is strong evidence that the Eddington ratio shapes the inner dust structure, most notably the wind-launching region and wind direction., Comment: 16 pages, 15 figures, 5 tables. Accepted for publication in MNRAS

Bootstrapping is often applied to get confidence limits for semiparametric inference of a target parameter in the presence of nuisance parameters. Bootstrapping with replacement can be computationally expensive and problematic when cross-validation is used in the estimation algorithm due to duplicate observations in the bootstrap samples. We provide a valid, fast, easy-to-implement subsampling bootstrap method for constructing confidence intervals for asymptotically linear estimators and discuss its application to semiparametric causal inference. Our method, inspired by the Cheap Bootstrap (Lam, 2022), leverages the quantiles of a t-distribution and has the desired coverage with few bootstrap replications. We show that the method is asymptotically valid if the subsample size is chosen appropriately as a function of the sample size. We illustrate our method with data from the LEADER trial (Marso et al., 2016), obtaining confidence intervals for a longitudinal targeted minimum loss-based estimator (van der Laan and Gruber, 2012). Through a series of empirical experiments, we also explore the impact of subsample size, sample size, and the number of bootstrap repetitions on the performance of the confidence interval.

We present a novel universal gradient method for solving convex optimization problems. Our algorithm -- Dual Averaging with Distance Adaptation (DADA) -- is based on the classical scheme of dual averaging and dynamically adjusts its coefficients based on observed gradients and the distance between iterates and the starting point, eliminating the need for problem-specific parameters. DADA is a universal algorithm that simultaneously works for a broad spectrum of problem classes, provided the local growth of the objective function around its minimizer can be bounded. Particular examples of such problem classes are nonsmooth Lipschitz functions, Lipschitz-smooth functions, H\"older-smooth functions, functions with high-order Lipschitz derivative, quasi-self-concordant functions, and $(L_0,L_1)$-smooth functions. Crucially, DADA is applicable to both unconstrained and constrained problems, even when the domain is unbounded, without requiring prior knowledge of the number of iterations or desired accuracy.

Early detection of forest fires is crucial to minimizing the environmental and socioeconomic damage they cause. Indeed, a fire's duration directly correlates with the difficulty and cost of extinguishing it. For instance, a fire burning for 1 minute might require 1 liter of water to extinguish, while a 2-minute fire could demand 100 liters, and a 10-minute fire might necessitate 1,000 liters. On the other hand, existing fire detection systems based on novel technologies (e.g., remote sensing, PTZ cameras, UAVs) are often expensive and require human intervention, making continuous monitoring of large areas impractical. To address this challenge, this work proposes a low-cost forest fire detection system that utilizes a central gateway device with computer vision capabilities to monitor a 360{\deg} field of view for smoke at long distances. A deep reinforcement learning agent enhances surveillance by dynamically controlling the camera's orientation, leveraging real-time sensor data (smoke levels, ambient temperature, and humidity) from distributed IoT devices. This approach enables automated wildfire monitoring across expansive areas while reducing false positives., Comment: Accepted for publication in the proceedings of the 11th International Conference on Automation, Robotics, and Applications (ICARA 2025)

In this paper, we elaborate on how AI can support diversity and inclusion and exemplify research projects conducted in that direction. We start by looking at the challenges and progress in making large language models (LLMs) more transparent, inclusive, and aware of social biases. Even though LLMs like ChatGPT have impressive abilities, they struggle to understand different cultural contexts and engage in meaningful, human like conversations. A key issue is that biases in language processing, especially in machine translation, can reinforce inequality. Tackling these biases requires a multidisciplinary approach to ensure AI promotes diversity, fairness, and inclusion. We also highlight AI's role in identifying biased content in media, which is important for improving representation. By detecting unequal portrayals of social groups, AI can help challenge stereotypes and create more inclusive technologies. Transparent AI algorithms, which clearly explain their decisions, are essential for building trust and reducing bias in AI systems. We also stress AI systems need diverse and inclusive training data. Projects like the Child Growth Monitor show how using a wide range of data can help address real world problems like malnutrition and poverty. We present a project that demonstrates how AI can be applied to monitor the role of search engines in spreading disinformation about the LGBTQ+ community. Moreover, we discuss the SignON project as an example of how technology can bridge communication gaps between hearing and deaf people, emphasizing the importance of collaboration and mutual trust in developing inclusive AI. Overall, with this paper, we advocate for AI systems that are not only effective but also socially responsible, promoting fair and inclusive interactions between humans and machines., Comment: 14 pages, 2 figures

In this article, models for assessing national intangible resources are analysed through a lecture in the literature, and the best-known evaluation methods are categorized into academic models and models of international organizations, with the most important differences being identified. The European Innovation Scoreboard (EIS) and the World Economic Forum annual reports on Global Competitiveness were considered to assess Romania's position in the international context in terms of intangible assets. Despite the importance of intangible resources at national level and the fact that they are an important factor in determining economic growth in the current knowledge-based economy, this article concludes that Romania's position in the international context regarding intangible assets is very weak, with many weak points in research and innovation performance compared to other EU Member States. Therefore, there is a need in our country to re-evaluate the areas where all efforts need to be focused to stimulate innovation performance, to properly manage national intangible resources, a crucial process for improving the quality of life.

Analog gravity systems have the unique opportunity to probe theoretical aspects of black hole physics in a controlled laboratory environment that one cannot easily observe for astrophysical black holes. In this work, we address the question of whether one could use controlled initial perturbations to excite the black hole ringdown and infer the effective black hole metric. Using a theory-agnostic ansatz for the effective metric described by the Rezzolla-Zhidenko metric and evolving perturbations on that background, we quantify with Bayesian analysis what regions of the effective spacetime could be constrained in experiments. In contrast to standard ringdown analyses based on quasi-normal mode extraction, a laboratory-controlled setup, in combination with our framework, allows one to model the entire signal, including the prompt response and possible effects of late-time tails. Therefore, it has the intriguing advantage of not relying on start and end times when the superposition of quasi-normal modes is a good signal approximation. It also avoids the non-trivial question of how many modes are present. We demonstrate that this approach is feasible in principle and discuss opportunities beyond this study., Comment: 13 pages, 11 figures

In this work we estimate the explosion and progenitor properties of six Type II supernovae (SNe) at 0.675 <= z <= 3.61 discovered by the JWST Advanced Deep Extragalactic Survey (JADES) transient survey by modeling their light curves. This high-redshift Type II SN sample allows us to compare low-redshift Type II SNe to their high-redshift counterparts. Two Type II SNe are found to have high explosion energies of 3e51 erg, while the other four Type II SNe are estimated to have typical explosion energies found in the local Universe [(0.5-2)e51 erg]. The fraction of Type II SNe with high explosion energies might be higher at high redshifts because of, e.g., lower metallicity, but it is still difficult to draw a firm conclusion because of the small sample size and potential observational biases. We found it difficult to constrain the progenitor masses for Type II SNe in our sample because of the sparse light-curve data. We found two Type II SN light curves can be better reproduced by introducing confined, dense circumstellar matter. Thus, the confined, dense circumstellar matter frequently observed in nearby Type II SNe is likely to exist in Type II SNe at high redshifts as well. Two Type II SNe are estimated to have high host galaxy extinctions, showing the ability of JWST to discover dust-obscured SNe at high redshifts. More high-redshift Type II SNe are required to investigate the differences in the properties of Type II SNe near and far, but here we show the first glimpse into the high-redshift population of Type II SNe., Comment: 12 pages, 12 figures, 2 tables, submitted to Publications of the Astronomical Society of Japan

Fixpoints are ubiquitous in computer science and when dealing with quantitative semantics and verification one is commonly led to consider least fixpoints of (higher-dimensional) functions over the nonnegative reals. We show how to approximate the least fixpoint of such functions, focusing on the case in which they are not known precisely, but represented by a sequence of approximating functions that converge to them. We concentrate on monotone and non-expansive functions, for which uniqueness of fixpoints is not guaranteed and standard fixpoint iteration schemes might get stuck at a fixpoint that is not the least. Our main contribution is the identification of an iteration scheme, a variation of Mann iteration with a dampening factor, which, under suitable conditions, is shown to guarantee convergence to the least fixpoint of the function of interest. We then argue that these results are relevant in the context of model-based reinforcement learning for Markov decision processes (MDPs), showing that the proposed iteration scheme instantiates to MDPs and allows us to derive convergence to the optimal expected return. More generally, we show that our results can be used to iterate to the least fixpoint almost surely for systems where the function of interest can be approximated with given probabilistic error bounds, as it happens for probabilistic systems, such as simple stochastic games, that can be explored via sampling.

The current biodiversity loss crisis makes animal monitoring a relevant field of study. In light of this, data collected through monitoring can provide essential insights, and information for decision-making aimed at preserving global biodiversity. Despite the importance of such data, there is a notable scarcity of datasets featuring videos of birds, and none of the existing datasets offer detailed annotations of bird behaviors in video format. In response to this gap, our study introduces the first fine-grained video dataset specifically designed for bird behavior detection and species classification. This dataset addresses the need for comprehensive bird video datasets and provides detailed data on bird actions, facilitating the development of deep learning models to recognize these, similar to the advancements made in human action recognition. The proposed dataset comprises 178 videos recorded in Spanish wetlands, capturing 13 different bird species performing 7 distinct behavior classes. In addition, we also present baseline results using state of the art models on two tasks: bird behavior recognition and species classification.

Fast radio bursts (FRBs) are extraordinary astrophysical phenomena characterized by short radio pulses that last only a few milliseconds, yet their power can surpass that of 500 million suns. To date, most detected FRBs originate from beyond our galaxy. However, if an FRB were to originate within the Milky Way, it could be detected using small antennas. In this paper, we propose a compact and ad-hoc antenna array designed for the efficient detection and localization of FRBs within the Milky Way. The antenna operates within the 1200-1800 MHz range and consists of three sub-arrays placed in an L-shape for source localization, occupying a total volume of 80x25x6 cm^3. Each sub-array consists of 4 miniaturized, dual-polarized, half-space radiation antenna elements, forming a one-dimensional array that allows shaping the radiation pattern to match the form of the Milky Way without exhibiting grating lobes. A prototype was constructed and characterized to validate the design. The measured results exhibit good agreement with the simulations. In addition to having a custom elongated radiation pattern, the array has attractive merits, such as low reflections at the input ports, high radiation efficiency, and a distribution that inhibits the existence of phase ambiguities, thus facilitating source localization.

The \emph{coloring number} $\mathrm{col}(G)$ of a graph $G$, which is equal to the \emph{degeneracy} of $G$ plus one, provides a very useful measure for the uniform sparsity of $G$. The coloring number is generalized by three series of measures, the \emph{generalized coloring numbers}. These are the \emph{$r$-admissibility} $\mathrm{adm}_r(G)$, the \emph{strong $r$-coloring number} $\mathrm{col}_r(G)$ and the \emph{weak $r$-coloring number} $\mathrm{wcol}_r(G)$, where $r$ is an integer parameter. The generalized coloring numbers measure the edge density of bounded-depth minors and thereby provide an even more uniform measure of sparsity of graphs. They have found many applications in graph theory and in particular play a key role in the theory of bounded expansion and nowhere dense graph classes introduced by Ne\v{s}et\v{r}il and Ossona de Mendez. We overview combinatorial and algorithmic applications of the generalized coloring numbers, emphasizing new developments in this area. We also present a simple proof for the existence of uniform orders and improve known bounds, e.g., for the weak coloring numbers on graphs with excluded topological minors.

The demand for artificially generated data for the development, training and testing of new algorithms is omnipresent. Quantum computing (QC), does offer the hope that its inherent probabilistic functionality can be utilised in this field of generative artificial intelligence. In this study, we use quantum-classical hybrid generative adversarial networks (QuGANs) to artificially generate graphs of shipping routes. We create a training dataset based on real shipping data and investigate to what extent QuGANs are able to learn and reproduce inherent distributions and geometric features of this data. We compare hybrid QuGANs with classical Generative Adversarial Networks (GANs), with a special focus on their parameter efficiency. Our results indicate that QuGANs are indeed able to quickly learn and represent underlying geometric properties and distributions, although they seem to have difficulties in introducing variance into the sampled data. Compared to classical GANs of greater size, measured in the number of parameters used, some QuGANs show similar result quality. Our reference to concrete use cases, such as the generation of shipping data, provides an illustrative example and demonstrate the potential and diversity in which QC can be used.

We develop double microwave shielding, which has recently enabled evaporative cooling to the first Bose-Einstein condensate of polar molecules [Bigagli et al., Nature 631, 289 (2024)]. Two microwave fields of different frequency and polarization are employed to effectively shield polar molecules from inelastic collisions and three-body recombination. Here, we describe in detail the theory of double microwave shielding. We demonstrate that double microwave shielding effectively suppresses two- and three-body losses. Simultaneously, dipolar interactions and the scattering length can be flexibly tuned, enabling comprehensive control over interactions in ultracold gases of polar molecules. We show that this approach works for a wide range of molecules. This opens the door to studying many-body physics with strongly interacting dipolar quantum matter.

Robotic systems are increasingly employed for industrial automation, with contact-rich tasks like polishing requiring dexterity and compliant behaviour. These tasks are difficult to model, making classical control challenging. Deep reinforcement learning (RL) offers a promising solution by enabling the learning of models and control policies directly from data. However, its application to real-world problems is limited by data inefficiency and unsafe exploration. Adaptive hybrid RL methods blend classical control and RL adaptively, combining the strengths of both: structure from control and learning from RL. This has led to improvements in data efficiency and exploration safety. However, their potential for hardware applications remains underexplored, with no evaluations on physical systems to date. Such evaluations are critical to fully assess the practicality and effectiveness of these methods in real-world settings. This work presents an experimental demonstration of the hybrid RL algorithm CHEQ for robotic polishing with variable impedance, a task requiring precise force and velocity tracking. In simulation, we show that variable impedance enhances polishing performance. We compare standalone RL with adaptive hybrid RL, demonstrating that CHEQ achieves effective learning while adhering to safety constraints. On hardware, CHEQ achieves effective polishing behaviour, requiring only eight hours of training and incurring just five failures. These results highlight the potential of adaptive hybrid RL for real-world, contact-rich tasks trained directly on hardware.

Future multifunction RF systems will be able to not only perform various different radar, communication and electronic warfare functionalities but also to perform them simultaneously on the same aperture. This ability of concurrent operations requires new, cognitive approaches of resource management compared to classical methods. This paper presents such a new approach using a combination of quality of service based resource management and Monte Carlo tree search., Comment: 6 pages, 9 figures, presented at 2024 International Radar Conference (RADAR2024)

Wind power is expected to play a crucial role in future net-zero energy systems, but wind power simulations to support deployment strategies vary drastically in their results, hindering reliable design decisions. Therefore, we present a transparent, open source, validated and evaluated, global wind power simulation tool ETHOS.RESKit.Wind with high spatial resolution and customizable designs for both onshore and offshore wind turbines. The tool provides a comprehensive validation and calibration procedure using over 16 million global measurements from metrerological masts and wind turbine sites. We achieve a global average capacity factor mean error of 0.006 and Pearson correlation of 0.865. In addition, we evaluate its performance against several aggregated and statistical sources of wind power generation. The release of ETHOS.RESKit.Wind is a step towards a fully open source and open data approach to accurate wind power modeling by incorporating the most comprehensive simulation advances in one model., Comment: Edgar Ubaldo Pe\~na-S\'anchez, Philipp Dunkel and Christoph Winkler contributed euqally to the paper. Main article: 20 pages, 7 figures; supplementary material: 20 pages, 10 figures. This version includes minor updates: Fix appearance of toolname in online abstract. Fix figure numbering in appendix

We present a new photometric pipeline for the detection of pre-supernova (pre-SN) emission in the Young Supernova Experiment (YSE) sky survey. The method described is applied to SN 2020tlf, a type II SN (SN II) with precursor emission in the last ~100 days before first light. We re-analyze the YSE griz-band light curves of SN 2020tlf and provide revised pre-explosion photometry that includes a robust list of confident detection and limiting magnitudes. Compared to the results of Jacobson-Galan et al. 2022a, this new analysis yields fewer total r/i/z-band pre-SN detections at phases > -100 days. Furthermore, we discourage the use of the blackbody modeling of the pre-explosion spectral energy distribution, the pre-SN bolometric light curve and the blackbody model parameters presented in Jacobson-Galan et al. 2022a. Nevertheless, binned photometry of SN 2020tlf confirms a consistent progenitor luminosity of ~10$^{40}$ erg s$^{-1}$ before explosion., Comment: 5 pages, 1 figure. Published in RNAAS

Many AI companies are training their large language models (LLMs) on data without the permission of the copyright owners. The permissibility of doing so varies by jurisdiction: in countries like the EU and Japan, this is allowed under certain restrictions, while in the United States, the legal landscape is more ambiguous. Regardless of the legal status, concerns from creative producers have led to several high-profile copyright lawsuits, and the threat of litigation is commonly cited as a reason for the recent trend towards minimizing the information shared about training datasets by both corporate and public interest actors. This trend in limiting data information causes harm by hindering transparency, accountability, and innovation in the broader ecosystem by denying researchers, auditors, and impacted individuals access to the information needed to understand AI models. While this could be mitigated by training language models on open access and public domain data, at the time of writing, there are no such models (trained at a meaningful scale) due to the substantial technical and sociological challenges in assembling the necessary corpus. These challenges include incomplete and unreliable metadata, the cost and complexity of digitizing physical records, and the diverse set of legal and technical skills required to ensure relevance and responsibility in a quickly changing landscape. Building towards a future where AI systems can be trained on openly licensed data that is responsibly curated and governed requires collaboration across legal, technical, and policy domains, along with investments in metadata standards, digitization, and fostering a culture of openness.

Combining brightness and nanoscale size of short-wave infrared (SWIR) emitters is equally essential in the fields of bioimaging, photonics, and quantum science, but such nano-emitters are still lacking. Here we report that when functionalized with luminescent color centers, ultrashort carbon nanotubes with lengths much shorter than 100 nm, are surprisingly bright in the near-infrared second-biological window (NIR-II) of the SWIR. We discuss the origin of this exceptional brightness based on the uncontrollable presence of quenching defects in solubilized carbon nanotubes. The observed NIR-II brightness exceeds that of well-known visible emitters, including quantum dots. After being made biocompatible, ultrashort carbon nanotubes with luminescent color centers open the route toward point-spread function engineering and 3-dimensional single-particle studies for nanoscale NIR-II imaging in thick brain tissue., Comment: 33 pages, 5 figures

Doping an antiferromagnetic Mott insulator is central to our understanding of a variety of phenomena in strongly-correlated electrons, including high-temperature superconductors. To describe the competition between tunneling $t$ of hole dopants and antiferromagnetic (AFM) spin interactions $J$, theoretical and numerical studies often focus on the paradigmatic $t$-$J$ model, and the direct analog quantum simulation of this model in the relevant regime of high-particle density has long been sought. Here, we realize a doped quantum antiferromagnet with next-nearest neighbour (NNN) tunnelings $t'$ and hard-core bosonic holes using a Rydberg tweezer platform. We utilize coherent dynamics between three Rydberg levels, encoding spins and holes, to implement a tunable bosonic $t$-$J$-$V$ model allowing us to study previously inaccessible parameter regimes. We observe dynamical phase separation between hole and spin domains for $|t/J|\ll 1$, and demonstrate the formation of repulsively bound hole pairs in a variety of spin backgrounds. The interference between NNN tunnelings $t'$ and perturbative pair tunneling gives rise to light and heavy pairs depending on the sign of $t$. Using the single-site control allows us to study the dynamics of a single hole in 2D square lattice (anti)ferromagnets. The model we implement extends the toolbox of Rydberg tweezer experiments beyond spin-1/2 models to a larger class of $t$-$J$ and spin-$1$ models., Comment: 8 pages, 5 figures

This paper addresses the issue of intellectual property management in the knowledge-based economy. The starting point in carrying out the study is the presentation of some concepts regarding in the first phase, the intellectual capital. Arguments are made that the knowledge-based economy is a challenge for the current century. The subject of intellectual property is approached through the prism of a topical concept operationalized in the current global economic context. The main institutions that are directly related to this concept are mentioned. The topic of patents related to WOS indexed scientific papers is also debated, along with a series of statistics and studies on the state of patent protection worldwide in the top fields. The last part of the paper contains the conclusions and own points of view on the debated topic.

We observe that BatchBALD, a popular acquisition function for batch Bayesian active learning for classification, can conflate epistemic and aleatoric uncertainty, leading to suboptimal performance. Motivated by this observation, we propose to focus on the predictive probabilities, which only exhibit epistemic uncertainty. The result is an acquisition function that not only performs better, but is also faster to evaluate, allowing for larger batches than before., Comment: 7 pages, 2 figures; presented as a lightning talk at the NeurIPS Workshop on Bayesian Decision-making and Uncertainty (BDU; 2024)

This study explores in depth rotating detonation engines (RDEs) fueled by premixed stoichiometric hydrogen/air mixtures through two-dimensional numerical simulations including a detailed chemical kinetic mechanism. To model the spatial reactant non-uniformities observed in practical RDE combustors, the referred simulations incorporate different numbers of discrete inlet nozzles. The primary focus here is to analyze the influence of reactant non-uniformities on detonation combustion dynamics in RDEs. By systematically varying the number of reactant injection nozzles (from 15 to 240), while maintaining a constant total injection area, the study delves into how this variation influences the behavior of rotating detonation waves (RDWs) and the associated overall flow field structure. The numerical results obtained here reveal significant effects of the number of inlets employed on both RDE stability (self-sustaining detonation wave) and performance. RDE configurations with a lower number of inlets exhibit a detonation front with chaotic behavior (pressure oscillations) due to an increased amount of unburned gas ahead of the detonation wave. This chaotic behavior can lead to the flame extinguishing or decreasing in intensity, ultimately diminishing the engine's overall performance. Conversely, RDE configurations with a higher number of inlets feature smoother detonation propagations without chaotic transients, leading to more stable and reliable performance metrics. This study uses high-fidelity numerical techniques such as adaptive mesh refinement (AMR) and the PeleC compressible reacting flow solver. This comprehensive approach enables a thorough evaluation of critical RDE characteristics including detonation velocity, fuel mass flow rate, impulse, thrust, and reverse pressure waves under varying reactant injection conditions.

We present radio polarimetric images of 12 Seyfert and Low-Ionization Nuclear Emission-line Region (LINER) galaxies belonging to the Centre for Astrophysics (CfA)+12 micron sample exhibiting kiloparsec-scale radio outflows (KSRs). These observations have been carried out at 10 GHz with Karl G. Jansky Very Large Array (VLA) in D-array and at 1.4 GHz with the BnA$\rightarrow$A array configurations. We find signatures of organized magnetic (B-) field structures in the cores, jets and lobes of these galaxies. The linear polarization fraction varies from a few per cent in the cores to $47\pm18$ per cent in the lobes. The inferred B-fields are toroidal in the cores of several sources making them consistent with the presence of either a sheath-like or a wind-like component surrounding the jet. The in-band spectral index images typically show the presence of flat/inverted spectrum cores and steep spectrum lobes. Radio cores with flatter spectra are found to have lower Eddington ratios while the steeper ones have higher. A strong correlation is observed between the Seyfert/LINER radio outflow properties and the mass of the supermassive black holes (SMBHs); correlations with Eddington ratios are weaker. We find signatures of jet-medium interaction and both positive and negative AGN feedback in these sources. Overall, our study indicates that radio-quiet (RQ) AGN with KSRs possess radio outflows driven by magnetic fields anchored to their black holes - accretion disks, which significantly impact their environments., Comment: 36 pages, 20 figures, 6 tables. Accepted for publication in The Astrophysical Journal

The advent of both multi-messenger and time-domain astrophysics over the last decade has seen a large interest in the development of small-scale, cheap, and robust gamma-ray detectors. This has been further encouraged by the availability of CubeSat platforms. Of particular interest are detectors capable of producing spectral and localization measurements of X and gamma-ray transients to allow for accurate follow-up measurements at different wavelengths. A vast number of the instruments developed for such purposes in the last years use a combination of scintillators and Silicon Photomultipliers (SiPMs) for photon detection. Here, we present the design, performance, and space qualification of a readout system capable of reading out 64 SiPM channels. This low-power and low-cost system was originally designed for the POLAR-2 mission, a large scale gamma-ray polarimeter. However, its flexible design makes it equally suitable for use on various CubeSat missions. The system was found to perform well when reading out both plastic and high Z scintillators using a total of 1.8~W. The space qualified design furthermore relies on commercial off-the-shelf components, thereby also removing most international export issues. In this paper, we will present the overall design, the performance of the electronics, its performance when reading out various scintillators and the successful space-qualification of this design., Comment: Submitted for publication in JINST

For mathematical programs with complementarity constraints (MPCC), we refine the convergence analysis of the Scholtes regularization. Our goal is to relate nondegenerate C-stationary points of MPCC with nondegenerate Karush-Kuhn-Tucker points of its Scholtes regularization. We detected the following anomalies: (i) in a neighborhood of a nondegenerate C-stationary point there could be degenerate Karush-Kuhn-Tucker points of the Scholtes regularization; (ii) even if nondegenerate, they might be locally non-unique; (iii) if nevertheless unique, their quadratic index potentially differs from the C-index of the C-stationary point under consideration. Thus, a change of the topological type for Karush-Kuhn-Tucker points of the Scholtes regularization is possible. In particular, a nondegenerate minimizer of MPCC might be approximated by saddle points. In order to bypass the mentioned anomalies, an additional generic condition for nondegenerate C-stationary points of MPCC is identified. Then, we uniquely trace nondegenerate Karush-Kuhn-Tucker points of the Scholtes regularization and successively maintain their topological type., Comment: 25 pages

Learning from tabular data is of paramount importance, as it complements the conventional analysis of image and video data by providing a rich source of structured information that is often critical for comprehensive understanding and decision-making processes. We present Multi-task Contrastive Masked Tabular Modeling (MT-CMTM), a novel method aiming to enhance tabular models by leveraging the correlation between tabular data and corresponding images. MT-CMTM employs a dual strategy combining contrastive learning with masked tabular modeling, optimizing the synergy between these data modalities. Central to our approach is a 1D Convolutional Neural Network with residual connections and an attention mechanism (1D-ResNet-CBAM), designed to efficiently process tabular data without relying on images. This enables MT-CMTM to handle purely tabular data for downstream tasks, eliminating the need for potentially costly image acquisition and processing. We evaluated MT-CMTM on the DVM car dataset, which is uniquely suited for this particular scenario, and the newly developed HIPMP dataset, which connects membrane fabrication parameters with image data. Our MT-CMTM model outperforms the proposed tabular 1D-ResNet-CBAM, which is trained from scratch, achieving a relative 1.48% improvement in relative MSE on HIPMP and a 2.38% increase in absolute accuracy on DVM. These results demonstrate MT-CMTM's robustness and its potential to advance the field of multi-modal learning.

This paper addresses the domain adaptation challenge for semantic segmentation in medical imaging. Despite the impressive performance of recent foundational segmentation models like SAM on natural images, they struggle with medical domain images. Beyond this, recent approaches that perform end-to-end fine-tuning of models are simply not computationally tractable. To address this, we propose a novel SAM adapter approach that minimizes the number of trainable parameters while achieving comparable performances to full fine-tuning. The proposed SAM adapter is strategically placed in the mask decoder, offering excellent and broad generalization capabilities and improved segmentation across both fully supervised and test-time domain adaptation tasks. Extensive validation on four datasets showcases the adapter's efficacy, outperforming existing methods while training less than 1% of SAM's total parameters., Comment: WACV25

Modern software systems are typically configurable, a fundamental prerequisite for wide applicability and reusability. This flexibility poses an extraordinary challenge for quality assurance, as the enormous number of possible configurations makes it impractical to test each of them separately. This is where t-wise interaction sampling can be used to systematically cover the configuration space and detect unknown feature interactions. Over the last two decades, numerous algorithms for computing small interaction samples have been studied, providing improvements for a range of heuristic results; nevertheless, it has remained unclear how much these results can still be improved. We present a significant breakthrough: a fundamental framework, based on the mathematical principle of duality, for combining near-optimal solutions with provable lower bounds on the required sample size. This implies that we no longer need to work on heuristics with marginal or no improvement, but can certify the solution quality by establishing a limit on the remaining gap; in many cases, we can even prove optimality of achieved solutions. This theoretical contribution also provides extensive practical improvements: Our algorithm SampLNS was tested on 47 small and medium-sized configurable systems from the existing literature. SampLNS can reliably find samples of smaller size than previous methods in 85% of the cases; moreover, we can achieve and prove optimality of solutions for 63% of all instances. This makes it possible to avoid cumbersome efforts of minimizing samples by researchers as well as practitioners, and substantially save testing resources for most configurable systems.

The monetary transmission channel is disrupted by many factors, especially securitization and liquidity traps. In our study we try to estimate the effect of securitization on the interest elasticity and to identify if a liquidity trap occurred during 1954Q3-2019Q3. The yield curve inversion mechanism shows us that economic cycles are very sensitive to decreasing profitability of banks. However there is no evidence that restoring their profits will ensure a strong recovery. In this regard, we research the low effect of Quantitative Easing (QE) upon economic growth and analyze whether securitization and liquidity traps posed challenges to QE or is it the mainstream theory flawed. In this regard we will examine the main weaknesses of QE, respectively the speculative behavior induced by artificial low rates and its unequal distribution. We propose a new form of QE that will relief households and not reward banks for their risky behavior before recession.

The discipline of software engineering (SE) combines social and technological dimensions. It is an interdisciplinary research field. However, interdisciplinary research submitted to software engineering venues may not receive the same level of recognition as more traditional or technical topics such as software testing. For this paper, we conducted an online survey of 73 SE researchers and used a mixed-method data analysis approach to investigate their challenges and recommendations when publishing interdisciplinary research in SE. We found that the challenges of publishing interdisciplinary research in SE can be divided into topic-related and reviewing-related challenges. Furthermore, while our initial focus was on publishing interdisciplinary research, the impact of current reviewing practices on marginalized groups emerged from our data, as we found that marginalized groups are more likely to receive negative feedback. In addition, we found that experienced researchers are less likely to change their research direction due to feedback they receive. To address the identified challenges, our participants emphasize the importance of highlighting the impact and value of interdisciplinary work for SE, collaborating with experienced researchers, and establishing clearer submission guidelines and new interdisciplinary SE publication venues. Our findings contribute to the understanding of the current state of the SE research community and how we could better support interdisciplinary research in our field., Comment: 12 pages, 1 figure, 4 tables, 47th International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS 2025)

This study explores the use of artificial intelligence (AI) as a complementary tool for grading essay-type questions in higher education, focusing on its consistency with human grading and potential to reduce biases. Using 70 handwritten exams from an introductory sociology course, we evaluated generative pre-trained transformers (GPT) models' performance in transcribing and scoring students' responses. GPT models were tested under various settings for both transcription and grading tasks. Results show high similarity between human and GPT transcriptions, with GPT-4o-mini outperforming GPT-4o in accuracy. For grading, GPT demonstrated strong correlations with the human grader scores, especially when template answers were provided. However, discrepancies remained, highlighting GPT's role as a "second grader" to flag inconsistencies for assessment reviewing rather than fully replace human evaluation. This study contributes to the growing literature on AI in education, demonstrating its potential to enhance fairness and efficiency in grading essay-type questions., Comment: 10 figures, 2 tables

Modern cosmology is built on the assumption that the Universe is homogeneous and isotropic on large scales - but this is challenged by results of the Ellis-Baldwin test that show an unexplained anomaly in the distribution of distant galaxies and quasars., Comment: 5 pages, 1 figure; Invited Comment for Nature Reviews Physics

Recent photorealistic Novel View Synthesis (NVS) advances have increasingly gained attention. However, these approaches remain constrained to small indoor scenes. While optimization-based NVS models have attempted to address this, generalizable feed-forward methods, offering significant advantages, remain underexplored. In this work, we train PixelNeRF, a feed-forward NVS model, on the large-scale UrbanScene3D dataset. We propose four training strategies to cluster and train on this dataset, highlighting that performance is hindered by limited view overlap. To address this, we introduce Aug3D, an augmentation technique that leverages reconstructed scenes using traditional Structure-from-Motion (SfM). Aug3D generates well-conditioned novel views through grid and semantic sampling to enhance feed-forward NVS model learning. Our experiments reveal that reducing the number of views per cluster from 20 to 10 improves PSNR by 10%, but the performance remains suboptimal. Aug3D further addresses this by combining the newly generated novel views with the original dataset, demonstrating its effectiveness in improving the model's ability to predict novel views., Comment: IROS 2024 Workshop, 9 Pages, 7 Figures

The radiative transfer equation models various physical processes ranging from plasma simulations to radiation therapy. In practice, these phenomena are often subject to uncertainties. Modeling and propagating these uncertainties requires accurate and efficient solvers for the radiative transfer equations. Due to the equation's high-dimensional phase space, fine-grid solutions of the radiative transfer equation are computationally expensive and memory-intensive. In recent years, dynamical low-rank approximation has become a popular method for solving kinetic equations due to the development of computationally inexpensive, memory-efficient and robust algorithms like the augmented basis update \& Galerkin integrator. In this work, we propose a low-rank Monte Carlo estimator and combine it with a control variate strategy based on multi-fidelity low-rank approximations for variance reduction. We investigate the error analytically and numerically and find that a joint approach to balance rank and grid size is necessary. Numerical experiments further show that the efficiency of estimators can be improved using dynamical low-rank approximation, especially in the context of control variates., Comment: 16 pages, 2 figures, 1 table, Submitted to Proceedings of MCQMC 2024

Local refinement is vital for efficient numerical simulations. In the context of Isogeometric Analysis (IGA), hierarchical B-splines have gained prominence. The work applies the methodology of truncated hierarchical B-splines (THB-splines) as they keep additional properties. The framework is further enriched with B\'{e}zier extraction, resulting in the multi-level B\'{e}zier extraction method. We apply this discretization method to 2D magnetostatic problems. The implementation is based on an open-source Octave/MATLAB IGA code called GeoPDEs, which allows us to compare our routines with globally refined spline models as well as locally refined ones where the solver does not rely on B\'{e}zier extraction.

The IceCube Neutrino Observatory includes low energy extensions such as the existing DeepCore subarray and the upcoming IceCube Upgrade, which will consist of seven new strings of photosensors with denser instrumentation than the existing array. The setup will allow for the study of neutrino oscillations with greater sensitivity compared to the existing instrumentation, improve neutrino mass ordering studies, and test for the unitarity of the PMNS mixing matrix with high precision. A critical component in these low-energy physics analyses is the accurate reconstruction of event information, particularly the zenith angle of incoming neutrinos. In this study, we discuss the processes that limit the zenith resolution, which include the transverse spread of the hadronic shower, in-ice photon scattering, module resolutions, and module noise. By considering approximations to these processes, we aim to approach the intrinsic zenith resolution limits for purely hadronic events., Comment: 24 pages, 12 figures

In this paper we study multivariate kinetic-type equations in a general setup, which includes in particular the spatially homogeneous Boltzmann equation with Maxwellian molecules, both with elastic and inelastic collisions. Using a representation of the collision operator derived in Bassetti, Ladelli, Matthes (2015) and Dolera, Regazzini (2014), we prove the existence and uniqueness of time-dependent solutions with the help of continuous-time branching random walks, under assumptions as weak as possible. Our main objective is a characterisation of the set of stationary solutions, e.g. equilibrium solutions for inelastic kinetic-type equations, which we describe as mixtures of multidimensional stable laws.

In this article we compute Seshadri constants of ample line bundles on the blowup of Hirzebruch surface $\mathbb{F}_e$ at $r\leqslant e+3$ very general points. Similarly, we compute Seshadri constants on the blowups of certain decomposable ruled surfaces over smooth curves of non-zero genus. We also prove some results related to bounded negativity of blowups of Hirzebruch surfaces and ruled surfaces., Comment: Comments are most welcome

Accurate load forecasting is crucial for predictive control in many energy domain applications, with significant economic and ecological implications. To address these implications, this study provides an extensive benchmark of state-of-the-art deep learning models for short-term load forecasting in energy communities. Namely, LSTM, xLSTM, and Transformers are compared with benchmarks such as KNNs, synthetic load models, and persistence forecasting models. This comparison considers different scales of aggregation (e.g., number of household loads) and varying training data availability (e.g., training data time spans). Further, the impact of transfer learning from synthetic (standard) load profiles and the deep learning model size (i.e., parameter count) is investigated in terms of forecasting error. Implementations are publicly available and other researchers are encouraged to benchmark models using this framework. Additionally, a comprehensive case study, comprising an energy community of 50 households and a battery storage demonstrates the beneficial financial implications of accurate predictions. Key findings of this research include: (1) Simple persistence benchmarks outperform deep learning models for short-term load forecasting when the available training data is limited to six months or less; (2) Pretraining with publicly available synthetic load profiles improves the normalized Mean Absolute Error (nMAE) by an average of 1.28%pt during the first nine months of training data; (3) Increased aggregation significantly enhances the performance of deep learning models relative to persistence benchmarks; (4) Improved load forecasting, with an nMAE reduction of 1.1%pt, translates to an economic benefit of approximately 600EUR per year in an energy community comprising 50 households., Comment: This preprint was submitted to the Elsevier journal Energy and AI on December 18, 2024

Recent studies have suggested that large language models (LLMs) underperform on mathematical and computer science tasks when these problems are translated from Romanian into English, compared to their original Romanian format. Accurate translation is critical for applications ranging from automatic translations in programming competitions to the creation of high-quality educational materials, as well as minimizing errors or fraud in human translations. This study shows that robust large language models (LLMs) can maintain or even enhance their performance in translating less common languages when given well-structured prompts. Our findings suggest that LLMs, with appropriate supervision, can be reliably used for the automatic translation of IOI (International Olympiad in Informatics)-style tasks. We evaluate several translation methods across multiple LLMs, including OpenRoLLM, Llama 3.1 8B, Llama 3.2 3B and GPT-4o, assessing their translation accuracy and performance stability through repeated runs. Additionally, we augment the OJI (Romanian County-Level Informatics Olympiad) Romanian dataset with accurate English translations, enhancing its utility for future LLM training and evaluation. Through detailed syntactic and semantic analyses, we confirm that with human oversight, LLMs can serve as a viable solution for multilingual problem-solving. We also compare the translation quality of LLMs against human translators, as evaluated by a certified expert, underscoring the potential of LLMs in realworld scenarios., Comment: 12 pages

We introduce a novel approach to modelling the nebular emission from star-forming galaxies by combining the contributions from many HII regions incorporating loose trends in physical properties, random dust attenuation, a predefined Halpha luminosity function and a diffuse ionized-gas component. Using a machine-learning-based regression artificial neural network trained on a grid of models generated by the photoionization code Cloudy, we efficiently predict emission-line properties of individual HII regions over a wide range of physical conditions. We generate 250,000 synthetic star-forming galaxies composed of up to 3000 HII regions and explore how variations in parameters affect their integrated emission-line properties. Our results highlight systematic biases in oxygen-abundance estimates derived using traditional methods, emphasizing the importance of accounting for the composite nature of star-forming galaxies when interpreting integrated nebular emission. Future work will leverage this approach to explore in detail its impact on parameter estimates of star-forming galaxies., Comment: Accepted for publication in MNRAS

Unlike human-engineered systems such as aeroplanes, where each component's role and dependencies are well understood, the inner workings of AI models remain largely opaque, hindering verifiability and undermining trust. This paper introduces SemanticLens, a universal explanation method for neural networks that maps hidden knowledge encoded by components (e.g., individual neurons) into the semantically structured, multimodal space of a foundation model such as CLIP. In this space, unique operations become possible, including (i) textual search to identify neurons encoding specific concepts, (ii) systematic analysis and comparison of model representations, (iii) automated labelling of neurons and explanation of their functional roles, and (iv) audits to validate decision-making against requirements. Fully scalable and operating without human input, SemanticLens is shown to be effective for debugging and validation, summarizing model knowledge, aligning reasoning with expectations (e.g., adherence to the ABCDE-rule in melanoma classification), and detecting components tied to spurious correlations and their associated training data. By enabling component-level understanding and validation, the proposed approach helps bridge the "trust gap" between AI models and traditional engineered systems. We provide code for SemanticLens on https://github.com/jim-berend/semanticlens and a demo on https://semanticlens.hhi-research-insights.eu., Comment: 74 pages (18 pages manuscript, 7 pages references, 49 pages appendix)

Every maneuver of a vehicle redistributes risks between road users. While human drivers do this intuitively, autonomous vehicles allow and require deliberative algorithmic risk management. But how should traffic risks be distributed among road users? In a global experimental study in eight countries with different cultural backgrounds and almost 11,000 participants, we compared risk distribution preferences. It turns out that risk preferences in road traffic are strikingly similar between the cultural zones. The vast majority of participants in all countries deviates from a guiding principle of minimizing accident probabilities in favor of weighing up the probability and severity of accidents. At the national level, the consideration of accident probability and severity hardly differs between countries. The social dilemma of autonomous vehicles detected in deterministic crash scenarios disappears in risk assessments of everyday traffic situations in all countries. In no country do cyclists receive a risk bonus that goes beyond their higher vulnerability. In sum, our results suggest that a global consensus on the risk ethics of autonomous driving is easier to establish than on the ethics of crashing.

Reinforcement learning (RL) in the real world necessitates the development of procedures that enable agents to explore without causing harm to themselves or others. The most successful solutions to the problem of safe RL leverage offline data to learn a safe-set, enabling safe online exploration. However, this approach to safe-learning is often constrained by the demonstrations that are available for learning. In this paper we investigate the influence of the quantity and quality of data used to train the initial safe learning problem offline on the ability to learn safe-RL policies online. Specifically, we focus on tasks with spatially extended goal states where we have few or no demonstrations available. Classically this problem is addressed either by using hand-designed controllers to generate data or by collecting user-generated demonstrations. However, these methods are often expensive and do not scale to more complex tasks and environments. To address this limitation we propose an unsupervised RL-based offline data collection procedure, to learn complex and scalable policies without the need for hand-designed controllers or user demonstrations. Our research demonstrates the significance of providing sufficient demonstrations for agents to learn optimal safe-RL policies online, and as a result, we propose optimistic forgetting, a novel online safe-RL approach that is practical for scenarios with limited data. Further, our unsupervised data collection approach highlights the need to balance diversity and optimality for safe online exploration., Comment: Initially submitted to ICML 2023

LocalSGD and SCAFFOLD are widely used methods in distributed stochastic optimization, with numerous applications in machine learning, large-scale data processing, and federated learning. However, rigorously establishing their theoretical advantages over simpler methods, such as minibatch SGD (MbSGD), has proven challenging, as existing analyses often rely on strong assumptions, unrealistic premises, or overly restrictive scenarios. In this work, we revisit the convergence properties of LocalSGD and SCAFFOLD under a variety of existing or weaker conditions, including gradient similarity, Hessian similarity, weak convexity, and Lipschitz continuity of the Hessian. Our analysis shows that (i) LocalSGD achieves faster convergence compared to MbSGD for weakly convex functions without requiring stronger gradient similarity assumptions; (ii) LocalSGD benefits significantly from higher-order similarity and smoothness; and (iii) SCAFFOLD demonstrates faster convergence than MbSGD for a broader class of non-quadratic functions. These theoretical insights provide a clearer understanding of the conditions under which LocalSGD and SCAFFOLD outperform MbSGD.

The last five years of research on distributed graph algorithms have seen huge leaps of progress, both regarding algorithmic improvements and impossibility results: new strong lower bounds have emerged for many central problems and exponential improvements over the state of the art have been achieved for the runtimes of many algorithms. Nevertheless, there are still large gaps between the best known upper and lower bounds for many important problems. The current lower bound techniques for deterministic algorithms are often tailored to obtaining a logarithmic bound and essentially cannot be used to prove lower bounds beyond $\Omega(\log n)$. In contrast, the best deterministic upper bounds are often polylogarithmic, raising the fundamental question of how to resolve the gap between logarithmic lower and polylogarithmic upper bounds and finally obtain tight bounds. We develop a novel algorithm design technique aimed at closing this gap. In essence, each node finds a carefully chosen local solution in $O(\log n)$ rounds and we guarantee that this solution is consistent with the other nodes' solutions without coordination. The local solutions are based on a distributed version of Hall's theorem that may be of independent interest and motivates the title of this work. We showcase our framework by improving on the state of the art for the following fundamental problems: edge coloring, bipartite saturating matchings and hypergraph sinkless orientation. In particular, we obtain an asymptotically optimal $O(\log n)$-round algorithm for $3\Delta/2$-edge coloring in bounded degree graphs. The previously best bound for the problem was $O(\log^4 n)$ rounds, obtained by plugging in the state-of-the-art maximal independent set algorithm from arXiv:2303.16043 into the $3\Delta/2$-edge coloring algorithm from arXiv:1711.05469 ., Comment: To appear at SODA'25

Scientific collaborations require a strong computing infrastructure to successfully process and analyze data. While large-scale collaborations have access to resources such as Analysis Facilities, small-scale collaborations often lack the resources to establish and maintain such an infrastructure and instead operate with fragmented analysis environments, resulting in inefficiencies, hindering reproducibility and thus creating additional challenges for the collaboration that are not related to the experiment itself. We present a scalable, lightweight and maintainable Analysis Facility developed for the DARWIN collaboration as an example study case. Grid computing and storage resources are integrated into the facility, allowing for distributed computing and a common entry point for storage. The authentication and authorization infrastructure for all services is token-based, using an Indigo IAM instance. We discuss the architecture of the facility, its provided services, the user experience, and how it can serve as a sustainable blueprint for small-scale collaborations.