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Nanoscaling interstitial metal hydrides offers opportunities for hydrogenation applications by enhancing kinetics, increasing surface area, and allowing for tunable properties. The introduction of interfaces impacts hydrogen absorption properties and distribution heterogeneously, making it however challenging to examine the multiple concurrent mechanisms, especially at the atomic level. Here we demonstrate the effect of proximity on interstitial hydrogen in ultrathin single crystalline vanadium films, by comparing hydride formation in identically strained Fe/V- and Cr/V-superlattices. Pressure concentration and excess resistivity isotherms show higher absolute solubility of hydrogen, higher critical temperature and concentration in the Cr/V-superlattice. Direct measurements of hydrogen site location and thermal vibrations show identical occupation of octahedral z sites at room temperature with a vibrational amplitude of 0.20-0.25 {\AA} over a wide range of hydrogen concentrations. Our findings are consistent with a more extended region of hydrogen depletion in the vicinity of Fe compared to Cr, which showcases an inverse of the hydrogen spillover effect. Advancing the understanding of interface effects resolves previously puzzling differences in the hydrogen loading of Fe/V- and Cr/V-superlattices and is relevant for advancing both catalysis and storage.

PDS 453 is a rare highly inclined disk where the stellar photosphere is seen at grazing incidence on the disk surface. Our goal is take advantage of this geometry to constrain the structure and composition of this disk, in particular the fact that it shows a 3.1 $\mu$m water ice band in absorption that can be related uniquely to the disk. We observed the system in polarized intensity with the VLT/SPHERE instrument, as well as in polarized light and total intensity using the HST/NICMOS camera. Infrared archival photometry and a spectrum showing the water ice band are used to model the spectral energy distribution under Mie scattering theory. Based on these data, we fit a model using the radiative transfer code MCFOST to retrieve the geometry and dust and ice content of the disk. PDS 453 has the typical morphology of a highly inclined system with two reflection nebulae where the disk partially attenuates the stellar light. The upper nebula is brighter than the lower nebula and shows a curved surface brightness profile in polarized intensity, indicating a ring-like structure. With an inclination of 80{\deg} estimated from models, the line-of-sight crosses the disk surface and a combination of absorption and scattering by ice-rich dust grains produces the water ice band. PDS 453 is seen highly inclined and is composed of a mixture of silicate dust and water ice. The radial structure of the disk includes a significant jump in density and scale height at a radius of 70 au in order to produce a ring-like image. The depth of the 3.1 $\mu$m water ice band depends on the amount of water ice, until it saturates when the optical thickness along the line-of-sight becomes too large. Therefore, quantifying the exact amount of water from absorption bands in edge-on disks requires a detailed analysis of the disk structure and tailored radiative transfer modeling., Comment: 11 pages, 11 figures

Demand forecasting faces challenges induced by Peak Events (PEs) corresponding to special periods such as promotions and holidays. Peak events create significant spikes in demand followed by demand ramp down periods. Neural networks like MQCNN and MQT overreact to demand peaks by carrying over the elevated PE demand into subsequent Post-Peak-Event (PPE) periods, resulting in significantly over-biased forecasts. To tackle this challenge, we introduce a neural forecasting model called Split Peak Attention DEcomposition, SPADE. This model reduces the impact of PEs on subsequent forecasts by modeling forecasting as consisting of two separate tasks: one for PEs; and the other for the rest. Its architecture then uses masked convolution filters and a specialized Peak Attention module. We show SPADE's performance on a worldwide retail dataset with hundreds of millions of products. Our results reveal a reduction in PPE degradation by 4.5% and an improvement in PE accuracy by 3.9%, relative to current production models.

We present a provisory scattered light detection of the Vega debris disk using deep Hubble Space Telescope coronagraphy (PID 16666). At only 7.7 parsecs, Vega is immensely important in debris disk studies both for its prominence and also because it allows the highest physical resolution among all debris systems relative to temperature zones around the star. We employ the STIS coronagraph's widest wedge position and classical Reference Differential Imaging to achieve among the lowest surface brightness sensitivities to date ($\sim 4\,\mu Jy/arcsec^{2}$) at wide separations using 32 orbits in Cycle 29. We detect a halo extending from the inner edge of our effective inner working angle at $10^{\prime\prime}.5$ out to the photon noise floor at $30^{\prime\prime}$ (80 - 230 au). The face-on orientation of the system and the lack of a perfectly color-matched PSF star have provided significant challenges to the reductions, particularly regarding artifacts from the imperfect color matching. However, we find that a halo of small dust grains provides the best explanation for the observed signal. Unlike Fomalhaut (a close twin to Vega in luminosity, distance, and age), there is no clear distinction in scattered light between the parent planetesimal belt observed with ALMA and the extended dust halo. These HST observations complement JWST GTO Cycle 1 observations of the system with NIRCam and MIRI.

We present images of the Vega planetary debris disk obtained at 15.5, 23, and 25.5 microns with the Mid-Infrared Instrument (MIRI) on JWST. The debris system is remarkably symmetric and smooth, and centered accurately on the star. There is a broad Kuiper-belt-analog ring at 80 to 170 au that coincides with the planetesimal belt detected with ALMA at 1.34 mm. The interior of the broad belt is filled with warm debris that shines most efficiently at mid-infrared along with a shallow flux dip/gap at 60 au from the star. These qualitative characteristics argue against any Saturn-mass planets orbiting the star outside of about 10 au assuming the unseen planet would be embedded in the very broad planetesimal disk from a few to hundred au. We find that the distribution of dust detected interior to the broad outer belt is consistent with grains being dragged inward by the Poynting-Robertson effect. Tighter constraints can be derived for planets in specific locations, for example any planet shepherding the inner edge of the outer belt is likely to be less than 6 Earth masses. The disk surface brightness profile along with the available infrared photometry suggest a disk inner edge near 3-5 au, disconnected from the sub-au region that gives rise to the hot near-infrared excess. The gap between the hot, sub-au zone and the inner edge of the warm debris might be shepherded by a modest mass, Neptune-size planet., Comment: accepted for publication in ApJ

The most prominent of the IRAS debris disk systems, $\alpha$ Lyrae (Vega), at a distance of 7.7 pc, has been observed by both the NIRCam and MIRI instruments on the James Webb Space Telescope (JWST). This paper describes NIRCam coronagraphic observations which have achieved F444W contrast levels of 3$\times10^{-7}$ at 1\arcsec\ (7.7 au), 1$\times10^{-7}$ at 2\arcsec\ (15 au) and few $\times 10^{-8}$ beyond 5\arcsec\ (38 au), corresponding to masses of $<$ 3, 2 and 0.5 MJup for a system age of 700 Myr. Two F444W objects are identified in the outer MIRI debris disk, around 48 au. One of these is detected by MIRI, appears to be extended and has a spectral energy distribution similar to those of distant extragalactic sources. The second one also appears extended in the NIRCam data suggestive of an extragalactic nature.The NIRCam limits within the inner disk (1\arcsec\ --10\arcsec) correspond to a model-dependent masses of 2$\sim$3 \mj. \citet{Su2024} argue that planets larger even 0.3 MJup would disrupt the smooth disk structure seen at MIRI wavelengths. Eight additional objects are found within 60\arcsec\ of Vega, but none has astrometric properties or colors consistent with planet candidates. These observations reach a level consistent with expected Jeans Mass limits. Deeper observations achieving contrast levels $<10^{-8}$ outside of $\sim$4\arcsec\ and reaching masses below that of Saturn are possible, but may not reveal a large population of new objects., Comment: 19 pages, 12 figures. accepted for Astronomical Journal

We report on the low-temperature fabrication (300$\deg$C) of ultrathin 2D amorphous carbon layers on III-V semiconductors by plasma-enhanced chemical vapor deposition as a universal template for remote epitaxy. We present growth and detailed characterization of 2D amorphous carbon layers on various host substrates and their subsequent remote epitaxial overgrowth by solid-source molecular beam epitaxy. We present the fabrication of ultra-smooth monolayer thick amorphous carbon layers with roughness $\leq 0.3$ nm determined by atomic-force microscopy and X-ray reflectivity measurement. We show that precisely tailoring the carbon layer thickness allows superior tunability of the substrate-layer interaction. Further, X-ray photoelectron and Raman spectroscopy measurements reveal predominantly sp$^2$-hybridised carbon in the amorphous layers. We observe that a low-temperature nucleation step is favorable for nucleation of III-V material growth on substrates coated with thin amorphous carbon layers. Under optimized preparation conditions, we obtain high-quality, single-crystalline, (001)-oriented GaAs, cubic-AlN, cubic-GaN and In(x)Ga(1-x)As, respectively, and various carbon-coated (001)-oriented substrates as GaAs, InP and 3C-SiC. Transmission electron microscopy images of the substrate-carbon-layer interface reveal a stretching of the atomic bonds at the interface and high-resolution X-ray diffraction measurements reveal high crystal quality and low dislocation densities $<1\times 10^7 \text{cm}^{-2}$. Our results show the universality of our carbon deposition process to fabricate templates for remote epitaxy, e. g., for remote epitaxy on temperature sensitive substrates like GaAs or InP and growth of metastable phases. Lift-off of layers from their substrates is demonstrated by employing a Ni stressor.

The celebrated Dvoretzky theorem asserts that every $N$-dimensional convex body admits central sections of dimension $d = \Omega(\log N)$, which is nearly spherical. For many instances of convex bodies, typically unit balls with respect to some norm, much better lower bounds on $d$ have been obtained, with most research focusing on such lower bounds and on the degree of approximation of the section by a $k$-dimensional Euclidean ball. In this note we focus on another parameter, namely the radius of the approximating ball. We focus on the case of the unit ball of the space $\ell_1^N$ (the so-called cross-polytope), which is relevant to various questions of interest in theoretical computer science. We will also survey other instances where similar questions for other normed spaces (most often $\ell_p$-spaces or their non-commutative analogues) were found relevant to problems in various areas of mathematics and its applications, and state some open problems. Finally, in view of the computer science ramifications, we will comment on the algorithmic aspects of finding nearly spherical sections., Comment: 12 pages

We study the Fubini-Study currents and equilibrium metrics of continuous Hermitian metrics on sequences of holomorphic line bundles over a fixed compact K\"ahler manifold. We show that the difference between the Fubini-Study currents and the curvature of the equilibrium metric, when appropriately scaled, converges to 0 in the sense of currents. As a consequence, we obtain sufficient conditions for the scaled Fubini-Study currents to converge weakly.

XTE J1829-098 is a transient X-ray pulsar with a period of ~7.8 s. It is a candidate Be star system, although the evidence for this is not yet definitive. We investigated the twenty-year long X-ray light curve using the Rossi X-ray Timing Explorer Proportional Counter Array (PCA), Neil Gehrels Swift Observatory Burst Alert Telescope (BAT), and the Monitor of All-sky X-ray Image (MAXI). We find that all three light curves are clearly modulated on the ~244 day orbital period previously reported from PCA monitoring observations, with outbursts confined to a narrow phase range. The light curves also show that XTE J1829-098 was in an inactive state between approximately December 2008 and April 2018 and no strong outbursts occurred. Such behavior is typical of Be X-ray binary systems, with the absence of outbursts likely related to the dissipation of the Be star's decretion disk. The mean outburst shapes can be approximated with a triangular profile and, from a joint fit of this to all three light curves, we refine the orbital period to 243.95 +/- 0.04 days. The mean outburst profile does not show any asymmetry and has a total phase duration of 0.140 +/- 0.007. However, the PCA light curve shows that there is considerable cycle-to-cycle variability of the individual outbursts. We compare the properties of XTE J1829-098 with other sources that show short phase-duration outbursts, in particular GS 1843-02 (2S 1845-024) which has a very similar orbital period, but longer pulse period, and whose orbit is known to be highly eccentric., Comment: Accepted for publication in the Astrophysical Journal. 21 pages

We present JWST broadband images of the highly inclined protoplanetary disk SSTc2d J163131.2-242627 (Oph163131) from 2.0 to 21$\mu$m. The images show a remarkable evolution in disk structure with wavelength, quite different from previous JWST observations of other edge-on disks. At 2.0 and 4.4$\mu$m, Oph163131 shows two scattering surfaces separated by a dark lane, typical of highly inclined disks. Starting at 7.7$\mu$m however, 1) the two linear nebulosities flanking the dark lane disappear; 2) the brighter nebula tracing the disk upper surface transitions into a compact central source distinctly larger than the JWST PSF and whose intrinsic size increases with wavelength; and 3) patches of extended emission appear at low latitudes, and at surprisingly large radii nearly twice that of the scattered light seen with $HST$ and NIRCam, and of the gas. We interpret the compact central source as thermal emission from the star and the inner disk that is not seen directly, but which instead is able to progressively propagate to greater distances at longer wavelengths. The lack of sharp-edged structures in the extended patchy emission argues against the presence of shocks and suggests photoexcitation or stochastic heating of material smoothly flowing away from the star along the disk surface. Finally, the dark lane thickness decreases significantly between 0.6$\mu$m and 4.4$\mu$m which indicates that the surface layers of Oph163131 lack grains larger than 1$\mu$m., Comment: Accepted for publication in the Astrophysical Journal. 11 pages, 7 figures

Ensuring robust performance on long-tail examples is an important problem for many real-world applications of machine learning, such as autonomous driving. This work focuses on the problem of identifying rare examples within a corpus of unlabeled data. We propose a simple and scalable data mining approach that leverages the knowledge contained within a large vision language model (VLM). Our approach utilizes a VLM to summarize the content of an image into a set of keywords, and we identify rare examples based on keyword frequency. We find that the VLM offers a distinct signal for identifying long-tail examples when compared to conventional methods based on model uncertainty. Therefore, we propose a simple and general approach for integrating signals from multiple mining algorithms. We evaluate the proposed method on two diverse tasks: 2D image classification, in which inter-class variation is the primary source of data diversity, and on 3D object detection, where intra-class variation is the main concern. Furthermore, through the detection task, we demonstrate that the knowledge extracted from 2D images is transferable to the 3D domain. Our experiments consistently show large improvements (between 10\% and 50\%) over the baseline techniques on several representative benchmarks: ImageNet-LT, Places-LT, and the Waymo Open Dataset.

Recent constraints on neutron star mass and radius have advanced our understanding of the equation of state of cold dense matter. Some of them have been obtained by modeling the pulses of three millisecond X-ray pulsars observed by the Neutron Star Interior Composition Explorer (NICER). Here, we present a Bayesian parameter inference for a fourth pulsar, PSR J1231-1411, using the same technique with NICER and XMM-Newton data. When applying a broad mass-inclination prior from radio timing measurements and the emission region geometry model that can best explain the data, we find likely-converged results only when using a limited radius prior. If limiting the radius to be consistent with the previous observational constraints and equation of state analyses, we infer the radius to be $12.6 \pm 0.3$ km and the mass to be $1.04_{-0.03}^{+0.05}$ $M_\odot$, each reported as the posterior credible interval bounded by the $16\,\%$ and $84\,\%$ quantiles. If using an uninformative prior but limited between $10$ and $14$ km, we find otherwise similar results, but $R_{\mathrm{eq}} = 13.5_{-0.5}^{+0.3}$ km for the radius. In both cases, we find a non-antipodal hot region geometry where one emitting spot is at the equator or slightly above, surrounded by a large colder region, and where a non-circular hot region lies close to southern rotational pole. If using a wider radius prior, we only find solutions that fit the data significantly worse. We discuss the challenges in finding the better fitting solutions, possibly related to the weak interpulse feature in the pulse profile., Comment: 22 pages, 13 figures (2 of which are figure sets), 3 tables, accepted for publication in ApJ

This study investigates the automated classification of Calls to Action (CTAs) within the 2021 German Instagram election campaign to advance the understanding of mobilization in social media contexts. We analyzed over 2,208 Instagram stories and 712 posts using fine-tuned BERT models and OpenAI's GPT-4 models. The fine-tuned BERT model incorporating synthetic training data achieved a macro F1 score of 0.93, demonstrating a robust classification performance. Our analysis revealed that 49.58% of Instagram posts and 10.64% of stories contained CTAs, highlighting significant differences in mobilization strategies between these content types. Additionally, we found that FDP and the Greens had the highest prevalence of CTAs in posts, whereas CDU and CSU led in story CTAs., Comment: Accepted Archival Paper for the CPSS Workshop at KONVENS 2024. Camera Ready Submission

We introduce the Tidal Tales Plugin, a Firefox extension for efficiently collecting and archiving of Instagram stories, addressing the challenges of ephemeral data in social media research. It enables an automated collection of story metadata and media files without risking account bans. It contributes to Web Science by facilitating expansive, long-term studies with enhanced data access and integrity.

Not every directed acyclic graph (DAG) whose underlying undirected graph is planar admits an upward planar drawing. We are interested in pushing the notion of upward drawings beyond planarity by considering upward $k$-planar drawings of DAGs in which the edges are monotonically increasing in a common direction and every edge is crossed at most $k$ times for some integer $k \ge 1$. We show that the number of crossings per edge in a monotone drawing is in general unbounded for the class of bipartite outerplanar, cubic, or bounded pathwidth DAGs. However, it is at most two for outerpaths and it is at most quadratic in the bandwidth in general. From the computational point of view, we prove that upward-$k$-planarity testing is NP-complete already for $k =1$ and even for restricted instances for which upward planarity testing is polynomial. On the positive side, we can decide in linear time whether a single-source DAG admits an upward $1$-planar drawing in which all vertices are incident to the outer face., Comment: This is the extended version, with full appendix, of a paper to appear in the Proc. 32nd Int. Symp. Graph Drawing & Network Visualization (GD 2024)

The optimal rate of reliable communication over a quantum channel can be enhanced by pre-shared entanglement. Whereas the enhancement may be unbounded in infinite-dimensional settings even when the input power is constrained, a long-standing conjecture asserts that the ratio between the entanglement-assisted and unassisted classical capacities is bounded in finite-dimensional settings [Bennett et al., Phys. Rev. Lett. 83, 3081 (2002)]. In this work, we prove this conjecture by showing that their ratio is upper bounded by $o(d^2)$, where $d$ is the input dimension of the channel. An application to quantum communication with noisy encoders and decoders is given., Comment: 7 pages, 1 figure

For visual recognition, knowledge distillation typically involves transferring knowledge from a large, well-trained teacher model to a smaller student model. In this paper, we introduce an effective method to distill knowledge from an off-the-shelf vision-language model (VLM), demonstrating that it provides novel supervision in addition to those from a conventional vision-only teacher model. Our key technical contribution is the development of a framework that generates novel text supervision and distills free-form text into a vision encoder. We showcase the effectiveness of our approach, termed VLM-KD, across various benchmark datasets, showing that it surpasses several state-of-the-art long-tail visual classifiers. To our knowledge, this work is the first to utilize knowledge distillation with text supervision generated by an off-the-shelf VLM and apply it to vanilla randomly initialized vision encoders.

Emergent language research has made significant progress in recent years, but still largely fails to explore how communication emerges in more complex and situated multi-agent systems. Existing setups often employ a reference game, which limits the range of language emergence phenomena that can be studied, as the game consists of a single, purely language-based interaction between the agents. In this paper, we address these limitations and explore the emergence and utility of token-based communication in open-ended multi-agent environments, where situated agents interact with the environment through movement and communication over multiple time-steps. Specifically, we introduce two novel cooperative environments: Multi-Agent Pong and Collectors. These environments are interesting because optimal performance requires the emergence of a communication protocol, but moderate success can be achieved without one. By employing various methods from explainable AI research, such as saliency maps, perturbation, and diagnostic classifiers, we are able to track and interpret the agents' language channel use over time. We find that the emerging communication is sparse, with the agents only generating meaningful messages and acting upon incoming messages in states where they cannot succeed without coordination., Comment: 10 pages, 4 figures, 4 tables, preprint

Memory safety is an essential correctness property of software systems. For programs operating on linked heap-allocated data structures, the problem of proving memory safety boils down to analyzing the possible shapes of data structures, leading to the field of shape analysis. This paper presents a novel reduction-based approach to memory safety analysis that relies on two forms of abstraction: flow abstraction, representing global properties of the heap graph through local flow equations; and view abstraction, which enable verification tools to reason symbolically about an unbounded number of heap objects. In combination, the two abstractions make it possible to reduce memory-safety proofs to proofs about heap-less imperative programs that can be discharged using off-the-shelf software verification tools without built-in support for heap reasoning. Using an empirical evaluation on a broad range of programs, the paper shows that the reduction approach can effectively verify memory safety for sequential and concurrent programs operating on different kinds of linked data structures, including singly-linked, doubly-linked, and nested lists as well as trees.

This paper is centered around the approximation of dynamical systems by means of Gaussian processes. To this end, trajectories of such systems must be collected to be used as training data. The measurements of these trajectories are typically noisy, which implies that both the regression inputs and outputs are corrupted by noise. However, most of the literature considers only noise in the regression outputs. In this paper, we show how to account for the noise in the regression inputs in an extended Gaussian process framework to approximate scalar and multidimensional systems. We demonstrate the potential of our framework by comparing it to different state-of-the-art methods in several simulation examples., Comment: 6 pages

We present GERestaurant, a novel dataset consisting of 3,078 German language restaurant reviews manually annotated for Aspect-Based Sentiment Analysis (ABSA). All reviews were collected from Tripadvisor, covering a diverse selection of restaurants, including regional and international cuisine with various culinary styles. The annotations encompass both implicit and explicit aspects, including all aspect terms, their corresponding aspect categories, and the sentiments expressed towards them. Furthermore, we provide baseline scores for the four ABSA tasks Aspect Category Detection, Aspect Category Sentiment Analysis, End-to-End ABSA and Target Aspect Sentiment Detection as a reference point for future advances. The dataset fills a gap in German language resources and facilitates exploration of ABSA in the restaurant domain., Comment: Accepted in KONVENS 2024. Camera Ready submission

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.

In this paper the accuracy and robustness of quality measures for the assessment of machine learning models are investigated. The prediction quality of a machine learning model is evaluated model-independent based on a cross-validation approach, where the approximation error is estimated for unknown data. The presented measures quantify the amount of explained variation in the model prediction. The reliability of these measures is assessed by means of several numerical examples, where an additional data set for the verification of the estimated prediction error is available. Furthermore, the confidence bounds of the presented quality measures are estimated and local quality measures are derived from the prediction residuals obtained by the cross-validation approach., Comment: accepted for publication in Engineering Modelling, Analysis & Simulation (EMAS)

The treewidth is a structural parameter that measures the tree-likeness of a graph. Many algorithmic and combinatorial results are expressed in terms of the treewidth. In this paper, we study the treewidth of outer $k$-planar graphs, that is, graphs that admit a straight-line drawing where all the vertices lie on a circle, and every edge is crossed by at most $k$ other edges. Wood and Telle [New York J. Math., 2007] showed that every outer $k$-planar graph has treewidth at most $3k + 11$ using so-called planar decompositions, and later, Auer et al. [Algorithmica, 2016] proved that the treewidth of outer $1$-planar graphs is at most $3$, which is tight. In this paper, we improve the general upper bound to $1.5k + 2$ and give a tight bound of $4$ for $k = 2$. We also establish a lower bound: we show that, for every even $k$, there is an outer $k$-planar graph with treewidth $k+2$. Our new bound immediately implies a better bound on the cop number, which answers an open question of Durocher et al. [GD 2023] in the affirmative. Our treewidth bound relies on a new and simple triangulation method for outer $k$-planar graphs that yields few crossings with graph edges per edge of the triangulation. Our method also enables us to obtain a tight upper bound of $k + 2$ for the separation number of outer $k$-planar graphs, improving an upper bound of $2k + 3$ by Chaplick et al. [GD 2017]. We also consider outer min-$k$-planar graphs, a generalization of outer $k$-planar graphs, where we achieve smaller improvements., Comment: Appears in the Proceedings of the 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024)

The Antillean manatee (\emph{Trichechus manatus}) is an endangered herbivorous aquatic mammal whose role as an ecological balancer and umbrella species underscores the importance of its conservation. An innovative approach to monitor manatee populations is passive acoustic monitoring (PAM), where vocalisations are extracted from submarine audio. We propose a novel end-to-end approach to detect manatee vocalisations building on the Audio Spectrogram Transformer (AST). In a transfer learning spirit, we fine-tune AST to detect manatee calls by redesigning its filterbanks and adapting a real-world dataset containing partial positive labels. Our experimental evaluation reveals the two key features of the proposed model: i) it performs on par with the state of the art without requiring hand-tuned denoising or detection stages, and ii) it can successfully identify missed vocalisations in the training dataset, thus reducing the workload of expert bioacoustic labellers. This work is a preliminary relevant step to develop novel, user-friendly tools for the conservation of the different species of manatees., Comment: Accepted at MLSP 2024

We present an Integer Linear Programming based approach to finding the optimal fusion strategy for combinator-based parallel programs. While combinator-based languages or libraries provide a convenient interface for programming parallel hardware, fusing combinators to more complex operations is essential to achieve the desired performance. Our approach is not only suitable for languages with the usual map, fold, scan, indexing and scatter operations, but also gather operations, which access arrays in arbitrary order, and therefore goes beyond the traditional producer-consumer fusion. It can be parametrised with appropriate cost functions, and is fast enough to be suitable for just-in-time compilation., Comment: 12 pages, 6 figures, submitted to FProPer '24

We present a review of popular simulation engines and frameworks used in reinforcement learning (RL) research, aiming to guide researchers in selecting tools for creating simulated physical environments for RL and training setups. It evaluates nine frameworks (Brax, Chrono, Gazebo, MuJoCo, ODE, PhysX, PyBullet, Webots, and Unity) based on their popularity, feature range, quality, usability, and RL capabilities. We highlight the challenges in selecting and utilizing physics engines for RL research, including the need for detailed comparisons and an understanding of each framework's capabilities. Key findings indicate MuJoCo as the leading framework due to its performance and flexibility, despite usability challenges. Unity is noted for its ease of use but lacks scalability and simulation fidelity. The study calls for further development to improve simulation engines' usability and performance and stresses the importance of transparency and reproducibility in RL research. This review contributes to the RL community by offering insights into the selection process for simulation engines, facilitating informed decision-making., Comment: 11 pages, 3 figures

This comprehensive study, being part of the European XFEL R\&D effort, elucidates the influence of medium temperature (mid-T) heat treatments between 250{\deg}C and 350{\deg}C on the performance of 1.3~GHz superconducting radiofrequency (SRF) niobium cavities. Utilizing a refurbished niobium retort furnace equipped with an inter-vacuum chamber and cryopumps at DESY, we have embarked on an investigation to enhance the state-of-the-art SRF cavity technology. Our research reveals that mid-T heat treatments significantly boost the quality factor ($Q_0$) of the cavities, achieving values between $2\cdot10^{10}$ to $5\cdot10^{10}$ at field strengths around 16~MV/m, while the maximum field strengths are limited to 25-35~MV/m and enhanced sensitivity to trapped magnetic flux is observed. Moreover, we delve into the effects of surface impurity concentration changes, particularly the diffusion of oxygen content, and its impact on performance enhancements. By categorizing treatments based on calculated diffusion lengths using the whole temperature profile, we recognize patterns that suggest an optimal diffusion length conducive to optimizing cavity performance. SIMS results from samples confirm the calculated oxygen diffusion lengths in most instances. Deviations are primarily attributed to grain boundaries in fine-grain materials, necessitating repeated measurements on single-crystal materials to further investigate this phenomenon. Investigations into cooling rates and the resulting spatial temperature gradients across the cavities ranging from 0.04 to 0.2~K/mm reveal no significant correlation with performance following a mid-T heat treatment. However, the increased sensitivity to trapped magnetic flux leads to new challenges in the quest for next-generation accelerator technologies since the requirement for magnetic hygiene gets stricter., Comment: 15 pages, 24 figures

Hardware trojan detection methods, based on machine learning (ML) techniques, mainly identify suspected circuits but lack the ability to explain how the decision was arrived at. An explainable methodology and architecture is introduced based on the existing hardware trojan detection features. Results are provided for explaining digital hardware trojans within a netlist using trust-hub trojan benchmarks.

We explore the relation of weak conjugacy in the group of homeomorphisms isotopic to the identity, for surfaces., Comment: 18 pages, 2 figures

We present a novel approach to achieve spatial variations in the degree of non-covalent functionalization of twisted bilayer graphene (tBLG). The tBLG with twist angles varying between ~ 5{\deg} and 7{\deg} was non-covalently functionalized with 1,4,5,8,9,11-hexaazatriphenylenehexacarbonitrile (HATCN) molecules. Our results show a correlation between the degree of functionalization and the twist angle of tBLG. This correlation was determined through Raman spectroscopy, where areas with larger twist angles exhibited a lower HATCN peak intensity compared to areas with smaller twist angles. We suggest that the HATCN adsorption follows the moir\'e pattern of tBLG by avoiding AA-stacked areas and attach predominantly to areas with a local AB-stacking order of tBLG, forming an overall ABA-stacking configuration. This is supported by density functional theory (DFT) calculations. Our work highlights the role of the moir\'e lattice in controlling the non-covalent functionalization of tBLG. Our approach can be generalized for designing nanoscale patterns on two-dimensional (2D) materials using moir\'e structures as a template.

PSR J0740+6620 is the neutron star with the highest precisely determined mass, inferred from radio observations to be $2.08\pm0.07\,\rm M_\odot$. Measurements of its radius therefore hold promise to constrain the properties of the cold, catalyzed, high-density matter in neutron star cores. Previously, Miller et al. (2021) and Riley et al. (2021) reported measurements of the radius of PSR J0740+6620 based on Neutron Star Interior Composition Explorer (NICER) observations accumulated through 17 April 2020, and an exploratory analysis utilizing NICER background estimates and a data set accumulated through 28 December 2021 was presented in Salmi et al. (2022). Here we report an updated radius measurement, derived by fitting models of X-ray emission from the neutron star surface to NICER data accumulated through 21 April 2022, totaling $\sim1.1$ Ms additional exposure compared to the data set analyzed in Miller et al. (2021) and Riley et al. (2021), and to data from X-ray Multi-Mirror (XMM-Newton) observations. We find that the equatorial circumferential radius of PSR J0740+6620 is $12.92_{-1.13}^{+2.09}$ km (68% credibility), a fractional uncertainty $\sim83\%$ the width of that reported in Miller et al. (2021), in line with statistical expectations given the additional data. If we were to require the radius to be less than 16 km, as was done in Salmi et al. (2024), then our 68% credible region would become $R=12.76^{+1.49}_{-1.02}$ km, which is close to the headline result of Salmi et al. (2024). Our updated measurements, along with other laboratory and astrophysical constraints, imply a slightly softer equation of state than that inferred from our previous measurements., Comment: 18 pages, 8 figures, +appendices. Accepted in ApJ

We report an updated analysis of the radius, mass, and heated surface regions of the massive pulsar PSR J0740+6620 using Neutron Star Interior Composition Explorer (NICER) data from 2018 September 21 to 2022 April 21, a substantial increase in data set size compared to previous analyses. Using a tight mass prior from radio timing measurements and jointly modeling the new NICER data with XMM-Newton data, the inferred equatorial radius and gravitational mass are $12.49_{-0.88}^{+1.28}$ km and $2.073_{-0.069}^{+0.069}$ $M_\odot$ respectively, each reported as the posterior credible interval bounded by the $16\,\%$ and $84\,\%$ quantiles, with an estimated systematic error $\lesssim 0.1$ km. This result was obtained using the best computationally feasible sampler settings providing a strong radius lower limit but a slightly more uncertain radius upper limit. The inferred radius interval is also close to the $R=12.76_{-1.02}^{+1.49}$ km obtained by Dittmann et al., when they require the radius to be less than $16$ km as we do. The results continue to disfavor very soft equations of state for dense matter, with $R<11.15$ km for this high-mass pulsar excluded at the $95\,\%$ probability. The results do not depend significantly on the assumed cross-calibration uncertainty between NICER and XMM-Newton. Using simulated data that resemble the actual observations, we also show that our pipeline is capable of recovering parameters for the inferred models reported in this paper., Comment: 17 pages, 9 figures (2 of which are figure sets), 2 tables, published in ApJ

In 2021, the high-mass X-ray binary EXO 2030+375 underwent a giant X-ray outburst, the first since 2006, that reached a peak flux of ${\sim}600\,\mathrm{mCrab}$ (3-50\,keV). The goal of this work is to study the spectral evolution over the course of the outburst, search for possible cyclotron resonance scattering features (CRSFs), and to associate spectral components with the emission pattern of the accretion column. We used broadband spectra taken with the Nuclear Spectroscopic Telescope Array (NuSTAR), the Neutron Star Interior Composition Explorer (NICER), and Chandra near the peak and during the decline phase of the outburst. We describe the data with established empirical continuum models and perform pulse-phase-resolved spectroscopy. We compare the spectral evolution with pulse phase using a proposed geometrical emission model. We find a significant spectral hardening toward lower luminosity, a behavior that is expected for super-critical sources. The continuum shape and evolution cannot be described by a simple power-law model with exponential cutoff; it requires additional absorption or emission components. We can confirm the presence of a narrow absorption feature at ${\sim}10\,\mathrm{keV}$ in both NuSTAR observations. The absence of harmonics puts into question the interpretation of this feature as a CRSF. The empirical spectral components cannot be directly associated with identified emission components from the accretion column., Comment: 12 pages, 8 figures, accepted in A&A

Blood glucose prediction is an important component of biomedical technology for managing diabetes with automated insulin delivery systems. Machine learning and deep learning algorithms hold the potential to advance this technology. However, the lack of standardized methodologies impedes direct comparisons of emerging algorithms. This study addresses this challenge by developing GluPredKit, a software platform designed to standardize the training, testing, and comparison of blood glucose prediction algorithms. GluPredKit features a modular, open-source architecture, complemented by a command-line interface, comprehensive documentation, and a video tutorial to enhance usability. To ensure the platform's effectiveness and user-friendliness, we conducted preliminary testing and a user study. In this study, four participants interacted with GluPredKit and provided feedback through the System Usability Scale (SUS) and open-ended questions. The findings indicate that GluPredKit effectively addresses the standardization challenge and offers high usability, facilitating direct comparisons between different algorithms. Additionally, it serves an educational purpose by making advanced methodologies more accessible. Future directions include continuously enhancing the software based on user feedback. We also invite community contributions to further expand GluPredKit with state-of-the-art components and foster a collaborative effort in standardizing blood glucose prediction research, leading to more comparable studies., Comment: 18 pages, 5 figures, submitted to the IEEE-EMBS International Conference on Biomedical and Health Informatics

Explainability is needed to establish confidence in machine learning results. Some explainable methods take a post hoc approach to explain the weights of machine learning models, others highlight areas of the input contributing to decisions. These methods do not adequately explain decisions, in plain terms. Explainable property-based systems have been shown to provide explanations in plain terms, however, they have not performed as well as leading unexplainable machine learning methods. This research focuses on the importance of metrics to explainability and contributes two methods yielding performance gains. The first method introduces a combination of explainable and unexplainable flows, proposing a metric to characterize explainability of a decision. The second method compares classic metrics for estimating the effectiveness of neural networks in the system, posing a new metric as the leading performer. Results from the new methods and examples from handwritten datasets are presented.

Pulse Profile Modeling (PPM), the technique used to infer mass, radius and geometric parameters for rotation-powered millisecond pulsars using data from the Neutron Star Interior Composition Explorer (NICER), relies on relativistic ray-tracing of thermal X-ray photons from hot spots on the neutron star surface to the observer. To verify our ray-tracing codes we have in the past conducted cross-tests for simple hot spot geometries, focusing primarily on the implementation of the space-time model. In this paper, we present verification for test problems that explore the more complex hot spot geometries that are now being employed in the NICER PPM analyses. We conclude that the accuracy of our computed waveforms is in general sufficiently high for analyses of current NICER data sets. We have however identified some extreme configurations where extra care may be needed., Comment: Updated to version accepted by The Astrophysical Journal following their review process

Cover songs are alternate versions of a song by a different artist. Long being a vital part of the music industry, cover songs significantly influence music culture and are commonly heard in public venues. The rise of online music platforms has further increased their prevalence, often as background music or video soundtracks. While current automatic identification methods serve adequately for original songs, they are less effective with cover songs, primarily because cover versions often significantly deviate from the original compositions. In this paper, we propose a novel method for cover song detection that utilizes the lyrics of a song. We introduce a new dataset for cover songs and their corresponding originals. The dataset contains 5078 cover songs and 2828 original songs. In contrast to other cover song datasets, it contains the annotated lyrics for the original song and the cover song. We evaluate our method on this dataset and compare it with multiple baseline approaches. Our results show that our method outperforms the baseline approaches., Comment: 6 pages, 3 figures

The contextual integrity model is a widely accepted way of analyzing the plurality of norms that are colloquially called "privacy norms". Contextual integrity systematically describes such norms by distinguishing the type of data concerned, the three social agents involved (subject, sender, and recipient) and the transmission principle governing the transfer of information. It allows analyzing privacy norms in terms of their impact on the interaction of those agents with one another. This paper places contextual integrity in a strict game theoretic framework. When such description is possible it has three key advantages: Firstly, it allows indisputable utilitarian justification of some privacy norms. Secondly, it better relates privacy to topics which are well understood by stakeholders whose education is predominantly quantitative, such as engineers and economists. Thirdly, it is an absolute necessity when describing ethical constraints to machines such as AI agents. In addition to describing games which capture paradigmatic informational norms, the paper also analyzes cases in which the game, per se, does not encourage normative behavior. The paper discusses two main forms of mechanisms which can be applied to the game in such cases, and shows that they reflect accepted privacy regulation and technologies.

In this study, we investigate the potential of fast-to-evaluate surrogate modeling techniques for developing a hybrid digital twin of a steel-reinforced concrete beam, serving as a representative example of a civil engineering structure. As surrogates, two distinct models are developed utilizing physics-informed neural networks, which integrate experimental data with given governing laws of physics. The experimental data (sensor data) is obtained from a previously conducted four-point bending test. The first surrogate model predicts strains at fixed locations along the center line of the beam for various time instances. This time-dependent surrogate model is inspired by the motion of a harmonic oscillator. For this study, we further compare the physics-based approach with a purely data-driven method, revealing the significance of physical laws for the extrapolation capabilities of models in scenarios with limited access to experimental data. Furthermore, we identify the natural frequency of the system by utilizing the physics-based model as an inverse solver. For the second surrogate model, we then focus on a fixed instance in time and combine the sensor data with the equations of linear elasticity to predict the strain distribution within the beam. This example reveals the importance of balancing different loss components through the selection of suitable loss weights.

Pseudospectral approximation provides a means to approximate the dynamics of delay differential equations (DDE) by ordinary differential equations (ODE). This article develops a computer-aided algorithm to determine the distance between the unstable manifold of a DDE and the unstable manifold of the approximating pseudospectral ODE. The algorithm is based upon the parametrization method. While a-priori the parametrization method for a vector-valued ODE involves computing a sequence of vector-valued Taylor coefficients, we show that for the pseudospectral ODE, due to its specific structure, the problem reduces to finding a sequence of scalars, which significantly simplifies the problem.

Contemporary quantum devices are reaching new limits in size and complexity, allowing for the experimental exploration of emergent quantum modes. However, this increased complexity introduces significant challenges in device tuning and control. Here, we demonstrate autonomous tuning of emergent Majorana zero modes in a minimal realization of a Kitaev chain. We achieve this task using cross-platform transfer learning. First, we train a tuning model on a theory model. Next, we retrain it using a Kitaev chain realization in a two-dimensional electron gas. Finally, we apply this model to tune a Kitaev chain realized in quantum dots coupled through a semiconductor-superconductor section in a one-dimensional nanowire. Utilizing a convolutional neural network, we predict the tunneling and Cooper pair splitting rates from differential conductance measurements, employing these predictions to adjust the electrochemical potential to a Majorana sweet spot. The algorithm successfully converges to the immediate vicinity of a sweet spot (within 1.5 mV in 67.6% of attempts and within 4.5 mV in 80.9% of cases), typically finding a sweet spot in 45 minutes or less. This advancement is a stepping stone towards autonomous tuning of emergent modes in interacting systems, and towards foundational tuning machine learning models that can be deployed across a range of experimental platforms.

With the rapid expansion of academic literature and the proliferation of preprints, researchers face growing challenges in manually organizing and labeling large volumes of articles. The NSLP 2024 FoRC Shared Task I addresses this challenge organized as a competition. The goal is to develop a classifier capable of predicting one of 123 predefined classes from the Open Research Knowledge Graph (ORKG) taxonomy of research fields for a given article.This paper presents our results. Initially, we enrich the dataset (containing English scholarly articles sourced from ORKG and arXiv), then leverage different pre-trained language Models (PLMs), specifically BERT, and explore their efficacy in transfer learning for this downstream task. Our experiments encompass feature-based and fine-tuned transfer learning approaches using diverse PLMs, optimized for scientific tasks, including SciBERT, SciNCL, and SPECTER2. We conduct hyperparameter tuning and investigate the impact of data augmentation from bibliographic databases such as OpenAlex, Semantic Scholar, and Crossref. Our results demonstrate that fine-tuning pre-trained models substantially enhances classification performance, with SPECTER2 emerging as the most accurate model. Moreover, enriching the dataset with additional metadata improves classification outcomes significantly, especially when integrating information from S2AG, OpenAlex and Crossref. Our best-performing approach achieves a weighted F1-score of 0.7415. Overall, our study contributes to the advancement of reliable automated systems for scholarly publication categorization, offering a potential solution to the laborious manual curation process, thereby facilitating researchers in efficiently locating relevant resources., Comment: 8 pages, 2 figures, NSLP2024 conference

We present the discovery, with the Neutron Star Interior Composition Explorer (NICER), that SRGA J144459.2-604207 is a 447.9 Hz accreting millisecond X-ray pulsar (AMXP), which underwent a four-week long outburst starting on 2024 February 15. The AMXP resides in a 5.22 hr binary, orbiting a low-mass companion donor with $M_d>0.1M_\odot$. We report on the temporal and spectral properties from NICER observations during the early days of the outburst, from 2024 February 21 through 2024 February 23, during which NICER also detected a type-I X-ray burst that exhibited a plateau lasting ~6 s. The spectra of the persistent emission were well described by an absorbed thermal blackbody and power-law model, with blackbody temperature $kT\approx0.9{\rm\,keV}$ and power-law photon index $\Gamma\approx1.9$. Time-resolved burst spectroscopy confirmed the thermonuclear nature of the burst, where an additional blackbody component reached a maximum temperature of nearly $kT\approx3{\rm\,keV}$ at the peak of the burst. We discuss the nature of the companion as well as the type-I X-ray burst., Comment: 11 pages, 4 figures, 3 tables. Accepted by ApJL

Retrieving the optical depth of the Martian clouds ($\tau_\mathrm{cld}$) is a powerful way to monitor their spatial and temporal evolution. However, such retrievals from nadir imagery rely on several assumptions, including the vertical structure of the clouds in the atmosphere. Here we compare the results of cloud optical depth retrievals at 320 nm from the Emirates eXploration Imager (EXI) onboard the Emirates Mars Mission (EMM) "Hope" orbiter performed using a basic uniform cloud profile used in previous studies and using derived cloud profiles obtained from near-simultaneous Solar Occultation observations in the 3.1-3.4 $\mu$m spectral range from the Middle-Infrared channel of the Atmospheric Chemistry Suite (ACS) instrument onboard the ESA Trace Gas Orbiter (TGO). We show that the latitudinal dependence of the cloud vertical profiles can have a strong impact on the nadir retrievals; neglecting it can lead to a significant underestimation of $\tau_\mathrm{cld}$ in the polar regions (up to 25 % to 50 %, depending on the vertical distribution of the dust in the atmosphere) and to a lesser extent, to an overestimation of $\tau_\mathrm{cld}$ around the equator. We also discuss the impact of a vertically-dependent particle size profile, as previous studies have shown the presence of very small water ice particles at the top of the clouds. From this analysis, we provide recommendations for the improvement of water ice cloud parameterization in radiative transfer algorithms in nadir atmospheric retrievals.

According to the World Health Organization, the involvement of Vulnerable Road Users (VRUs) in traffic accidents remains a significant concern, with VRUs accounting for over half of traffic fatalities. The increase of automation and connectivity levels of vehicles has still an uncertain impact on VRU safety. By deploying the Collective Perception Service (CPS), vehicles can include information about VRUs in Vehicle-to-Everything (V2X) messages, thus raising the general perception of the environment. Although an increased awareness is considered positive, one could argue that the awareness ratio, the metric used to measure perception, is only implicitly connected to the VRUs' safety. This paper introduces a tailored metric, the Risk Factor (RF), to measure the risk level for the interactions between Connected Automated Vehicles (CAVs) and VRUs. By evaluating the RF, we assess the impact of V2X communication on VRU risk mitigation. Our results show that high V2X penetration rates can reduce mean risk, quantified by our proposed metric, by up to 44%. Although the median risk value shows a significant decrease, suggesting a reduction in overall risk, the distribution of risk values reveals that CPS's mitigation effectiveness is overestimated, which is indicated by the divergence between RF and awareness ratio. Additionally, by analyzing a real-world traffic dataset, we pinpoint high-risk locations within a scenario, identifying areas near intersections and behind parked cars as especially dangerous. Our methodology can be ported and applied to other scenarios in order to identify high-risk areas. We value the proposed RF as an insightful metric for quantifying VRU safety in a highly automated and connected environment., Comment: Accepted for 15th IEEE Vehicular Networking Conference (VNC) 2024

Ensuring the safety of Vulnerable Road Users (VRUs) is a critical concern in transportation, demanding significant attention from researchers and engineers. Recent advancements in Vehicle-to-Everything (V2X) technology offer promising solutions to enhance VRU safety. Notably, VRUs often travel in groups, exhibiting similar movement patterns that facilitate the formation of clusters. The standardized Collective Perception Message (CPM) and VRU Awareness Message in ETSI's Release 2 consider this clustering behavior, allowing for the description of VRU clusters. Given the constraints of narrow channel bandwidth, the selection of an appropriate geometric shape for representing a VRU cluster becomes crucial for efficient data transmission. In our study we conduct a comprehensive evaluation of different geometric shapes used to describe VRU clusters. We introduce two metrics: Cluster Accuracy (CA) and Comprehensive Area Density Information (CADI), to assess the precision and efficiency of each shape. Beyond comparing predefined shapes, we propose an adaptive algorithm that selects the preferred shape for cluster description, prioritizing accuracy while maintaining a high level of efficiency. The study culminates by demonstrating the benefits of clustering on data transmission rates. We simulate VRU movement using real-world data and the transmission of CPMs by a roadside unit. The results reveal that broadcasting cluster information, as opposed to individual object data, can reduce the data transmission volume by two-thirds on average. This finding underscores the potential of clustering in V2X communications to enhance VRU safety while optimizing network resources., Comment: Accepted for 15th IEEE Vehicular Networking Conference (VNC)

Liquid phase exfoliation (LPE) has been used for the successful fabrication of nanosheets from a large number of van der Waals materials. While this allows to study fundamental changes of material properties' associated with reduced dimensions, it also changes the chemistry of many materials due to a significant increase of the effective surface area, often accompanied with enhanced reactivity and accelerated oxidation. To prevent material decomposition, LPE and processing in inert atmosphere have been developed, which enables the preparation of pristine nanomaterials, and to systematically study compositional changes over time for different storage conditions. Here, we demonstrate the inert exfoliation of the oxidation-sensitive van der Waals crystal, CrTe$_3$. The pristine nanomaterial was purified and size-selected by centrifugation, nanosheet dimensions in the fractions quantified by atomic force microscopy and studied by Raman, X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray spectroscopy (EDX) and photo spectroscopic measurements. We find a dependence of the relative intensities of the CrTe$_3$ Raman modes on the propagation direction of the incident light, which prevents a correlation of the Raman spectral profile to the nanosheet dimensions. XPS and EDX reveal that the contribution of surface oxides to the spectra is reduced after exfoliation compared to the bulk material. Further, the decomposition mechanism of the nanosheets was studied by time-dependent extinction measurements after water titration experiments to initially dry solvents, which suggest that water plays a significant role in the material decomposition.