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Context. The supernova remnant (SNR) W44 and its surroundings are a prime target for studying the acceleration of cosmic rays (CRs). Several previous studies established an extended gamma-ray emission that is set apart from the radio shell of W44. This emission is thought to originate from escaped high-energy CRs that interact with a surrounding dense molecular cloud complex. Aims. We present a detailed analysis of Fermi-LAT data with an emphasis on the spatial and spectral properties of W44 and its surroundings. We also report the results of the observations performed with the MAGIC telescopes of the northwestern region of W44. Finally, we present an interpretation model to explain the gamma-ray emission of the SNR and its surroundings. Methods. We first performed a detailed spatial analysis of 12 years of Fermi-LAT data at energies above 1 GeV, in order to exploit the better angular resolution, while we set a threshold of 100MeV for the spectral analysis. We performed a likelihood analysis of 174 hours of MAGIC data above 130 GeV using the spatial information obtained with Fermi-LAT. Results. The combined spectra of Fermi-LAT and MAGIC, extending from 100MeV to several TeV, were used to derive constraints on the escape of CRs. Using a time-dependent model to describe the particle acceleration and escape from the SNR, we show that the maximum energy of the accelerated particles has to be ' 40 GeV. However, our gamma-ray data suggest that a small number of lower-energy particles also needs to escape. We propose a novel model, the broken-shock scenario, to account for this effect and explain the gamma-ray emission.

Mrk 421 was in its most active state around early 2010, which led to the highest TeV gamma-ray flux ever recorded from any active galactic nuclei. We aim to characterize the multiwavelength behavior during this exceptional year for Mrk 421, and evaluate whether it is consistent with the picture derived with data from other less exceptional years. We investigated the period from November 5, 2009, (MJD 55140) until July 3, 2010, (MJD 55380) with extensive coverage from very-high-energy (VHE; E$\,>\,$100$\,$GeV) gamma rays to radio with MAGIC, VERITAS, Fermi-LAT, RXTE, Swift, GASP-WEBT, VLBA, and a variety of additional optical and radio telescopes. We investigated the variability and correlation behavior among different energy bands in great detail. We find the strongest variability in X-rays and VHE gamma rays, and PSDs compatible with power-law functions. We observe strong correlations between X-rays and VHE gamma rays. We also report a marginally significant positive correlation between high-energy (HE; E$\,>\,$100$\,$MeV) gamma rays and the ultraviolet band. We detected marginally significant correlations between the HE and VHE gamma rays, and between HE gamma rays and the X-ray, that disappear when the large flare in February 2010 is excluded from the correlation study. The activity of Mrk 421 also yielded the first ejection of features in the VLBA images of the jet of Mrk 421. Yet the large uncertainties in the ejection times of these radio features prevent us from firmly associating them to the specific flares recorded during the campaign. We also show that the collected multi-instrument data are consistent with a scenario where the emission is dominated by two regions, a compact and extended zone, which could be considered as a simplified implementation of an energy-stratified jet as suggested by recent IXPE observations., Comment: Accepted for publication in Astronomy & Astrophysics. Corresponding authors: Felix Schmuckermaier, David Paneque, Axel Arbet Engels

The Apollo Advanced Telecommunications Computing Architecture (ATCA) platform is an open-source design consisting of a generic "Service Module" (SM) and a customizable "Command Module" (CM), allowing for cost-effective use in applications such as the readout of the inner tracker and the Level-1 track trigger for the CMS Phase-II upgrade at the HL-LHC. The SM integrates an intelligent IPMC, robust power entry and conditioning systems, a powerful system-on-module computer, and flexible clock and communication infrastructure. The CM is designed around two Xilinx Ultrascale+ FPGAs and high-density, high-bandwidth optical transceivers capable of 25 Gb/s. Crates of Apollo blades are currently being tested at Boston University, Cornell University, and CERN., Comment: 7 pages, 4 figures, TWEPP2024 conference proceedings

We present results from a high cadence multi-wavelength observational campaign of the enigmatic changing look AGN 1ES 1927+654 from May 2022- April 2024, coincident with an unprecedented radio flare (an increase in flux by a factor of $\sim 60$ over a few months) and the emergence of a spatially resolved jet at $0.1-0.3$ pc scales (Meyer et al. 2024). Companion work has also detected a recurrent quasi-periodic oscillation (QPO) in the $2-10$ keV band with an increasing frequency ($1-2$ mHz) over the same period (Masterson et al., 2025). During this time, the soft X-rays ($0.3-2$ keV) monotonically increased by a factor of $\sim 8$, while the UV emission remained near-steady with $<30\%$ variation and the $2-10$ keV flux showed variation by a factor $\lesssim 2$. The weak variation of the $2-10$ keV X-ray emission and the stability of the UV emission suggest that the magnetic energy density and accretion rate are relatively unchanged, and that the jet could be launched due to a reconfiguration of the magnetic field (toroidal to poloidal) close to the black hole. Advecting poloidal flux onto the event horizon would trigger the Blandford-Znajek (BZ) mechanism, leading to the onset of the jet. The concurrent softening of the coronal slope (from $\Gamma= 2.70\pm 0.04$ to $\Gamma=3.27\pm 0.04$), the appearance of a QPO, and low coronal temperature ($kT_{e}=8_{-3}^{+8}$ keV) during the radio outburst suggest that the poloidal field reconfiguration can significantly impact coronal properties and thus influence jet dynamics. These extraordinary findings in real time are crucial for coronal and jet plasma studies, particularly as our results are independent of coronal geometry., Comment: Submitted to ApJ after minor referee comments

This paper proposes a theoretical framework for analyzing Modified Incomplete LU (MILU) preconditioners. Considering a generalized MILU preconditioner on a weighted undirected graph with self-loops, we extend its applicability beyond matrices derived by Poisson equation solvers on uniform grids with compact stencils. A major contribution is, a novel measure, the \textit{Localized Estimator of Condition Number (LECN)}, which quantifies the condition number locally at each vertex of the graph. We prove that the maximum value of the LECN provides an upper bound for the condition number of the MILU preconditioned system, offering estimation of the condition number using only local measurements. This localized approach significantly simplifies the condition number estimation and provides a powerful tool or analyzing the MILU preconditioner applied to previously unexplored matrix structures. To demonstrate the usability of LECN analysis, we present three cases: (1) revisit to existing results of MILU preconditioners on uniform grids, (2) analysis of high-order implicit finite difference schemes on wide stencils, and (3) analysis of variable coefficient Poisson equations on hierarchical adaptive grids such as quadtree and octree. For the third case, we also validate LECN analysis numerically on a quadtree.

Conversational question answering (ConvQA) is a convenient means of searching over RDF knowledge graphs (KGs), where a prevalent approach is to translate natural language questions to SPARQL queries. However, SPARQL has certain shortcomings: (i) it is brittle for complex intents and conversational questions, and (ii) it is not suitable for more abstract needs. Instead, we propose a novel two-pronged system where we fuse: (i) SQL-query results over a database automatically derived from the KG, and (ii) text-search results over verbalizations of KG facts. Our pipeline supports iterative retrieval: when the results of any branch are found to be unsatisfactory, the system can automatically opt for further rounds. We put everything together in a retrieval augmented generation (RAG) setup, where an LLM generates a coherent response from accumulated search results. We demonstrate the superiority of our proposed system over several baselines on a knowledge graph of BMW automobiles., Comment: Accepted at BTW 2025, 10 pages

The BL Lacertae object VER J0521+211 underwent a notable flaring episode in February 2020. A short-term monitoring campaign, led by the MAGIC (Major Atmospheric Gamma Imaging Cherenkov) collaboration, covering a wide energy range from radio to very-high-energy (VHE, 100 GeV < E < 100 TeV) gamma rays was organised to study its evolution. These observations resulted in a consistent detection of the source over six consecutive nights in the VHE gamma-ray domain. Combining these nightly observations with an extensive set of multiwavelength data made modelling of the blazar's spectral energy distribution (SED) possible during the flare. This modelling was performed with a focus on two plausible emission mechanisms: i) a leptonic two-zone synchrotron-self-Compton scenario, and ii) a lepto-hadronic one-zone scenario. Both models effectively replicated the observed SED from radio to the VHE gamma-ray band. Furthermore, by introducing a set of evolving parameters, both models were successful in reproducing the evolution of the fluxes measured in different bands throughout the observing campaign. Notably, the lepto-hadronic model predicts enhanced photon and neutrino fluxes at ultra-high energies (E > 100 TeV). While the photon component, generated via decay of neutral pions, is not directly observable as it is subject to intense pair production (and therefore extinction) through interactions with the cosmic microwave background photons, neutrino detectors (e.g. IceCube) can probe the predicted neutrino component. Finally, the analysis of the gamma-ray spectra, as observed by MAGIC and the Fermi-LAT telescopes, yielded a conservative 95\% confidence upper limit of z \leq 0.244 for the redshift of this blazar., Comment: Accepted for publication in A&A

Fully Homomorphic Encryption (FHE) enables operations on encrypted data, making it extremely useful for privacy-preserving applications, especially in cloud computing environments. In such contexts, operations like ranking, order statistics, and sorting are fundamental functionalities often required for database queries or as building blocks of larger protocols. However, the high computational overhead and limited native operations of FHE pose significant challenges for an efficient implementation of these tasks. These challenges are exacerbated by the fact that all these functionalities are based on comparing elements, which is a severely expensive operation under encryption. Previous solutions have typically based their designs on swap-based techniques, where two elements are conditionally swapped based on the results of their comparison. These methods aim to reduce the primary computational bottleneck: the comparison depth, which is the number of non-parallelizable homomorphic comparisons. The current state of the art solution for sorting by Lu et al. (IEEE S&P'21), for instance, achieves a comparison depth of O(log^2(N)). In this paper, we address the challenge of reducing the comparison depth by shifting away from the swap-based paradigm. We present solutions for ranking, order statistics, and sorting, that all achieve a comparison depth of O(1), making our approach highly parallelizable. Leveraging the SIMD capabilities of the CKKS FHE scheme, our approach re-encodes the input vector under encryption to allow for simultaneous comparisons of all elements with each other. The homomorphic re-encoding incurs a minimal computational overhead of O(log(N)) rotations. Experimental results show that our approach ranks a 128-element vector in approximately 2.64s, computes its argmin/argmax in 14.18s, and sorts it in 21.10s.

When considering risky events or actions, we must not downplay the role of involved objects: a charged battery in our phone averts the risk of being stranded in the desert after a flat tyre, and a functional firewall mitigates the risk of a hacker intruding the network. The Common Ontology of Value and Risk (COVER) highlights how the role of objects and their relationships remains pivotal to performing transparent, complete and accountable risk assessment. In this paper, we operationalize some of the notions proposed by COVER -- such as parthood between objects and participation of objects in events/actions -- by presenting a new framework for risk assessment: DODGE. DODGE enriches the expressivity of vetted formal models for risk -- i.e., fault trees and attack trees -- by bridging the disciplines of ontology and formal methods into an ontology-aware formal framework composed by a more expressive modelling formalism, Object-Oriented Disruption Graphs (ODGs), logic (ODGLog) and an intermediate query language (ODGLang). With these, DODGE allows risk assessors to pose questions about disruption propagation, disruption likelihood and risk levels, keeping the fundamental role of objects at risk always in sight.

Many recent studies have pointed out significant discrepancies between observations and models of stellar populations in the near-infrared (NIR). With current and future observing facilities being focused in this wavelength range, properly assessing and solving these issues is of utmost importance. Here, we present the first application of the extragalactic globular cluster (GC) near-infrared spectroscopy survey, and present evidence that these GCs reveal an age zero-point problem of stellar population synthesis (SPS) models. This problem has already been identified in the optical range for the GCs of the Milky Way. Such an issue arises when derived GC spectroscopic ages appear older than the Universe itself. We extend this discussion for the first time to the NIR, specifically using the Pa$_{\rm\beta}$ line at 1.28~microns. We focus on the GCs of the nearby Centaurus~A galaxy using their NIR spectra. This work broadens our understanding of the age zero-point problem and emphasises the necessity to revisit and refine SPS models, especially in the NIR domain., Comment: 11 pages, 6 figures, accepted for publication in A&A

One way to constrain the evolutionary histories of galaxies is to analyse their stellar populations. In the local Universe, our understanding of the stellar population properties of galaxies has traditionally relied on the study of optical absorption and emission-line features. In order to overcome limitations intrinsic to this wavelength range, such as the age-metallicity degeneracy and the high sensitivity to dust reddening, we must use wavelength ranges beyond the optical. The near-infrared (NIR) offers a possibility to extract information on spectral signatures that are not as obvious in traditional optical bands. Moreover, with the current and forthcoming generation of instrumentation focusing on the NIR, it is mandatory to explore possibilities within this wavelength range for nearby-Universe galaxies. However, although the NIR shows great potential, we are only beginning to understand it. Widely used techniques such as a full spectral fitting and line strength indices need to be tested on systems that are as close to simple stellar populations as possible, and the result from the techniques need to be compared to the yields from a traditional optical analysis. We present a NIR spectral survey of extragalactic globular clusters (GCs). The set was composed of 21 GCs from NGC5128 that were obtained with SOAR/TripleSpec4, which covered the ~1.0-2.4$\mu$m range with a spectral resolution R=$\lambda/\Delta\lambda$ of 3500. These spectra cover H$\beta$ equivalent widths between 0.98$\r{A}$ and 4.32$\r{A}$, and [MgFe]' between 0.24$\r{A}$ and 3.76$\r{A}$. This set is ideal for performing absorption band measurements and a full spectral fitting, and it can be used for kinematic studies and age and abundance measurements. With this library, we expect to be able to probe the capabilities of NIR models, as well as to further improve stellar population estimates for the GCs around NGC5128., Comment: 10 pages, 3 figures

Classically simulating circuits with bosonic codes is a challenging task due to the prohibitive cost of simulating quantum systems with many, possibly infinite, energy levels. We propose an algorithm to simulate circuits with encoded Gottesman-Kitaev-Preskill states, specifically for odd-dimensional encoded qudits. Our approach is tailored to be especially effective in the most challenging but practically relevant regime, where the codeword states exhibit high (but finite) squeezing. Our algorithm leverages the Zak-Gross Wigner function introduced by J. Davis et al. [arXiv:2407.18394], which represents infinitely squeezed encoded stabilizer states positively. The runtime of the algorithm scales with the amount of negativity of this Wigner function, enabling fast simulation of certain large-scale circuits with a high degree of squeezing., Comment: 22 pages, 2 figures

A numerical integration of the equations of motion of the Sun-planets-an object system is used to study the evolution of orbits close to the orbit of the P/1996 R2 object, which is a Jupiter-crossing object, and to the asteroidal orbit of the P/1996 N2 object, which, at the moment of its detection, had a tail similar to a cometary one. Small variations in the initial data considerably affect the evolution of orbits close to that of the P/1996 R2 object. The time elapsed up to the ejection of the object into a hyperbolic orbit varied from 3*10^4 to 2.7*10^7 yr. Some objects were in resonances with Jupiter and Saturn for a long time. For about 20 percent of the runs, objects reached the Earth's orbit during evolution. Orbital elements of the P/1996 N2 object changed quasi-periodically over the considered time span of 200 Myr. Variations in the semimajor axis, eccentricity, and inclination were equal to 0.04 AU, 0.11 deg, and 3.5 deg, respectively., Comment: 14 pages, 2 figures

Retrieval Augmented Generation (RAG) works as a backbone for interacting with an enterprise's own data via Conversational Question Answering (ConvQA). In a RAG system, a retriever fetches passages from a collection in response to a question, which are then included in the prompt of a large language model (LLM) for generating a natural language (NL) answer. However, several RAG systems today suffer from two shortcomings: (i) retrieved passages usually contain their raw text and lack appropriate document context, negatively impacting both retrieval and answering quality; and (ii) attribution strategies that explain answer generation typically rely only on similarity between the answer and the retrieved passages, thereby only generating plausible but not causal explanations. In this work, we demonstrate RAGONITE, a RAG system that remedies the above concerns by: (i) contextualizing evidence with source metadata and surrounding text; and (ii) computing counterfactual attribution, a causal explanation approach where the contribution of an evidence to an answer is determined by the similarity of the original response to the answer obtained by removing that evidence. To evaluate our proposals, we release a new benchmark ConfQuestions: it has 300 hand-created conversational questions, each in English and German, coupled with ground truth URLs, completed questions, and answers from 215 public Confluence pages. These documents are typical of enterprise wiki spaces with heterogeneous elements. Experiments with RAGONITE on ConfQuestions show the viability of our ideas: contextualization improves RAG performance, and counterfactual explanations outperform standard attribution., Comment: Accepted at WSDM 2025, 8 pages

We use the angular cross-correlation between a Luminous Red Galaxy (LRG) sample from the DR9 DESI Legacy Survey and the $Planck$ PR4 CMB lensing maps to constrain the local primordial non-Gaussianity parameter $f_{\rm NL}$ using the scale-dependent galaxy bias effect. The galaxy sample covers $\sim$ 40% of the sky and contains galaxies up to $z \sim 1.4$, and is calibrated with the LRG spectra that have been observed for the DESI Survey Validation. We apply a nonlinear imaging systematics treatment based on neural networks to remove observational effects that could potentially bias the $f_{\rm NL}$ measurement. Our measurement is performed without blinding, but the full analysis pipeline is tested with simulations including systematics. Using the two-point angular cross-correlation between LRG and CMB lensing only ($C_\ell^{\kappa G}$) we find $f_{\rm NL} = 39_{-38}^{+40}$ at 68% confidence level, and our result is robust in terms of systematics and cosmology assumptions. If we combine this information with the autocorrelation of LRG ($C_\ell^{GG}$) applying a $\ell_{\rm min}$ scale cut to limit the impact of systematics, we find $f_{\rm NL} = 24_{-21}^{+20}$ at 68% confidence level. Our results motivate the use of CMB lensing cross-correlations for measuring $f_{\rm NL}$ with future datasets given its stability in terms of observational systematics compared to the angular auto-correlation., Comment: 13 pages, 11 figures, to be submitted to A&A

In this paper we focus on inserting a given human (specifically, a single image of a person) into a novel scene. Our method, which builds on top of Stable Diffusion, yields natural looking images while being highly controllable with text and pose. To accomplish this we need to train on pairs of images, the first a reference image with the person, the second a "target image" showing the same person (with a different pose and possibly in a different background). Additionally we require a text caption describing the new pose relative to that in the reference image. In this paper we present a novel dataset following this criteria, which we create using pairs of frames from human-centric and action-rich videos and employing a multimodal LLM to automatically summarize the difference in human pose for the text captions. We demonstrate that identity preservation is a more challenging task in scenes "in-the-wild", and especially scenes where there is an interaction between persons and objects. Combining the weak supervision from noisy captions, with robust 2D pose improves the quality of person-object interactions.

User prompts for generative AI models are often underspecified, leading to sub-optimal responses. This problem is particularly evident in text-to-image (T2I) generation, where users commonly struggle to articulate their precise intent. This disconnect between the user's vision and the model's interpretation often forces users to painstakingly and repeatedly refine their prompts. To address this, we propose a design for proactive T2I agents equipped with an interface to (1) actively ask clarification questions when uncertain, and (2) present their understanding of user intent as an understandable belief graph that a user can edit. We build simple prototypes for such agents and verify their effectiveness through both human studies and automated evaluation. We observed that at least 90% of human subjects found these agents and their belief graphs helpful for their T2I workflow. Moreover, we develop a scalable automated evaluation approach using two agents, one with a ground truth image and the other tries to ask as few questions as possible to align with the ground truth. On DesignBench, a benchmark we created for artists and designers, the COCO dataset (Lin et al., 2014), and ImageInWords (Garg et al., 2024), we observed that these T2I agents were able to ask informative questions and elicit crucial information to achieve successful alignment with at least 2 times higher VQAScore (Lin et al., 2024) than the standard single-turn T2I generation. Demo: https://github.com/google-deepmind/proactive_t2i_agents.

The first spectroscopy of $^{52}$K was investigated via in-beam $\gamma$-ray spectroscopy at the RIKEN Radioactive Isotope Beam Factory after one-proton and one-neutron knockout from $^{53}$Ca and $^{53}$K beams impinging on a 15-cm liquid hydrogen target at $\approx$ 230~MeV/nucleon. The energy level scheme of $^{52}$K was built using single $\gamma$ and $\gamma$-$\gamma$ coincidence spectra. The spins and parities of the excited states were established based on momentum distributions of the fragment after the knockout reaction and based on exclusive cross sections. The results were compared to state-of-the-art shell model calculations with the SDPF-Umod interaction and ab initio IMSRG calculations with chiral effective field theory nucleon-nucleon and three-nucleon forces.

We survey clinical document corpora, with focus on German textual data. Due to rigid data privacy legislation in Germany these resources, with only few exceptions, are stored in safe clinical data spaces and locked against clinic-external researchers. This situation stands in stark contrast with established workflows in the field of natural language processing where easy accessibility and reuse of data collections are common practice. Hence, alternative corpus designs have been examined to escape from this data poverty. Besides machine translation of English clinical datasets and the generation of synthetic corpora with fictitious clinical contents, several other types of domain proxies have come up as substitutes for authentic clinical documents. Common instances of close proxies are medical journal publications, clinical therapy guidelines, drug labels, etc., more distant proxies include online encyclopedic medical articles or medical contents from social media channels. After PRISM-conformant screening of 359 hits from four bibliographic systems, 75 relevant documents were finally selected for this review and 59 distinct corpora were determined. We identified 24 real clinical corpora (from 40 publications) out of which only 5 are publicly distributable. 2 translations of real corpora and 3 synthetic ones complement the set of clinical corpora. 14 corpora were categorized as close domain proxies, 16 as distant ones. There is a clear divide between the large number of non-accessible authentic clinical German-language corpora and their publicly accessible substitutes: translated or synthetic, close or more distant proxies. So on first sight, the data bottleneck seems broken. Intuitively yet, differences in genre-specific writing style, wording and medical domain expertise in this typological space are also obvious. This raises the question how valid alternative corpus designs really are.

Reconstruction of the baryon acoustic oscillation (BAO) signal has been a standard procedure in BAO analyses over the past decade and has helped to improve the BAO parameter precision by a factor of ~2 on average. The Dark Energy Spectroscopic Instrument (DESI) BAO analysis for the first year (DR1) data uses the ``standard'' reconstruction framework, in which the displacement field is estimated from the observed density field by solving the linearized continuity equation in redshift space, and galaxy and random positions are shifted in order to partially remove nonlinearities. There are several approaches to solving for the displacement field in real survey data, including the multigrid (MG), iterative Fast Fourier Transform (iFFT), and iterative Fast Fourier Transform particle (iFFTP) algorithms. In this work, we analyze these algorithms and compare them with various metrics including two-point statistics and the displacement itself using realistic DESI mocks. We focus on three representative DESI samples, the emission line galaxies (ELG), quasars (QSO), and the bright galaxy sample (BGS), which cover the extreme redshifts and number densities, and potential wide-angle effects. We conclude that the MG and iFFT algorithms agree within 0.4% in post-reconstruction power spectrum on BAO scales with the RecSym convention, which does not remove large-scale redshift space distortions (RSDs), in all three tracers. The RecSym convention appears to be less sensitive to displacement errors than the RecIso convention, which attempts to remove large-scale RSDs. However, iFFTP deviates from the first two; thus, we recommend against using iFFTP without further development. In addition, we provide the optimal settings for reconstruction for five years of DESI observation. The analyses presented in this work pave the way for DESI DR1 analysis as well as future BAO analyses., Comment: 51 pages, 28 figures. Supporting publication of DESI 2024 III: Baryon Acoustic Oscillations from Galaxies and Quasars

We are interested in computing an approximation of the maximum flow in large (brain) connectivity networks. The maximum flow in such networks is of interest in order to better understand the routing of information in the human brain. However, the runtime of $O(|V||E|^2)$ for the classic Edmonds-Karp algorithm renders computations of the maximum flow on networks with millions of vertices infeasible, where $V$ is the set of vertices and $E$ is the set of edges. In this contribution, we propose a new Monte Carlo algorithm which is capable of computing an approximation of the maximum flow in networks with millions of vertices via subsampling. Apart from giving a point estimate of the maximum flow, our algorithm also returns valid confidence bounds for the true maximum flow. Importantly, its runtime only scales as $O(B \cdot |\tilde{V}| |\tilde{E}|^2)$, where $B$ is the number of Monte Carlo samples, $\tilde{V}$ is the set of subsampled vertices, and $\tilde{E}$ is the edge set induced by $\tilde{V}$. Choosing $B \in O(|V|)$ and $|\tilde{V}| \in O(\sqrt{|V|})$ (implying $|\tilde{E}| \in O(|V|)$) yields an algorithm with runtime $O(|V|^{3.5})$ while still guaranteeing the usual "root-n" convergence of the confidence interval of the maximum flow estimate. We evaluate our proposed algorithm with respect to both accuracy and runtime on simulated graphs as well as graphs downloaded from the Brain Networks Data Repository (https://networkrepository.com).

We present three biometric datasets (iCarB-Face, iCarB-Fingerprint, iCarB-Voice) containing face videos, fingerprint images, and voice samples, collected inside a car from 200 consenting volunteers. The data was acquired using a near-infrared camera, two fingerprint scanners, and two microphones, while the volunteers were seated in the driver's seat of the car. The data collection took place while the car was parked both indoors and outdoors, and different "noises" were added to simulate non-ideal biometric data capture that may be encountered in real-life driver recognition. Although the datasets are specifically tailored to in-vehicle biometric recognition, their utility is not limited to the automotive environment. The iCarB datasets, which are available to the research community, can be used to: (i) evaluate and benchmark face, fingerprint, and voice recognition systems (we provide several evaluation protocols); (ii) create multimodal pseudo-identities, to train/test multimodal fusion algorithms; (iii) create Presentation Attacks from the biometric data, to evaluate Presentation Attack Detection algorithms; (iv) investigate demographic and environmental biases in biometric systems, using the provided metadata. To the best of our knowledge, ours are the largest and most diverse publicly available in-vehicle biometric datasets. Most other datasets contain only one biometric modality (usually face), while our datasets consist of three modalities, all acquired in the same automotive environment. Moreover, iCarB-Fingerprint seems to be the first publicly available in-vehicle fingerprint dataset. Finally, the iCarB datasets boast a rare level of demographic diversity among the 200 data subjects, including a 50/50 gender split, skin colours across the whole Fitzpatrick-scale spectrum, and a wide age range (18-60+). So, these datasets will be valuable for advancing biometrics research., Comment: 8 pages, 13 figures, 4 tables

The physical size of the sound horizon at recombination is a powerful source of information for early-time measurements of the Hubble constant $H_0$, and many proposed solutions to the Hubble tension therefore involve modifications to this scale. In light of this, there has been growing interest in measuring $H_0$ independently of the sound horizon. We present the first such measurement to use data from the Dark Energy Spectroscopic Instrument (DESI), jointly analyzing the full-shape galaxy power spectra of DESI luminous red galaxies, emission line galaxies, quasars, and the bright galaxy sample, in a total of six redshift bins. Information from the sound horizon scale is removed from our constraints via a rescaling procedure at the power spectrum level, with our sound horizon-marginalized measurement being driven instead primarily by the matter-radiation equality scale. This measurement is then combined with additional sound horizon-free information from Planck+ACT CMB lensing, uncalibrated type Ia supernovae, and the DESI Lyman-$\alpha$ forest. We agnostically combine with the DESY5, Pantheon+, and Union3 supernova datasets, with our tightest respective constraints being $H_0=66.7^{+1.7}_{-1.9},~67.9^{+1.9}_{-2.1},$ and $67.8^{+2.0}_{-2.2}$ km s-1 Mpc-1. This corresponds to a sub-3% sound horizon-free constraint of the Hubble constant, and is the most precise measurement of its kind to date. Even without including information from the sound horizon, our measurement is still in 2.2-3.0$\sigma$ tension with SH0ES. Additionally, the consistency between our result and other measurements that do rely on the sound horizon scale provides no evidence for new early-Universe physics (e.g. early dark energy). Future DESI data releases will allow unprecedented measurements of $H_0$ and place strong constraints on models that use beyond-$\Lambda$CDM physics to ameliorate the Hubble tension., Comment: 17+7 pages; 5 figures. Submitted to JCAP

Run-and-Tumble Particles (RTPs) are a key model of active matter. They are characterized by alternating phases of linear travel and random direction reshuffling. By this dynamic behavior, they break time reversibility and energy conservation at the microscopic level. It leads to complex out-of-equilibrium phenomena such as collective motion, pattern formation, and motility-induced phase separation (MIPS). In this work, we study two fundamental dynamical models of a pair of RTPs with jamming interactions and provide a rigorous link between their discrete- and continuous-space descriptions. We demonstrate that as the lattice spacing vanishes, the discrete models converge to a continuous RTP model on the torus, described by a Piecewise Deterministic Markov Process (PDMP). This establishes that the invariant measures of the discrete models converge to that of the continuous model, which reveals finite mass at jamming configurations and exponential decay away from them. This indicates effective attraction, which is consistent with MIPS. Furthermore, we quantitatively explore the convergence towards the invariant measure. Such convergence study is critical for understanding and characterizing how MIPS emerges over time. Because RTP systems are non-reversible, usual methods may fail or are limited to qualitative results. Instead, we adopt a coupling approach to obtain more accurate, non-asymptotic bounds on mixing times. The findings thus provide deeper theoretical insights into the mixing times of these RTP systems, revealing the presence of both persistent and diffusive regimes.

We present cosmological constraints on deviations from general relativity (GR) from the first-year of clustering observations from the Dark Energy Spectroscopic Instrument (DESI) in combination with other datasets. We first consider the $\mu(a,k)$-$\Sigma(a,k)$ modified gravity (MG) parametrization (as well as $\eta(a,k)$) in flat $\Lambda$CDM and $w_0 w_a$CDM backgrounds. Using a functional form for time-only evolution gives $\mu_0= 0.11^{+0.44}_{-0.54}$ from DESI(FS+BAO)+BBN and a wide prior on $n_{s}$. Using DESI(FS+BAO)+CMB+DESY3+DESY5-SN, we obtain $\mu_0 = 0.05\pm 0.22$ and $\Sigma_0 = 0.008\pm 0.045$ in the $\Lambda$CDM background. In $w_0 w_a$CDM, we obtain $\mu_0 =-0.24^{+0.32}_{-0.28}$ and $\Sigma_0 = 0.006\pm 0.043$, consistent with GR, and we still find a preference of the data for dynamical dark energy with $w_0>-1$ and $w_a<0$. We then use binned forms in the two backgrounds starting with two bins in redshift and then combining them with two bins in scale for a total of 4 and 8 MG parameters, respectively. All MG parameters are found consistent with GR. We also find that the tension reported for $\Sigma_0$ with GR when using Planck PR3 goes away when we use the recent LoLLiPoP+HiLLiPoP likelihoods. As noted previously, this seems to indicate that the tension is related to the CMB lensing anomaly in PR3 which is also resolved when using these likelihoods. We then constrain the class of Horndeski theory in the effective field theory of dark energy. We consider both EFT-basis and $\alpha$-basis. Assuming a power law parametrization for the function $\Omega$, which controls non-minimal coupling, we obtain $\Omega_0 = 0.012^{+0.001}_{-0.012}$ and $s_0 = 0.996^{+0.54}_{-0.20}$ from DESI(FS+BAO)+DESY5SN+CMB in a $\Lambda$CDM background. Similar results are obtained when using the $\alpha$-basis, where we constrain $c_M<1.14$, and are all consistent with GR. [Abridged.], Comment: 55 pages, 13 figures. This DESI Collaboration Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/). Added 3 figures and more discussions

We present an in-depth analysis of the fiber assignment incompleteness in the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1). This incompleteness is caused by the restricted mobility of the robotic fiber positioner in the DESI focal plane, which limits the number of galaxies that can be observed at the same time, especially at small angular separations. As a result, the observed clustering amplitude is suppressed in a scale-dependent manner, which, if not addressed, can severely impact the inference of cosmological parameters. We discuss the methods adopted for simulating fiber assignment on mocks and data. In particular, we introduce the fast fiber assignment (FFA) emulator, which was employed to obtain the power spectrum covariance adopted for the DR1 full-shape analysis. We present the mitigation techniques, organised in two classes: measurement stage and model stage. We then use high fidelity mocks as a reference to quantify both the accuracy of the FFA emulator and the effectiveness of the different measurement-stage mitigation techniques. This complements the studies conducted in a parallel paper for the model-stage techniques, namely the $\theta$-cut approach. We find that pairwise inverse probability (PIP) weights with angular upweighting recover the "true" clustering in all the cases considered, in both Fourier and configuration space. Notably, we present the first ever power spectrum measurement with PIP weights from real data., Comment: 42 pages, 19 figures

Emission Line Galaxies (ELGs) are one of the main tracers that the Dark Energy Spectroscopic Instrument (DESI) uses to probe the universe. However, they are afflicted by strong spurious correlations between target density and observing conditions known as imaging systematics. We present the imaging systematics mitigation applied to the DESI Data Release 1 (DR1) large-scale structure catalogs used in the DESI 2024 cosmological analyses. We also explore extensions of the fiducial treatment. This includes a combined approach, through forward image simulations in conjunction with neural network-based regression, to obtain an angular selection function that mitigates the imaging systematics observed in the DESI DR1 ELGs target density. We further derive a line-of-sight selection function from the forward model that removes the strong redshift dependence between imaging systematics and low redshift ELGs. Combining both angular and redshift-dependent systematics, we construct a 3D selection function and assess the impact of all selection functions on clustering statistics. We quantify differences between these extended treatments and the fiducial treatment in terms of the measured 2-point statistics. We find that the results are generally consistent with the fiducial treatment and conclude that the differences are far less than the imaging systematics uncertainty included in DESI 2024 full-shape measurements. We extend our investigation to the ELGs at $0.6

We present cosmological results from the measurement of clustering of galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). We adopt the full-shape (FS) modeling of the power spectrum, including the effects of redshift-space distortions, in an analysis which has been validated in a series of supporting papers. In the flat $\Lambda$CDM cosmological model, DESI (FS+BAO), combined with a baryon density prior from Big Bang Nucleosynthesis and a weak prior on the scalar spectral index, determines matter density to $\Omega_\mathrm{m}=0.2962\pm 0.0095$, and the amplitude of mass fluctuations to $\sigma_8=0.842\pm 0.034$. The addition of the cosmic microwave background (CMB) data tightens these constraints to $\Omega_\mathrm{m}=0.3056\pm 0.0049$ and $\sigma_8=0.8121\pm 0.0053$, while further addition of the the joint clustering and lensing analysis from the Dark Energy Survey Year-3 (DESY3) data leads to a 0.4% determination of the Hubble constant, $H_0 = (68.40\pm 0.27)\,{\rm km\,s^{-1}\,Mpc^{-1}}$. In models with a time-varying dark energy equation of state, combinations of DESI (FS+BAO) with CMB and type Ia supernovae continue to show the preference, previously found in the DESI DR1 BAO analysis, for $w_0>-1$ and $w_a<0$ with similar levels of significance. DESI data, in combination with the CMB, impose the upper limits on the sum of the neutrino masses of $\sum m_\nu < 0.071\,{\rm eV}$ at 95% confidence. DESI data alone measure the modified-gravity parameter that controls the clustering of massive particles, $\mu_0=0.11^{+0.45}_{-0.54}$, while the combination of DESI with the CMB and the clustering and lensing analysis from DESY3 constrains both modified-gravity parameters, giving $\mu_0 = 0.04\pm 0.22$ and $\Sigma_0 = 0.044\pm 0.047$, in agreement with general relativity. [Abridged.], Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/). 55 pages, 10 figures

We present the samples of galaxies and quasars used for DESI 2024 cosmological analyses, drawn from the DESI Data Release 1 (DR1). We describe the construction of large-scale structure (LSS) catalogs from these samples, which include matched sets of synthetic reference `randoms' and weights that account for variations in the observed density of the samples due to experimental design and varying instrument performance. We detail how we correct for variations in observational completeness, the input `target' densities due to imaging systematics, and the ability to confidently measure redshifts from DESI spectra. We then summarize how remaining uncertainties in the corrections can be translated to systematic uncertainties for particular analyses. We describe the weights added to maximize the signal-to-noise of DESI DR1 2-point clustering measurements. We detail measurement pipelines applied to the LSS catalogs that obtain 2-point clustering measurements in configuration and Fourier space. The resulting 2-point measurements depend on window functions and normalization constraints particular to each sample, and we present the corrections required to match models to the data. We compare the configuration- and Fourier-space 2-point clustering of the data samples to that recovered from simulations of DESI DR1 and find they are, generally, in statistical agreement to within 2\% in the inferred real-space over-density field. The LSS catalogs, 2-point measurements, and their covariance matrices will be released publicly with DESI DR1., Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/)

We present the measurements and cosmological implications of the galaxy two-point clustering using over 4.7 million unique galaxy and quasar redshifts in the range $0.1

We analyse the robustness of the DESI 2024 cosmological inference from fits to the full shape of the galaxy power spectrum to uncertainties in the Halo Occupation Distribution (HOD) model of the galaxy-halo connection and the choice of priors on nuisance parameters. We assess variations in the recovered cosmological parameters across a range of mocks populated with different HOD models and find that shifts are often greater than 20% of the expected statistical uncertainties from the DESI data. We encapsulate the effect of such shifts in terms of a systematic covariance term, $\mathsf{C}_{\rm HOD}$, and an additional diagonal contribution quantifying the impact of our choice of nuisance parameter priors on the ability of the effective field theory (EFT) model to correctly recover the cosmological parameters of the simulations. These two covariance contributions are designed to be added to the usual covariance term, $\mathsf{C}_{\rm stat}$, describing the statistical uncertainty in the power spectrum measurement, in order to fairly represent these sources of systematic uncertainty. This approach is more general and robust to choices of model free parameters or additional external datasets used in cosmological fits than the alternative approach of adding systematic uncertainties at the level of the recovered marginalised parameter posteriors. We compare the approaches within the context of a fixed $\Lambda$CDM model and demonstrate that our method gives conservative estimates of the systematic uncertainty that nevertheless have little impact on the final posteriors obtained from DESI data., Comment: This DESI Collaboration Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/). 26 pages, 10 figures

Since its beginnings, every Cycles and Colourings workshop holds one or two open problem sessions; this document contains the problems (together with notes regarding the current state of the art and related bibliography) presented by participants of the 32nd edition of the workshop which took place in Poprad, Slovakia during September 8-13, 2024 (see the workshop webpage https://candc.upjs.sk)., Comment: Problems presented during the problem sessions of the 32nd Workshop on Cycles and Colours (Poprad, Slovakia, 8-13 September 2024)

This paper presents an approach to joint wireless and computing resource management in slice-enabled metaverse networks, addressing the challenges of inter-slice and intra-slice resource allocation in the presence of in-network computing. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and derive an optimal solution using standard optimization techniques. Through extensive simulations, we demonstrate that our proposed method significantly improves system performance by effectively balancing the allocation of radio and computing resources across multiple slices. Our approach outperforms existing benchmarks, particularly in scenarios with high user demand and varying computational tasks.

Posiform planting is a method for constructing QUBO problems with a single unique planted solution that can be tailored to arbitrary connectivity graphs. In this study we investigate making posiform planted QUBOs computationally harder by fusing many smaller random discrete coefficient spin-glass Ising models, whose global minimum energy is computed classically using classical binary integer programming optimization software, with posiform-planted QUBOs. The single unique ground-state solution of the resulting QUBO problem is the concatenation of (exactly one of) the ground-states of each of the smaller problems. We apply these modified posiform planted QUBOs to the task of benchmarking programmable D-Wave quantum annealers. The proposed method enables generating binary variable combinatorial optimization problems that cover the entire quantum annealing processor hardware graph, have a unique solution, are entirely hardware-graph-native, and can have tunable computational hardness. We benchmark the capabilities of three D-Wave superconducting qubit quantum annealing processors, having from 563 up to 5627 qubits, to sample the optimal unique planted solution of problems generated by our proposed method and compare them against simulated annealing and Gurobi. We find that the D-Wave QPU ground-state sampling success rate does not change with respect to the size of the random QUBOs we employ. Surprisingly, we find that some of these classes of QUBOs are solved at very high success rates at short annealing times compared to longer annealing times for the Zephyr connectivity graph QPUs.

An unresolved question in studies of active galactic nuclei (AGN) is whether their different classes probe different evolutionary stages of black hole--host galaxy interaction. We present the projected two-point cross-correlation function between a sample of Dark Energy Spectroscopic Instrument (DESI)-matched AGN selected from Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) optical + Wide-field Infrared Survey Explorer ($WISE$) mid-IR photometry, and DESI-designated luminous red galaxies, for $z\in 0.5-1.0$. The total overlap area is 43.4 deg$^2$, including $\sim27,000$ spectroscopic LRGs in our redshift range. We visually classified 1,991 matched HSC-DESI objects in our redshift range, spectroscopically confirming that 1,517 ($76\%$) of them are AGN. Of these 1,517 objects, $73\%$ are broad-line AGN, $27\%$ are obscured AGN. We infer that the parent HSC+$WISE$ AGN catalog has a number density of at least $\sim 240$ deg$^{-2}$, confirming it is one of the most complete optical/infrared AGN catalog to date. We investigate the AGN clustering as a function of the spectroscopic classification and infer the halo mass for each sample. The inferred average mass of the halos $\langle M_h\rangle$ that host unobscured broad-line AGN ($M_h \approx 10^{13.4}h^{-1}M_\odot$) is $\sim 5.5\times$ larger than the halos that host obscured AGN ($M_h \approx 10^{12.6}\, h^{-1}M_\odot$), at $2.8\sigma$ significance, in the same sense as our prior work based on photometric redshifts. This suggests that we may relax our concerns about systematic shifts in the inferred redshift distribution producing this halo mass difference. While we do not yet find statistically significant spectroscopic evidence that unobscured AGN reside in more massive halos than their obscured counterparts, further analyses are necessary to distinguish if more complex evolutionary histories are needed to model these AGN populations., Comment: 27 pages, 12 figures, submitted to ApJ

Recent advances in quantum information processing with superconducting qubits have fueled a growing demand for scaling and miniaturizing circuit layouts. Despite significant progress, predicting the Hamiltonian of complex circuits remains a challenging task. Here, we propose an improved method for quantizing superconducting circuits that incorporates material- and geometry-dependent kinetic inductance. Our approach models superconducting films as reactive boundary elements, seamlessly integrating into the conventional circuit quantization framework without adding computational complexity. We experimentally validate our method using superconducting devices fabricated with 35 nm-thick disordered niobium films, demonstrating significantly improved accuracy in predicting the Hamiltonian based solely on the device layout and material properties of superconducting films and Josephson junctions. Specifically, conventional methods exhibit an average error of 5.4% in mode frequencies, while our method reduces it to 1.1%. Our method enables systematic studies of superconducting devices with disordered films or compact elements, facilitating precise engineering of superconducting circuits at scale., Comment: 12 pages, 4 figures, 1 table

State conversion is a fundamental task in quantum information processing. Quantum resource theories allow to analyze and bound conversions that use restricted sets of operations. In the context of continuous-variable systems, state conversions restricted to Gaussian operations are crucial for both fundamental and practical reasons -- particularly in state preparation and quantum computing with bosonic codes. However, previous analysis did not consider the relevant case of approximate state conversion. In this work, we introduce a framework for assessing approximate Gaussian state conversion by extending the stellar rank to the approximate stellar rank, which serves as an operational measure of non-Gaussianity. We derive bounds for Gaussian state conversion under both approximate and probabilistic conditions, yielding new no-go results for non-Gaussian state preparation and enabling a reliable assessment of the performance of generic Gaussian conversion protocols.

We study a flaring activity of the HSP Mrk421 that was characterized from radio to very-high-energy (VHE; E $>0.1$TeV) gamma rays with MAGIC, Fermi-LAT, Swift, XMM-Newton and several optical and radio telescopes. These observations included, for the first time for a gamma-ray flare of a blazar, simultaneous X-ray polarization measurements with IXPE. We find substantial variability in both X-rays and VHE gamma rays throughout the campaign, with the highest VHE flux above 0.2 TeV occurring during the IXPE observing window, and exceeding twice the flux of the Crab Nebula. However, the VHE and X-ray spectra are on average softer, and the correlation between these two bands weaker that those reported in previous flares of Mrk421. IXPE reveals an X-ray polarization degree significantly higher than that at radio and optical frequencies. The X-ray polarization angle varies by $\sim$100$^\circ$ on timescales of days, and the polarization degree changes by more than a factor 4. The highest X-ray polarization degree reaches 26%, around which a X-ray counter-clockwise hysteresis loop is measured with XMM-Newton. It suggests that the X-ray emission comes from particles close to the high-energy cutoff, hence possibly probing an extreme case of the Turbulent Extreme Multi-Zone model. We model the broadband emission with a simplified stratified jet model throughout the flare. The polarization measurements imply an electron distribution in the X-ray emitting region with a very high minimum Lorentz factor, which is expected in electron-ion plasma, as well as a variation of the emitting region size up to a factor of three during the flaring activity. We find no correlation between the fluxes and the evolution of the model parameters, which indicates a stochastic nature of the underlying physical mechanism. Such behaviour would be expected in a highly turbulent electron-ion plasma crossing a shock front., Comment: Submitted to Astronomy and Astrophysics. Corresponding authors: Axel Arbet-Engels, Lea Heckmann, David Paneque

When solving inverse problems, one has to deal with numerous potential sources of model inexactnesses, like object motion, calibration errors, or simplified data models. Regularized Sequential Subspace Optimization (ReSeSOp) allows to compensate for such inaccuracies within the reconstruction step by employing consecutive projections onto suitably defined subspaces. However, this approach relies on a priori estimates for the model inexactness levels which are typically unknown. In dynamic imaging applications, where inaccuracies arise from the unpredictable dynamics of the object, these estimates are particularly challenging to determine in advance. To overcome this limitation, we propose a learned version of ReSeSOp which allows to approximate inexactness levels on the fly. The proposed framework generalizes established unrolled iterative reconstruction schemes to inexact forward operators and is particularly tailored to the structure of dynamic problems. We also present a comprehensive mathematical analysis regarding the effect of dependencies within the forward problem, clarifying when and why dividing the overall problem into subproblems is essential. The proposed method is evaluated on various examples from dynamic imaging, including datasets from a rheological CT experiment, brain MRI, and real-time cardiac MRI. The respective results emphasize improvements in reconstruction quality while ensuring adequate data consistency., Comment: 21 pages, 7 figures, 4 tables

Testis size is known to be one of the main predictors of male fertility, usually assessed in clinical workup via palpation or imaging. Despite its potential, population-level evaluation of testicular volume using imaging remains underexplored. Previous studies, limited by small and biased datasets, have demonstrated the feasibility of machine learning for testis volume segmentation. This paper presents an evaluation of segmentation methods for testicular volume using Magnet Resonance Imaging data from the UKBiobank. The best model achieves a median dice score of $0.87$, compared to median dice score of $0.83$ for human interrater reliability on the same dataset, enabling large-scale annotation on a population scale for the first time. Our overall aim is to provide a trained model, comparative baseline methods, and annotated training data to enhance accessibility and reproducibility in testis MRI segmentation research.

The winding number is the topological invariant that classifies chiral symmetric Hamiltonians with one-dimensional parametric dependence. In this work we complete our study of the winding number statistics in a random matrix model belonging to the chiral unitary class AIII. We show that in the limit of large matrix dimensions the winding number distribution becomes Gaussian. Our results include expressions for the statistical moments of the winding number and for the k-point correlation function of the winding number density.

We present capivara, a fast and scalable multi-decomposition package designed to study astrophysical properties within distinct structural components of galaxies. Our spectro-decomposition code for analyzing integral field unit (IFU) data enables a more holistic approach, moving beyond conventional radial gradients and the bulge-plus-disk dichotomy. It facilitates comprehensive comparisons of integrated stellar ages and metallicities across various galactic structures. Our classification method naturally identifies outliers and organizes the different pixels based on their dominant spectral features. The algorithm leverages the scalability and GPU acceleration of Torch, outputting both a one-dimensional spectrum and a full data cube for each galaxy component, without relying on Voronoi binning. We demonstrate the capabilities of our approach using a sample of galaxies from the MaNGA survey, processing the resulting data cubes with the starlight spectral fitting code to derive both stellar population and ionized gas properties of the galaxy components. Our method effectively groups regions with similar spectral properties in both the continuum and emission lines. By aggregating the spectra of these regions, we enhance the signal-to-noise ratio of our analysis while significantly speeding up computations by reducing the number of spectra processed simultaneously. capivara will be freely available on GitHub., Comment: Submitted to MNRAS. Comments and suggestions are very welcome

Cells use signalling pathways as windows into the environment to gather information, transduce it into their interior, and use it to drive behaviours. MAPK (ERK) is a highly conserved signalling pathway in eukaryotes, directing multiple fundamental cellular behaviours such as proliferation, migration, and differentiation, making it of few central hubs in the signalling circuitry of cells. Despite this versatility of behaviors, population-level measurements have reported low information content (\textless 1 bit) relayed through the ERK pathway, rendering the population barely able to distinguish the presence or absence of stimuli. Here, we contrast the information transmitted by a single cell and a population of cells. Using a combination of optogenetic experiments, data analysis based on information theory framework, and numerical simulations we quantify the amount of information transduced from the receptor to ERK, from responses to singular, brief and sparse input pulses. We show that single cells are indeed able to resolve between graded stimuli, yielding over 2 bit of information, however showing a large population heterogeneity., Comment: 13 pages, 5 figures, 10 pages of supplemental material

OT 081 is a well-known, luminous blazar that is remarkably variable in many energy bands. We present the first broadband study of the source which includes very-high-energy (VHE, $E>$100\,GeV) $\gamma$-ray data taken by the MAGIC and H.E.S.S. imaging Cherenkov telescopes. The discovery of VHE $\gamma$-ray emission happened during a high state of $\gamma$-ray activity in July 2016, observed by many instruments from radio to VHE $\gamma$-rays. We identify four states of activity of the source, one of which includes VHE $\gamma$-ray emission. Variability in the VHE domain is found on daily timescales. The intrinsic VHE spectrum can be described by a power-law with index $3.27\pm0.44_{\rm stat}\pm0.15_{\rm sys}$ (MAGIC) and $3.39\pm0.58_{\rm stat}\pm0.64_{\rm sys}$ (H.E.S.S.) in the energy range of 55--300\,GeV and 120--500\,GeV, respectively. The broadband emission cannot be sucessfully reproduced by a simple one-zone synchrotron self-Compton model. Instead, an additional external Compton component is required. We test a lepto-hadronic model that reproduces the dataset well and a proton-synchrotron dominated model that requires an extreme proton luminosity. Emission models that are able to successfully represent the data place the emitting region well outside of the Broad Line Region (BLR) to a location at which the radiative environment is dominated by the infrared thermal radiation field of the dusty torus. In the scenario described by this flaring activity, the source appears to be an FSRQ, in contrast with past categorizations. This suggests that the source can be considered to be a transitional blazar, intermediate between BL~Lac and FSRQ objects., Comment: Accepted on MNRAS Corresponding authors: M. Manganaro, J. Becerra Gonz\'alez, M. Seglar-Arroyo, D. A. Sanchez

Computational physics simulation can be a powerful tool to accelerate industry deployment of new scientific technologies. However, it must address the challenge of computationally tractable, moderately accurate prediction at large industry scales, and training a model without data at such large scales. A recently proposed component reduced order modeling (CROM) tackles this challenge by combining reduced order modeling (ROM) with discontinuous Galerkin domain decomposition (DG-DD). While it can build a component ROM at small scales that can be assembled into a large scale system, its application is limited to linear physics equations. In this work, we extend CROM to nonlinear steady Navier-Stokes flow equation. Nonlinear advection term is evaluated via tensorial approach or empirical quadrature procedure. Application to flow past an array of objects at moderate Reynolds number demonstrates $\sim23.7$ times faster solutions with a relative error of $\sim 2.3\%$, even at scales $256$ times larger than the original problem., Comment: 6 pages, 1 figure

Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predictions in the deployment process, though traditional numerical methods are computationally intractable even for intermediate pilot plant scales. Recently, component reduced order modeling method is developed to tackle this challenge by combining projection reduced order modeling and discontinuous Galerkin domain decomposition. However, while many scientific or engineering applications involve nonlinear physics, this method has been only demonstrated for various linear systems. In this work, the component reduced order modeling method is extended to steady Navier-Stokes flow, with application to general nonlinear physics in view. Large-scale, global domain is decomposed into combination of small-scale unit component. Linear subspaces for flow velocity and pressure are identified via proper orthogonal decomposition over sample snapshots collected at small scale unit component. Velocity bases are augmented with pressure supremizer, in order to satisfy inf-sup condition for stable pressure prediction. Two different nonlinear reduced order modeling methods are employed and compared for efficient evaluation of nonlinear advection: 3rd-order tensor projection operator and empirical quadrature procedure. The proposed method is demonstrated on flow over arrays of five different unit objects, achieving $23$ times faster prediction with less than $4\%$ relative error up to $256$ times larger scale domain than unit components. Furthermore, a numerical experiment with pressure supremizer strongly indicates the need of supremizer for stable pressure prediction. A comparison between tensorial approach and empirical quadrature procedure is performed, which suggests a slight advantage for empirical quadrature procedure., Comment: 34 pages, 7 figures

Imaging atmospheric Cherenkov telescopes (IACTs) are used to observe very high-energy photons from the ground. Gamma rays are indirectly detected through the Cherenkov light emitted by the air showers they induce. The new generation of experiments, in particular the Cherenkov Telescope Array Observatory (CTAO), sets ambitious goals for discoveries of new gamma-ray sources and precise measurements of the already discovered ones. To achieve these goals, both hardware and data analysis must employ cutting-edge techniques. This also applies to the LST-1, the first IACT built for the CTAO, which is currently taking data on the Canary island of La Palma. This paper introduces a new event reconstruction technique for IACT data, aiming to improve the image reconstruction quality and the discrimination between the signal and the background from misidentified hadrons and electrons. The technique models the development of the extensive air shower signal, recorded as a waveform per pixel, seen by CTAO telescopes' cameras. Model parameters are subsequently passed to random forest regressors and classifiers to extract information on the primary particle. The new reconstruction was applied to simulated data and to data from observations of the Crab Nebula performed by the LST-1. The event reconstruction method presented here shows promising performance improvements. The angular and energy resolution, and the sensitivity, are improved by 10 to 20% over most of the energy range. At low energy, improvements reach up to 22%, 47%, and 50%, respectively. A future extension of the method to stereoscopic analysis for telescope arrays will be the next important step., Comment: Accepted in A&A

We present revised point-spread functions (PSFs) for the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO). These PSFs provide a robust estimate of the light diffracted by the meshes holding the entrance and focal plane filters and the light that is diffusely scattered over medium- to long-distance by the micro-roughness of the mirrors. We first calibrate the diffracted light using flare images. Our modeling of the diffracted light provides reliable determinations of the mesh parameters and finds that about 24 to 33% of the collected light is diffracted, depending on the AIA channel. Then, we fit for the diffuse scattered light using partially lunar occulted images. We find that the diffuse scattered light can be modeled as a superposition of two power law functions that scatter light over the entire length of the detector. The amount of diffuse scattered light ranges from 10 to 35 %, depending on the AIA channel. In total, AIA diffracts and scatters about 40 to 60 % of the collected light over medium to long distances. When correcting for this, bright image regions increase in intensity by about 30 %, dark image regions decrease in intensity by up to 90 %, and the associated differential emission measure analysis of solar features are affected accordingly. Finally, we compare the image reconstructions using our new PSFs to those using the AIA team PSFs and the PSFs of Poduval et al. (2013). We find that our PSFs outperform the others; our PSFs correct well for the flare diffraction pattern and predict accurately the long-distance scattered light in lunar occultations.

This paper presents a comprehensive overview of the data preparation pipeline developed for the OpenGPT-X project, a large-scale initiative aimed at creating open and high-performance multilingual large language models (LLMs). The project goal is to deliver models that cover all major European languages, with a particular focus on real-world applications within the European Union. We explain all data processing steps, starting with the data selection and requirement definition to the preparation of the final datasets for model training. We distinguish between curated data and web data, as each of these categories is handled by distinct pipelines, with curated data undergoing minimal filtering and web data requiring extensive filtering and deduplication. This distinction guided the development of specialized algorithmic solutions for both pipelines. In addition to describing the processing methodologies, we provide an in-depth analysis of the datasets, increasing transparency and alignment with European data regulations. Finally, we share key insights and challenges faced during the project, offering recommendations for future endeavors in large-scale multilingual data preparation for LLMs.