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Food insecurity is widespread among college students in the United States. Food benefits delivered through the CalFresh program, California's version of the federal Supplemental Nutrition Assistance Program (SNAP), can reduce hunger by helping students pay for groceries, but may not reach all eligible students. To date, higher education systems have lacked good estimates of the share of their students who are eligible for CalFresh and the share who actually receive benefits. To address this information gap, the California Policy Lab (CPL) partnered with the California Community College (CCC) Chancellor's Office, the University of California Office of the President (UCOP), the California Department of Social Services (CDSS), and the California Student Aid Commission (CSAC) to build a linked database of student-level administrative data on college enrollment, financial aid, and CalFresh participation. This database covers all students enrolled at CCC or UC campuses from academic years 2010-11 through 2021-22, along with corresponding FAFSA submissions and CalFresh participation. Using these data, researchers are able to measure how many college students are likely eligible for CalFresh, and of those how many participate. This report was updated in August 2024 with updated eligibility statistics that reflect results from a refined simulation of student eligibility for CalFresh. Using the improved simulation, the researchers found that among California Community College students, 20% of students were eligible for CalFresh (an increase from the 16% estimate in the June version of this report), among University of California (UC) undergraduates, 33% were eligible (an increase from 31% in the June report), and among UC graduate students, 7% were eligible (an increase from 6%).

This study pioneers the application of the market microstructure framework to an informal financial market. By scraping data from websites and social media about the Cuban informal currency market, we model the dynamics of bid/ask intentions using a Limit Order Book (LOB). This approach enables us to study key characteristics such as liquidity, stability and volume profiles. We continue exploiting the Avellaneda-Stoikov model to explore the impact of introducing a Market Maker (MM) into this informal setting, assessing its influence on the market structure and the bid/ask dynamics. We show that the Market Maker improves the quality of the market. Beyond their academic significance, we believe that our findings are relevant for policymakers seeking to intervene informal markets with limited resources., Comment: 27 pages, 10 figures, 1 table. Submitted to arXiv

A parasitic reconfigurable antenna array is a low-power approach for beamforming using passive tunable elements. Prior work on reconfigurable antennas in communication theory is based on ideal radiation pattern abstractions. It does not address the problem of physical realizability. Beamforming with parasitic elements is inherently difficult because mutual coupling creates non-linearity in the beamforming gain objective. We develop a multi-port circuit-theoretic model of the hybrid array with parasitic elements and antennas with active RF chain validated through electromagnetic simulations with a dipole array. We then derive the beamforming weight of the parasitic element using the theoretical beam pattern expression for the case of a single active antenna and multiple parasitic elements. We show that the parasitic beamforming is challenging because the weights are subject to coupled magnitude and phase constraints. We simplify the beamforming optimization problem using a shift-of-origin transformation to the typical unit-modulus beamforming weight. With this transformation, we derive a closed-form solution for the reconfigurable parasitic reactance. We generalize this solution to the multi-active multi-parasitic hybrid array operating in a multi-path channel. Our proposed hybrid architecture with parasitic elements outperforms conventional architectures in terms of energy efficiency., Comment: Submitted to IEEE Transactions on Wireless Communications

In July 2024, Sunrise completed its third successful science flight. The Sunrise III observatory had been upgraded significantly after the two previous successful flights in 2009 and 2013. Three completely new instruments focus on the small-scale physical processes and their complex interaction from the deepest observable layers in the photosphere up to chromospheric heights. Previously poorly explored spectral regions and lines are exploited to paint a three-dimensional picture of the solar atmosphere with unprecedented completeness and level of detail. The full polarimetric information is captured by all three instruments to reveal the interaction between the magnetic fields and the hydrodynamic processes. Two slit-based spectropolarimeters, the Sunrise UV Spectropolarimeter and Imager (SUSI) and the Sunrise Chromospheric Infrared spectro-Polarimeter (SCIP), focus on the near-ultraviolet and the near-infrared regions respectively, and the imaging spectropolarimeter Tunable Magnetograph (TuMag) simultaneously obtains maps of the full field-of-view of $46 \times 46$ Mm$^2$ in the photosphere and the chromosphere in the visible. The instruments are operated in an orchestrated mode, benefiting from a new Image Stabilization and Light Distribution unit (ISLiD), with the Correlating Wavefront Sensor (CWS) providing the autofocus control and an image stability with a root-mean-square value smaller than 0.005''. A new gondola was constructed to significantly improve the telescope pointing stability, required to achieve uninterrupted observations over many hours. Sunrise III was launched successfully on July 10, 2024, from the Esrange Space Center near Kiruna (Sweden). It reached the landing site between the Mackenzie River and the Great Bear Lake in Canada after a flight duration of 6.5 days. In this paper, we give an overview of the Sunrise III observatory and its instruments., Comment: 67 pages, 25 figures; to be published in Solar Physics Topical Collection "The Sunrise III Solar Observatory" (https://link.springer.com/collections/jegdciedig)

Multiple-input multiple-output (MIMO) communication has led to immense enhancements in data rates and efficient spectrum management. The evolution of MIMO, though, has been accompanied by increased hardware complexity and array sizes, causing the system power consumption to increase. Despite past advances in power-efficient hybrid architectures, new solutions are needed to enable extremely large-scale MIMO deployments for 6G and beyond. In this paper, we introduce a novel architecture that integrates low-power reconfigurable antennas with both digital and analog precoding. This \emph{tri-hybrid} approach addresses key limitations in traditional and hybrid MIMO systems by improving power consumption and adds a new layer for signal processing. We provide an analysis of the proposed architecture and compare its performance with existing solutions, including fully-digital and hybrid MIMO systems. The results demonstrate significant improvements in energy efficiency, highlighting the potential of the tri-hybrid system to meet the growing demands of future wireless networks. We conclude the paper with a summary of design and implementation challenges, including the need for technological advancements in reconfigurable array hardware and tunable antenna parameters., Comment: IEEE Transactions on Communications (invited)

The HAWC Observatory collected 6 years of extensive data, providing an ideal platform for long-term monitoring of blazars in the Very High Energy (VHE) band, without bias towards specific flux states. HAWC continuously monitors blazar activity at TeV energies, focusing on sources with a redshift of {z \lt 0.3}, based on the Third Fermi-LAT Catalog of High-Energy sources. We specifically focused our analysis on Mrk 421 and Mrk 501, as they are the brightest blazars observed by the HAWC Observatory. With a dataset of 2143 days, this work significantly extends the monitoring previously published, which was based on 511 days of observation. By utilizing HAWC data for the VHE {\gamma}-ray emission in the 300 GeV to 100 TeV energy range, in conjunction with Swift-XRT data for the 0.3 to 10 keV X-ray emission, we aim to explore potential correlations between these two bands. For Mrk 501, we found evidence of a long-term correlation. Additionally, we identified a period in the light curve where the flux was very low for more than two years. On the other hand, our analysis of Mrk 421 measured a strong linear correlation for quasi-simultaneous observations collected by HAWC and Swift-XRT. This result is consistent with a linear dependence and a multiple-zone synchrotron self-Compton model to explain the X-ray and the {\gamma}-ray emission. Finally, as suggested by previous findings, we confirm a harder-when-brighter behavior in the spectral evolution of the flux properties for Mrk 421. These findings contribute to the understanding of blazar emissions and their underlying mechanisms.

New observations obtained with JWST of the proto-stellar HH~270 jet and the "deflected" HH 110 system, show that HH 110 has a morphology of a series of distorted working surfaces. These working surfaces appear to be "deflected versions" of the heads of the incident, HH 270 jet. We compute a series of 3D numerical simulations, in which we explore the possible parameters of a shearing environment that could give origin to the deflection of HH 270 into the HH 110 flow. We find that we need a quite high sideways velocity for the streaming environment (of ~30km/s) in order to produce the complex structure observed in HH 110. This high velocity would be possible in an environment which has been strongly perturbed by the passage of other outflows., Comment: Accepted for publication in ApJ

Chemical reactions resulting from the ingress of carbonates into the cement matrix modify the properties of its pore solution, as well as its pore distribution and size. These changes lead to corrosion of the steel in reinforced concrete. The nature of conventional testing for the estimation of carbonation in cement-based materials is time-consuming and destructive. This paper presents a set of non-destructive ultrasound-based indexes, obtained solely from non-linear and linear analyses of ultrasonic signals, for measuring the carbonation of Portland cement pastes. Class 30RS cement pastes with three water/cement ratios by weight (0.4, 0.5, and 0.6) were considered. Carbonation was carried out for 120 days with a constant CO2 level of 4% by volume under controlled temperature and humidity, considering a unidirectional carbonation, parallel to the longitudinal axis of the samples. The level of carbonation was validated by FTIR measurements. From these analyses, different indexes with high correlation were obtained, estimated only from the ultrasonic signals and as a function of the days of exposure to carbonation, as well as of the percentage of carbonation. Further study is required for the evaluation of the reliability of these promising indexes for the determination of carbonation in cement-based materials.

In the quest to improve services, companies offer customers the option to interact with agents via texting. Such contact centers face unique challenges compared to traditional call centers, as measuring customer experience proxies like abandonment and patience involves uncertainty. A key source of this uncertainty is silent abandonment, where customers leave without notifying the system, wasting agent time and leaving their status unclear. Silent abandonment also obscures whether a customer was served or left. Our goals are to measure the magnitude of silent abandonment and mitigate its effects. Classification models show that 3%-70% of customers across 17 companies abandon silently. In one study, 71.3% of abandoning customers did so silently, reducing agent efficiency by 3.2% and system capacity by 15.3%, incurring $5,457 in annual costs per agent. We develop an expectation-maximization (EM) algorithm to estimate customer patience under uncertainty and identify influencing covariates. We find that companies should use classification models to estimate abandonment scope and our EM algorithm to assess patience. We suggest strategies to operationally mitigate the impact of silent abandonment by predicting suspected silent-abandonment behavior or changing service design. Specifically, we show that while allowing customers to write while waiting in the queue creates a missing data challenge, it also significantly increases patience and reduces service time, leading to reduced abandonment and lower staffing requirements., Comment: 75% of the paper is an updated version of arXiv:2304.11754

Recent solar observations of bipolar light bridges (BLBs) in sunspots have, in a few individual cases, revealed magnetic fields up to 8.2 kG, which is at least twice as strong as typical values measured in sunspot umbrae. However, the small number of such observations hinted that such strong fields in these bright photospheric features that separate two opposite-polarity umbrae, are a rare phenomenon. We determine the field strength in a large sample of BLBs with the aim of establishing how prevalent such strong fields are in BLBs. We apply a state-of-the-art inversion technique that accounts for the degradation of the data by the intrinsic point spread function of the telescope, to the so far largest set of spectropolarimetric observations, by Hinode/Solar Optical Telescope spectropolarimeter, of sunspots containing BLBs. We identified 98 individual BLBs within 51 distinct sunspot groups. Since 66.3% of the BLBs were observed multiple times, a total of 630 spectropolarimetric scans of these 98 BLBs were analysed. All analysed BLBs contain magnetic fields stronger than 4.5 kG at unit optical depth. The field strengths decrease faster with height than the fields in umbrae and penumbrae. BLBs display a unique continuum intensity and field strength combination, forming a population well separated from umbrae and the penumbrae. The high brightness of BLBs in spite of their very strong magnetic fields points to the presence of a so far largely unexplored regime of magnetoconvection., Comment: Accepted for publication in ApJL

Increasing the size and complexity of quantum information systems requires highly-multiplexed readout architectures, as well as amplifier chains operating near the quantum limit (QL) of added noise. While documented prior efforts in KITWPA integration in quantum systems are scarce, in this work we demonstrate integration of a KI-TWPA with a multiplexed-qubit device. To quantify the system noise improvement we perform an ac Stark shift calibration to precisely determine noise power levels on-chip (at each cavity's reference plane) and the total system gain. We then characterize the qubit state measurement fidelity and the corresponding signal-to-noise ratio (SNR). To conduct the most faithful measurement of the benefits offered by the KI-TWPA we perform these measurements for readout chains where the high electron mobility transistor (HEMT) amplifier is the first-stage amplifier (FSA) - with none of the external hardware required to operate the KI-TWPA - and with the KI-TWPA as the FSA. While some readout cavities fall outside the KI-TWPA bandwidth, for those inside the bandwidth we demonstrate a maximum improvement in the state measurement SNR by a factor of 1.45, and increase the fidelity from 96.2% to 97.8%. These measurements demonstrate a system noise below 5 quanta referenced on-chip and we bound the KI-TWPA excess noise to be below 4 quanta for the six cavities inside its bandwidth. These results show a promising path forward for realizing quantum-limited readout chains in large qubit systems using a single parametric amplifier.

To ensure and monitor large language models (LLMs) reliably, various evaluation metrics have been proposed in the literature. However, there is little research on prescribing a methodology to identify a robust threshold on these metrics even though there are many serious implications of an incorrect choice of the thresholds during deployment of the LLMs. Translating the traditional model risk management (MRM) guidelines within regulated industries such as the financial industry, we propose a step-by-step recipe for picking a threshold for a given LLM evaluation metric. We emphasize that such a methodology should start with identifying the risks of the LLM application under consideration and risk tolerance of the stakeholders. We then propose concrete and statistically rigorous procedures to determine a threshold for the given LLM evaluation metric using available ground-truth data. As a concrete example to demonstrate the proposed methodology at work, we employ it on the Faithfulness metric, as implemented in various publicly available libraries, using the publicly available HaluBench dataset. We also lay a foundation for creating systematic approaches to select thresholds, not only for LLMs but for any GenAI applications., Comment: 16 pages, 8 figures, 4 tables. 2-columns

The Compton Spectrometer and Imager balloon payload (COSI-Balloon) is a wide-field-of-view Compton ${\gamma}$-ray telescope that operates in the 0.2 - 5 MeV bandpass. COSI-Balloon had a successful 46-day flight in 2016 during which the instrument observed the Crab Nebula, Cygnus X-1, and Centaurus A. Using the data collected by the COSI-Balloon instrument during this flight, we present the source flux extraction of signals from the variable balloon background environment and produce images of these background-dominated sources by performing Richardson-Lucy deconvolutions. We also present the spectra measured by the COSI-Balloon instrument, compare and combine them with measurements from other instruments, and fit the data. The Crab Nebula was observed by COSI-Balloon and we obtain a measured flux in the energy band 325 - 480 keV of (4.5 ${\pm}$ 1.6) ${\times}$ 10$^{-3}$ ph cm$^{-2}$ s$^{-1}$. The model that best fits the COSI-Balloon data combined with measurements from NuSTAR and Swift-BAT is a broken power law with a measured photon index ${\Gamma}$ = 2.20 ${\pm}$ 0.02 above the 43 keV break. Cygnus X-1 was also observed during this flight, and we obtain a measured flux of (1.4 ${\pm}$ 0.2) ${\times}$ 10$^{-3}$ ph cm$^{-2}$ s$^{-1}$ in the same energy band and a best-fit result (including data from NuSTAR, Swift-BAT, and INTEGRAL/ IBIS) was to a cutoff power law with a high-energy cutoff energy of 138.3 ${\pm}$ 1.0 keV and a photon index of ${\Gamma}$ = 1.358 ${\pm}$ 0.002. Lastly, we present the measured spectrum of Centaurus A and our best model fit to a power law with a photon index of ${\Gamma}$ = 1.73 ${\pm}$ 0.01.

Group equivariance has emerged as a valuable inductive bias in deep learning, enhancing generalization, data efficiency, and robustness. Classically, group equivariant methods require the groups of interest to be known beforehand, which may not be realistic for real-world data. Additionally, baking in fixed group equivariance may impose overly restrictive constraints on model architecture. This highlights the need for methods that can dynamically discover and apply symmetries as soft constraints. For neural network architectures, equivariance is commonly achieved through group transformations of a canonical weight tensor, resulting in weight sharing over a given group $G$. In this work, we propose to learn such a weight-sharing scheme by defining a collection of learnable doubly stochastic matrices that act as soft permutation matrices on canonical weight tensors, which can take regular group representations as a special case. This yields learnable kernel transformations that are jointly optimized with downstream tasks. We show that when the dataset exhibits strong symmetries, the permutation matrices will converge to regular group representations and our weight-sharing networks effectively become regular group convolutions. Additionally, the flexibility of the method enables it to effectively pick up on partial symmetries., Comment: 19 pages, 14 figures, 4 tables

Dynamic metasurface antennas (DMAs) beamform through low-powered components that enable reconfiguration of each radiating element. Previous research on a single-user multiple-input-single-output (MISO) system with a dynamic metasurface antenna at the transmitter has focused on maximizing the beamforming gain at a fixed operating frequency. The DMA, however, has a frequency-selective response that leads to magnitude degradation for frequencies away from the resonant frequency of each element. This causes reduction in beamforming gain if the DMA only operates at a fixed frequency. We exploit the frequency reconfigurability of the DMA to dynamically optimize both the operating frequency and the element configuration, maximizing the beamforming gain. We leverage this approach to develop a single-shot beam training procedure using a DMA sub-array architecture that estimates the receiver's angular direction with a single OFDM pilot signal. We evaluate the beamforming gain performance of the DMA array using the receiver's angular direction estimate obtained from beam training. Our results show that it is sufficient to use a limited number of resonant frequency states to do both beam training and beamforming instead of using an infinite resolution DMA beamformer.

The intense debate about the presence of methane in the Martian atmosphere has stimulated the study of methanogens adapted to terrestrial habitats that mimic Martian environments. We examinate the environmental conditions, energy sources and ecology of terrestrial methanogens thriving in deep crystalline fractures, sub-sea hypersaline lakes and subglacial water bodies considered as analogs of a hypothetical habitable Martian subsurface. We combine this information with recent data on the distribution of buried water or ice and radiogenic elements on Mars and with models of the subsurface thermal regime of this planet to identify a 4.3-8.8 km-deep regolith habitat at the mid-latitude location of Acidalia Planitia, that might fit the requirements for hosting putative Martian methanogens analogous to the methanogenic families Methanosarcinaceae and Methanomicrobiaceae., Comment: 76 pages, 6 Figures, 3 Tables, Accepted in AstroBiology. Literature dataset is available on request

This document summarizes the prospective physics plans of the French QCD and Heavy-Ion community, including the experimental programs at the LHC Run 5 and beyond and future colliders at CERN, within the context of the French contribution to the update of the European Strategy in Particle Physics (ESPPU 2025), as discussed in the workshop on European Strategy for Particle Physics Update 2025 organised by the QCD GdR in Oct. 2024., Comment: 6.5 pages without title page and without references, no figures. Keywords: QCD, Heavy Ions, Quark Gluon Plasma, LHC, FCC

The speed of response by search and rescue teams at sea is of vital importance, as survival may depend on it. Recent technological advancements have led to the development of more efficient systems for locating individuals involved in a maritime incident, such as the use of Unmanned Aerial Vehicles (UAVs) equipped with cameras and other integrated sensors. Over the past decade, several researchers have contributed to the development of automatic systems capable of detecting people using aerial images, particularly by leveraging the advantages of deep learning. In this article, we provide a comprehensive review of the existing literature on this topic. We analyze the methods proposed to date, including both traditional techniques and more advanced approaches based on machine learning and neural networks. Additionally, we take into account the use of synthetic data to cover a wider range of scenarios without the need to deploy a team to collect data, which is one of the major obstacles for these systems. Overall, this paper situates the reader in the field of detecting people at sea using aerial images by quickly identifying the most suitable methodology for each scenario, as well as providing an in-depth discussion and direction for future trends.

We analyze the dynamics of a gravitational bounded cloud of generic bosons or a boson star in the non-relativistic regime, assuming from the very beginning that the boson star is in a Bose-Einstein condensed state. Also, we have taken into account multi-particle interactions within the boson star, which are described through the so-called logarithmic potential. Assuming energy conservation conditions at any time, we are able to describe the boson star as a some kind of gravitational quantum liquid, or more specifically, as a macroscopic gravitational quantum droplet. The assumption of logarithmic interactions, lead us to establish several scenarios in which the system is able to oscillates around the equilibrium radius. The approach analyzed in the present report shows that the interpretation of a boson star as a macroscopic gravitational quantum droplet can be applied to the dynamics analysis of a large number of systems on different size scales., Comment: 15 pages, 10 figures

Stacking monolayers of transition metal dichalcogenides (TMDs) has led to the discovery of a plethora of new exotic phenomena, resulting from moir\'e pattern formation. Due to the atomic thickness and high surface-to-volume ratio of heterostructures, the interfaces play a crucial role. Fluctuations in the interlayer distance affect interlayer coupling and moir\'e effects. Therefore, to access the intrinsic properties of the TMD stack, it is essential to obtain a clean and uniform interface between the layers. Here, we show that this is achieved by ironing with the tip of an atomic force microscope. This post-stacking procedure dramatically improves the homogeneity of the interfaces, which is reflected in the optical response of the interlayer exciton. We demonstrate that ironing improves the layer coupling, enhancing moir\'e effects and reducing disorder. This is crucial for the investigation of TMD heterostructure physics, which currently suffers from low reproducibility.

Convergent-beam electron diffraction (CBED) is a well-established probe for spatial symmetries of crystalline samples, mainly exploiting the well-defined mapping between the diffraction groups (symmetry group of CBED patterns) and the point-group symmetries of the crystalline sample. In this work, we extend CBED to determine magnetic point groups. We construct all magnetic CBED groups, of which there exist 125. Then, we provide the complete mapping of the 122 magnetic point groups to corresponding magnetic CBED groups for all crystal orientations. In order to verify the group-theoretical considerations, we conduct electron-scattering simulations on antiferromagnetic crystals and provide guidelines for the experimental realization. Based on its feasibility using existing technology, as well as on its accuracy, high spatial resolution, and small required sample size, magnetic CBED promises to be become a valuable alternative method for magnetic structure determination.

Measurements of the EMC effect in the tritium and helium-3 mirror nuclei are reported. The data were obtained by the MARATHON Jefferson Lab experiment, which performed deep inelastic electron scattering from deuterium and the three-body nuclei, using a cryogenic gas target system and the High Resolution Spectrometers of the Hall A Facility of the Lab. The data cover the Bjorken $x$ range from 0.20 to 0.83, corresponding to a squared four-momentum transfer $Q^2$ range from 2.7 to $11.9\gevsq$, and to an invariant mass $W$ of the final hadronic state greater than 1.84 GeV/${\it c}^2$. The tritium EMC effect measurement is the first of its kind. The MARATHON experimental results are compared to results from previous measurements by DESY-HERMES and JLab-Hall C experiments, as well as with few-body theoretical predictions., Comment: arXiv admin note: text overlap with arXiv:2104.05850

Unmanned Aerial Vehicles autonomously perform tasks with the use of state-of-the-art control algorithms. These control algorithms rely on the freshness and correctness of sensor readings. Incorrect control actions lead to catastrophic destabilization of the process. In this work, we propose a multi-part \emph{Sensor Deprivation Attacks} (SDAs), aiming to stealthily impact process control via sensor reconfiguration. In the first part, the attacker will inject messages on local buses that connect to the sensor. The injected message reconfigures the sensors, e.g.,~to suspend the sensing. In the second part, those manipulation primitives are selectively used to cause adversarial sensor values at the controller, transparently to the data consumer. In the third part, the manipulated sensor values lead to unwanted control actions (e.g. a drone crash). We experimentally investigate all three parts of our proposed attack. Our findings show that i)~reconfiguring sensors can have surprising effects on reported sensor values, and ii)~the attacker can stall the overall Kalman Filter state estimation, leading to a complete stop of control computations. As a result, the UAV becomes destabilized, leading to a crash or significant deviation from its planned trajectory (over 30 meters). We also propose an attack synthesis methodology that optimizes the timing of these SDA manipulations, maximizing their impact. Notably, our results demonstrate that these SDAs evade detection by state-of-the-art UAV anomaly detectors. Our work shows that attacks on sensors are not limited to continuously inducing random measurements, and demonstrate that sensor reconfiguration can completely stall the drone controller. In our experiments, state-of-the-art UAV controller software and countermeasures are unable to handle such manipulations. Hence, we also discuss new corresponding countermeasures.

A major feature of efflux pumps is multidrug polyspecificity, which to date still eludes some of the traditional dogmas of drug binding within protein science. We propose a combinatorial approach to explore, in the vast sequence space of polypeptide chains, the neighbourhood of some superfamilies of efflux pumps. Creating new functional structures as permutations of existing ones has the potential to unveil some of the hidden mechanisms behind the functionality of efflux pumps., Comment: 3 pages

The interplay between missing data and model uncertainty -- two classic statistical problems -- leads to primary questions that we formally address from an objective Bayesian perspective. For the general regression problem, we discuss the probabilistic justification of Rubin's rules applied to the usual components of Bayesian variable selection, arguing that prior predictive marginals should be central to the pursued methodology. In the regression settings, we explore the conditions of prior distributions that make the missing data mechanism ignorable. Moreover, when comparing multiple linear models, we provide a complete methodology for dealing with special cases, such as variable selection or uncertainty regarding model errors. In numerous simulation experiments, we demonstrate that our method outperforms or equals others, in consistently producing results close to those obtained using the full dataset. In general, the difference increases with the percentage of missing data and the correlation between the variables used for imputation. Finally, we summarize possible directions for future research.

The miniaturisation of transistors is approaching its limits due to challenges in heat management and information transfer speed. To overcome these obstacles, emerging technologies such as spintronics are being developed, which leverage the electron's spin in addition to its charge. Local phenomena at interfaces or structural defects will greatly influence the efficiency of spin-based devices, making the ability to study and control spin wave propagation at the nano- and atomic scales a key challenge. The development of high-spatial-resolution tools to probe spin waves, also called magnons, at relevant lengthscales is thus essential to understand how their properties are affected by such local features. Here, we show the first experimental detection of bulk magnons at the nanoscale using scanning transmission electron microscopy. By employing high-resolution electron energy loss spectroscopy with hybrid-pixel direct electron detectors optimized for low acceleration voltages, we successfully overcome the challenges posed by weak signals and identify magnon excitations in a thin NiO nanocrystal. Advanced inelastic electron scattering simulations corroborate our findings. These results open new avenues for detecting magnons, exploring their dispersions and their modifications arising from nanoscale structural or chemical defects. This marks an important milestone in magnonics and presents exciting opportunities for the future development of spintronic devices., Comment: The revised version includes several updates, including an improved Fig. 2, a new Fig. 4 showcasing spatially resolved magnon EELS measurements across a NiO thin film, and new Extended Data Figs. 3 and 4, which present magnon dispersions at different temperatures and a direct comparison between experiment and theory, respectively. Additionally, we have incorporated supplementary material

We present a computational study of phonon relaxation in face-centered cubic (fcc) nickel (Ni), focusing on ultra-fast magnetization dynamics. The phonons are excited beyond their thermal equilibrium population, and the relaxation behavior is analyzed as a function of both wave vector ($\vec{q}$) and phonon frequency ($\omega$). To efficiently investigate these excitations, we introduce a trajectory post-processing technique, the frozen-trajectory excitation, which facilitates the $(\vec{q},\omega)$-resolved analysis. Molecular dynamics simulations combined with the frozen-phonon multislice method predict relaxation signatures observable with time-resolved transmission electron microscopy (TEM) at 10 -- 20 fs resolution. Our findings indicate mode dependence in the relaxation processes, highlighting the importance of phonon-specific behavior consideration in ultrafast dynamics., Comment: 10 pages, 8 figures

Uniaxially strained Fe--Co disordered alloys have emerged as promising candidates for cost-effective rare-earth-free permanent magnets due to their high magnetocrystalline anisotropy energy (MAE). Using first-principles, fully relativistic calculations within the coherent potential approximation and PBE exchange-correlation potential, we explore the MAE of tetragonal Fe--Co alloys under uniaxial compression. Our results reveal a previously uncharted high-MAE region, distinct from known structures and accessible through uniaxial compression., Comment: 6 pages, 3 figures

Relative representations are an established approach to zero-shot model stitching, consisting of a non-trainable transformation of the latent space of a deep neural network. Based on insights of topological and geometric nature, we propose two improvements to relative representations. First, we introduce a normalization procedure in the relative transformation, resulting in invariance to non-isotropic rescalings and permutations. The latter coincides with the symmetries in parameter space induced by common activation functions. Second, we propose to deploy topological densification when fine-tuning relative representations, a topological regularization loss encouraging clustering within classes. We provide an empirical investigation on a natural language task, where both the proposed variations yield improved performance on zero-shot model stitching.

This study presents the design, simulation, and experimental characterization of a superconducting transmon qubit circuit prototype for potential applications in dark matter detection experiments. We describe a planar circuit design featuring two non-interacting transmon qubits, one with fixed frequency and the other flux tunable. Finite-element simulations were employed to extract key Hamiltonian parameters and optimize component geometries. The qubit was fabricated and then characterized at $20$ mK, allowing for a comparison between simulated and measured qubit parameters. Good agreement was found for transition frequencies and anharmonicities (within 1\% and 10\% respectively) while coupling strengths exhibited larger discrepancies (30\%). We discuss potential causes for measured coherence times falling below expectations ($T_1\sim\,$1-2 \textmu s) and propose strategies for future design improvements. Notably, we demonstrate the application of a hybrid 3D-2D simulation approach for energy participation ratio evaluation, yielding a more accurate estimation of dielectric losses. This work represents an important first step in developing planar Quantum Non-Demolition (QND) single-photon counters for dark matter searches, particularly for axion and dark photon detection schemes.

We propose HyperSteiner -- an efficient heuristic algorithm for computing Steiner minimal trees in the hyperbolic space. HyperSteiner extends the Euclidean Smith-Lee-Liebman algorithm, which is grounded in a divide-and-conquer approach involving the Delaunay triangulation. The central idea is rephrasing Steiner tree problems with three terminals as a system of equations in the Klein-Beltrami model. Motivated by the fact that hyperbolic geometry is well-suited for representing hierarchies, we explore applications to hierarchy discovery in data. Results show that HyperSteiner infers more realistic hierarchies than the Minimum Spanning Tree and is more scalable to large datasets than Neighbor Joining.

We present a complete analysis of Fermi Large Area Telescope (LAT) data of GRB 221009A, the brightest Gamma-Ray Burst (GRB) ever detected. The burst emission above 30 MeV detected by the LAT preceded by 1 s the low-energy (< 10 MeV) pulse that triggered the Fermi Gamma-Ray Burst Monitor (GBM), as has been observed in other GRBs. The prompt phase of GRB 221009A lasted a few hundred seconds. It was so bright that we identify a Bad Time Interval (BTI) of 64 seconds caused by the extremely high flux of hard X-rays and soft gamma rays, during which the event reconstruction efficiency was poor and the dead time fraction quite high. The late-time emission decayed as a power law, but the extrapolation of the late-time emission during the first 450 seconds suggests that the afterglow started during the prompt emission. We also found that high-energy events observed by the LAT are incompatible with synchrotron origin, and, during the prompt emission, are more likely related to an extra component identified as synchrotron self-Compton (SSC). A remarkable 400 GeV photon, detected by the LAT 33 ks after the GBM trigger and directionally consistent with the location of GRB 221009A, is hard to explain as a product of SSC or TeV electromagnetic cascades, and the process responsible for its origin is uncertain. Because of its proximity and energetic nature, GRB 221009A is an extremely rare event., Comment: 60 pages, 38 figures, 9 tables

We present the most comprehensive and deepest X-ray study to date of the properties of the richest Wolf-Rayet (WR) population observed in a single stellar cluster, Westerlund 1 (Wd1). This work is based on 36 Chandra observations obtained from the "Extended Westerlund 1 and 2 Open Clusters Survey" (EWOCS) project, plus 8 archival Chandra observations. The overall exposure depth (~1.1 Ms) and baseline of the EWOCS observations extending over more than one year enable us to perform a detailed photometric, colour, and spectral analysis, as well as to search for short- and long-term periodicity. In X-rays, we detect 20 out of the 24 known Wolf-Rayet stars in Wd1 down to an observed luminosity of ~7$\times10^{29}$erg s$^{-1}$ (assuming a distance of 4.23 kpc to Wd1), with 8 WR stars being detected in X-rays for the first time. Nine stars show clear evidence of variability over the year-long baseline, with clear signs of periodicity. The X-ray colours and spectral analysis reveal that the vast majority of the WR stars are hard X-ray sources (kT$\geq$2.0keV). The Fe XXV emission line at ~6.7 keV, which commonly originates from the wind-wind collision zone in binary systems, is detected for the first time in the spectra of 17 WR stars in Wd1. In addition the ~6.4 keV fluorescent line is observed in the spectra of three stars, indicating that dense cold material coexists with the hot gas in these systems. Overall, our X-ray results alone suggest a very high binary fraction ($\geq$80%) for the WR star population in Wd1. When combining our results with properties of the WR population from other wavelengths, we estimate a binary fraction of $\geq$92%, which could even reach unity. This suggests that either all the most massive stars are found in binary systems within Wd1, or that binarity is essential for the formation of such a rich population of WR stars., Comment: 20 pages, 11 figures, 6 tables, accepted for publication in A&A

Peer analysis is a critical component of investment management, often relying on expert-provided categorization systems. These systems' consistency is questioned when they do not align with cohorts from unsupervised clustering algorithms optimized for various metrics. We investigate whether unsupervised clustering can reproduce ground truth classes in a labeled dataset, showing that success depends on feature selection and the chosen distance metric. Using toy datasets and fund categorization as real-world examples we demonstrate that accurately reproducing ground truth classes is challenging. We also highlight the limitations of standard clustering evaluation metrics in identifying the optimal number of clusters relative to the ground truth classes. We then show that if appropriate features are available in the dataset, and a proper distance metric is known (e.g., using a supervised Random Forest-based distance metric learning method), then an unsupervised clustering can indeed reproduce the ground truth classes as distinct clusters., Comment: 9 pages, 4 tables 28 figures

Surrogate models that combine dimensionality reduction and regression techniques are essential to reduce the need for costly high-fidelity computational fluid dynamics data. New approaches using $\beta$-Variational Autoencoder ($\beta$-VAE) architectures have shown promise in obtaining high-quality low-dimensional representations of high-dimensional flow data while enabling physical interpretation of their latent spaces. We propose a surrogate model based on latent space regression to predict pressure distributions on a transonic wing given the flight conditions: Mach number and angle of attack. The $\beta$-VAE model, enhanced with Principal Component Analysis (PCA), maps high-dimensional data to a low-dimensional latent space, showing a direct correlation with flight conditions. Regularization through $\beta$ requires careful tuning to improve overall performance, while PCA preprocessing helps to construct an effective latent space, improving autoencoder training and performance. Gaussian Process Regression is used to predict latent space variables from flight conditions, showing robust behavior independent of $\beta$, and the decoder reconstructs the high-dimensional pressure field data. This pipeline provides insight into unexplored flight conditions. Furthermore, a fine-tuning process of the decoder further refines the model, reducing the dependence on $\beta$ and enhancing accuracy. Structured latent space, robust regression performance, and significant improvements in fine-tuning collectively create a highly accurate and efficient surrogate model. Our methodology demonstrates the effectiveness of $\beta$-VAEs for aerodynamic surrogate modeling, offering a rapid, cost-effective, and reliable alternative for aerodynamic data prediction., Comment: 19 pages, 12 figures

TThe Period-Luminosity (PL) relation for Cepheid variable stars in the Large Magellanic Cloud (LMC) is crucial for distance measurements in astronomy. This study analyzes the impact of using the median rather than the mean on the PL relation's slope and zero point. It also examines the persistence of the break at approximately 10 days and addresses specification issues in the PL relation model. Using $VI$-band median and mean magnitudes from the OGLE-IV survey, corrected for extinction, we fit the PL relation employing robust $MM$-regression, which features a high breakdown point and robust standard errors. Statistical tests and residual analysis are conducted to identify and correct model deficiencies. Our findings indicate a significant change in the PL relation for Cepheids with periods of 10 days or longer, regardless of whether median or mean magnitudes are used. A bias in the zero point and slope estimators is observed when using median magnitudes instead of mean magnitudes, especially in the $V$-band. By identifying and correcting regression issues and considering the period break, our estimators for slope and zero point are more accurate for distance calculations. Comparative analysis of the models for each band quantifies the bias introduced by using median magnitudes, highlighting the importance of considering the Cepheids' period for accurate location measure results, similar to those obtained using mean magnitudes.

In recent years, linear complementary pairs (LCP) of codes and linear complementary dual (LCD) codes have gained significant attention due to their applications in coding theory and cryptography. In this work, we construct explicit LCPs of codes and LCD codes from function fields of genus $g \geq 1$. To accomplish this, we present pairs of suitable divisors giving rise to non-special divisors of degree $g-1$ in the function field. The results are applied in constructing LCPs of algebraic geometry codes and LCD algebraic geometry (AG) codes in Kummer extensions, hyperelliptic function fields, and elliptic curves.