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Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. This work presents a general method for driver identification in extreme climate events. A novel framework (STCO-FS) is proposed to identify key immediate (short-term) HW drivers by combining clustering algorithms with an ensemble evolutionary algorithm. The framework analyzes spatio-temporal data, reduces dimensionality by grouping similar geographical nodes for each variable, and develops driver selection in spatial and temporal domains, identifying the best time lags between predictive variables and HW occurrences. The proposed method has been applied to analyze HWs in the Adda river basin in Italy. The approach effectively identifies significant variables influencing HWs in this region. This research can potentially enhance our understanding of HW drivers and predictability., Comment: 28 pages, 10 figures, 4 tables

Inflammation of the umbilical cord can be seen as a result of ascending intrauterine infection or other inflammatory stimuli. Acute fetal inflammatory response (FIR) is characterized by infiltration of the umbilical cord by fetal neutrophils, and can be associated with neonatal sepsis or fetal inflammatory response syndrome. Recent advances in deep learning in digital pathology have demonstrated favorable performance across a wide range of clinical tasks, such as diagnosis and prognosis. In this study we classified FIR from whole slide images (WSI). We digitized 4100 histological slides of umbilical cord stained with hematoxylin and eosin(H&E) and extracted placental diagnoses from the electronic health record. We build models using attention-based whole slide learning models. We compared strategies between features extracted by a model (ConvNeXtXLarge) pretrained on non-medical images (ImageNet), and one pretrained using histopathology images (UNI). We trained multiple iterations of each model and combined them into an ensemble. The predictions from the ensemble of models trained using UNI achieved an overall balanced accuracy of 0.836 on the test dataset. In comparison, the ensembled predictions using ConvNeXtXLarge had a lower balanced accuracy of 0.7209. Heatmaps generated from top accuracy model appropriately highlighted arteritis in cases of FIR 2. In FIR 1, the highest performing model assigned high attention to areas of activated-appearing stroma in Wharton's Jelly. However, other high-performing models assigned attention to umbilical vessels. We developed models for diagnosis of FIR from placental histology images, helping reduce interobserver variability among pathologists. Future work may examine the utility of these models for identifying infants at risk of systemic inflammatory response or early onset neonatal sepsis.

This study analyzes the behavior of a differentially heated channel flow by means of a direct numerical simulations (DNS) with variable thermophysical properties under low-speed conditions focusing on the impact of the temperature gradient on the turbulence structures near the channel walls. The simulations were conducted at a mean friction Reynolds number of Re{\tau}m = 400 with a temperature ratio between the walls of Thot/Tcold = 2. Results show significant differences between the hot and cold walls that lead to an increased heat transfer at the hot wall and a higher turbulent production in the cold wall., Comment: Proceedings paper, ECCOMAS 2024

This paper addresses the critical issue of psychological safety in the design and operation of autonomous vehicles, which are increasingly integrated with artificial intelligence technologies. While traditional safety standards focus primarily on physical safety, this paper emphasizes the psychological implications that arise from human interactions with autonomous vehicles, highlighting the importance of trust and perceived risk as significant factors influencing user acceptance. Through a review of existing safety techniques, the paper defines psychological safety in the context of autonomous vehicles, proposes a risk model to identify and assess psychological risks, and adopts a system-theoretic analysis method. The paper illustrates the potential psychological hazards using a scenario involving a family's experience with an autonomous vehicle, aiming to systematically evaluate situations that could lead to psychological harm. By establishing a framework that incorporates psychological safety alongside physical safety, the paper contributes to the broader discourse on the safe deployment of autonomous vehicle and aims to guide future developments in user-cantered design and regulatory practices.

Fine-tuning large language models (LLMs) is essential for enhancing their performance on specific tasks but is often resource-intensive due to redundant or uninformative data. To address this inefficiency, we introduce DELIFT (Data Efficient Language model Instruction Fine-Tuning), a novel algorithm that systematically optimizes data selection across the three key stages of fine-tuning: (1) instruction tuning, (2) task-specific fine-tuning (e.g., reasoning, question-answering), and (3) continual fine-tuning (e.g., incorporating new data versions). Unlike existing methods that focus on single-stage optimization or rely on computationally intensive gradient calculations, DELIFT operates efficiently across all stages. Central to our approach is a pairwise utility metric that quantifies how beneficial a data sample is for improving the model's responses to other samples, effectively measuring the informational value relative to the model's current capabilities. By leveraging different submodular functions applied to this metric, DELIFT selects diverse and optimal subsets that are useful across all stages of fine-tuning. Experiments across various tasks and model scales demonstrate that DELIFT can reduce the fine-tuning data size by up to 70% without compromising performance, offering significant computational savings and outperforming existing methods in both efficiency and efficacy.

Generative Artificial Intelligence (AI) technologies are in a phase of unprecedented rapid development following the landmark release of Chat-GPT, which brought the phenomenon to wide public attention. As the deployment of AI products rises geometrically, considerable attention is being given to the threats and opportunities that AI technologies offer, and to the need for regulatory and standards initiatives to ensure that use of the technology aligns with societal needs and generates broad benefits while mitigating risks and threats. This manuscript is the third of a series of White Papers informing the development of IEEE-SA's p3995 {\it `Standard for the Implementation of Safeguards, Controls, and Preventive Techniques for Artificial Intelligence Models'} \cite{P3395}, Chair Marina Cort\^{e}s. This part focuses on assessing calmly and objectively, as far as is possible, the current state of Artificial Intelligence (AI) technology development and identifying predominant trends, prospects, and ensuing risks. It necessarily forms a snapshot of the current instant of a rapidly-evolving landscape, with new products and innovations emerging continuously. While our main focus is on software and hardware developments and their corporate context, we also briefly review progress on robotics within the AI context and describe some implications of the substantial and growing AI energy demand., Comment: This is an interim version of our p3395 WG White Paper, Part III. We will update this version, until publication by IEEE-SA, Sponsor Committee - Artificial Intelligence Standards Committee (C/AISC); https://standards.ieee.org/ieee/3395/11378/ This White Paper is companion to Part I available at arXiv:2410.01808

In many research fields, researchers aim to identify significant associations between a set of explanatory variables and a response while controlling the false discovery rate (FDR). To this aim, we develop a fully Bayesian generalization of the classical model-X knockoff filter. Knockoff filter introduces controlled noise in the model in the form of cleverly constructed copies of the predictors as auxiliary variables. In our approach we consider the joint model of the covariates and the response and incorporate the conditional independence structure of the covariates into the prior distribution of the auxiliary knockoff variables. We further incorporate the estimation of a graphical model among the covariates, which in turn aids knockoffs generation and improves the estimation of the covariate effects on the response. We use a modified spike-and-slab prior on the regression coefficients, which avoids the increase of the model dimension as typical in the classical knockoff filter. Our model performs variable selection using an upper bound on the posterior probability of non-inclusion. We show how our model construction leads to valid model-X knockoffs and demonstrate that the proposed characterization is sufficient for controlling the BFDR at an arbitrary level, in finite samples. We also show that the model selection is robust to the estimation of the precision matrix. We use simulated data to demonstrate that our proposal increases the stability of the selection with respect to classical knockoff methods, as it relies on the entire posterior distribution of the knockoff variables instead of a single sample. With respect to Bayesian variable selection methods, we show that our selection procedure achieves comparable or better performances, while maintaining control over the FDR. Finally, we show the usefulness of the proposed model with an application to real data.

Multi-view data from the same source often exhibit correlation. This is mirrored in correlation between the latent spaces of separate variational autoencoders (VAEs) trained on each data-view. A multi-view VAE approach is proposed that incorporates a joint prior with a non-zero correlation structure between the latent spaces of the VAEs. By enforcing such correlation structure, more strongly correlated latent spaces are uncovered. Using conditional distributions to move between these latent spaces, missing views can be imputed and used for downstream analysis. Learning this correlation structure involves maintaining validity of the prior distribution, as well as a successful parameterization that allows end-to-end learning., Comment: Accepted at 'UniReps: 2nd Edition of the Workshop on Unifying Representations in Neural Models', a workshop at NeurIPS 2024

The placenta forms a critical barrier to infection through pregnancy, labor and, delivery. Inflammatory processes in the placenta have short-term, and long-term consequences for offspring health. Digital pathology and machine learning can play an important role in understanding placental inflammation, and there have been very few investigations into methods for predicting and understanding Maternal Inflammatory Response (MIR). This work intends to investigate the potential of using machine learning to understand MIR based on whole slide images (WSI), and establish early benchmarks. To that end, we use Multiple Instance Learning framework with 3 feature extractors: ImageNet-based EfficientNet-v2s, and 2 histopathology foundation models, UNI and Phikon to investigate predictability of MIR stage from histopathology WSIs. We also interpret predictions from these models using the learned attention maps from these models. We also use the MIL framework for predicting white blood cells count (WBC) and maximum fever temperature ($T_{max}$). Attention-based MIL models are able to classify MIR with a balanced accuracy of up to 88.5% with a Cohen's Kappa ($\kappa$) of up to 0.772. Furthermore, we found that the pathology foundation models (UNI and Phikon) are both able to achieve higher performance with balanced accuracy and $\kappa$, compared to ImageNet-based feature extractor (EfficientNet-v2s). For WBC and $T_{max}$ prediction, we found mild correlation between actual values and those predicted from histopathology WSIs. We used MIL framework for predicting MIR stage from WSIs, and compared effectiveness of foundation models as feature extractors, with that of an ImageNet-based model. We further investigated model failure cases and found them to be either edge cases prone to interobserver variability, examples of pathologist's overreach, or mislabeled due to processing errors.

Quantum electrodynamics in 1 + 1D (QED2) shares intriguing properties with QCD, including confinement, string breaking, and interesting phase diagram when the non-trivial topological $\theta$-term is considered. Its lattice regularization is a commonly used toy model for quantum simulations of gauge theories on near-term quantum devices. In this work, we address algorithms for adiabatic state preparation in digital quantum simulations of QED2. We demonstrate that, for specific choices of parameters, the existing adiabatic procedure leads to level crossing between states of different charge sectors, preventing the correct preparation of the ground state. We further propose a new adiabatic Hamiltonian and verify its efficiency in targeting systems with a nonzero topological $\theta$-term and in studying string breaking phenomena., Comment: 12 pages, 5 figures

Biologists study Diatoms, a fundamental algae, to assess the health of aquatic systems. Diatom specimens have traditionally been preserved on analog slides, where a single slide can contain thousands of these microscopic organisms. Digitization of these collections presents both metadata challenges and opportunities. This paper reports on metadata research aimed at providing access to a digital portion of the Academy of Natural Sciences' Diatom Herbarium, Drexel University. We report results of a 3-part study covering 1) a review of relevant metadata standards and a microscopy metadata framework shared by Hammer et al., 2) a baseline metadata alignment mapping current diatom metadata properties to standard metadata types, and 3) a metadata risk analysis associated with the course of standard data curation practices. This research is part of an effort involving the transfer of these digital slides to an new system, DataFed, to support global accessible. The final section of this paper includes a conclusion and discusses next steps., Comment: 13 pages, 2 figures, 1 table, to be published in MTSR 2024 conference proceedings

Given a surjective ring morphism, this paper constructs the twist functor around the induced derived restriction of scalars functor. We prove that the twist around ring morphisms is a derived autoequivalence in two settings: (1) Twists for Gorenstein orders, (2) twists induced by Frobenius exact categories. As a corollary, it is shown that the noncommutative twist introduced by Donovan and Wemyss is in fact a spherical twist. We then use the technology developed in (1) and (2) to obtain new spherical twists for very singular schemes, and discuss how our result extends previous works on spherical twists induced by crepant contractions., Comment: 48 pages, comments welcome

This paper proposes the RePAIR dataset that represents a challenging benchmark to test modern computational and data driven methods for puzzle-solving and reassembly tasks. Our dataset has unique properties that are uncommon to current benchmarks for 2D and 3D puzzle solving. The fragments and fractures are realistic, caused by a collapse of a fresco during a World War II bombing at the Pompeii archaeological park. The fragments are also eroded and have missing pieces with irregular shapes and different dimensions, challenging further the reassembly algorithms. The dataset is multi-modal providing high resolution images with characteristic pictorial elements, detailed 3D scans of the fragments and meta-data annotated by the archaeologists. Ground truth has been generated through several years of unceasing fieldwork, including the excavation and cleaning of each fragment, followed by manual puzzle solving by archaeologists of a subset of approx. 1000 pieces among the 16000 available. After digitizing all the fragments in 3D, a benchmark was prepared to challenge current reassembly and puzzle-solving methods that often solve more simplistic synthetic scenarios. The tested baselines show that there clearly exists a gap to fill in solving this computationally complex problem., Comment: NeurIPS 2024, Track Datasets and Benchmarks, 10 pages

Transmission matrices, mapping the propagation of light from one end of the tissue to the other, form an important mathematical tool in the analysis of tissue scattering and the design of wavefront shaping systems. To understand the relationship between their content and the volumetric structure of the tissue, we wish to fit them with multi-slice models, composed of a set of planar aberrations spaced throughout the volume. The number of layers used in such a model would largely affect the amount of information compression and the ease in which we can use such layered models in a wavefront-shaping system. This work offers a theoretical study of such multi-layered models. We attempt to understand how many layers are required for a good fit, and how does the approximation degrade when a smaller number of such layers is used. We show analytically that transmission matrices can be well fitted with very sparse layers. This leads to optimistic predictions on our ability to use them to design future wavefront shaping systems which can correct tissue aberration over a wide field-of-view.

Artificial intelligence may assist healthcare systems in meeting increasing demand for pathology services while maintaining diagnostic quality and reducing turnaround time and costs. We aimed to investigate the performance of an institutionally developed system for prostate cancer detection, grading, and workflow optimization and to contrast this with commercial alternatives. From August 2021 to March 2023, we scanned 21,396 slides from 1,147 patients with positive biopsies. We developed models for cancer detection, grading, and screening of equivocal cases for IHC ordering. We compared a task-specific model trained using the PANDA dataset of prostate cancer biopsies with one built using features extracted by the general-purpose histology foundation model, UNI and compare their performance in an unfiltered prospectively collected dataset that reflects our patient population (1737 slides,95 patients). We evaluated the contributions of a bespoke model designed to improve sensitivity in detecting small cancer foci and scoring of broader patterns observed at lower resolution. We found high concordance between the developed systems and pathologist reference in detection (AUC 98.5, sensitivity 95.0, and specificity 97.8), ISUP grading (quadratic Cohen's kappa 0.869), grade group 3 or higher (AUC 97.5, sensitivity 94.9, specificity 96.6) and comparable to published data from commercial systems. Screening could reduce IHC ordering for equivocal cases by 44.5% with an overall error rate of 1.8% (1.4% false positive, 0.4% false negative rates). Institutions like academic medical centers that have high scanning volumes and report abstraction capabilities can develop accurate computational pathology models for internal use. These models have the potential to aid in quality control role and to improve workflow in the pathology lab to help meet future challenges in prostate cancer diagnosis.

Mouse-tracking data, which record computer mouse trajectories while participants perform an experimental task, provide valuable insights into subjects' underlying cognitive processes. Neuroscientists are interested in clustering the subjects' responses during computer mouse-tracking tasks to reveal patterns of individual decision-making behaviors and identify population subgroups with similar neurobehavioral responses. These data can be combined with neuro-imaging data to provide additional information for personalized interventions. In this article, we develop a novel hierarchical shrinkage partition (HSP) prior for clustering summary statistics derived from the trajectories of mouse-tracking data. The HSP model defines a subjects' cluster as a set of subjects that gives rise to more similar (rather than identical) nested partitions of the conditions. The proposed model can incorporate prior information about the partitioning of either subjects or conditions to facilitate clustering, and it allows for deviations of the nested partitions within each subject group. These features distinguish the HSP model from other bi-clustering methods that typically create identical nested partitions of conditions within a subject group. Furthermore, it differs from existing nested clustering methods, which define clusters based on common parameters in the sampling model and identify subject groups by different distributions. We illustrate the unique features of the HSP model on a mouse tracking dataset from a pilot study and in simulation studies. Our results show the ability and effectiveness of the proposed exploratory framework in clustering and revealing possible different behavioral patterns across subject groups., Comment: Biometrics

Demixing properties of planetary major constituents influence the interior structure and evolution of planets. Comparing experimental and computational data on the miscibility of hydrogen and water to adiabatic profiles suggests phase separation between these components occurs in the ice giants Uranus and Neptune. We aim to predict the atmospheric water abundance and transition pressure between the water-poor outer envelope and the water-rich deep interior in Uranus and Neptune. We construct seven H2-H2O phase diagrams from the available experimental and computational data. We compute interior adiabatic structure models and compare these to the phase diagrams to infer whether demixing is occurring. We obtain a strong water depletion in the top layer due to rain-out of water and find upper limits on the atmospheric water mass fraction Z_atm of 0.21 for Uranus and 0.16 for Neptune. The transition from the water-poor to the water-rich layer is sharp and occurs at pressures P_Z between 4 and 11 GPa. Using these constraints on Z_atm and P_Z, we find that the observed gravitational harmonics J2 and J4 can be reproduced if P_Z > 10 GPa in Uranus and > 5 GPa in Neptune, and if the deep interior has a high primordial water mass fraction of 0.8, unless rocks are also present. The agreement with J4 is improved if rocks are confined deeper than P_Z, for instance below a rock cloud level at 2000 K (20-30 GPa). These findings confirm classical few-layer models and suggest that a layered structure may result from a combination of primordial mass accretion and subsequent phase separation. Reduced observational uncertainty in J4 and its dynamic contribution, atmospheric water abundance measurements from an Orbiter with a Probe mission to Uranus (UOP) or Neptune, and better understanding of the mixing behaviour of constituents are needed to constrain the interiors of ice giants., Comment: Accepted for publication in Astronomy and Astrophysics

Progressive dimensionality reduction algorithms allow for visually investigating intermediate results, especially for large data sets. While different algorithms exist that progressively increase the number of data points, we propose an algorithm that allows for increasing the number of dimensions. Especially in spatio-temporal data, where each spatial location can be seen as one data point and each time step as one dimension, the data is often stored in a format that supports quick access to the individual dimensions of all points. Therefore, we propose Progressive Glimmer, a progressive multidimensional scaling (MDS) algorithm. We adapt the Glimmer algorithm to support progressive updates for changes in the data's dimensionality. We evaluate Progressive Glimmer's embedding quality and runtime. We observe that the algorithm provides more stable results, leading to visually consistent results for progressive rendering and making the approach applicable to streaming data. We show the applicability of our approach to spatio-temporal simulation ensemble data where we add the individual ensemble members progressively., Comment: 6 pages, 5 figures, presented at 2024 IEEE VIS Workshop on Progressive Data Analysis and Visualization (PDAV)

The production of six energetic jets in proton-proton (pp) collisions at the LHC is studied as a means to directly observe for the first time the simultaneous scattering of three partons. The single-parton-scattering (SPS) cross sections for the production 2-, 3-, 4-, and 6-jets in pp collisions at center-of-mass energies of $\sqrt{s} = 14$ TeV, are calculated up to next-to-leading-order (NLO) accuracy in perturbative quantum chromodynamics with the MadGraph5_aMC@NLO and ALPGEN codes complemented with Pythia-8 for parton showering, hadronization, and decays. Jets are reconstructed using the anti-$k_T$ algorithm with distance parameter $R=0.4$. Assuming factorization of multiple hard-scattering probabilities in terms of SPS cross sections, the contributions to six-jet production from double- (DPS) and triple- (TPS) parton scatterings are derived. We find that the TPS contributions represent a ${\approx}20\%$ (${\approx}1\%$) fraction of the total 6-jets yields for minimum jet transverse momenta of $p_{T,min} = 20$ ($40$) GeV. A detailed multivariate analysis with realistic simulations of fully reconstructed jet samples for the TPS signal and DPS and SPS backgrounds indicates that TPS can be observed in events with six jets with $p_{T,min}=40$ GeV each, by collecting an integrated luminosity of $\mathcal{O}(50$ pb$^{-1}$) in a dedicated low-pileup run at the LHC., Comment: 8 pages, 2 Figures. DIS'24 Proceedings

We study families of deformed ADE surfaces by probing them with a D2-brane in Type IIA string theory. The geometry of the total space $X$ of such a family can be encoded in a scalar field $\Phi$, which lives in the corresponding ADE algebra and depends on the deformation parameters. The superpotential of the probe three dimensional (3d) theory incorporates a term that depends on the field $\Phi$. By varying the parameters on which $\Phi$ depends, one generates a family of 3d theories whose moduli space always includes a geometric branch, isomorphic to the deformed surface. By fibering this geometric branch over the parameter space, the total space $X$ of the family of ADE surfaces is reconstructed. We explore various cases, including when $X$ is the universal flop of length $\ell=1,2$. The effective theory, obtained after the introduction of $\Phi$, provides valuable insights into the geometric features of $X$, such as the loci in parameter space where the fiber becomes singular and, more notably, the conditions under which this induces a singularity in the total space. By analyzing the monopole operators in the 3d theory, we determine the charges of certain M2-brane states arising in M-theory compactifications on $X$., Comment: 51 pages, 11 figures

Jigsaw puzzle solving is a challenging task for computer vision since it requires high-level spatial and semantic reasoning. To solve the problem, existing approaches invariably use color and/or shape information but in many real-world scenarios, such as in archaeological fresco reconstruction, this kind of clues is often unreliable due to severe physical and pictorial deterioration of the individual fragments. This makes state-of-the-art approaches entirely unusable in practice. On the other hand, in such cases, simple geometrical patterns such as lines or curves offer a powerful yet unexplored clue. In an attempt to fill in this gap, in this paper we introduce a new challenging version of the puzzle solving problem in which one deliberately ignores conventional color and shape features and relies solely on the presence of linear geometrical patterns. The reconstruction process is then only driven by one of the most fundamental principles of Gestalt perceptual organization, namely Wertheimer's {\em law of good continuation}. In order to tackle this problem, we formulate the puzzle solving problem as the problem of finding a Nash equilibrium of a (noncooperative) multiplayer game and use classical multi-population replicator dynamics to solve it. The proposed approach is general and allows us to deal with pieces of arbitrary shape, size and orientation. We evaluate our approach on both synthetic and real-world data and compare it with state-of-the-art algorithms. The results show the intrinsic complexity of our purely line-based puzzle problem as well as the relative effectiveness of our game-theoretic formulation., Comment: to be published in ACCV2024

We propose a self-contained, resilient and fully distributed solution for locating the maximum of an unknown 3D scalar field using a swarm of robots that travel at constant speeds. Unlike conventional reactive methods relying on gradient information, our methodology enables the swarm to determine an ascending direction so that it approaches the source with arbitrary precision. Our source-seeking solution consists of three algorithms. The first two algorithms run sequentially and distributively at a high frequency providing barycentric coordinates and the ascending direction respectively to the individual robots. The third algorithm is the individual control law for a robot to track the estimated ascending direction. We show that the two algorithms with higher frequency have an exponential convergence to their eventual values since they are based on the standard consensus protocol for first-order dynamical systems; their high frequency depends on how fast the robots travel through the scalar field. The robots are not constrained to any particular geometric formation, and we study both discrete and continuous distributions of robots within swarm shapes. The shape analysis reveals the resiliency of our approach as expected in robot swarms, i.e., by amassing robots we ensure the source-seeking functionality in the event of missing or misplaced individuals or even if the robot network splits into two or more disconnected subnetworks. In addition, we also enhance the robustness of the algorithm by presenting conditions for \emph{optimal} swarm shapes, in the sense that the ascending directions can be closely parallel to the field's gradient. We exploit such an analysis so that the swarm can adapt to unknown environments by morphing its shape and maneuvering while still following an ascending direction., Comment: 15 pages, submitted version to T-RO. This version does not contain the field experiments. arXiv admin note: text overlap with arXiv:2309.02937

Thermophotovoltaics (TPVs) have the potential to exhibit higher power conversion efficiencies than traditional photovoltaics (PVs), with a broad range of applicability from waste recovery systems to aerospace solutions. They operate by preferentially radiating above bandgap photons via a high temperature optical emitter, whose spectrum is tuned through choice of materials and geometry. For TPV to be practically implemented, the emitters must be designed with a simple optical structure while remaining thermally stable. Here, we demonstrate coating/substrate bilayer thin films as a solution to these design criteria. With the optical data of 53 high melting point materials, we simulate the bilayer emissivity as a function of coating thickness for each thermochemically stable emitter operating at 1,800 {\deg}C. Emitter-cell systems are characterized by the cell power density and TPV conversion efficiency, constituting a TPV performance metric space. For a given bilayer and bandgap these coating thickness parameterized figures of merit form a performance metric curve, with the best points defining a tradeoff zone. We screen the resulting performance metric curves, identifying trends based on optical properties of the system, finding a high degree of tunability through the material selection step. For GaSb cells, >49% efficiency is achieved using AlN/W, a 5.6% increase over bulk W. By calculating the figures of merit for all systems with varying bandgap, we find unique emitter choices per PV cell for achieving the highest potential efficiency. Our materials screening approach uses physical data to identify improvements to emitters for experimental TPV designs., Comment: 27 pages, 14 figures (5 main text 9 supplemental), to be submitted in ACS Applied Energy Materials

Data collected over networks can be modelled as noisy observations of an unknown function over the nodes of a graph or network structure, fully described by its nodes and their connections, the edges. In this context, function estimation has been proposed in the literature and typically makes use of the network topology such as relative node arrangement, often using given or artificially constructed node Euclidean coordinates. However, networks that arise in fields such as hydrology (for example, river networks) present features that challenge these established modelling setups since the target function may naturally live on edges (e.g., river flow) and/or the node-oriented modelling uses noisy edge data as weights. This work tackles these challenges and develops a novel lifting scheme along with its associated (second) generation wavelets that permit data decomposition across the network edges. The transform, which we refer to under the acronym LG-LOCAAT, makes use of a line graph construction that first maps the data in the line graph domain. We thoroughly investigate the proposed algorithm's properties and illustrate its performance versus existing methodologies. We conclude with an application pertaining to hydrology that involves the denoising of a water quality index over the England river network, backed up by a simulation study for a river flow dataset.

An isometric immersion $X: \Sigma^n \longrightarrow \mathbb{E}^{n+1}$ is biharmonic if $\Delta^2 X = 0$, i.e. if $\Delta H =0$, where $\Delta$ and $H$ are the metric Laplacian and the mean curvature vector field of $\Sigma^n$ respectively. More generally, biconservative hypersurfaces (BCH) are isometric immersions for which only the tangential part of the biharmonic equation vanishes. We study and construct BCH that are holonomic, i.e. for which the principal curvature directions define an integrable net, and we deduce that $\Sigma^n$ is a holonomic biharmonic hypersurface iff it is minimal.

Bars are remarkable stellar structures that can transport gas toward centers and drive the secular evolution of galaxies. In this context, it is important to locate dynamical resonances associated with bars. For this study, we used ${Spitzer}$ near-infrared images as a proxy for the stellar gravitational potential and the ALMA CO(J=2-1) gas distribution from the PHANGS survey to determine the position of the main dynamical resonances associated with the bars in the PHANGS sample of 74 nearby star-forming galaxies. We used the gravitational torque method to estimate the location of the bar corotation radius ($R_{\rm CR}$), where stars and gas rotate at the same angular velocity as the bar. Of the 46 barred galaxies in PHANGS, we have successfully determined the corotation (CR) for 38 of them. The mean ratio of the $R_{\rm CR}$ to the bar radius ($R_{\rm bar}$) is $\mathcal{R} = R_{\rm CR}/R_{\rm bar} = 1.12$, with a standard deviation of $0.39$. This is consistent with the average value expected from theory and suggests that bars are predominantly fast. We also compared our results with other bar CR measurements from the literature, which employ different methods, and find good agreement ($\rho = 0.64$). Finally, using rotation curves, we have estimated other relevant resonances such as the inner Lindblad resonance (ILR) and the outer Lindblad resonance (OLR), which are often associated with rings. This work provides a useful catalog of resonances for a large sample of nearby galaxies and emphasizes the clear connection between bar dynamics and morphology.

Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a massive-scale benchmark for multilingual and multicultural, visually grounded language understanding. This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date. It includes tasks for identifying dish names and their origins. We provide evaluation datasets in two sizes (12k and 60k instances) alongside a training dataset (1 million instances). Our findings show that while VLMs perform better with correct location context, they struggle with adversarial contexts and predicting specific regional cuisines and languages. To support future research, we release a knowledge base with annotated food entries and images along with the VQA data., Comment: Preprint

Triangular cross-section nanodevices are among the leading approaches for integrating color centers with photonics for applications in quantum information processing. We design periodic and aperiodic fishbone triangular grating couplers in silicon carbide. We optimize the designs for achieving up to $\sim$ 31% collection efficiency from color center integrated triangular devices to a microscopy system. Using an ion beam angle etching process, we demonstrate proof-of-principle fabrication of the designed devices for future implementation in wafer-scale quantum nanophotonics.

Given a digraph $D=(V,A)$ with vertex-set $V=\{1,\ldots,n\}$ and arc-set $A$, we denote by $Q(D)$ the set of all real $n\times n$ matrices $B=[b_{u,w}]$ with $b_{u,u}\not=0$ for all $u\in V$, $b_{u,w} \not= 0$ if $u\not=w$ and there is an arc from $u$ to $w$, and $b_{u,w}=0$ if $u\not=w$ and there is no arc from $u$ to $w$. We say that a matrix $B\in Q(D)$ has the Asymmetric Strong Arnold Property (ASAP) if $X\circ B = 0$, $X^T B = 0$, and $B X^T = 0$ implies $X=0$. We define the stable maximum nullity, $\overrightarrow{\nu}(D)$, of a digraph $D$ as the largest nullity of any matrix $A\in Q(D)$ that has the ASAP\@. We show that a digraph $D$ has $\overrightarrow{\nu}(D)\leq 1$ if and only $D$ and $\overleftarrow{D}$ a partial $1$-DAGs.

6G networks will be highly dynamic, re-configurable, and resilient. To enable and support such features, employing AI has been suggested. Integrating AIin networks will likely require distributed AI deployments with resilient connectivity, e.g., for communication between RL agents and environment. Such approaches need to be validated in realistic network environments. In this demo, we use ContainerNet to emulate AI-capable and autonomic networks that employ the routing protocol KIRA to provide resilient connectivity and service discovery. As an example AI application, we train and infer deep RL agents learning medium access control (MAC) policies for a wireless network environment in the emulated network., Comment: Accepted for presentation in the Demo Session at the IEEE International Conference on Network Protocols (ICNP), 2024

We consider a wave equation with a time-dependent propagation speed, whose potential oscillations are controlled through bounds on its first and second derivatives and by limiting the integral of the difference with a fixed constant. We investigate when the wave equation exhibits generalized energy conservation (GEC), meaning that the energy of all solutions remains bounded for all times by a multiple of the initial energy. When GEC is not satisfied, we provide upper bounds for the growth of the energy. These upper bounds are derived by analyzing the growth of the Fourier components of the solution. Depending on the frequency and the time interval, different energy inequalities are employed to fully exploit our assumptions on the propagation speed. Finally, we present counterexamples that demonstrate the optimality of our upper bound estimates., Comment: 31 pages

The chemical abundances of gas-giant exoplanet atmospheres hold clues to the formation and evolution pathways that sculpt the exoplanet population. Recent ground-based high-resolution spectroscopic observations of the non-transiting hot Jupiter $\tau$ Bo\"otis b from different instruments have resulted in a tension on the presence of water vapour in the planet's atmosphere, which impact the planet's inferred C/O and metallicity. To investigate this, we revisit the archival CRIRES observations of the planet's dayside in the wavelength range 2.28 to 2.33 $\mu$m. We reanalyse them using the latest methods for correcting stellar and telluric systematics, and free-chemistry Bayesian atmospheric retrieval. We find that a spurious detection of CH$_{4}$ can arise from inadequate telluric correction. We confirm the detection of CO and constrain its abundance to be near solar $\log_{10}(\mathrm{CO})$ = -3.44$^{+1.63}_{-0.85}$ VMR. We find a marginal evidence for H$_{2}$O with $\log_{10}(\mathrm{H_{2}O})$ = -5.13$^{+1.22}_{-6.37}$ VMR. This translates to super solar C/O (0.95$^{+0.06}_{-0.31}$), marginally sub-solar metallicity (-0.21 $^{+1.66}_{-0.87}$). Due to the relatively large uncertainty on H$_{2}$O abundance, we cannot confidently resolve the tension on the presence of H$_{2}$O and the super-solar atmospheric metallicity of $\tau$ Bo\"otis b. We recommend further observations of $\tau$ Bo\"otis b in the wavelength ranges simultaneously covering CO and $\mathrm{H_{2}O}$ to confirm the peculiar case of the planet's super-solar C/O and metallicity., Comment: 16 pages, 13 figures; Accepted for publication in MNRAS

Deep InfoMax (DIM) is a well-established method for self-supervised representation learning (SSRL) based on maximization of the mutual information between the input and the output of a deep neural network encoder. Despite the DIM and contrastive SSRL in general being well-explored, the task of learning representations conforming to a specific distribution (i.e., distribution matching, DM) is still under-addressed. Motivated by the importance of DM to several downstream tasks (including generative modeling, disentanglement, outliers detection and other), we enhance DIM to enable automatic matching of learned representations to a selected prior distribution. To achieve this, we propose injecting an independent noise into the normalized outputs of the encoder, while keeping the same InfoMax training objective. We show that such modification allows for learning uniformly and normally distributed representations, as well as representations of other absolutely continuous distributions. Our approach is tested on various downstream tasks. The results indicate a moderate trade-off between the performance on the downstream tasks and quality of DM., Comment: 22 pages, 3 fugures

The influence of Active Galactic Nuclei (AGN) on star formation within their host galaxies remains a topic of intense debate. One of the primary challenges in quantifying the star formation rate (SFR) within AGN hosts arises from the prevalent assumption in most methodologies, which attribute gas excitation to young stars alone. However, this assumption does not consider the contribution of the AGN to the ionization of the gas in their environment. To address this issue, we evaluate the use of strong optical emission lines to obtain the SFR surface density ($\Sigma{{\rm SFR_{AGN}}}$) in regions predominantly ionized by an AGN, using a sample of 293 AGN hosts from the MaNGA survey, with SFR measurements available through stellar population fitting. We propose calibrations involving the H$\alpha$ and [O\,{\sc iii}]$\lambda$5007 emission lines, which can be used to determine $\Sigma{{\rm SFR_{AGN}}}$, resulting in values consistent with those estimated through stellar population fitting., Comment: Accepted for publication in MNRAS

View synthesis using Neural Radiance Fields (NeRF) and Gaussian Splatting (GS) has demonstrated impressive fidelity in rendering real-world scenarios. However, practical methods for accurate and efficient epistemic Uncertainty Quantification (UQ) in view synthesis are lacking. Existing approaches for NeRF either introduce significant computational overhead (e.g., ``10x increase in training time" or ``10x repeated training") or are limited to specific uncertainty conditions or models. Notably, GS models lack any systematic approach for comprehensive epistemic UQ. This capability is crucial for improving the robustness and scalability of neural view synthesis, enabling active model updates, error estimation, and scalable ensemble modeling based on uncertainty. In this paper, we revisit NeRF and GS-based methods from a function approximation perspective, identifying key differences and connections in 3D representation learning. Building on these insights, we introduce PH-Dropout (Post hoc Dropout), the first real-time and accurate method for epistemic uncertainty estimation that operates directly on pre-trained NeRF and GS models. Extensive evaluations validate our theoretical findings and demonstrate the effectiveness of PH-Dropout., Comment: 21 pages, in submision

We address the problem of solving strongly convex and smooth minimization problems using stochastic gradient descent (SGD) algorithm with a constant step size. Previous works suggested to combine the Polyak-Ruppert averaging procedure with the Richardson-Romberg extrapolation technique to reduce the asymptotic bias of SGD at the expense of a mild increase of the variance. We significantly extend previous results by providing an expansion of the mean-squared error of the resulting estimator with respect to the number of iterations $n$. More precisely, we show that the mean-squared error can be decomposed into the sum of two terms: a leading one of order $\mathcal{O}(n^{-1/2})$ with explicit dependence on a minimax-optimal asymptotic covariance matrix, and a second-order term of order $\mathcal{O}(n^{-3/4})$ where the power $3/4$ can not be improved in general. We also extend this result to the $p$-th moment bound keeping optimal scaling of the remainders with respect to $n$. Our analysis relies on the properties of the SGD iterates viewed as a time-homogeneous Markov chain. In particular, we establish that this chain is geometrically ergodic with respect to a suitably defined weighted Wasserstein semimetric.

Photon entanglement is indispensable for optical quantum technologies. Measurement-based optical quantum computing and all-optical quantum networks rely on multiphoton cluster states consisting of indistinguishable entangled photons. A promising method for creating such cluster states on demand is spin-photon entanglement using the spin of a resident charge carrier in a quantum dot, precessing in a weak external magnetic field. In this work, we show theoretically and experimentally that spin-photon entanglement is strongly affected by the hidden anisotropy of quantum dots, which can arise from mechanical stress, shape anisotropy and even specific crystal structure. In the measurements of time-resolved photoluminescence and cross-polarized second-order photon correlation function in a magnetic field, the anisotropy manifests itself in the spin dynamics and, as a consequence, in the spin-photon concurrence. The measured time-filtered spin-photon Bell state fidelity depends strongly on the excitation polarization and reaches an extremely high value of 94% at maximum. We specify the magnetic field and excitation polarization directions that maximize spin-photon entanglement and thereby enhance the fidelity of multiphoton entangled states.

Starting from the well-known relationship $|{{\mathrm{e}}}^z| = {{\mathrm{e}}}^{{\mathrm Re}(z)}$, we consider the question whether $|E_{\alpha,\beta}(z)|$ and $E_{\alpha,\beta}({\mathrm Re}(z))$ are comparable, as functions of the complex variable $z$, where $E_{\alpha,\beta}$ denotes the two-parameter Mittag-Leffler function, a generalization of the exponential function. For some ranges of the parameters $\alpha$ and $\beta$ we prove inequalities between $|E_{\alpha,\beta}(z)|$ and $E_{\alpha,\beta}({\mathrm Re}(z))$ holding globally for all $z\in \mathbb{C}$. In some other ranges of $\alpha$ and $\beta$ the same inequalities are proved to hold asymptotically, i.e. for sufficiently small or large $z$. There are moreover some values of $\alpha$ and $\beta$ for which the situation is less clear, and some conjectures, motivated by numerical observations, are proposed.

This work describes the architecture and vision of designing and implementing a new test infrastructure for 6G physical layer research at KU Leuven. The Testbed is designed for physical layer research and experimentation following several emerging trends, such as cell-free networking, integrated communication, sensing, open disaggregated Radio Access Networks, AI-Native design, and multiband operation. The software is almost entirely based on free and open-source software, making contributing and reusing any component easy. The open Testbed is designed to provide real-time and labeled data on all parts of the physical layer, from raw IQ data to synchronization statistics, channel state information, or symbol/bit/packet error rates. Real-time labeled datasets can be collected by synchronizing the physical layer data logging with a positioning and motion capture system. One of the main goals of the design is to make it open and accessible to external users remotely. Most tests and data captures can easily be automated, and experiment code can be remotely deployed using standard containers (e.g., Docker or Podman). Finally, the paper describes how the Testbed can be used for our research on joint communication and sensing, over-the-air synchronization, distributed processing, and AI in the loop.

We have been developing an x-ray imaging system, Multi-Image X-ray Interferometer Module (MIXIM), to achieve a high angular resolution with a compact system size. MIXIM is comprised of a mask with equally-spaced apertures and an x-ray detector. The aperture size and mask-detector distance determine the system's angular resolution. Although a smaller aperture gives a better resolution, the degree of improvement is limited by a diffraction effect. MIXIM circumvents this problem by utilizing the Talbot effect. Our experiment with the previous model equipped with a multi-pinhole mask obtained an angular resolution of 0.5" with a mask-detector distance of 92 cm. A major downside of the multi-pinhole mask is, however, that it has a very low opening fraction, which results in a very low effective area. Here, we newly adopt to MIXIM a multiple coded aperture (MCA) mask, an array of coded aperture patterns. Our proof-of-concept experiment demonstrates that the Talbot effect works even for the MCA mask with a high opening fraction of ~50% at 12.4 keV. Consequently, the new MIXIM realizes about 25 times as large an effective area as that of the previous model, while maintaining a high angular resolution of 0.2" and a compact size of ~1.5 m., Comment: 27 pages, 11 figures, accepted for publication in JATIS

Neurological disorders that affect speech production, such as Alzheimer's Disease (AD), significantly impact the lives of both patients and caregivers, whether through social, psycho-emotional effects or other aspects not yet fully understood. Recent advancements in Large Language Model (LLM) architectures have developed many tools to identify representative features of neurological disorders through spontaneous speech. However, LLMs typically lack interpretability, meaning they do not provide clear and specific reasons for their decisions. Therefore, there is a need for methods capable of identifying the representative features of neurological disorders in speech and explaining clearly why these features are relevant. This paper presents an explainable LLM method, named SLIME (Statistical and Linguistic Insights for Model Explanation), capable of identifying lexical components representative of AD and indicating which components are most important for the LLM's decision. In developing this method, we used an English-language dataset consisting of transcriptions from the Cookie Theft picture description task. The LLM Bidirectional Encoder Representations from Transformers (BERT) classified the textual descriptions as either AD or control groups. To identify representative lexical features and determine which are most relevant to the model's decision, we used a pipeline involving Integrated Gradients (IG), Linguistic Inquiry and Word Count (LIWC), and statistical analysis. Our method demonstrates that BERT leverages lexical components that reflect a reduction in social references in AD and identifies which further improve the LLM's accuracy. Thus, we provide an explainability tool that enhances confidence in applying LLMs to neurological clinical contexts, particularly in the study of neurodegeneration., Comment: 27 pages, 6 figures, authors Marina Ribeiro and B\'arbara Malcorra have equal contribution, C\'esar Renn\'o-Costa is the corresponding author

We have conducted extremely ultra-deep pencil beam observations for new satellites around both Uranus and Neptune. Tens of images on several different nights in 2021, 2022 and 2023 were obtained and shifted and added together to reach as faint as 26.9 and 27.2 magnitudes in the r-band around Uranus and Neptune, respectively. One new moon of Uranus, S/2023 U1, and two new moons of Neptune, S/2021 N1 and S/2002 N5, were found. S/2023 U1 was 26.6 mags, is about 7 km in diameter and has a distant, eccentric and inclined retrograde orbit similar to Caliban and Stephano, implying these satellites are fragments from a once larger parent satellite. S/2023 U1 almost completely overlaps Stephano in orbital phase space. S/2021 N1 was 26.9 mags, about 14 km in size and has a retrograde orbit similar to Neso and Psamathe, indicating they are a dynamical family. We find S/2021 N1 is in a Kozai-Lidov orbital resonance. S/2002 N5 was 25.9 mags, is about 23 km in size and it makes a family of distant prograde satellites with Sao and Laomedeia. All three new moons show for the first time dynamical groups of moons exist around both Uranus and Neptune. The creation of these groups likely produced dust that could be the source of red material seen on the leading hemispheres of some larger inner satellites like Titania, Oberon and Umbriel. We also detected all known outer moons of Uranus and Neptune on multiple nights. This survey mostly completes the outer satellites of Uranus to about 8 km and Neptune to about 14 km in diameter. The size distributions of satellite dynamical families around the giant planets shows a strong steepening in the power law size distribution smaller than 5 km in diameter. The satellites of a family become much more common smaller than 5 km and their size distribution is consistent with a collisional break-up of a once larger parent satellite., Comment: Accepted Astronomical Journal

In this work, we focus on the analytical and numerical study of a mathematical model for brain tumors with radiotherapy influence. Under certain assumptions on the given data in the model, we prove existence and uniqueness of a weak nonnegative (biological relevant) solution. Then, assuming only more regular initial data, we obtain the extra regularity of this solution. Besides, we analyze the optimal control of the advection coefficient responding for the radiotherapy effect on the tumor cell population. Finally, we provide numerical illustration to all obtained analytical results., Comment: 37 pages, 3 figures

Covariate-dependent graph learning has gained increasing interest in the graphical modeling literature for the analysis of heterogeneous data. This task, however, poses challenges to modeling, computational efficiency, and interpretability. The parameter of interest can be naturally represented as a three-dimensional array with elements that can be grouped according to two directions, corresponding to node level and covariate level, respectively. In this article, we propose a novel dual group spike-and-slab prior that enables multi-level selection at covariate-level and node-level, as well as individual (local) level sparsity. We introduce a nested strategy with specific choices to address distinct challenges posed by the various grouping directions. For posterior inference, we develop a tuning-free Gibbs sampler for all parameters, which mitigates the difficulties of parameter tuning often encountered in high-dimensional graphical models and facilitates routine implementation. Through simulation studies, we demonstrate that the proposed model outperforms existing methods in its accuracy of graph recovery. We show the practical utility of our model via an application to microbiome data where we seek to better understand the interactions among microbes as well as how these are affected by relevant covariates.

Count data play a crucial role in sports analytics, providing valuable insights into various aspects of the game. Models that accurately capture the characteristics of count data are essential for making reliable inferences. In this paper, we propose the use of the Conway-Maxwell-Poisson (CMP) model for analyzing count data in sports. The CMP model offers flexibility in modeling data with different levels of dispersion. Here we consider a bivariate CMP model that models the potential correlation between home and away scores by incorporating a random effect specification. We illustrate the advantages of the CMP model through simulations. We then analyze data from baseball and soccer games before, during, and after the COVID-19 pandemic. The performance of our proposed CMP model matches or outperforms standard Poisson and Negative Binomial models, providing a good fit and an accurate estimation of the observed effects in count data with any level of dispersion. The results highlight the robustness and flexibility of the CMP model in analyzing count data in sports, making it a suitable default choice for modeling a diverse range of count data types in sports, where the data dispersion may vary., Comment: Journal of Quantitative Analysis in Sports (in press)

Device-independent quantum key distribution (DIQKD) provides the strongest form of quantum security, as it allows two honest users to establish secure communication channels even when using fully uncharacterized quantum devices. The security proof of DIQKD is derived from the violation of a Bell inequality, mitigating side-channel attacks by asserting the presence of nonlocality. This enhanced security comes at the cost of a challenging implementation, especially over long distances, as losses make Bell tests difficult to conduct successfully. Here, we propose a photonic realization of DIQKD, utilizing a heralded preparation of a single-photon path entangled state between the honest users. Being based on single-photon interference effects, the obtained secret key rate scales with the square root of the quantum channel transmittance. This leads to positive key rates over distances of up to hundreds of kilometers, making the proposed setup a promising candidate for securing long-distance communication in quantum networks.

We present the results of a James Webb Space Telescope (JWST) NIRCam and NIRSpec investigation into the young massive star cluster (YMC) population of NGC 3256, the most cluster-rich luminous infrared galaxy (LIRG) in the Great Observatories All Sky LIRG Survey. We detect 3061 compact YMC candidates with a $S/N \geq 3$ at F150W, F200W, and F335M. Based on yggdrasil stellar population models, we identify 116/3061 sources with F150W - F200W $> 0.47$ and F200W - F355M $> -1.37$ colors suggesting they are young (t $\leq 5$ Myr), dusty ($A_{V} = 5 - 15$), and massive ($M_{\odot} > 10^{5}$). This increases the sample of dust-enshrouded YMCs detected in this system by an order of magnitude relative to previous HST studies. With NIRSpec IFU pointings centered on the northern and southern nucleus, we extract the Pa$\alpha$ and 3.3$\mu$m PAH equivalent widths for 8 bright and isolated YMCs. Variations in both the F200W - F335M color and 3.3$\mu$m PAH emission with the Pa$\alpha$ line strength suggest a rapid dust clearing ($< 3 - 4$ Myr) for the emerging YMCs in the nuclei of NGC 3256. Finally, with both the age and dust emission accurately measured we use yggdrasil to derive the color excess (E(B - V)) for all 8 YMCs. We demonstrate that YMCs with strong 3.3$\mu$m PAH emission (F200W - F335M $> 0$) correspond to sources with E(B - V) $> 3$, which are typically missed in UV-optical studies. This underscores the importance of deep near-infrared imaging for finding and characterizing these very young and dust-embedded sources., Comment: 18 pages, 7 figures, 1 table; Accepted for publication in ApJL

The TROPOMI-SWIR HgCdTe detector on the Sentinel-5 Precursor mission has been performing in-orbit measurements of molecular absorption in Earth's atmosphere since its launch in October 2017. In its polar orbit the detector is continuously exposed to potentially harmful energetic particles. Calibration measurements taken during the eclipse are used to inspect the performance of this detector. This paper explores the in-orbit degradation of the HgCdTe detector. After five years, the detector is still performing within specifications, even though pixels are continuously hit by cosmic radiation. The bulk of the impacts have no lasting effects, and most of the damaged pixels (95%) appear to recover on the order of a few days to several months, attributed to a slow spontaneous recovery of defects in the HgCdTe detector material. This is observed at the operational temperature of 140 K. The distribution of the observed recovery times has a mean around nine days with a significant tail towards several months. Pixels that have degraded have a significant probability to degrade again. The location of faulty pixels follows a Poissonian distribution across the detector. No new clusters have appeared, revealing that impacts are dominated by relatively low energetic protons and electrons. Due to the observed spontaneous recovery of pixels, the fraction of pixels meeting all quality requirements in the nominal operations phase has always been over 98.7%. The observed performance of the TROPOMI-SWIR detector in-flight impacts selection criteria of HgCdTe detectors for future space instrumentation., Comment: 18 pages, accepted for publication in MST

Bayesian Personalized Ranking (BPR), a collaborative filtering approach based on matrix factorization, frequently serves as a benchmark for recommender systems research. However, numerous studies often overlook the nuances of BPR implementation, claiming that it performs worse than newly proposed methods across various tasks. In this paper, we thoroughly examine the features of the BPR model, indicating their impact on its performance, and investigate open-source BPR implementations. Our analysis reveals inconsistencies between these implementations and the original BPR paper, leading to a significant decrease in performance of up to 50% for specific implementations. Furthermore, through extensive experiments on real-world datasets under modern evaluation settings, we demonstrate that with proper tuning of its hyperparameters, the BPR model can achieve performance levels close to state-of-the-art methods on the top-n recommendation tasks and even outperform them on specific datasets. Specifically, on the Million Song Dataset, the BPR model with hyperparameters tuning statistically significantly outperforms Mult-VAE by 10% in NDCG@100 with binary relevance function., Comment: This paper is accepted at the Reproducibility track of the ACM RecSys '24 conference

The task of content-type detection -- which entails identifying the data encoded in an arbitrary byte sequence -- is critical for operating systems, development, reverse engineering environments, and a variety of security applications. In this paper, we introduce Magika, a novel AI-powered content-type detection tool. Under the hood, Magika employs a deep learning model that can execute on a single CPU with just 1MB of memory to store the model's weights. We show that Magika achieves an average F1 score of 99% across over a hundred content types and a test set of more than 1M files, outperforming all existing content-type detection tools today. In order to foster adoption and improvements, we open source Magika under an Apache 2 license on GitHub and make our model and training pipeline publicly available. Our tool has already seen adoption by the Gmail email provider for attachment scanning, and it has been integrated with VirusTotal to aid with malware analysis. We note that this paper discusses the first iteration of Magika, and a more recent version already supports more than 200 content types. The interested reader can see the latest development on the Magika GitHub repository, available at https://github.com/google/magika.