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When water is confined within walls only a few molecular diameters apart, it displays unique behaviors that differ significantly from bulk water. This confinement reveals fascinating mechanical, thermodynamic, and dielectric anomalies. Nature has effectively used the confinement "trick" to achieve superior functionalities with abundant elements and water, avoiding scarce materials. The challenge, however, is to replicate this principle in scalable artificial device engineering. Here, we introduce the "blue battery", a scalable supercapacitive device utilizing pure water confined in 1-nm clay channels as its sole electrolyte. Made entirely from Earth-abundant materials via scalable nano-engineering, it preserves nearly 100% coulombic efficiency over 60,000 charge-discharge cycles, operates at voltages up to 1.65 V, and delivers competitive power and energy densities. Thus, achieving a high degree of sustainability via just the confinement effect, our concept establishes a versatile blueprint for environmentally neutral technologies, enabling the design of other "blue devices" for micro- to bulk-scale energy storage applications, even in extreme environments like Mars. Our research opens possibilities for environmentally neutral energy solutions inspired by nature., Comment: Main text: 17 pages, 4 figures, 55 references. Supplementary: 28 pages, 26 figures, 3 tables, 3 references

This paper addresses the incompatible case of parallel batch scheduling, where compatible jobs belong to the same family, and jobs from different families cannot be processed together in the same batch. Existing constraint programming (CP) models for this problem fail to synchronize the processing of the jobs within their batch, resulting in batch interruptions. In the context of the diffusion area in the semiconductor manufacturing process, these interrupted solutions would disrupt the thermal stability required for a uniform dopant distribution on the wafers. This paper proposes three new CP models that directly tackle these interruptions in the formulation, including two adaptions of existing models and a novel Redundant Synchronized (RS) model. These existing and novel models are compared on standard test cases, demonstrating the superiority of the RS model in finding optimal or near-optimal solutions quickly., Comment: 11 pages, 6 figures

In medical image synthesis, the precision of localized structural details is crucial, particularly when addressing specific clinical requirements such as the identification and measurement of fine structures. Traditional methods for image translation and synthesis are generally optimized for global image reconstruction but often fall short in providing the finesse required for detailed local analysis. This study represents a step toward addressing this challenge by introducing a novel anatomical feature-prioritized (AFP) loss function into the synthesis process. This method enhances reconstruction by focusing on clinically significant structures, utilizing features from a pre-trained model designed for a specific downstream task, such as the segmentation of particular anatomical regions. The AFP loss function can replace or complement global reconstruction methods, ensuring a balanced emphasis on both global image fidelity and local structural details. Various implementations of this loss function are explored, including its integration into different synthesis networks such as GAN-based and CNN-based models. Our approach is applied and evaluated in two contexts: lung MR to CT translation, focusing on high-quality reconstruction of bronchial structures, using a private dataset; and pelvis MR to CT synthesis, targeting the accurate representation of organs and muscles, utilizing a public dataset from the Synthrad2023 challenge. We leverage embeddings from pre-trained segmentation models specific to these anatomical regions to demonstrate the capability of the AFP loss to prioritize and accurately reconstruct essential features. This tailored approach shows promising potential for enhancing the specificity and practicality of medical image synthesis in clinical applications.

With the rapid development and early industrialization of quantum technologies, it is of great interest to analyze their overall energy consumption before planning for their wide-scale deployments. The evaluation of the total energy requirements of quantum networks is a challenging task: different networks require very disparate techniques to create, distribute, manipulate, detect, and process quantum signals. This paper aims to lay the foundations of a framework to model the energy requirements of different quantum technologies and protocols applied to near-term quantum networks. Different figures of merit are discussed and a benchmark on the energy consumption of bipartite and multipartite network protocols is presented. An open-source software to estimate the energy consumption of photonic setups is also provided.

Designing topological materials with specific topological indices is a complex inverse problem, traditionally tackled through manual, intuition-driven methods that are neither scalable nor efficient for exploring the vast space of possible material configurations. In this work, we develop an algorithm that leverages the covariance matrix adaptation evolution strategy to optimize the Fourier representation of the periodic functions shaping the designer material's characteristics. This includes mass profiles or dielectric tensors for phononic and photonic crystals, respectively, as much as synthetic potentials applicable to electronic and ultra-cold atomic systems. We demonstrate our methodology with a detailed characterization of a class of topological bands known as "fragile topological", showcasing the algorithm's capability to address both topological characteristics and spectral quality. This automation not only streamlines the design process but also significantly expands the potential for identifying and constructing high quality designer topological materials across the wide range of platforms, and is readily extendable to other setups, including higher-dimensional and non-linear systems., Comment: 4 pages + 45 pages SI, 4 + 49 figures

The physics of spin-orbit entangled magnetic moments of $4d$ and $5d$ transition metal ions on a honeycomb lattice has been much explored in search for unconventional magnetic orders or quantum spin liquids expected for compass spin models, where different bonds in the lattice favour different orientations for the magnetic moments. Realizing such physics with rare-earth ions is a promising route to achieve exotic ground states in the extreme spin orbit limit, however this regime has remained experimentally largely unexplored due to major challenges in materials synthesis. Here we report successful synthesis of powders and single crystals of $\beta$-Na$_2$PrO$_3$, with $4f^{1}$ Pr$^{4+}$ $j_\mathrm{eff}\!=\!1/2$ magnetic moments arranged on a hyperhoneycomb lattice with the same threefold coordination as the planar honeycomb. We find a strongly noncollinear magnetic order with highly dispersive gapped excitations that we argue arise from frustration between bond-dependent, anisotropic off-diagonal exchanges, a compass quantum spin model not explored experimentally so far. Our results show that rare-earth ions on threefold coordinated lattices offer a platform for the exploration of quantum compass spin models in the extreme spin orbit regime, with qualitatively distinct physics from that of $4d$ and $5d$ Kitaev materials., Comment: 22 pages in total, main text (9 pages) + supplementary information

Anonymization plays a key role in protecting sensible information of individuals in real world datasets. Self-driving cars for example need high resolution facial features to track people and their viewing direction to predict future behaviour and react accordingly. In order to protect people's privacy whilst keeping important features in the dataset, it is important to replace the full body of a person with a highly detailed anonymized one. In contrast to doing face anonymization, full body replacement decreases the ability of recognizing people by their hairstyle or clothes. In this paper, we propose a workflow for full body person anonymization utilizing Stable Diffusion as a generative backend. Text-to-image diffusion models, like Stable Diffusion, OpenAI's DALL-E or Midjourney, have become very popular in recent time, being able to create photorealistic images from a single text prompt. We show that our method outperforms state-of-the art anonymization pipelines with respect to image quality, resolution, Inception Score (IS) and Frechet Inception Distance (FID). Additionally, our method is invariant with respect to the image generator and thus able to be used with the latest models available.

We present a novel framework for free-viewpoint facial performance relighting using diffusion-based image-to-image translation. Leveraging a subject-specific dataset containing diverse facial expressions captured under various lighting conditions, including flat-lit and one-light-at-a-time (OLAT) scenarios, we train a diffusion model for precise lighting control, enabling high-fidelity relit facial images from flat-lit inputs. Our framework includes spatially-aligned conditioning of flat-lit captures and random noise, along with integrated lighting information for global control, utilizing prior knowledge from the pre-trained Stable Diffusion model. This model is then applied to dynamic facial performances captured in a consistent flat-lit environment and reconstructed for novel-view synthesis using a scalable dynamic 3D Gaussian Splatting method to maintain quality and consistency in the relit results. In addition, we introduce unified lighting control by integrating a novel area lighting representation with directional lighting, allowing for joint adjustments in light size and direction. We also enable high dynamic range imaging (HDRI) composition using multiple directional lights to produce dynamic sequences under complex lighting conditions. Our evaluations demonstrate the models efficiency in achieving precise lighting control and generalizing across various facial expressions while preserving detailed features such as skintexture andhair. The model accurately reproduces complex lighting effects like eye reflections, subsurface scattering, self-shadowing, and translucency, advancing photorealism within our framework., Comment: 18 pages, SIGGRAPH Asia 2024 Conference Papers (SA Conference Papers '24), December 3--6, 2024, Tokyo, Japan. Project page: https://www.eyelinestudios.com/research/diffrelight.html

Carcinogenesis is a proteiform phenomenon, with tumors emerging in various locations and displaying complex, diverse shapes. At the crucial intersection of research and clinical practice, it demands precise and flexible assessment. However, current biomarkers, such as RECIST 1.1's long and short axis measurements, fall short of capturing this complexity, offering an approximate estimate of tumor burden and a simplistic representation of a more intricate process. Additionally, existing supervised AI models face challenges in addressing the variability in tumor presentations, limiting their clinical utility. These limitations arise from the scarcity of annotations and the models' focus on narrowly defined tasks. To address these challenges, we developed ONCOPILOT, an interactive radiological foundation model trained on approximately 7,500 CT scans covering the whole body, from both normal anatomy and a wide range of oncological cases. ONCOPILOT performs 3D tumor segmentation using visual prompts like point-click and bounding boxes, outperforming state-of-the-art models (e.g., nnUnet) and achieving radiologist-level accuracy in RECIST 1.1 measurements. The key advantage of this foundation model is its ability to surpass state-of-the-art performance while keeping the radiologist in the loop, a capability that previous models could not achieve. When radiologists interactively refine the segmentations, accuracy improves further. ONCOPILOT also accelerates measurement processes and reduces inter-reader variability, facilitating volumetric analysis and unlocking new biomarkers for deeper insights. This AI assistant is expected to enhance the precision of RECIST 1.1 measurements, unlock the potential of volumetric biomarkers, and improve patient stratification and clinical care, while seamlessly integrating into the radiological workflow.

In this study, we present a novel Survival Analysis algorithm designed to efficiently handle large-scale longitudinal data. Our approach draws inspiration from Reinforcement Learning principles, particularly the Deep Q-Network paradigm, extending Temporal Learning concepts to Survival Regression. A central idea in our method is temporal consistency, a hypothesis that past and future outcomes in the data evolve smoothly over time. Our framework uniquely incorporates temporal consistency into large datasets by providing a stable training signal that captures long-term temporal relationships and ensures reliable updates. Additionally, the method supports arbitrarily complex architectures, enabling the modeling of intricate temporal dependencies, and allows for end-to-end training. Through numerous experiments we provide empirical evidence demonstrating our framework's ability to exploit temporal consistency across datasets of varying sizes. Moreover, our algorithm outperforms benchmarks on datasets with long sequences, demonstrating its ability to capture long-term patterns. Finally, ablation studies show how our method enhances training stability.

Image-text representation learning forms a cornerstone in vision-language models, where pairs of images and textual descriptions are contrastively aligned in a shared embedding space. Since visual and textual concepts are naturally hierarchical, recent work has shown that hyperbolic space can serve as a high-potential manifold to learn vision-language representation with strong downstream performance. In this work, for the first time we show how to fully leverage the innate hierarchical nature of hyperbolic embeddings by looking beyond individual image-text pairs. We propose Compositional Entailment Learning for hyperbolic vision-language models. The idea is that an image is not only described by a sentence but is itself a composition of multiple object boxes, each with their own textual description. Such information can be obtained freely by extracting nouns from sentences and using openly available localized grounding models. We show how to hierarchically organize images, image boxes, and their textual descriptions through contrastive and entailment-based objectives. Empirical evaluation on a hyperbolic vision-language model trained with millions of image-text pairs shows that the proposed compositional learning approach outperforms conventional Euclidean CLIP learning, as well as recent hyperbolic alternatives, with better zero-shot and retrieval generalization and clearly stronger hierarchical performance., Comment: 23 pages, 12 figures, 8 tables

Many countries use automated decision-making (ADM) systems, often based on artificial intelligence (AI), to manage migration at their borders. This interdisciplinary paper explores two questions. What are the main ways that automated decision-making is used at EU borders? Does such automated decision-making bring risks related to human rights, and if so: which risks? The paper introduces a taxonomy of four types of ADM systems at EU borders. Three types are used at borders: systems for (1) identification and verification by checking biometrics, (2) risk assessment, and (3) border monitoring. In addition, (4) polygraphs and emotion detectors are being tested at EU borders. We discuss three categories of risks of such automated decision-making, namely risks related to the human rights to (1) privacy and data protection, (2) nondiscrimination, and (3) a fair trial and effective remedies. The paper is largely based on a literature review that we conducted about the use of automated decision-making at borders. The paper combines insights from several disciplines, including social sciences, law, computer science, and migration studies.

In this work, we tackle a challenging and extreme form of subpopulation shift, which is termed compositional shift. Under compositional shifts, some combinations of attributes are totally absent from the training distribution but present in the test distribution. We model the data with flexible additive energy distributions, where each energy term represents an attribute, and derive a simple alternative to empirical risk minimization termed compositional risk minimization (CRM). We first train an additive energy classifier to predict the multiple attributes and then adjust this classifier to tackle compositional shifts. We provide an extensive theoretical analysis of CRM, where we show that our proposal extrapolates to special affine hulls of seen attribute combinations. Empirical evaluations on benchmark datasets confirms the improved robustness of CRM compared to other methods from the literature designed to tackle various forms of subpopulation shifts., Comment: Preprint. Under Review

Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE (Structure-preserving High-dimensional Analysis with Density-based Exploration), the first deep clustering algorithm that incorporates density-connectivity into its loss function. Similar to existing deep clustering algorithms, SHADE supports high-dimensional and large data sets with the expressive power of a deep autoencoder. In contrast to most existing deep clustering methods that rely on a centroid-based clustering objective, SHADE incorporates a novel loss function that captures density-connectivity. SHADE thereby learns a representation that enhances the separation of density-connected clusters. SHADE detects a stable clustering and noise points fully automatically without any user input. It outperforms existing methods in clustering quality, especially on data that contain non-Gaussian clusters, such as video data. Moreover, the embedded space of SHADE is suitable for visualization and interpretation of the clustering results as the individual shapes of the clusters are preserved., Comment: Short version accepted at ICDM 2024

The aim of this second part of the article is to study with several concrete cases the absolute definition of the seawater entropy described in Part I. Observed vertical profiles and polar transects, as well as analysed surface data, show that very different temperature and salinity values can organise to create new isentropic regions that can only be revealed by the absolute formulation of the entropy of seawater (Arctic Ocean; Bay of Bengal; Mediterranean, Black and Caspian Seas). Existing hypotheses to explain these results include the possible impact of turbulent processes that must be applied to the entropies of the atmosphere and oceans., Comment: Submitted on the 7th of October 2024 to the Comptes Rendus (Geoscience) Paris, France (16 pages, 7 Figures). V2: arXiv reference for the Part-I

The absolute entropy of seawater is defined as an improved version of the relationship defined by Franck Millero in 1976 and 1983. The first improvements concern the complex non-linear dependence of entropy on pressure, temperature and salinity, with the use of the standard TEOS10 formulation based on a fit of the oceanic Gibbs function to more recent observations. On the other hand, more recent thermodynamic tables have been used to increase the accuracy of the Millero's salinity increment to this standard formulation, to deduce the absolute version of entropy with new values for the pure-water and sea-salts absolute reference entropies. The differences between the values of the seawater entropy calculated with the Millero and TEOS10 formulations (standard and absolute) are documented, before a more complete study shown in the second part of the paper of the absolute seawater entropy computed from observed vertical profiles and analysed surface datasets., Comment: Submitted on the 7th of October 2024 to the Comptes Rendus (Geoscience) Paris, France (17 pages, 1 Figure). V2: arXiv reference for the Part-II

EK Draconis, a nearby young solar-type star (G1.5V, 50-120 Myr), is known as one of the best proxies for inferring the environmental conditions of the young Sun. The star frequently produces superflares and Paper I presented the first evidence of an associated gigantic prominence eruption observed as a blueshifted H$\alpha$ Balmer line emission. In this paper, we present the results of dynamical modeling of the stellar eruption and examine its relationship to the surface starspots and large-scale magnetic fields observed concurrently with the event. By performing a one-dimensional free-fall dynamical model and a one dimensional hydrodynamic simulation of the flow along the expanding magnetic loop, we found that the prominence eruption likely occurred near the stellar limb (12$^{+5}_{-5}$-16$^{+7}_{-7}$ degrees from the limb) and was ejected at an angle of 15$^{+6}_{-5}$-24$^{+6}_{-6}$ degrees relative to the line of sight, and the magnetic structures can expand into a coronal mass ejection (CME). The observed prominence displayed a terminal velocity of $\sim$0 km s$^{-1}$ prior to disappearance, complicating the interpretation of its dynamics in Paper I. The models in this paper suggest that prominence's H$\alpha$ intensity diminishes at around or before its expected maximum height, explaining the puzzling time evolution in observations. The TESS light curve modeling and (Zeeman) Doppler Imaging revealed large mid-latitude spots with polarity inversion lines and one polar spot with dominant single polarity, all near the stellar limb during the eruption. This suggests that mid-latitude spots could be the source of the pre-existing gigantic prominence we reported in Paper I. These results provide valuable insights into the dynamic processes that likely influenced the environments of early Earth, Mars, Venus, and young exoplanets., Comment: 25 pages, 14 figures, 5 tables. Accepted for publication in The Astrophysical Journal

The precise and safe control of heavy material handling machines presents numerous challenges due to the hard-to-model hydraulically actuated joints and the need for collision-free trajectory planning with a free-swinging end-effector tool. In this work, we propose an RL-based controller that commands the cabin joint and the arm simultaneously. It is trained in a simulation combining data-driven modeling techniques with first-principles modeling. On the one hand, we employ a neural network model to capture the highly nonlinear dynamics of the upper carriage turn hydraulic motor, incorporating explicit pressure prediction to handle delays better. On the other hand, we model the arm as velocity-controllable and the free-swinging end-effector tool as a damped pendulum using first principles. This combined model enhances our simulation environment, enabling the training of RL controllers that can be directly transferred to the real machine. Designed to reach steady-state Cartesian targets, the RL controller learns to leverage the hydraulic dynamics to improve accuracy, maintain high speeds, and minimize end-effector tool oscillations. Our controller, tested on a mid-size prototype material handler, is more accurate than an inexperienced operator and causes fewer tool oscillations. It demonstrates competitive performance even compared to an experienced professional driver., Comment: Presented at IROS 2024, Abu Dhabi, as oral presentation

Graph generation is fundamental in diverse scientific applications, due to its ability to reveal the underlying distribution of complex data, and eventually generate new, realistic data points. Despite the success of diffusion models in this domain, those face limitations in sampling efficiency and flexibility, stemming from the tight coupling between the training and sampling stages. To address this, we propose DeFoG, a novel framework using discrete flow matching for graph generation. DeFoG employs a flow-based approach that features an efficient linear interpolation noising process and a flexible denoising process based on a continuous-time Markov chain formulation. We leverage an expressive graph transformer and ensure desirable node permutation properties to respect graph symmetry. Crucially, our framework enables a disentangled design of the training and sampling stages, enabling more effective and efficient optimization of model performance. We navigate this design space by introducing several algorithmic improvements that boost the model performance, consistently surpassing existing diffusion models. We also theoretically demonstrate that, for general discrete data, discrete flow models can faithfully replicate the ground truth distribution - a result that naturally extends to graph data and reinforces DeFoG's foundations. Extensive experiments show that DeFoG achieves state-of-the-art results on synthetic and molecular datasets, improving both training and sampling efficiency over diffusion models, and excels in conditional generation on a digital pathology dataset.

Construction sites are challenging environments for autonomous systems due to their unstructured nature and the presence of dynamic actors, such as workers and machinery. This work presents a comprehensive panoptic scene understanding solution designed to handle the complexities of such environments by integrating 2D panoptic segmentation with 3D LiDAR mapping. Our system generates detailed environmental representations in real-time by combining semantic and geometric data, supported by Kalman Filter-based tracking for dynamic object detection. We introduce a fine-tuning method that adapts large pre-trained panoptic segmentation models for construction site applications using a limited number of domain-specific samples. For this use case, we release a first-of-its-kind dataset of 502 hand-labeled sample images with panoptic annotations from construction sites. In addition, we propose a dynamic panoptic mapping technique that enhances scene understanding in unstructured environments. As a case study, we demonstrate the system's application for autonomous navigation, utilizing real-time RRT* for reactive path planning in dynamic scenarios. The dataset (https://leggedrobotics.github.io/panoptic-scene-understanding.github.io/) and code (https://github.com/leggedrobotics/rsl_panoptic_mapping) for training and deployment are publicly available to support future research., Comment: 9 pages, 7 figures, 4 tables, submitted to 2024 Australasian Conference on Robotics and Automation (ACRA 2024)

This paper introduces Idempotent Test-Time Training (IT$^3$), a novel approach to addressing the challenge of distribution shift. While supervised-learning methods assume matching train and test distributions, this is rarely the case for machine learning systems deployed in the real world. Test-Time Training (TTT) approaches address this by adapting models during inference, but they are limited by a domain specific auxiliary task. IT$^3$ is based on the universal property of idempotence. An idempotent operator is one that can be applied sequentially without changing the result beyond the initial application, that is $f(f(x))=f(x)$. At training, the model receives an input $x$ along with another signal that can either be the ground truth label $y$ or a neutral "don't know" signal $0$. At test time, the additional signal can only be $0$. When sequentially applying the model, first predicting $y_0 = f(x, 0)$ and then $y_1 = f(x, y_0)$, the distance between $y_0$ and $y_1$ measures certainty and indicates out-of-distribution input $x$ if high. We use this distance, that can be expressed as $||f(x, f(x, 0)) - f(x, 0)||$ as our TTT loss during inference. By carefully optimizing this objective, we effectively train $f(x,\cdot)$ to be idempotent, projecting the internal representation of the input onto the training distribution. We demonstrate the versatility of our approach across various tasks, including corrupted image classification, aerodynamic predictions, tabular data with missing information, age prediction from face, and large-scale aerial photo segmentation. Moreover, these tasks span different architectures such as MLPs, CNNs, and GNNs.

ConceptLens is an innovative tool designed to illuminate the intricate workings of deep neural networks (DNNs) by visualizing hidden neuron activations. By integrating deep learning with symbolic methods, ConceptLens offers users a unique way to understand what triggers neuron activations and how they respond to various stimuli. The tool uses error-margin analysis to provide insights into the confidence levels of neuron activations, thereby enhancing the interpretability of DNNs. This paper presents an overview of ConceptLens, its implementation, and its application in real-time visualization of neuron activations and error margins through bar charts.

In chronic liver disease, liver fibrosis develops as excessive deposition of extracellular matrix macromolecules, predominantly collagens, progressively form fibrous scars that disrupt the hepatic architecture, and fibrosis, iron, and fat are interrelated. Fibrosis is the best predictor of morbidity and mortality in chronic liver disease but liver biopsy, the reference method for diagnosis and staging, is invasive and limited by sampling and interobserver variability and risks of complications. The overall objective of this study was to develop a new non-invasive method to quantify fibrosis using diamagnetic susceptibility sources with histology validation in ex vivo liver explants., Comment: 17 pages, 5 figures

The emergence of anomalous Hall effects (AHE) in antiferromagnets presents an intriguing phenomenon with potential spintronic device applications due to their ultrafast switching dynamics. Mn3NiN antiperovskite stands out as a promising candidate owing to its noncollinear antiferromagnetism. In this study, we report a significant AHE observed in epitaxial Mn3NiN thin films below the N\'eel temperature, comparable to those observed in typical ferromagnets. Through a combination of magnetometry, neutron diffraction and ab-initio data analyses, we propose that the substantial AHE can be attributed to the non-vanishing Berry curvature arising from the {\Gamma}4g-type spin structure, coupled with uncompensated magnetic domains featuring a weak canted moment. This coupling, due to robust antiferromagnetic domains, enables deterministic detection and switching of AHE via the application of a magnetic field.

We introduce a novel machine learning model for credit risk by combining tree-boosting with a latent spatio-temporal Gaussian process model accounting for frailty correlation. This allows for modeling non-linearities and interactions among predictor variables in a flexible data-driven manner and for accounting for spatio-temporal variation that is not explained by observable predictor variables. We also show how estimation and prediction can be done in a computationally efficient manner. In an application to a large U.S. mortgage credit risk data set, we find that both predictive default probabilities for individual loans and predictive loan portfolio loss distributions obtained with our novel approach are more accurate compared to conventional independent linear hazard models and also linear spatio-temporal models. Using interpretability tools for machine learning models, we find that the likely reasons for this outperformance are strong interaction and non-linear effects in the predictor variables and the presence of large spatio-temporal frailty effects.

The next generation of radio astronomy telescopes are challenging existing data analysis paradigms, as they have an order of magnitude larger collecting area and bandwidth. The two primary problems encountered when processing this data are the need for storage and that processing is primarily I/O limited. An example of this is the data deluge expected from the SKA-Low Telescope of about 300 PB per year. To remedy these issues, we have demonstrated lossy and lossless compression of data on an existing precursor telescope, the Australian Square Kilometre Array Pathfinder (ASKAP), using MGARD and ADIOS2 libraries. We find data processing is faster by a factor of 7 and give compression ratios from a factor of 7 (lossless) up to 37 (lossy with an absolute error bound of 1e-3). We discuss the effectiveness of lossy MGARD compression and its adherence to the designated error bounds, the trade-off between these error bounds and the corresponding compression ratios, as well as the potential consequences of these I/O and storage improvements on the science quality of the data products., Comment: 9 pages, 10 figures. Included in the conference proceedings of Cray User Group Conference 2024

Cosmological neutral hydrogen (HI) surveys provide a promising tomographic probe of the post-reionization era and of the standard model of cosmology. Simulations of this signal are crucial for maximizing the utility of these surveys. We present a fast method for simulating the cosmological distribution of HI based on a halo model approach. Employing the approximate $\texttt{PINOCCHIO}$ code, we generate the past light cone of dark matter halos. Subsequently, the halos are populated with HI according to a HI-halo mass relation. The nature of 21 cm intensity mapping demands large-volume simulations with a high halo mass resolution. To fulfill both requirements, we simulate a past light cone for declinations between -15{\deg} and -35{\deg} in the frequency range from 700 to 800 MHz, matching HIRAX, the Hydrogen Intensity and Real-time Analysis eXperiment. We run $\texttt{PINOCCHIO}$ for a 1 h$^{-3}$Gpc$^3$ box with 6700$^3$ simulation particles. With this configuration, halos with masses as low as M$_\text{min}$ = 4.3 $\times$ 10$^{9}$M$_{\odot}$ are simulated, resulting in the recovery of more than 97% of the expected HI density. From the dark matter and HI past light cone, maps with a width of 5 MHz are created. To validate the simulations, we have implemented and present here an analytical dark matter and HI halo model in $\texttt{PyCosmo}$, a Python package tailored for theoretical cosmological predictions. We perform extensive comparisons between analytical predictions and the simulations for the mass function, mass density, power spectrum, and angular power spectrum for dark matter and HI. We find close agreement in the mass function and mass densities, with discrepancies within a few percent. For the three-dimensional power spectra and angular power spectra, we observe an agreement better than 10%., Comment: Submitted to JCAP. 29 pages, 10 figures, 1 table. The PyCosmo package is available on https://cosmology.ethz.ch/research/software-lab/PyCosmo.html and can be installed from the Python Package Index at https://pypi.org. The simulation dataset will be made available at the time of publication

We present the formation histories of 19 massive ($>3X10^{10}M_\odot$) quiescent (sSFR$<0.15Gyr^{-1}$) galaxy candidates at z~3.0-4.5 observed using JWST/NIRSpec. This completes the spectroscopic confirmation of the 24 K-selected quiescent galaxy sample from the ZFOURGE and 3DHST surveys (Schreiber et al. 2018a). Utilizing Prism $1-5\mu m$ spectroscopy, we confirm that all 12 sources that eluded confirmation by ground-based spectroscopy lie at z>3, resulting in a spectroscopically confirmed number density of $\sim1.4X10^{-5}Mpc^{-3}$ between $z\sim3-4$. Rest-frame U-V vs V-J color selections show high effectiveness in identifying quiescent galaxies, with a purity of~90%. Our analysis shows that parametric star-formation histories (SFHs) from FAST++ and binned SFHs from Prospector on average yield consistent results, revealing diverse formation and quenching times. The oldest galaxy formed $~6X10^{10}M_\odot$ by $z\sim10$ and has been quiescent for over 1 Gyr at z~3.2. We detect two galaxies with ongoing star formation and six with active galactic nuclei (AGN). We demonstrate that the choice of stellar population models, stellar libraries, and nebular or AGN contributions does not significantly affect the derived average SFHs of the galaxies. We demonstrate that extending spectral fitting beyond the rest-frame optical regime reduces the inferred average star formation rates in the earliest time bins of the SFH reconstruction. The assumed SFH prior influences the star formation rate at early times, where spectral diagnostic power is limited. Simulated z~3 quiescent galaxies from IllustrisTNG, SHARK, and Magneticum broadly match the average SFHs of the observed sample but struggle to capture the full diversity, particularly at early stages. Our results emphasize the need for mechanisms that rapidly build stellar mass and quench star formation within the first billion years of the Universe., Comment: Accepted for publication in the ApJ

We present the PANORAMIC survey, a pure parallel extragalactic imaging program with NIRCam observed during JWST Cycle 1. The survey obtained $\sim$530 sq arcmin of NIRCam imaging from 1-5$\mu$m, totaling $\sim$192 hours of science integration time. This represents the largest on-sky time investment of any Cycle 1 GO extragalactic NIRCam imaging program by nearly a factor of 2. The survey includes $\sim$432 sq arcmin of novel sky area not yet observed with JWST using at least $6$ NIRCam broad-band filters, increasing the existing area covered by similar Cycle 1 data by $\sim$60%. 70 square arcmin was also covered by a 7th filter (F410M). A fraction of PANORAMIC data ($\sim$200 sq arcmin) was obtained in or around extragalactic deep-fields, enhancing their legacy value. Pure parallel observing naturally creates a wedding cake survey with both wide and ultra-deep tiers, with 5$\sigma$ point source depths at F444W ranging from 27.8-29.4 (ABmag), and with minimized cosmic variance. The 6+ filter observing setup yields remarkably good photometric redshift performance, achieving similar median scatter and outlier fraction as CANDELS ($\sigma_{\rm NMAD}\sim0.07$; $\eta\sim0.2$), which enables a wealth of science across redshift without the need for followup or ancillary data. We overview the proposed survey, the data obtained as part of this program, and document the science-ready data products in the first data release. PANORAMIC has delivered wide-area and deep imaging with excellent photometric performance, demonstrating that pure parallel observations with JWST are a highly efficient observing mode that is key to acquiring a complete picture of galaxy evolution from rare bright galaxies to fainter, more abundant sources at all redshifts., Comment: Submitted to AAS Journals; comments welcome. Initial data release available at https://panoramic-jwst.github.io/

Dwarf galaxies hold the key to crucial frontiers of astrophysics, however, their faintness renders spectroscopy challenging. Here we present the JWST Cycle 2 survey, All the Little Things (ALT, PID 3516), which is designed to seek late-forming Pop III stars and the drivers of reionization at $z\sim6-7$. ALT has acquired the deepest NIRCam grism spectroscopy yet (7-27 hr), at JWST's most sensitive wavelengths (3-4 $\mu$m), covering the powerful lensing cluster Abell 2744. Over the same 30 arcmin$^2$, ALT's ultra-deep F070W+F090W imaging ($\sim$30 mag) enables selection of very faint sources at $z>6$. We demonstrate the success of ALT's novel ``butterfly" mosaic to solve spectral confusion and contamination, and introduce the ``Allegro" method for emission line identification. By collecting spectra for every source in the field of view, ALT has measured precise ($R\sim1600$) redshifts for 1630 sources at $z=0.2-8.5$. This includes one of the largest samples of distant dwarf galaxies: [1015, 475, 50] sources less massive than the SMC, Fornax, and Sculptor with $\log(M_{*}/M_{\odot})<$[8.5, 7.5, 6.5]. We showcase ALT's discovery space with: (i) spatially resolved spectra of lensed clumps in galaxies as faint as $M_{\rm{UV}}\sim-15$; (ii) large-scale clustering -- overdensities at $z$=[2.50, 2.58, 3.97, 4.30, 5.66, 5.77, 6.33] hosting massive galaxies with striking Balmer breaks; (iii) small-scale clustering -- a system of satellites around a Milky Way analog at $z\sim6$; (iv) spectroscopically confirmed multiple images that help constrain the lensing model underlying all science in this legacy field; (v) sensitive star-formation maps based on dust-insensitive tracers such as Pa$\alpha$; (vi) direct spectroscopic discovery of rare sources such as AGN with ionized outflows. These results provide a powerful proof of concept for how grism surveys maximize the potential of strong lensing fields., Comment: Submitted to the Open Journal of Astrophysics. Figs. 6 and 10 illustrate the quality of the spectra and imaging, while Fig. 12 summarizes the yield of the survey. Comments warmly welcomed and greatly appreciated. The ALT DR1 catalog is available at https://zenodo.org/records/13871850

Future large tokamaks will operate at high plasma currents and high stored plasma energies. To ensure machine protection in case of a sudden loss of plasma confinement (major disruption), a large fraction of the magnetic and thermal energy must be radiated to reduce thermal loads. The disruption mitigation system for ITER is based on massive material injection in the form of shattered pellet injection (SPI). To support ITER, a versatile SPI system was installed at the tokamak ASDEX Upgrade (AUG). The AUG SPI features three independent pellet generation cells and guide tubes, and each was equipped with different shatter heads for the 2022 experimental campaign. We dedicated over 200 plasma discharges to the study of SPI plasma termination, and in this manuscript report on the results of bolometry (total radiation) analysis. The amount of neon inside the pellets is the dominant factor determining the radiated energy fraction (frad). Large and fast fragments, produced by the $12.5^\circ$ rectangular shatter head, lead to somewhat higher values of frad compared to the $25^\circ$ circular or rectangular heads. This effect is strongest for neon content of $< 4\times10^{20}$ neon atoms injected, where a higher normal velocity component (larger fragments) seems slightly beneficial. While full-sized, 8 mm diameter, pure deuterium (D2) pellets lead to a disruption, the 4 mm or shortened 8 mm pellets of pure D2 did not lead to a disruption. The disruption threshold for pure D2 is found to be around $1\times10^{22}$ deuterium molecules inside the pellet. While the radiated energy fraction of non-disruptive SPI is below 20%, this is increased to 40% during the TQ and VDE phase of the disruptive injections. For deuterium-neon-mix pellets, frad values of $< 90$% are observed, and the curve saturates around 80% for 10% neon mixed into the 8 mm pellets ($2\times10^{21}$ neon atoms)., Comment: 15 pages, 9 figures

The MAgnetized Disk and Mirror Axion eXperiment (MADMAX) is a future experiment aiming to detect dark matter axions from the galactic halo by resonant conversion to photons in a strong magnetic field. It uses a stack of dielectric disks in front of a mirror, called booster, to enhance the potential signal from axion-photon conversion over a significant mass range. Several small scale prototype systems have been developed and tested to verify the experimental principles. The current status of the experiment and its prototypes, including the ongoing research and development and remaining challenges, are presented in this contribution., Comment: 6 pages, 4 Figures, proceedings of the 42nd International Conference on High Energy Physics (ICHEP2024)

We present the properties of a massive, large, dusty, metal-rich, star-forming galaxy at z_spec=6.73. GOODSN-100182 was observed with JWST/NIRSpec as part of the AURORA survey, and is also covered by public multi-wavelength HST and JWST imaging. While the large stellar mass of GOODSN-100182 (~10^10 M_sun) was indicated prior to JWST, NIRCam rest-optical imaging now reveals the presence of an extended disk (r_eff~1.5 kpc). In addition, the NIRSpec R~1000 spectrum of GOODSN-100182 includes the detection of a large suite of rest-optical nebular emission lines ranging in wavelength from [OII]3727 up to [NII]6583. The ratios of Balmer lines suggest significant dust attenuation (E(B-V)_gas=0.40+0.10/-0.09), consistent with the red rest-UV slope inferred for GOODSN-100182 (beta=-0.50+/-0.09). The star-formation rate based on dust-corrected H-alpha emission is log(SFR(H-alpha)/ M_sun/yr)=2.02+0.13/-0.14, well above the z~7 star-forming main sequence in terms of specific SFR. Strikingly, the ratio of [NII]6583/H-alpha emission suggests almost solar metallicity, as does the ratio ([OIII]5007/H-beta)/([NII]6583/H-alpha) and the detection of the faint [FeII]4360 emission feature. Overall, the excitation and ionization properties of GOODSN-100182 more closely resemble those of typical star-forming galaxies at z~2-3 rather than z~7. Based on public spectroscopy of the GOODS-N field, we find that GOODSN-100182 resides within a significant galaxy overdensity, and is accompanied by a spectroscopically-confirmed neighbor galaxy. GOODSN-100182 demonstrates the existence of mature, chemically-enriched galaxies within the first billion years of cosmic time, whose properties must be explained by galaxy formation models., Comment: 16 pages, 13 figures, accepted to ApJ

We report the first successful ALMA follow-up observations of a secure $z > 10$ JWST-selected galaxy, by robustly detecting ($6.6\sigma$) the [OIII]$_{88\mu m}\,$ line in JADES-GS-z14-0 (hereafter GS-z14). The ALMA detection yields a spectroscopic redshift of $z=14.1793\pm0.0007$, and increases the precision on the prior redshift measurement of $z=14.32_{-0.20}^{+0.08}$ from NIRSpec by $\gtrsim$180$\times$. Moreover, the redshift is consistent with that previously determined from a tentative detection ($3.6\sigma$) of CIII]$_{1907,1909}$ ($z=14.178\pm0.013$), solidifying the redshift determination via multiple line detections. We measure a line luminosity of $L_\mathrm{[OIII]88} = (2.1 \pm 0.5)\times10^8\,L_\odot$, placing GS-z14 at the lower end, but within the scatter of, the local $L_\mathrm{[OIII]88}$-star formation rate relation. No dust continuum from GS-z14 is detected, suggesting an upper limit on the dust-to-stellar mass ratio of $< 2 \times 10^{-3}$, consistent with dust production from supernovae with a yield $y_d < 0.3\,M_\odot$. Combining a previous JWST/MIRI photometric measurement of the [OIII]$\lambda\lambda$4959,5007$\mathrm{\mathring{A}}$ and H$\beta$ lines with Cloudy models, we find GS-z14 to be surprisingly metal-enriched ($Z\sim0.05 - 0.2\,Z_\odot$) a mere $300\,\mathrm{Myr}$ after the Big Bang. The detection of a bright oxygen line in GS-z14 thus reinforces the notion that galaxies in the early Universe undergo rapid evolution., Comment: 11 Pages, 6 Figures, 1 Table. Submitted to The Astrophysical Journal

The sample compression theory provides generalization guarantees for predictors that can be fully defined using a subset of the training dataset and a (short) message string, generally defined as a binary sequence. Previous works provided generalization bounds for the zero-one loss, which is restrictive notably when applied to deep learning approaches. In this paper, we present a general framework for deriving new sample compression bounds that hold for real-valued unbounded losses. Using the Pick-To-Learn (P2L) meta-algorithm, which transforms the training method of any machine-learning predictor to yield sample-compressed predictors, we empirically demonstrate the tightness of the bounds and their versatility by evaluating them on random forests and multiple types of neural networks.

In this paper, we analyze the well-posedness of a first order in time nonlinear wave equation with nonhomogeneous Dirichlet or Neumann type boundary conditions, also known as known as Hodge, Lions or Navier-slip boundary conditions, in fractional Sobolev spaces. We do this by first showing the well-posedness of an abstract system and then apply the results to concrete operators and function spaces that represent the different boundary conditions in an appropriate sense. The analysis of the abstract system is based on a spectral decomposition of a positive self adjoint operator. The regularity of the solution is given in terms of the domains of fractional powers of this operator. Using Galerkins method and Newton-Kantorovich Theorem, we prove well-posedness of the abstract nonlinear system with possibly nonhomogeneous boundary conditions. The connection between the domains of fractional powers of the operator and fractional Sobolev spaces makes it possible to obtain results for a system with nonhomogeneous Dirichlet boundary conditions with fractional Sobolev space regularity. For the Neumann type boundary conditions we show an integer valued Sobolev regularity of the solution.

The H{\alpha} nebular emission line is an optimal tracer for recent star formation in galaxies. With the advent of JWST, this line has recently become observable at z>3 for the first time. We present a catalog of 1013 H{\alpha} emitters at 3.73 obtained based purely on spectroscopic data, robustly tracing galaxy star formation rates (SFRs) beyond the peak of the cosmic star formation history. We compare our results with theoretical predictions from three different simulations and find good agreement at z~4-6. The UV LFs of this spectroscopically-confirmed sample are in good agreement with pre-JWST measurements obtained with photometrically-selected objects. Finally, we derive SFR functions and integrate these to compute the evolution of the cosmic star-formation rate densities across z~4-6, finding values in good agreement with recent UV estimates from Lyman-break galaxies, which imply a continuous decrease in SFR density by a factor of 3x over z~4 to z~6. Our work shows the power of NIRCam grism observations to efficiently provide new tests for early galaxy formation models based on emission line statistics., Comment: 17 pages, 14 figures

The vertical mixing in hot Jupiter atmospheres plays a critical role in the formation and spacial distribution of cloud particles in their atmospheres. This affects the observed spectra of a planet through cloud opacity, which can be influenced by the degree of cold trapping of refractory species in the deep atmosphere. We aim to isolate the effects of the internal temperature on the mixing efficiency in the atmospheres of Ultra Hot Jupiters (UHJ) and the spacial distribution of cloud particles across the globe. We couple a simplified tracer based cloud model, picket fence radiative-transfer scheme and mixing length theory to the Exo-FMS general circulation model. We run the model for five different internal temperatures at typical UHJ atmosphere system parameters. Our results show the convective eddy diffusion coefficient remains low throughout the vast majority of the atmosphere, with mixing dominated by advective flows. However, some regions can show convective mixing in the upper atmosphere for colder interior temperatures. The vertical extent of the clouds is reduced as the internal temperature is increased. Additionally, a global cloud layer gets formed below the radiative-convective boundary (RCB) in the cooler cases. Convection is generally strongly inhibited in UHJ atmospheres above the RCB due to their strong irradiation. Convective mixing plays a minor role compared to advective mixing in keeping cloud particles aloft in ultra hot Jupiters with warm interiors. Higher vertical turbulent heat fluxes and the advection of potential temperature inhibit convection in warmer interiors. Our results suggest isolated upper atmosphere regions above cold interiors may exhibit strong convective mixing in isolated regions around Rossby gyres, allowing aerosols to be better retained in these areas., Comment: accepted A&A (23 September 2024)

In a previous paper, we introduced a new tool called GaiaPMEX. It characterizes the mass and semi-major axis relative to the central star (sma) of a possible companion around any source observed with Gaia. It uses the value of RUWE, or, with both Gaia and Hipparcos, the value of proper motion anomaly (PMa), alone or combined with the RUWE. Our goal is to exploit the large volume of sources in Gaia's DR3 and find new exoplanet candidates. We wish to create a new input catalog of planet-candidate hosting systems to the disposal of future follow-up projects. Beyond G=14, this catalog would prepare the arrival of powerful instruments on the ELTs, that could include RV follow-up of faint stars and direct imaging of planets around main sequence Gyr-old stars. We used the mass-sma degenerate set of solutions obtained by GaiaPMEX from any value of RUWE to select a sample of bright (G<16) Gaia sources whose companions could be planetary, with a mass <13.5 MJup. It led us to identify a sample of 9,698 planet candidate hosting sources, whose companion may have a mass <13.5 MJup in the range of 1-3-au sma. We identified 19 systems that are also reported in the Nasa exoplanet archive. We detected 8 substellar companions with a 1-3-au sma, initially discovered and characterised with RV and astrometry. Moreover, we found 6 transiting-planet systems and 2 wide-orbit systems for whom we predict the existence of supplementary companions. Focusing on the subsample of sources observed with Hipparcos, combining RUWE and PMa, we confirmed the identification of 4 new planetary candidate systems HD 187129, HD 81697, CD-42 883, and HD 105330. Given the degeneracy of mass-sma, many of the candidates in this 9,698 sources catalog might have a larger mass, in the brown-dwarf and stellar domain, if their sma departs from the 1-3-au range. The vetting of this large catalog will be the subject of future studies., Comment: 13 pages, 14 figures + appendix; accepted for publication in A&A on 03/09/2024

The Gaia mission is expected to yield the detection of several thousands of exoplanets, perhaps at least doubling the number of known exoplanets. Although the harvest is expected to occur when the astrometric time series will be published with DR4 at the eve of 2026, the DR3 is already a precious database to search for exoplanet beyond 1 au. With this objective, we characterized multiple systems by exploiting two astrometric signatures derived from the DR3 astrometric solution of bright sources (G<16). We have the proper motion anomaly, or PMa, for sources also observed with Hipparcos, and the excess of residuals in the RUWE and the astrometric excess noise (AEN). Those astrometric signatures give an accurate measurement of the astrometric motion of a source seen with Gaia, even in the presence of calibration and measurement noises. We found that they can allow identifying stellar binaries and hint to companions with a mass in the planetary domain. We introduce a tool called GaiaPMEX, that is able, for a given source, to model its astrometric signatures, by a photocenter orbit due to a companion with certain mass and semi-major axis (sma). Comparing to their actual measurements from the DR3 and Hipparcos, GaiaPMEX calculates a confidence map of the possible companion's mass and sma. The constraints on mass are, as expected, degenerate, but when allowed, coupling the use of PMa and RUWE, may significantly narrow the space of solutions. Thanks to combining Gaia and Hipparcos, planets are expected to be most frequently found within 1-10 au from their star, at the scale of Earth-to-Saturn orbits. In this range, exoplanets with mass down to 0.1 MJup are more favorably detected around M-dwarfs closer than 10 pc. Some fraction, if not all, of companions identified with GaiaPMEX may be characterized in the future using the astrometric time series that will be published with the DR4., Comment: 26 pages, 28 figures + appendices; accepted for publication in A&A on 03/09/2024

By framing reinforcement learning as a sequence modeling problem, recent work has enabled the use of generative models, such as diffusion models, for planning. While these models are effective in predicting long-horizon state trajectories in deterministic environments, they face challenges in dynamic settings with moving obstacles. Effective collision avoidance demands continuous monitoring and adaptive decision-making. While replanning at every timestep could ensure safety, it introduces substantial computational overhead due to the repetitive prediction of overlapping state sequences -- a process that is particularly costly with diffusion models, known for their intensive iterative sampling procedure. We propose an adaptive generative planning approach that dynamically adjusts replanning frequency based on the uncertainty of action predictions. Our method minimizes the need for frequent, computationally expensive, and redundant replanning while maintaining robust collision avoidance performance. In experiments, we obtain a 13.5% increase in the mean trajectory length and a 12.7% increase in mean reward over long-horizon planning, indicating a reduction in collision rates and an improved ability to navigate the environment safely.

The adoption of agroecological practices will be crucial to address the challenges of climate change and biodiversity loss. Such practices favor the cultivation of plants in complex mixtures with layouts differing from the monoculture approach of conventional agriculture. Inspired by random sequential adsorption processes, we propose a one-dimensional model in which the plants are represented as line segments that start as points and grow at a constant rate until they reach length $\sigma$ after a time interval $\tau$. The planting positions and times are randomly chosen with the constraint that plant overlap is forbidden. We apply an exact, event-driven simulation to investigate the resulting spatiotemporal patterns and yields in both mono- and duocultures. After a transient period, with oscillations in the density and coverage, the field reaches a steady state in which the mean age of plants is one half of the time to maturity. The structure of the active plants is characterized by correlation functions between the fluctuation of the age of a plant and its $k$th neighbour. Nearest neighbours are negatively correlated, while next nearest neighbours tend to have similar ages. The steady state yield increases with the planting rate and approaches a maximum value of 4/3 plants per unit length per unit time. For two species with the same size at maturity but different growth rates, the more slowly growing species is enriched in the harvest compared to the seed mix composition. If two species have the same time to maturity but different sizes, the smaller one is enriched in the harvest and, at a sufficiently high planting rate, the larger species may be completely absent. For two species with the same ratio of $\sigma/\tau$ the selectivity is insensitive to the planting rate. The model may be extended to higher dimensions, more species and other planting strategies., Comment: 14 pages, 13 figures

We present a new multi-dimensional, robust, and cell-centered finite-volume scheme for the ideal MHD equations. This scheme relies on relaxation and splitting techniques and can be easily used at high order. A fully conservative version is not entropy satisfying but is observed experimentally to be more robust than standard constrained transport schemes at low plasma beta. At very low plasma beta and high Alfv\'en number, we have designed an entropy-satisfying version that is not conservative for the magnetic field but preserves admissible states and we switch locally a-priori between the two versions depending on the regime of plasma beta and Alfv\'en number. This strategy is robust in a wide range of standard MHD test cases, all performed at second order with a classic MUSCL-Hancock scheme., Comment: accepted in JCP

COVID-19 pandemic has brought to the fore epidemiological models which, though describing a rich variety of behaviors, have previously received little attention in the signal processing literature. During the pandemic, several works successfully leveraged state-of-the-art signal processing strategies to robustly infer epidemiological indicators despite the low quality of COVID-19 data. In the present work, a novel nonstationary autoregressive model is introduced, encompassing, but not reducing to, one of the most popular models for the propagation of viral epidemics. Using a variational framework, penalized likelihood estimators of the parameters of this new model are designed. In practice, the main bottleneck is that the estimation accuracy strongly depends on hyperparameters tuning. Without available ground truth, hyperparameters are selected by minimizing specifically designed data-driven oracles, used as proxy for the estimation error. Focusing on the nonstationary autoregressive Poisson model, the Stein's Unbiased Risk Estimate formalism is generalized to construct asymptotically unbiased risk estimators based on the derivation of an original autoregressive counterpart of Stein's lemma. The accuracy of these oracles and of the resulting estimates are assessed through intensive Monte Carlo simulations on synthetic data. Then, elaborating on recent epidemiological models, a novel weekly scaled Poisson model is proposed, enabling to better account for intrinsic variability of the contamination while being robust to reporting errors. Finally, the overall data-driven procedure is particularized to the estimation of COVID-19 reproduction number and exemplified on real COVID-19 infection counts in different countries and at different stages of the pandemic, demonstrating its ability to yield consistent estimates.

The aquaculture industry is constantly making efforts to improve fish welfare while maintaining the ethically sustainable farming practises. This work presents an enhanced tank environment designed for testing and developing novel combinations of technologies for analyzing and detecting behavioral responses in fish shoals/groups. Regular cameras are combined with event cameras and a scanning sonar to comprise a sensor suite that offers a more detailed and complex way of fish observation. The modified tank environment is designed to simulate the prevailing conditions on-site at cage based farms, particularly in terms of lighting conditions, while all tank systems and sensors are hidden behind specially designed enclosures, providing a "clean" environment (open arena) less likely to impact the fish behavior. The proposed sensor suite will be tested and demonstrated in the modified tank environment to benchmark its ability in monitoring fish, after which it will be adapted for use in a more industrially relevant situation with open cages.

Integrating structured knowledge from tabular formats poses significant challenges within natural language processing (NLP), mainly when dealing with complex, semi-structured tables like those found in the FeTaQA dataset. These tables require advanced methods to interpret and generate meaningful responses accurately. Traditional approaches, such as SQL and SPARQL, often fail to fully capture the semantics of such data, especially in the presence of irregular table structures like web tables. This paper addresses these challenges by proposing a novel approach that extracts triples straightforward from tabular data and integrates it with a retrieval-augmented generation (RAG) model to enhance the accuracy, coherence, and contextual richness of responses generated by a fine-tuned GPT-3.5-turbo-0125 model. Our approach significantly outperforms existing baselines on the FeTaQA dataset, particularly excelling in Sacre-BLEU and ROUGE metrics. It effectively generates contextually accurate and detailed long-form answers from tables, showcasing its strength in complex data interpretation., Comment: We are withdrawing this paper to address foundational aspects that are critical for ensuring its accuracy and integrity before any potential resubmission

This paper addresses the problem of approximating an unknown probability distribution with density $f$ -- which can only be evaluated up to an unknown scaling factor -- with the help of a sequential algorithm that produces at each iteration $n\geq 1$ an estimated density $q_n$.The proposed method optimizes the Kullback-Leibler divergence using a mirror descent (MD) algorithm directly on the space of density functions, while a stochastic approximation technique helps to manage between algorithm complexity and variability. One of the key innovations of this work is the theoretical guarantee that is provided for an algorithm with a fixed MD learning rate $\eta \in (0,1 )$. The main result is that the sequence $q_n$ converges almost surely to the target density $f$ uniformly on compact sets. Through numerical experiments, we show that fixing the learning rate $\eta \in (0,1 )$ significantly improves the algorithm's performance, particularly in the context of multi-modal target distributions where a small value of $\eta$ allows to increase the chance of finding all modes. Additionally, we propose a particle subsampling method to enhance computational efficiency and compare our method against other approaches through numerical experiments.

The integration of Artificial Intelligence (AI) in medical diagnostics is often hindered by model opacity, where high-accuracy systems function as "black boxes" without transparent reasoning. This limitation is critical in clinical settings, where trust and reliability are paramount. To address this, we have developed an explainable AI methodology tailored for medical imaging. By employing a Convolutional Neural Network (CNN) that analyzes MRI scans across both image and frequency domains, we introduce a novel approach that incorporates Uniform Manifold Approximation and Projection UMAP] for the visualization of latent input embeddings. This approach not only enhances early training efficiency but also deepens our understanding of how additional features impact the model predictions, thereby increasing interpretability and supporting more accurate and intuitive diagnostic inferences

SModelS is a public tool for fast reinterpretation of LHC searches for new physics based on a large database of simplified model results. While previous versions were limited to models with a Z2-type symmetry, such as R-parity conserving supersymmetry, version 3 can now handle arbitrary signal topologies. To this end, the tool was fully restructured and now relies on a graph-based description of simplified model topologies. In this work, we present the main conceptual changes and novel features of SModelS v3, together with the inclusion of new experimental searches for resonant production of spin-1 and spin-0 mediators with decays to quarks or to dark matter. Applying these results to a model containing two mediators, we discuss the interplay of resonance and missing energy searches, and the model's coverage by the currently available simplified model results., Comment: 38 pages, 21 figures, 5 tables. Version accepted for publication in JHEP. SModelS v3 is available at https://smodels.github.io/ . The data and code used to produce the results in section 5 are available at https://zenodo.org/doi/10.5281/zenodo.13772537

The precise delineation of blood vessels in medical images is critical for many clinical applications, including pathology detection and surgical planning. However, fully-automated vascular segmentation is challenging because of the variability in shape, size, and topology. Manual segmentation remains the gold standard but is time-consuming, subjective, and impractical for large-scale studies. Hence, there is a need for automatic and reliable segmentation methods that can accurately detect blood vessels from medical images. The integration of shape and topological priors into vessel segmentation models has been shown to improve segmentation accuracy by offering contextual information about the shape of the blood vessels and their spatial relationships within the vascular tree. To further improve anatomical consistency, we propose a new joint prior encoding mechanism which incorporates both shape and topology in a single latent space. The effectiveness of our method is demonstrated on the publicly available 3D-IRCADb dataset. More globally, the proposed approach holds promise in overcoming the challenges associated with automatic vessel delineation and has the potential to advance the field of deep priors encoding., Comment: 5 pages, 3 figures, conference