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In this paper, we investigate how moving objects can be detected when images are impacted by atmospheric turbulence. We present a geometric spatio-temporal point of view to the problem and show that it is possible to distinguish movement due to the turbulence vs. moving objects. To perform this task, we propose an extension of 2D cartoon+texture decomposition algorithms to 3D vector fields. Our algorithm is based on curvelet spaces which permit to better characterize the movement flow geometry. We present experiments on real data which illustrate the efficiency of the proposed method.

Within this work, we introduce LIMIS: The first purely language-based interactive medical image segmentation model. We achieve this by adapting Grounded SAM to the medical domain and designing a language-based model interaction strategy that allows radiologists to incorporate their knowledge into the segmentation process. LIMIS produces high-quality initial segmentation masks by leveraging medical foundation models and allows users to adapt segmentation masks using only language, opening up interactive segmentation to scenarios where physicians require using their hands for other tasks. We evaluate LIMIS on three publicly available medical datasets in terms of performance and usability with experts from the medical domain confirming its high-quality segmentation masks and its interactive usability.

As trajectories sampled by policies used by reinforcement learning (RL) and generative flow networks (GFlowNets) grow longer, credit assignment and exploration become more challenging, and the long planning horizon hinders mode discovery and generalization. The challenge is particularly pronounced in entropy-seeking RL methods, such as generative flow networks, where the agent must learn to sample from a structured distribution and discover multiple high-reward states, each of which take many steps to reach. To tackle this challenge, we propose an approach to incorporate the discovery of action abstractions, or high-level actions, into the policy optimization process. Our approach involves iteratively extracting action subsequences commonly used across many high-reward trajectories and `chunking' them into a single action that is added to the action space. In empirical evaluation on synthetic and real-world environments, our approach demonstrates improved sample efficiency performance in discovering diverse high-reward objects, especially on harder exploration problems. We also observe that the abstracted high-order actions are interpretable, capturing the latent structure of the reward landscape of the action space. This work provides a cognitively motivated approach to action abstraction in RL and is the first demonstration of hierarchical planning in amortized sequential sampling.

Superfluid ${}^4$He is an ideal candidate for the direct detection of light dark matter via nuclear recoils thanks to its low nuclear mass and the possibility to reach a low detection energy threshold by exploiting the generated quasiparticles. The design of future detectors based on this target, such as the DELight experiment, requires a proper understanding of the formation and partitioning of the signal for different energy depositions from various sources. This work presents an overview of the physical processes involved in the energy deposition of recoiling electrons and ions, and describes a Monte Carlo approach to the partitioning of the signal into different channels. Despite an overall good agreement with existing literature, differences in the region of interest for light dark matter searches below 200\,eV are observed., Comment: 14 pages, 16 figures, 1 table

The increased brightness temperature of young rocky protoplanets during their magma ocean epoch makes them potentially amenable to atmospheric characterization to distances from the solar system far greater than thermally equilibrated terrestrial exoplanets, offering observational opportunities for unique insights into the origin of secondary atmospheres and the near surface conditions of prebiotic environments. The Large Interferometer For Exoplanets (LIFE) mission will employ a space-based mid-infrared nulling interferometer to directly measure the thermal emission of terrestrial exoplanets. Here, we seek to assess the capabilities of various instrumental design choices of the LIFE mission concept for the detection of cooling protoplanets with transient high-temperature magma ocean atmospheres, in young stellar associations in particular. Using the LIFE mission instrument simulator (LIFEsim) we assess how specific instrumental parameters and design choices, such as wavelength coverage, aperture diameter, and photon throughput, facilitate or disadvantage the detection of protoplanets. We focus on the observational sensitivities of distance to the observed planetary system, protoplanet brightness temperature using a blackbody assumption, and orbital distance of the potential protoplanets around both G- and M-dwarf stars. Our simulations suggest that LIFE will be able to detect (S/N $\geq$ 7) hot protoplanets in young stellar associations up to distances of $\approx$100 pc from the solar system for reasonable integration times (up to $\sim$hours). Detection of an Earth-sized protoplanet orbiting a solar-sized host star at 1 AU requires less than 30 minutes of integration time. M-dwarfs generally need shorter integration times. The contribution from wavelength regions $<$6 $\mu$m is important for decreasing the detection threshold and discriminating emission temperatures., Comment: 18 pages, 19 figures; accepted for publication in A&A

Novel optical imaging techniques, such as hyperspectral imaging (HSI) combined with machine learning-based (ML) analysis, have the potential to revolutionize clinical surgical imaging. However, these novel modalities face a shortage of large-scale, representative clinical data for training ML algorithms, while preclinical animal data is abundantly available through standardized experiments and allows for controlled induction of pathological tissue states, which is not ethically possible in patients. To leverage this situation, we propose a novel concept called "xeno-learning", a cross-species knowledge transfer paradigm inspired by xeno-transplantation, where organs from a donor species are transplanted into a recipient species. Using a total of 11,268 HSI images from humans as well as porcine and rat models, we show that although spectral signatures of organs differ across species, shared pathophysiological mechanisms manifest as comparable relative spectral changes across species. Such changes learnt in one species can thus be transferred to a new species via a novel "physiology-based data augmentation" method, enabling the large-scale secondary use of preclinical animal data for humans. The resulting ethical, monetary, and performance benefits of the proposed knowledge transfer paradigm promise a high impact of the methodology on future developments in the field., Comment: Jan Sellner and Alexander Studier-Fischer contributed equally to this work

Many modern software applications present numerous ethical concerns due to conflicts between users' values and companies' priorities. Intersectional communities, those with multiple marginalized identities, are disproportionately affected by these ethical issues, leading to legal, financial, and reputational issues for software companies, as well as real-world harm for intersectional users. Historically, the voices of intersectional communities have been systematically marginalized and excluded from contributing their unique perspectives to software design, perpetuating software-related ethical concerns. This work aims to fill the gap in research on intersectional users' software-related perspectives and provide software practitioners with a starting point to address their ethical concerns. We aggregated and analyzed the intersectional users' ethical concerns over time and developed a prioritization method to identify critical concerns. To achieve this, we collected posts from over 700 intersectional subreddits discussing software applications, utilized deep learning to identify ethical concerns in these posts, and employed state-of-the-art techniques to analyze their content in relation to time and priority. Our findings revealed that intersectional communities report \textit{critical} complaints related to cyberbullying, inappropriate content, and discrimination, highlighting significant flaws in modern software, particularly for intersectional users. Based on these findings, we discuss how to better address the ethical concerns of intersectional users in software development.

In the last 15 years, since the discovery of the first low-mass planets beyond the solar system, there has been tremendous progress in understanding the diversity of (super-)Earth and sub-Neptune exoplanets. Especially the influence of the planetary interior on the surface evolution (including the atmosphere) of exoplanets has been studied in detail. The first studies focused on the characterization of planets, including their potential interior structure, using as key observables only mass and radius. Meanwhile, a new field of geosciences of exoplanets has emerged, linking the planet to its stellar environment, and by coupling interior chemistry and dynamics to surface regimes and atmospheric compositions. The new era of atmospheric characterization by JWST as well as the ELT will allow testing of these theoretical predictions of atmospheric diversity based on interior structure, evolution, and outgassing models., Comment: Preprint of a chapter for the 'Encyclopedia of Astrophysics' (Editor-in-Chief Ilya Mandel, Section Editor Dimitri Veras) to be published by Elsevier as a Reference Module

The miniaturization of electronic devices brings severe challenges in the deposition of metal interconnects in back end of line processing due to a continually decreasing volume available for metal deposition in the interconnect via. Cu is currently used as the interconnect metal and requires barrier and liner layers to prevent diffusion into silicon and promote deposition of smooth conducting metal films, respectively. However, these take up a significant volume in the interconnect vias, which are already extremely narrow due to the high aspect ratio required. Designing combined barrier/liner materials provides opportunities to promote deposition of smooth films and avoid islanding and frees up precious volume to deposit the required amount of interconnect metal. We present here the first atomistic kinetic Monte Carlo (kMC) investigation of the potential of Ru-modified TaN as a dual-function barrier/line material to promote 2D morphology of deposited Cu enabling optimization of the limited available interconnect volume. The kMC simulations use Density Functional Theory (DFT) activation barriers to predict the role of the combined barrier/liner material on the morphology of deposited Cu and give, for the first time, a tool to predict metal deposition on technologically important substrates. We study Cu deposition at different partial pressures on a series of substrates, including unmodified TaN and TaN with 25% or 50% surface Ru incorporation, at deposition temperatures of 300 K, 500 K and 800 K. The deposition simulations predict Cu film roughness, island size and number, substrate exposure area (as a measure of 2D vs. 3D morphology), layer occupation rate, compactness (as a measure of film quality) and the impact of annealing. As-deposited Cu on unmodified TaN exhibits significantly higher roughness and substrate exposure areas, demonstrating island formation..., Comment: 38 pages, 7 figures

Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer convergence between computational and experimental methods. In this review, we highlight some of the important gaps between computational simulations and experiments, discuss popular state-of-the-art computational techniques such as the Activation Relaxation Technique nouveau (ARTn) and Reverse Monte Carlo (RMC), and introduce more recent advances: machine learning interatomic potentials (MLIPs) and generative machine learning for simulations of amorphous matter, e.g., the Morphological Autoregressive Protocol (MAP). Examples are drawn from the amorphous silicon and silica literature as well as from molecular glasses. Our outlook stresses the need for new computational methods to extend the time- and length- scales accessible through numerical simulations.

We consider the $k$-min-sum-radii ($k$-MSR) clustering problem with fairness constraints. The $k$-min-sum-radii problem is a mixture of the classical $k$-center and $k$-median problems. We are given a set of points $P$ in a metric space and a number $k$ and aim to partition the points into $k$ clusters, each of the clusters having one designated center. The objective to minimize is the sum of the radii of the $k$ clusters (where in $k$-center we would only consider the maximum radius and in $k$-median we would consider the sum of the individual points' costs). Various notions of fair clustering have been introduced lately, and we follow the definitions due to Chierichetti, Kumar, Lattanzi and Vassilvitskii [NeurIPS 2017] which demand that cluster compositions shall follow the proportions of the input point set with respect to some given sensitive attribute. For the easier case where the sensitive attribute only has two possible values and each is equally frequent in the input, the aim is to compute a clustering where all clusters have a 1:1 ratio with respect to this attribute. We call this the 1:1 case. There has been a surge of FPT-approximation algorithms for the $k$-MSR problem lately, solving the problem both in the unconstrained case and in several constrained problem variants. We add to this research area by designing an FPT $(6+\epsilon)$-approximation that works for $k$-MSR under the mentioned general fairness notion. For the special 1:1 case, we improve our algorithm to achieve a $(3+\epsilon)$-approximation., Comment: Full version of the paper accepted at ISAAC 2024

We present two multilingual LLMs designed to embrace Europe's linguistic diversity by supporting all 24 official languages of the European Union. Trained on a dataset comprising around 60% non-English data and utilizing a custom multilingual tokenizer, our models address the limitations of existing LLMs that predominantly focus on English or a few high-resource languages. We detail the models' development principles, i.e., data composition, tokenizer optimization, and training methodologies. The models demonstrate competitive performance across multilingual benchmarks, as evidenced by their performance on European versions of ARC, HellaSwag, MMLU, and TruthfulQA.

Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently, broad conclusions are simply derived from mean performance values. In this paper, we argue that this common practice is often a misleading simplification as it ignores performance variability. Our contribution is threefold. (1) Analyzing all MICCAI segmentation papers (n = 221) published in 2023, we first observe that more than 50% of papers do not assess performance variability at all. Moreover, only one (0.5%) paper reported confidence intervals (CIs) for model performance. (2) To address the reporting bottleneck, we show that the unreported standard deviation (SD) in segmentation papers can be approximated by a second-order polynomial function of the mean Dice similarity coefficient (DSC). Based on external validation data from 56 previous MICCAI challenges, we demonstrate that this approximation can accurately reconstruct the CI of a method using information provided in publications. (3) Finally, we reconstructed 95% CIs around the mean DSC of MICCAI 2023 segmentation papers. The median CI width was 0.03 which is three times larger than the median performance gap between the first and second ranked method. For more than 60% of papers, the mean performance of the second-ranked method was within the CI of the first-ranked method. We conclude that current publications typically do not provide sufficient evidence to support which models could potentially be translated into clinical practice., Comment: Paper accepted at MICCAI 2024 conference

Post-quantum cryptography has gained attention due to the need for secure cryptographic systems in the face of quantum computing. Code-based and lattice-based cryptography are two prominent approaches, both heavily studied within the NIST standardization project. Code-based cryptography -- most prominently exemplified by the McEliece cryptosystem -- is based on the hardness of decoding random linear error-correcting codes. Despite the McEliece cryptosystem having been unbroken for several decades, it suffers from large key sizes, which has led to exploring variants using metrics than the Hamming metric, such as the Lee metric. This alternative metric may allow for smaller key sizes, but requires further analysis for potential vulnerabilities to lattice-based attack techniques. In this paper, we consider a generic Lee metric based McEliece type cryptosystem and evaluate its security against lattice-based attacks.

In this work, we introduce a spatial branching process to model the growth of the mycelial network of a filamentous fungus. In this model, each filament is described by the position of its tip, the trajectory of which is solution to a stochastic differential equation with a drift term which depends on all the other trajectories. Each filament can branch either at its tip or along its length, that is to say at some past position of its tip, at some time- and space-dependent rates. It can stop growing at some rate which also depends on the positions of the other tips. We first construct the measure-valued process corresponding to this dynamics, then we study its large population limit and we characterise the limiting process as the weak solution to a system of partial differential equations., Comment: 34 pages

Studying the orbital motion of stars around Sagittarius A* in the Galactic Center provides a unique opportunity to probe the gravitational potential near the supermassive black hole at the heart of our Galaxy. Interferometric data obtained with the GRAVITY instrument at the Very Large Telescope Interferometer (VLTI) since 2016 has allowed us to achieve unprecedented precision in tracking the orbits of these stars. GRAVITY data have been key to detecting the in-plane, prograde Schwarzschild precession of the orbit of the star S2, as predicted by General Relativity. By combining astrometric and spectroscopic data from multiple stars, including S2, S29, S38, and S55 - for which we have data around their time of pericenter passage with GRAVITY - we can now strengthen the significance of this detection to an approximately $10 \sigma$ confidence level. The prograde precession of S2's orbit provides valuable insights into the potential presence of an extended mass distribution surrounding Sagittarius A*, which could consist of a dynamically relaxed stellar cusp comprised of old stars and stellar remnants, along with a possible dark matter spike. Our analysis, based on two plausible density profiles - a power-law and a Plummer profile - constrains the enclosed mass within the orbit of S2 to be consistent with zero, establishing an upper limit of approximately $1200 \, M_\odot$ with a $1 \sigma$ confidence level. This significantly improves our constraints on the mass distribution in the Galactic Center. Our upper limit is very close to the expected value from numerical simulations for a stellar cusp in the Galactic Center, leaving little room for a significant enhancement of dark matter density near Sagittarius A*., Comment: Submitted to A&A on September 17, 2024

One of the fundamental questions of cosmology is the origin and mechanism(s) responsible for the reionization of the Universe beyond $z\sim6$. To address this question, many studies over the past decade have focused on local ($z\sim0.3$) galaxies which leak ionizing radiation (Lyman continuum or LyC). However, line-of-sight effects and data quality have prohibited deeper insight into the nature of LyC escape. To circumvent these limitations, we analyze stacks of a consolidated sample of {\it HST}/COS observations of the LyC in 89 galaxies at $z\sim0.3$. From fitting of the continuum, we obtain information about the underlying stellar populations and neutral ISM geometry. We find that most LyC non-detections are not leaking appreciable LyC ($f_{esc}^{\rm LyC}<1$\%) but also that exceptional cases point to spatial variations in the LyC escape fraction $f_{esc}^{\rm LyC}$. Stellar populations younger than 3 Myr lead to an increase in ionizing feedback, which in turn increases the isotropy of LyC escape. Moreover, mechanical feedback from supernovae in 8-10 Myr stellar populations is important for anisotropic gas distributions needed for LyC escape. While mechanical feedback is necessary for any LyC escape, high $f_{esc}^{\rm LyC}$ ($>5$\%) also requires a confluence of young stars and ionizing feedback. A two-stage burst of star formation could facilitate this optimal LyC escape scenario., Comment: Submitted to AAS publications, 42 pages, 25 figures

For the Moran model with strong or moderately strong selection we prove that the fluctuations around the deterministic limit of the line counting process of the ancestral selection graph converges to an Ornstein-Uhlenbeck process. To this purpose we provide an extension of a functional limit theorem by Ethier and Kurtz 1986. This result and a small adaptation of our arguments can also be used to obtain the scaling limit for the fluctuations of certain logistic branching processes.

Acquisition and maintenance are central problems in deploying high-definition (HD) maps for autonomous driving, with two lines of research prevalent in current literature: Online HD map generation and HD map change detection. However, the generated map's quality is currently insufficient for safe deployment, and many change detection approaches fail to precisely localize and extract the changed map elements, hence lacking explainability and hindering a potential fleet-based cooperative HD map update. In this paper, we propose the novel task of explainable element-based HD map change detection and update. In extending recent approaches that use online mapping techniques informed with an outdated map prior for HD map updating, we present ExelMap, an explainable element-based map updating strategy that specifically identifies changed map elements. In this context, we discuss how currently used metrics fail to capture change detection performance, while allowing for unfair comparison between prior-less and prior-informed map generation methods. Finally, we present an experimental study on real-world changes related to pedestrian crossings of the Argoverse 2 Map Change Dataset. To the best of our knowledge, this is the first comprehensive problem investigation of real-world end-to-end element-based HD map change detection and update, and ExelMap the first proposed solution., Comment: 17 pages, 3 figures

We present novel methods to perform plenoptic imaging at the diffraction limit by measuring intensity correlations of light. The first method is oriented towards plenoptic microscopy, a promising technique which allows refocusing and depth-of-field enhancement, in post-processing, as well as scanning free 3D imaging. To overcome the limitations of standard plenoptic microscopes, we propose an adaptation of Correlation Plenoptic Imaging (CPI) to the working conditions of microscopy. We consider and compare different architectures of CPI microscopes, and discuss the improved robustness with respect to previous protocols against turbulence around the sample. The second method is based on measuring correlations between the images of two reference planes, arbitrarily chosen within the tridimensional scene of interest, providing an unprecedented combination of image resolution and depth of field. The results lead the way towards the realization of compact designs for CPI devices., Comment: 8 pages, 2 figures. arXiv admin note: text overlap with arXiv:2007.12033

CPI is a novel imaging modality capable of addressing the intrinsic limitations of conventional plenoptic imaging - namely, the resolution loss and the sacrificed change of perspective, - while guaranteeing the typical advantages of plenotpic imaging: 3D imaging, refocusing of acquired pictures, in post-processing, and depth of field extension. In this work, we review a recently developed CPI scheme, named correlation plenoptic imaging between arbitrary planes, and derive the algorithm for image refocusing., Comment: 7 pages, 2 figures

Stars mostly form in clusters where neighboring stars can influence proto-planetary disc evolution. Besides gravitational interactions, external photoevaporation can shape these discs. Depending on the strength of photoevaporation, discs can be destroyed within 1-2 Myrs or more gradually. We use the chemcomp code, incorporating a viscous disc evolution model with pebble drift and evaporation, to calculate the chemical composition of protoplanetary discs. This code is extended to include external photoevaporation based on the FRIED grid. Initially, the disc evolves purely viscously, with the inner disc's C/O ratio decreasing due to inward drifting and evaporating water ice pebbles. Over time, the C/O ratio increases as water vapor accretes onto the star and carbon-rich gas migrates inward. Once external photoevaporation starts, the outer disc disperses, but the inner disc's chemical evolution follows that of a purely viscous disc, as most pebbles have already drifted inward within 1 Myr. At low viscosity, the inner disc's C/O ratio remains sub-solar until dispersion by photoevaporation. At high viscosity, the C/O ratio can reach super-solar values, due to faster accretion of water vapor and inward migration of carbon-rich gas, provided the disc survives a few Myrs. In both cases, there is no significant difference in the inner disc's chemical composition compared to a purely viscous model due to the rapid inward drift of pebbles. Our model predicts that inner disc chemistry should be similar for discs subject to external photoevaporation and isolated discs, consistent with JWST observations., Comment: 15 pages, 7 figures, accepted for publication in Astronomy and Astrophysics Journal

Hyperspectral imaging (HSI) is emerging as a promising novel imaging modality with various potential surgical applications. Currently available cameras, however, suffer from poor integration into the clinical workflow because they require the lights to be switched off, or the camera to be manually recalibrated as soon as lighting conditions change. Given this critical bottleneck, the contribution of this paper is threefold: (1) We demonstrate that dynamically changing lighting conditions in the operating room dramatically affect the performance of HSI applications, namely physiological parameter estimation, and surgical scene segmentation. (2) We propose a novel learning-based approach to automatically recalibrating hyperspectral images during surgery and show that it is sufficiently accurate to replace the tedious process of white reference-based recalibration. (3) Based on a total of 742 HSI cubes from a phantom, porcine models, and rats we show that our recalibration method not only outperforms previously proposed methods, but also generalizes across species, lighting conditions, and image processing tasks. Due to its simple workflow integration as well as high accuracy, speed, and generalization capabilities, our method could evolve as a central component in clinical surgical HSI., Comment: Oral at MICCAI 2024

Much of our knowledge of quantum systems is encapsulated in the expectation value of Hermitian operators, experimentally obtained by averaging projective measurements. However, dynamical properties are often described by products of operators evaluated at different times; such observables cannot be measured by individual projective measurements, which occur at a single time. For example, the dynamical structure factor describes the propagation of density excitations, such as phonons, and is derived from the spatial density operator evaluated at different times. Conventionally, this is measured by first exciting the system at a specific wavevector and frequency, then measuring the response. Here, we describe an alternative approach using a pair of time-separated weak measurements, and analytically show that their cross-correlation function directly recovers the dynamical structure factor. We provide numerical confirmation of this technique with a matrix product states simulation of the one-dimensional Bose-Hubbard model, weakly measured by phase contrast imaging. We explore the limits of the method and demonstrate its applicability to real experiments with limited imaging resolution., Comment: 6 pages, 4 figures

Feedback is widely recognized as an essential condition for Lyman continuum (LyC) escape in star-forming galaxies. However, the mechanisms by which galactic outflows clear neutral gas and dust remain unclear. In this paper, we model the Mg II 2796\r{A}, 2804\r{A} absorption + emission lines in 29 galaxies taken from the Low-z LyC Survey (LzLCS) to investigate the impact of (radiation + mechanical) feedback on LyC escape. Using constraints on Mg$^+$ and photoionization models, we map the outflows' neutral hydrogen content and predict $f_{esc}^{LyC}$ with a multiphase wind model. We measure mass, momentum, and energy loading factors for the neutral winds, which carry up to 10% of the momentum and 1% of the energy in SFR-based deposition rates. We use SED template fitting to determine the relative ages of stellar populations, allowing us to identify radiation feedback dominant systems. We then examine feedback related properties (stellar age, loading factors, etc.) under conditions that optimize feedback efficiency, specifically high star formation rate surface density and compact UV half-light radii. Our findings indicate that the strongest leakers are radiation feedback dominant, lack Mg II outflows, but have extended broad components in higher ionization lines like [O III] 5007\r{A}, as observed by Amor\'in et al. (2024). In contrast, galaxies experiencing supernovae feedback typically exhibit weaker $f_{esc}^{LyC}$ and show evidence of outflows in both Mg II and higher ionization lines. We attribute these findings to rapid or "catastrophic" cooling in the radiation-dominant systems, which, given the low metallicities in our sample, are likely experiencing delayed supernovae., Comment: 34 pages, 16 figures, 7 tables

We develop a hybrid oscillator-qubit processor framework for quantum simulation of strongly correlated fermions and bosons that avoids the boson-to-qubit mapping overhead encountered in qubit hardware. This framework gives exact decompositions of particle interactions such as density-density terms and gauge-invariant hopping, as well as approximate methods based on the Baker-Campbell Hausdorff formulas including the magnetic field term for the $U(1)$ quantum link model in $(2+1)$D. We use this framework to show how to simulate dynamics using Trotterisation, perform ancilla-free partial error detection using Gauss's law, measure non-local observables, estimate ground state energies using a oscillator-qubit variational quantum eigensolver as well as quantum signal processing, and we numerically study the influence of hardware errors in circuit QED experiments. To show the advantages over all-qubit hardware, we perform an end-to-end comparison of the gate complexity for the gauge-invariant hopping term and find an improvement of the asymptotic scaling with the boson number cutoff $S$ from $\mathcal{O}(\log(S)^2)$ to $\mathcal{O}(1)$ in our framework as well as, for bosonic matter, a constant factor improvement of better than $10^4$. We also find an improvement from $\mathcal{O}(\log(S))$ to $\mathcal{O}(1)$ for the $U(1)$ magnetic field term. While our work focusses on an implementation in superconducting hardware, our framework can also be used in trapped ion, and neutral atom hardware. This work establishes digital quantum simulation with hybrid oscillator-qubit hardware as a viable and advantageous method for the study of qubit-boson models in materials science, chemistry, and high-energy physics., Comment: 48+8 pages, 24+3 figures

Rationality is not a constructible property in families. In this article, we consider stronger notions of rationality and study their behavior in families of Fano varieties. We first show that being toric is a constructible property in families of Fano varieties. The second main result of this article concerns an intermediate notion that lies between toric and rational varieties, namely cluster type varieties. A cluster type $\mathbb Q$-factorial Fano variety contains an open dense algebraic torus, but the variety does not need to be endowed with a torus action. We prove that, in families of $\mathbb Q$-factorial terminal Fano varieties, being of cluster type is a constructible condition. As a consequence, we show that there are finitely many smooth families parametrizing $n$-dimensional smooth cluster type Fano varieties., Comment: 26 pages, comments welcome

Detecting Out-of-Distribution (OOD) sensory data and covariate distribution shift aims to identify new test examples with different high-level image statistics to the captured, normal and In-Distribution (ID) set. Existing OOD detection literature largely focuses on semantic shift with little-to-no consensus over covariate shift. Generative models capture the ID data in an unsupervised manner, enabling them to effectively identify samples that deviate significantly from this learned distribution, irrespective of the downstream task. In this work, we elucidate the ability of generative models to detect and quantify domain-specific covariate shift through extensive analyses that involves a variety of models. To this end, we conjecture that it is sufficient to detect most occurring sensory faults (anomalies and deviations in global signals statistics) by solely modeling high-frequency signal-dependent and independent details. We propose a novel method, CovariateFlow, for OOD detection, specifically tailored to covariate heteroscedastic high-frequency image-components using conditional Normalizing Flows (cNFs). Our results on CIFAR10 vs. CIFAR10-C and ImageNet200 vs. ImageNet200-C demonstrate the effectiveness of the method by accurately detecting OOD covariate shift. This work contributes to enhancing the fidelity of imaging systems and aiding machine learning models in OOD detection in the presence of covariate shift., Comment: ECCV 2024, typos corrected

A graph class $\mathcal{G}$ admits product structure if there exists a constant $k$ such that every $G \in \mathcal{G}$ is a subgraph of $H \boxtimes P$ for a path $P$ and some graph $H$ of treewidth $k$. Famously, the class of planar graphs, as well as many beyond-planar graph classes are known to admit product structure. However, we have only few tools to prove the absence of product structure, and hence know of only a few interesting examples of classes. Motivated by the transition between product structure and no product structure, we investigate subclasses of intersection graphs in the plane (e.g., disk intersection graphs) and present necessary and sufficient conditions for these to admit product structure. Specifically, for a set $S \subset \mathbb{R}^2$ (e.g., a disk) and a real number $\alpha \in [0,1]$, we consider intersection graphs of $\alpha$-free homothetic copies of $S$. That is, each vertex $v$ is a homothetic copy of $S$ of which at least an $\alpha$-portion is not covered by other vertices, and there is an edge between $u$ and $v$ if and only if $u \cap v \neq \emptyset$. For $\alpha = 1$ we have contact graphs, which are in most cases planar, and hence admit product structure. For $\alpha = 0$ we have (among others) all complete graphs, and hence no product structure. In general, there is a threshold value $\alpha^*(S) \in [0,1]$ such that $\alpha$-free homothetic copies of $S$ admit product structure for all $\alpha > \alpha^*(S)$ and do not admit product structure for all $\alpha < \alpha^*(S)$. We show for a large family of sets $S$, including all triangles and all trapezoids, that it holds $\alpha^*(S) = 1$, i.e., we have no product structure, except for the contact graphs (when $\alpha= 1$). For other sets $S$, including regular $n$-gons for infinitely many values of $n$, we show that $0 < \alpha^*(S) < 1$ by proving upper and lower bounds., Comment: An extended abstract of this paper appears in the proceedings of the 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024)

The Eye Movements on Machine-Generated Texts Corpus (EMTeC) is a naturalistic eye-movements-while-reading corpus of 107 native English speakers reading machine-generated texts. The texts are generated by three large language models using five different decoding strategies, and they fall into six different text type categories. EMTeC entails the eye movement data at all stages of pre-processing, i.e., the raw coordinate data sampled at 2000 Hz, the fixation sequences, and the reading measures. It further provides both the original and a corrected version of the fixation sequences, accounting for vertical calibration drift. Moreover, the corpus includes the language models' internals that underlie the generation of the stimulus texts: the transition scores, the attention scores, and the hidden states. The stimuli are annotated for a range of linguistic features both at text and at word level. We anticipate EMTeC to be utilized for a variety of use cases such as, but not restricted to, the investigation of reading behavior on machine-generated text and the impact of different decoding strategies; reading behavior on different text types; the development of new pre-processing, data filtering, and drift correction algorithms; the cognitive interpretability and enhancement of language models; and the assessment of the predictive power of surprisal and entropy for human reading times. The data at all stages of pre-processing, the model internals, and the code to reproduce the stimulus generation, data pre-processing and analyses can be accessed via https://github.com/DiLi-Lab/EMTeC/.

To date, most investigations on surprisal and entropy effects in reading have been conducted on the group level, disregarding individual differences. In this work, we revisit the predictive power of surprisal and entropy measures estimated from a range of language models (LMs) on data of human reading times as a measure of processing effort by incorporating information of language users' cognitive capacities. To do so, we assess the predictive power of surprisal and entropy estimated from generative LMs on reading data obtained from individuals who also completed a wide range of psychometric tests. Specifically, we investigate if modulating surprisal and entropy relative to cognitive scores increases prediction accuracy of reading times, and we examine whether LMs exhibit systematic biases in the prediction of reading times for cognitively high- or low-performing groups, revealing what type of psycholinguistic subject a given LM emulates. Our study finds that in most cases, incorporating cognitive capacities increases predictive power of surprisal and entropy on reading times, and that generally, high performance in the psychometric tests is associated with lower sensitivity to predictability effects. Finally, our results suggest that the analyzed LMs emulate readers with lower verbal intelligence, suggesting that for a given target group (i.e., individuals with high verbal intelligence), these LMs provide less accurate predictability estimates., Comment: ACL 2024 Findings

Robotic-assisted surgery (RAS) relies on accurate depth estimation for 3D reconstruction and visualization. While foundation models like Depth Anything Models (DAM) show promise, directly applying them to surgery often yields suboptimal results. Fully fine-tuning on limited surgical data can cause overfitting and catastrophic forgetting, compromising model robustness and generalization. Although Low-Rank Adaptation (LoRA) addresses some adaptation issues, its uniform parameter distribution neglects the inherent feature hierarchy, where earlier layers, learning more general features, require more parameters than later ones. To tackle this issue, we introduce Depth Anything in Robotic Endoscopic Surgery (DARES), a novel approach that employs a new adaptation technique, Vector Low-Rank Adaptation (Vector-LoRA) on the DAM V2 to perform self-supervised monocular depth estimation in RAS scenes. To enhance learning efficiency, we introduce Vector-LoRA by integrating more parameters in earlier layers and gradually decreasing parameters in later layers. We also design a reprojection loss based on the multi-scale SSIM error to enhance depth perception by better tailoring the foundation model to the specific requirements of the surgical environment. The proposed method is validated on the SCARED dataset and demonstrates superior performance over recent state-of-the-art self-supervised monocular depth estimation techniques, achieving an improvement of 13.3% in the absolute relative error metric. The code and pre-trained weights are available at https://github.com/mobarakol/DARES., Comment: 11 pages

Graph neural networks (GNN) have shown significant capabilities in handling structured data, yet their application to dynamic, temporal data remains limited. This paper presents a new type of graph attention network, called TempoKGAT, which combines time-decaying weight and a selective neighbor aggregation mechanism on the spatial domain, which helps uncover latent patterns in the graph data. In this approach, a top-k neighbor selection based on the edge weights is introduced to represent the evolving features of the graph data. We evaluated the performance of our TempoKGAT on multiple datasets from the traffic, energy, and health sectors involving spatio-temporal data. We compared the performance of our approach to several state-of-the-art methods found in the literature on several open-source datasets. Our method shows superior accuracy on all datasets. These results indicate that TempoKGAT builds on existing methodologies to optimize prediction accuracy and provide new insights into model interpretation in temporal contexts.

Accurately forecasting dynamic processes on graphs, such as traffic flow or disease spread, remains a challenge. While Graph Neural Networks (GNNs) excel at modeling and forecasting spatio-temporal data, they often lack the ability to directly incorporate underlying physical laws. This work presents TG-PhyNN, a novel Temporal Graph Physics-Informed Neural Network framework. TG-PhyNN leverages the power of GNNs for graph-based modeling while simultaneously incorporating physical constraints as a guiding principle during training. This is achieved through a two-step prediction strategy that enables the calculation of physical equation derivatives within the GNN architecture. Our findings demonstrate that TG-PhyNN significantly outperforms traditional forecasting models (e.g., GRU, LSTM, GAT) on real-world spatio-temporal datasets like PedalMe (traffic flow), COVID-19 spread, and Chickenpox outbreaks. These datasets are all governed by well-defined physical principles, which TG-PhyNN effectively exploits to offer more reliable and accurate forecasts in various domains where physical processes govern the dynamics of data. This paves the way for improved forecasting in areas like traffic flow prediction, disease outbreak prediction, and potentially other fields where physics plays a crucial role.

Robust semantic segmentation of intraoperative image data holds promise for enabling automatic surgical scene understanding and autonomous robotic surgery. While model development and validation are primarily conducted on idealistic scenes, geometric domain shifts, such as occlusions of the situs, are common in real-world open surgeries. To close this gap, we (1) present the first analysis of state-of-the-art (SOA) semantic segmentation models when faced with geometric out-of-distribution (OOD) data, and (2) propose an augmentation technique called "Organ Transplantation", to enhance generalizability. Our comprehensive validation on six different OOD datasets, comprising 600 RGB and hyperspectral imaging (HSI) cubes from 33 pigs, each annotated with 19 classes, reveals a large performance drop in SOA organ segmentation models on geometric OOD data. This performance decline is observed not only in conventional RGB data (with a dice similarity coefficient (DSC) drop of 46 %) but also in HSI data (with a DSC drop of 45 %), despite the richer spectral information content. The performance decline increases with the spatial granularity of the input data. Our augmentation technique improves SOA model performance by up to 67 % for RGB data and 90 % for HSI data, achieving performance at the level of in-distribution performance on real OOD test data. Given the simplicity and effectiveness of our augmentation method, it is a valuable tool for addressing geometric domain shifts in surgical scene segmentation, regardless of the underlying model. Our code and pre-trained models are publicly available at https://github.com/IMSY-DKFZ/htc., Comment: Silvia Seidlitz and Jan Sellner contributed equally

Ordinary differential equations (ODE) are a popular tool to model the spread of infectious diseases, yet they implicitly assume an exponential distribution to describe the flow from one infection state to another. However, scientific experience yields more plausible distributions where the likelihood of disease progression or recovery changes accordingly with the duration spent in a particular state of the disease. Furthermore, transmission dynamics depend heavily on the infectiousness of individuals. The corresponding nonlinear variation with the time individuals have already spent in an infectious state requires more realistic models. The previously mentioned items are particularly crucial when modeling dynamics at change points such as the implementation of nonpharmaceutical interventions. In order to capture these aspects and to enhance the accuracy of simulations, integro-differential equations (IDE) can be used. In this paper, we propose a generalized model based on integro-differential equations with eight infection states. The model allows for variable stay time distributions and generalizes the concept of ODE-based models as well as IDE-based age-of-infection models. In this, we include particular infection states for severe and critical cases to allow for surveillance of the clinical sector, avoiding bottlenecks and overloads in critical epidemic situations. We will extend a recently introduced nonstandard numerical scheme to solve a simpler IDE-based model. This scheme is adapted to our more advanced model and we prove important mathematical and biological properties for the numerical solution. Furthermore, we validate our approach numerically by demonstrating the convergence rate. Eventually, we also show that our novel model is intrinsically capable of better assessing disease dynamics upon the introduction of nonpharmaceutical interventions.

Upcoming surveys such as Euclid, the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Telescope (Roman) will detect hundreds of high-redshift (z > 7) quasars, but distinguishing them from the billions of other sources in these catalogues represents a significant data analysis challenge. We address this problem by extending existing selection methods by using both i) Bayesian model comparison on measured fluxes and ii) a likelihood-based goodness-of-fit test on images, which are then combined using an Fbeta statistic. The result is an automated, reproduceable and objective high-redshift quasar selection pipeline. We test this on both simulations and real data from the cross-matched Sloan Digital Sky Survey (SDSS) and UKIRT Infrared Deep Sky Survey (UKIDSS) catalogues. On this cross-matched dataset we achieve an AUC score of up to 0.795 and an F3 score of up to 0.79, sufficient to be applied to the Euclid, LSST and Roman data when available., Comment: 21 pages, 24 figures; to be submitted to the Open Journal of Astrophysics

With sepsis remaining a leading cause of mortality, early identification of septic patients and those at high risk of death is a challenge of high socioeconomic importance. The driving hypothesis of this study was that hyperspectral imaging (HSI) could provide novel biomarkers for sepsis diagnosis and treatment management due to its potential to monitor microcirculatory alterations. We conducted a comprehensive study involving HSI data of the palm and fingers from more than 480 patients on the day of their intensive care unit (ICU) admission. The findings demonstrate that HSI measurements can predict sepsis with an area under the receiver operating characteristic curve (AUROC) of 0.80 (95 % confidence interval (CI) [0.76; 0.84]) and mortality with an AUROC of 0.72 (95 % CI [0.65; 0.79]). The predictive performance improves substantially when additional clinical data is incorporated, leading to an AUROC of up to 0.94 (95 % CI [0.92; 0.96]) for sepsis and 0.84 (95 % CI [0.78; 0.89]) for mortality. We conclude that HSI presents novel imaging biomarkers for the rapid, non-invasive prediction of sepsis and mortality, suggesting its potential as an important modality for guiding diagnosis and treatment., Comment: Markus A. Weigand, Lena Maier-Hein and Maximilian Dietrich contributed equally

The Boulby UnderGround Screening (BUGS) facility, located at the Boulby Underground Laboratory, has significantly advanced its material screening capabilities by installing two XIA UltraLo-1800 alpha particle detectors. This study presents a comprehensive evaluation of one of these detectors, operated 1,100 meters underground at the Boulby Underground Laboratory, which provides significant shielding from cosmic radiation and maintains a low ambient radon activity of 2.30 $\pm$ 0.03 Bq/m$^3$. Our evaluation focuses on energy reconstruction accuracy, background radiation rates, and operational stability. The XIA UltraLo-1800 detector demonstrates remarkable stability in energy reconstruction, with less than 0.1 MeV variation over four years. Moreover, the implementation of a graphite-filled PTFE liner in the sample tray resulted in a significant reduction in background radiation levels compared to measurements with the original stainless steel tray, achieving an average activity of 0.15 $\pm$ 0.01 $\alpha$/cm$^2$/khr. Copper sample assays, performed before and after radon exposure, demonstrated the detector's ability to accurately identify and quantify $^{210}$Po contamination. By implementing the robust cleanliness procedures and protocols described in this article, we observed a reduction in $^{210}$Po activity from 0.504 $\pm$ 0.022 mBq to 0.336 $\pm$ 0.013 mBq, highlighting the crucial role of refined cleaning methods in minimizing background for sensitive experiments. Additionally, observations of elevated background activity levels post-high-activity sample measurements illustrate the need for careful management of assay conditions and environment to maintain low background levels. These results highlight the potential of the XIA UltraLo-1800 in enhancing the precision of material assays essential for reducing background interference in rare event experiments.