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Constraining the helium enhancement in stars is critical for understanding the formation mechanisms of multiple populations in star clusters. However, measuring helium variations for many stars within a cluster remains observationally challenging. We use Hubble Space Telescope photometry combined with MUSE spectroscopic data for over 7,200 red-giant branch stars in \omc\ to measure helium differences between distinct groups of stars as a function of metallicity separating the impact of helium enhancements from other abundance variations on the pseudo-color (chromosome) diagrams. Our results show that stars at all metallicities have subpopulations with significant helium enhancement ($\Delta Y_{min} \gtrsim$ 0.11). We find a rapid increase in helium enhancement from low metallicities ($\rm{[Fe/H] \simeq -2.05}$ to $\rm{[Fe/H] \simeq -1.92})$, with this enhancement leveling out at \deltay\ $= 0.154$ at higher metallicities. The fraction of helium-enhanced stars steadily increases with metallicity ranging from 10\% at $\rm{[Fe/H] \simeq -2.04}$ to over $90\%$ at $\rm{[Fe/H] \simeq -1.04}$. This study is the first to examine helium enhancement across the full range of metallicities in \omc{}, providing new insight into its formation history and additional constraints on enrichment mechanisms.

This report documents the development, test, and application of Large Language Models (LLMs) for automated text analysis, with a specific focus on gambling-like elements in digital games, such as lootboxes. The project aimed not only to analyse user opinions and attitudes towards these mechanics, but also to advance methodological research in text analysis. By employing prompting techniques and iterative prompt refinement processes, the study sought to test and improve the accuracy of LLM-based text analysis. The findings indicate that while LLMs can effectively identify relevant patterns and themes on par with human coders, there are still challenges in handling more complex tasks, underscoring the need for ongoing refinement in methodologies. The methodological advancements achieved through this study significantly enhance the application of LLMs in real-world text analysis. The research provides valuable insights into how these models can be better utilized to analyze complex, user-generated content.

Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT) and of the auto- and cross-correlation of galaxy position and weak lensing measurements (3$\times$2pt) in the Dark Energy Survey (DES). We consider the cosmological correlation between the different tracers and we account for the systematic uncertainties that are shared between the large-scale lensing correlation functions and the small-scale lensing-based cluster mass calibration. Marginalized over the remaining $\Lambda$CDM parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure $\Omega_\mathrm{m}=0.300\pm0.017$ and $\sigma_8=0.797\pm0.026$. Compared to constraints from Planck primary CMB anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 ($1.2\sigma$) for the two-parameter difference. We further obtain $S_8\equiv\sigma_8(\Omega_\mathrm{m}/0.3)^{0.5}=0.796\pm0.013$ which is lower than the Planck measurement at the $1.6\sigma$ level. The combined SPT cluster, DES 3$\times$2pt, and Planck datasets mildly prefer a non-zero positive neutrino mass, with a 95% upper limit $\sum m_\nu<0.25~\mathrm{eV}$ on the sum of neutrino masses. Assuming a $w$CDM model, we constrain the dark energy equation of state parameter $w=-1.15^{+0.23}_{-0.17}$ and when combining with Planck primary CMB anisotropies, we recover $w=-1.20^{+0.15}_{-0.09}$, a $1.7\sigma$ difference with a cosmological constant. The precision of our results highlights the benefits of multiwavelength multiprobe cosmology., Comment: Submitted to Phys. Rev. D

This paper presents the development and implementation of a versatile ad-hoc metropolitan-range Quantum Key Distribution (QKD) network. The approach presented integrates various types of physical channels and QKD protocols, and a mix of trusted and untrusted nodes. Unlike conventional QKD networks that predominantly depend on either fiber-based or free-space optical (FSO) links, the testbed presented amalgamates FSO and fiber-based links, thereby overcoming some inherent limitations. Various network deployment strategies have been considered, including permanent infrastructure and provisional ad-hoc links to eradicate coverage gaps. Furthermore, the ability to rapidly establish a network using portable FSO terminals and to investigate diverse link topologies is demonstrated. The study also showcases the successful establishment of a quantum-secured link to a cloud server.

The aim of the Canadian publications in Library and Information Science (LIS) database is to help break down the silos in which the two main target audiences - LIS faculty members and academic librarians - conduct their research. As part of a larger project entitled "Breaking down research silos", we created a database of research contributions by Canadian LIS researchers (academics and practitioners). This was motivated by a desire to make research by Canadian LIS scholars and practitioners more visible and foster collaboration between these two groups. The aim of this paper is to introduce the database, describe the process through which it was created, provide descriptive statistics of the database content, and highlight areas for future development.

We investigate automated in situ optimization of the potential landscape in a quantum point contact device, using a $3 \times 3$ gate array patterned atop the constriction. Optimization is performed using the covariance matrix adaptation evolutionary strategy, for which we introduce a metric for how "step-like" the conductance is as the channel becomes constricted. We first perform the optimization of the gate voltages in a tight-binding simulation and show how such in situ tuning can be used to mitigate a random disorder potential. The optimization is then performed in a physical device in experiment, where we also observe a marked improvement in the quantization of the conductance resulting from the optimization procedure., Comment: 8 pages, 4 figures (supplement: 6 pages, 3 figures)

The Coronagraphic Instrument onboard the Nancy Grace Roman Space Telescope is an important stepping stone towards the characterization of habitable, rocky exoplanets. In a technology demonstration phase conducted during the first 18 months of the mission (expected to launch in late 2026), novel starlight suppression technology may enable direct imaging of a Jupiter analog in reflected light. Here we summarize the current activities of the Observation Planning working group formed as part of the Community Participation Program. This working group is responsible for target selection and observation planning of both science and calibration targets in the technology demonstration phase of the Roman Coronagraph. We will discuss the ongoing efforts to expand target and reference catalogs, and to model astrophysical targets (exoplanets and circumstellar disks) within the Coronagraph's expected sensitivity. We will also present preparatory observations of high priority targets., Comment: Proceedings Volume 13092, Space Telescopes and Instrumentation 2024: Optical, Infrared, and Millimeter Wave; 1309255 (2024)

The Covering Tour Location Routing Problem (CTLRP) unites the well-known location routing problem with the possibility of covering customers through intermediary facilities. The objective is to select a subset of greenfield or brownfield depots and a subset of facilities, assign customers to suitable facilities, and design routes for a fleet of vehicles such that each customer's demand of a homogenous good can be satisfied and a given objective function is minimized. We focus particularly on the case when customers can not be directly visited, i.e. they have to pick up their orders from intermediary facilities, and the objective function consists of the strategic and operational costs, i.e. total routing costs plus costs for establishing or operating the selected depots and facilities. We introduce a mixed-integer programming model derived from a 2-commodity flow formulation, which can be seen as a baseline model for various applications. As such models are often impractical to solve for realistically-sized scenarios, a local search-based matheuristic to solve the problem at hand is developed. Furthermore, we construct benchmark instances for the CTLRP by expanding existing LRP instances to include information on facilities and customers. Extensive computational experiments on said instances with the aim of analyzing different heuristic configurations show that the proposed heuristic, if designed carefully, is able to produce very good results within a short period of time, while exact solving methods using the proposed MIP formulation frequently return highly suboptimal solutions and exhibit slow convergence speeds.

The processes driving the formation and evolution of late-type galaxies (LTGs) continue to be a debated subject in extragalactic astronomy. Investigating stellar kinematics, especially when combined with age estimates, provides crucial insights into the formation and subsequent development of galactic discs. Post-processing of exceptionally high-quality Integral Field Spectroscopy (IFS) data of NGC 4030 acquired with the Multi Unit Spectroscopic Explorer (MUSE), clearly reveals a striking grand design spiral pattern in the velocity dispersion map not previously detected in other galaxies. This pattern spatially correlates with HII regions, suggesting that stars currently being born exhibit lower velocity dispersion as compared to surrounding areas where star formation (SF) is less active. We examine the age-velocity relation (AVR) and propose that its configuration might be shaped by a combination of heating mechanisms, seemingly consistent with findings from recent high-resolution cosmological zoom-in simulations. The complex structure of the uncovered AVR of NGC 4030 support the hypothesis that stellar populations initially inherit the velocity dispersion {\sigma} of the progenitor cold molecular gas, which depends on formation time and galactocentric distance, subsequently experiencing kinematic heating by cumulative gravitational interactions during their lifetime. While advancing our understanding of the AVR, these findings offer a new framework for investigating disk heating mechanisms, and their role in the evolution of galactic disks., Comment: accepted for publication in A&A letters

A notion of support for objects in any Grothendieck category is introduced. This is based on the spectral category of a Grothendieck category and uses its Boolean lattice of localising subcategories. The support provides a classification of all subcategories that are closed under arbitrary coproducts, subobjects, and essential extensions. There is also a notion of exact support which classifies certain thick subcategories. As an application, the coproduct decompositions of objects are described in terms of Boolean lattices. Also, for any ring Crawley-Boevey's correspondence between definable subcategories of modules and closed subsets of the Ziegler spectrum is extended., Comment: 26 pages. V2: Substantial revision, including a new section on coproduct decompositions

We consider the problem of predicting perturbation effects via causal models. In many applications, it is a priori unknown which mechanisms of a system are modified by an external perturbation, even though the features of the perturbation are available. For example, in genomics, some properties of a drug may be known, but not their causal effects on the regulatory pathways of cells. We propose a generative intervention model (GIM) that learns to map these perturbation features to distributions over atomic interventions in a jointly-estimated causal model. Contrary to prior approaches, this enables us to predict the distribution shifts of unseen perturbation features while gaining insights about their mechanistic effects in the underlying data-generating process. On synthetic data and scRNA-seq drug perturbation data, GIMs achieve robust out-of-distribution predictions on par with unstructured approaches, while effectively inferring the underlying perturbation mechanisms, often better than other causal inference methods.

Brillouin Light Scattering (BLS) spectroscopy is a non-invasive, non-contact, label-free optical technique that can provide information on the mechanical properties of a material on the sub-micron scale. Over the last decade it has seen increased applications in the life sciences, driven by the observed significance of mechanical properties in biological processes, the realization of more sensitive BLS spectrometers and its extension to an imaging modality. As with other spectroscopic techniques, BLS measurements not only detect signals characteristic of the investigated sample, but also of the experimental apparatus, and can be significantly affected by measurement conditions. The aim of this consensus statement is to improve the comparability of BLS studies by providing reporting recommendations for the measured parameters and detailing common artifacts. Given that most BLS studies of biological matter are still at proof-of-concept stages and use different--often self-built--spectrometers, a consensus statement is particularly timely to assure unified advancement., Comment: Main Text & Supplementary Text: 56 pages, 3 Figures, 2 Supplementary Figures, 1 Supplementary Table

We explore synergies between the Nancy Grace Roman Space Telescope High Latitude Wide Area Survey (HLWAS) and CMB experiments, specifically Simons Observatory (SO) and CMB-Stage4 (S4). Our simulated analyses include weak lensing, photometric galaxy clustering, CMB lensing, thermal SZ, and cross-correlations between these probes. While we assume the nominal 16,500 square degree area for SO and S4, we consider multiple survey designs for Roman that overlap with Rubin Observatory's Legacy Survey of Space and Time (LSST): the 2000 square degree reference survey using four photometric bands, and two shallower single-band surveys that cover 10,000 and 18,000 square degree, respectively. We find a ~2x increase in the dark energy figure of merit when including CMB-S4 data for all Roman survey designs. We further find a strong increase in constraining power for the Roman wide survey scenario cases, despite the reduction in galaxy number density, and the increased systematic uncertainties assumed due to the single band coverage. Even when tripling the already worse systematic uncertainties in the Roman wide scenarios, which reduces the 10,000 square degree FoM from 269 to 178, we find that the larger survey area is still significantly preferred over the reference survey (FoM 64). We conclude that for the specific analysis choices and metrics of this paper, a Roman wide survey is unlikely to be systematics-limited (in the sense that one saturates the improvement that can be obtained by increasing survey area). We outline several specific implementations of a two-tier Roman survey (1000 square degree with 4 bands, and a second wide tier in one band) that can further mitigate the risk of systematics for Roman wide concepts., Comment: Comments welcome!

This is the interim publication of the first International Scientific Report on the Safety of Advanced AI. The report synthesises the scientific understanding of general-purpose AI -- AI that can perform a wide variety of tasks -- with a focus on understanding and managing its risks. A diverse group of 75 AI experts contributed to this report, including an international Expert Advisory Panel nominated by 30 countries, the EU, and the UN. Led by the Chair, these independent experts collectively had full discretion over the report's content., Comment: Available under the open government license at https://www.gov.uk/government/publications/international-scientific-report-on-the-safety-of-advanced-ai

We propose practical deep Gaussian process models on Riemannian manifolds, similar in spirit to residual neural networks. With manifold-to-manifold hidden layers and an arbitrary last layer, they can model manifold- and scalar-valued functions, as well as vector fields. We target data inherently supported on manifolds, which is too complex for shallow Gaussian processes thereon. For example, while the latter perform well on high-altitude wind data, they struggle with the more intricate, nonstationary patterns at low altitudes. Our models significantly improve performance in these settings, enhancing prediction quality and uncertainty calibration, and remain robust to overfitting, reverting to shallow models when additional complexity is unneeded. We further showcase our models on Bayesian optimisation problems on manifolds, using stylised examples motivated by robotics, and obtain substantial improvements in later stages of the optimisation process. Finally, we show our models to have potential for speeding up inference for non-manifold data, when, and if, it can be mapped to a proxy manifold well enough.

Robust modeling of non-linear scales is critical for accurate cosmological inference in Stage IV surveys. For weak lensing analyses in particular, a key challenge arises from the incomplete understanding of how non-gravitational processes, such as supernovae and active galactic nuclei - collectively known as baryonic feedback - affect the matter distribution. Several existing methods for modeling baryonic feedback treat it independently from the underlying cosmology, an assumption which has been found to be inaccurate by hydrodynamical simulations. In this work, we examine the impact of this coupling between baryonic feedback and cosmology on parameter inference at LSST Y1 precision. We build mock 3$\times$2pt data vectors using the Magneticum suite of hydrodynamical simulations, which span a wide range of cosmologies while keeping subgrid parameters fixed. We perform simulated likelihood analyses for two baryon mitigation techniques: (i) the Principal Component Analysis (PCA) method which identifies eigenmodes for capturing the effect baryonic feedback on the data vector and (ii) HMCode2020 (Mead et al. 2021) which analytically models the modification in the matter distribution using a halo model approach. Our results show that the PCA method is robust to the coupling between cosmology and baryonic feedback, whereas, when using HMCode2020 there can be up to $0.5\sigma$ bias in $\Omega_\text{m}$-$S_8$. For HMCode2020, the bias also correlates with the input cosmology while for PCA we find no such correlation., Comment: correction to acknowledgements

Precision cardiology based on cardiac digital twins requires accurate simulations of cardiac arrhythmias. However, detailed models, such as the monodomain model, are computationally costly and have limited applicability in practice. Thus, it desirable to have fast models that can still represent the main physiological features presented during cardiac arrhythmias. The eikonal model is an approximation of the monodomain model that is widely used to describe the arrival times of the electrical wave. However, the standard eikonal model does not generalize to the complex re-entrant dynamics that characterize the cardiac arrhythmias. In this work, we propose an eikonal model that includes the tissue re-excitability, which allows to describe re-entries. The re-excitability properties are inferred from the monodomain model. Our eikonal model also handles the tissue anisotropy and heterogeneity. We compare the eikonal model to the monodomain model in various numerical experiments in the atria and the ventricles. The eikonal model is qualitatively accurate in the simulation of re-entries and can be potentially ran in real-time, opening the door to its clinical applicability., Comment: 18 pages, 11 figures

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including Variational AutoEncoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, Diffusion models, and models based on Conditional Flow Matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in 1-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space., Comment: 204 pages, 100+ figures, 30+ tables

Statistical fluctuations of local tensorial fields beyond the mean are relevant to predict localized failure or overall behavior of the inelastic composites. The expression for second moments of the local fields can be established using Hill-Mandel condition. Complete estimation of statistical fluctuations via second moments is usually ignored despite its significance. In Eshelby-based mean-field approaches, the second moments are evaluated through derivatives of Hill's Polarization tensor $ \left ( \mathbb{P}_{\mathrm{o}} \right )$ using a singular approximation. Typically, semi-analytical procedures using numerical integration are used to evaluate the derivatives of the polarization tensor $\mathbb{P}_{\mathrm{o}}$. Here, new analytically derived explicit expressions are presented for calculating the derivatives, specifically for unidirectional fibrous composites with isotropic phases. Full-field homogenization using finite element is used to compute the statistical distribution of local fields (exact solution) for the class of random fibrous microstructures. The mean-field estimates are validated with the exact solution across different fiber volume fractions and aspect ratios. The results indicate that the fiber volume fraction significantly influences the fluctuation of stress tensor invariants, whereas the aspect ratio has minimal effect., Comment: 37 pages, 8 figures, 1 table

We introduce MODEL SELECTOR, a framework for label-efficient selection of pretrained classifiers. Given a pool of unlabeled target data, MODEL SELECTOR samples a small subset of highly informative examples for labeling, in order to efficiently identify the best pretrained model for deployment on this target dataset. Through extensive experiments, we demonstrate that MODEL SELECTOR drastically reduces the need for labeled data while consistently picking the best or near-best performing model. Across 18 model collections on 16 different datasets, comprising over 1,500 pretrained models, MODEL SELECTOR reduces the labeling cost by up to 94.15% to identify the best model compared to the cost of the strongest baseline. Our results further highlight the robustness of MODEL SELECTOR in model selection, as it reduces the labeling cost by up to 72.41% when selecting a near-best model, whose accuracy is only within 1% of the best model.

Reinforcement learning (RL) is ubiquitous in the development of modern AI systems. However, state-of-the-art RL agents require extensive, and potentially unsafe, interactions with their environments to learn effectively. These limitations confine RL agents to simulated environments, hindering their ability to learn directly in real-world settings. In this work, we present ActSafe, a novel model-based RL algorithm for safe and efficient exploration. ActSafe learns a well-calibrated probabilistic model of the system and plans optimistically w.r.t. the epistemic uncertainty about the unknown dynamics, while enforcing pessimism w.r.t. the safety constraints. Under regularity assumptions on the constraints and dynamics, we show that ActSafe guarantees safety during learning while also obtaining a near-optimal policy in finite time. In addition, we propose a practical variant of ActSafe that builds on latest model-based RL advancements and enables safe exploration even in high-dimensional settings such as visual control. We empirically show that ActSafe obtains state-of-the-art performance in difficult exploration tasks on standard safe deep RL benchmarks while ensuring safety during learning.

Recent efforts in fine-tuning language models often rely on automatic data selection, commonly using Nearest Neighbors retrieval from large datasets. However, we theoretically show that this approach tends to select redundant data, limiting its effectiveness or even hurting performance. To address this, we introduce SIFT, a data selection algorithm designed to reduce uncertainty about the model's response given a prompt, which unifies ideas from retrieval and active learning. Whereas Nearest Neighbor retrieval typically fails in the presence of information duplication, SIFT accounts for information duplication and optimizes the overall information gain of the selected examples. We focus our evaluations on fine-tuning at test-time for prompt-specific language modeling on the Pile dataset, and show that SIFT consistently outperforms Nearest Neighbor retrieval, with minimal computational overhead. Moreover, we show that our uncertainty estimates can predict the performance gain of test-time fine-tuning, and use this to develop an adaptive algorithm that invests test-time compute proportional to realized performance gains. We provide the $\texttt{activeft}$ (Active Fine-Tuning) library which can be used as a drop-in replacement for Nearest Neighbor retrieval.

SHAP values are a popular local feature-attribution method widely used in interpretable and explainable AI. We tackle the problem of efficiently computing these values. We cover both the model-agnostic (black-box) setting, where one only has query access to the model and also the case of (ensembles of) trees where one has access to the structure of the tree. For both the black-box and the tree setting we propose a two-stage approach for estimating SHAP values. Our algorithm's first step harnesses recent results showing that many real-world predictors have a spectral bias that allows us to either exactly represent (in the case of ensembles of decision trees), or efficiently approximate them (in the case of neural networks) using a compact Fourier representation. In the second step of the algorithm, we use the Fourier representation to exactly compute SHAP values. The second step is computationally very cheap because firstly, the representation is compact and secondly, we prove that there exists a closed-form expression for SHAP values for the Fourier basis functions. Furthermore, the expression we derive effectively linearizes the computation into a simple summation and is amenable to parallelization on multiple cores or a GPU. Since the function approximation (first step) is only done once, it allows us to produce Shapley values in an amortized way. We show speedups compared to relevant baseline methods equal levels of accuracy for both the tree and black-box settings. Moreover, this approach introduces a reliable and fine-grained continuous trade-off between computation and accuracy through the sparsity of the Fourier approximation, a feature previously unavailable in all black-box methods., Comment: Submitted as a conference paper to ICLR 2025

Pre-trained generalist policies are rapidly gaining relevance in robot learning due to their promise of fast adaptation to novel, in-domain tasks. This adaptation often relies on collecting new demonstrations for a specific task of interest and applying imitation learning algorithms, such as behavioral cloning. However, as soon as several tasks need to be learned, we must decide which tasks should be demonstrated and how often? We study this multi-task problem and explore an interactive framework in which the agent adaptively selects the tasks to be demonstrated. We propose AMF (Active Multi-task Fine-tuning), an algorithm to maximize multi-task policy performance under a limited demonstration budget by collecting demonstrations yielding the largest information gain on the expert policy. We derive performance guarantees for AMF under regularity assumptions and demonstrate its empirical effectiveness to efficiently fine-tune neural policies in complex and high-dimensional environments.

In this note, we prove that $K_{2i} (\mathbb{Z}/p^n) \neq 0$ if and only if $p-1$ divides $i$ and $0 \leq i \leq (p-1) p^{n-2}$, refining the even vanishing theorem of Antieau, Nikolaus and the first author in this case. As a corollary of our proof, we determine that the nilpotence order of $v_1$ in $\pi_* K(\mathbb{Z}/p^n)/p$ is equal to $\frac{p^n-1}{p-1}$. Our proof combines the recent crystallinity result for reduced syntomic cohomology of Hahn, Levy and the second author with the explicit complex computing the syntomic cohomology of $\mathcal{O}_K /\varpi^n$ constructed by Antieau, Nikolaus and the first author., Comment: 10 pages

In recent years, there has been a renewed interest in preconditioning for multilevel Toeplitz systems, a research field that has been extensively explored over the past several decades. This work introduces novel preconditioning strategies using multilevel $\tau$ matrices for both symmetric and nonsymmetric multilevel Toeplitz systems. Our proposals constitute a general framework, as they are constructed solely based on the generating function of the multilevel Toeplitz coefficient matrix, when it can be defined. We begin with nonsymmetric systems, where we employ a symmetrization technique by permuting the coefficient matrix to produce a real symmetric multilevel Hankel structure. We propose a multilevel $\tau$ preconditioner tailored to the symmetrized system and prove that the eigenvalues of the preconditioned matrix sequence cluster at $\pm 1$, leading to rapid convergence when using the preconditioned minimal residual method. The high effectiveness of this approach is demonstrated through its application in solving space fractional diffusion equations. Next, for symmetric systems we introduce another multilevel $\tau$ preconditioner and show that the preconditioned conjugate gradient method can achieve an optimal convergence rate, namely a rate that is independent of the matrix size, when employed for a class of ill-conditioned multilevel Toeplitz systems. Numerical examples are provided to critically assess the effectiveness of our proposed preconditioners compared to several leading existing preconditioned solvers, highlighting their superior performance.

Bayesian optimization (BO) is a powerful framework to optimize black-box expensive-to-evaluate functions via sequential interactions. In several important problems (e.g. drug discovery, circuit design, neural architecture search, etc.), though, such functions are defined over large $\textit{combinatorial and unstructured}$ spaces. This makes existing BO algorithms not feasible due to the intractable maximization of the acquisition function over these domains. To address this issue, we propose $\textbf{GameOpt}$, a novel game-theoretical approach to combinatorial BO. $\textbf{GameOpt}$ establishes a cooperative game between the different optimization variables, and selects points that are game $\textit{equilibria}$ of an upper confidence bound acquisition function. These are stable configurations from which no variable has an incentive to deviate$-$ analog to local optima in continuous domains. Crucially, this allows us to efficiently break down the complexity of the combinatorial domain into individual decision sets, making $\textbf{GameOpt}$ scalable to large combinatorial spaces. We demonstrate the application of $\textbf{GameOpt}$ to the challenging $\textit{protein design}$ problem and validate its performance on four real-world protein datasets. Each protein can take up to $20^{X}$ possible configurations, where $X$ is the length of a protein, making standard BO methods infeasible. Instead, our approach iteratively selects informative protein configurations and very quickly discovers highly active protein variants compared to other baselines.

Self-enrichment is one of the leading explanations for chemical anomalies in globular clusters. In this scenario, various candidate polluter stars have been proposed to eject gas with altered chemical composition during the self-enrichment process. Most of the proposed polluters will also eject radioactive $^{26}$Al into the surroundings. Hence, any detection of $^{26}$Al in young massive star clusters (YMCs) would support the self-enrichment scenario if YMCs were indeed the progenitors of globular clusters. Observations of gamma-ray data from COMPTEL and INTEGRAL, as well as detections of $^{26}$AlF molecules by the Atacama Large Millimeter-submillimeter Array (ALMA), indicate the maturing of $^{26}$Al detection methods. Detection possibilities will be enhanced in the short- to mid-term by the upcoming launch of the Compton Spectrometer and Imager (COSI). The Square Kilometer Array (SKA) could in principle also detect radio recombination lines of the positronium formed from the decay products of $^{26}$Al. Here, we show for a sample of YMCs in the nearby Universe, where self-enrichment could plausibly take place. For some nearby galaxies, this could enhance $^{26}$Al by an order of one magnitude. Detecting $^{26}$AlF with ALMA appears feasible for many candidate self-enrichment clusters, although significant challenges remain with other detection methods. The Large Magellanic Cloud, with its YMC R136, stands out as the most promising candidate. Detecting a 1.8~MeV radioactive decay line of $^{26}$Al here would require at least 15 months of targeted observation with COSI, assuming ongoing self-enrichment in R136., Comment: 13 pages, 5 figures

We present age estimates for over 8100 sub-giant branch (SGB) stars in Omega Centauri ($\omega$ Cen) to study its star formation history. Our large data set, which combines multi-wavelength HST photometry with MUSE metallicities, provides an unprecedented opportunity to measure individual stellar ages. We do this by fitting each star's photometry and metallicity with theoretical isochrones, that are embedded with an empirical [C+N+O]-[Fe/H] relation specifically for $\omega$ Cen. The bulk of the stars have ages between 13 and 10 Gyr, with the mean stellar age being 12.08$\pm$0.01 Gyrs and the median age uncertainty being 0.68 Gyrs. From these ages we construct the most complete age-metallicity relation (AMR) for $\omega$ Cen to date. We find that the mean age of stars decreases with increasing metallicity and find two distinct streams in the age-metallicity plane, hinting at different star formation pathways. We derive an intrinsic spread in the ages of 0.75$\pm$0.01 Gyr for the whole cluster, with the age spread showing a clear increase with metallicity. We verify the robustness of our age estimations by varying isochrone parameters and constraining our systematics. We find the C+N+O relation to be the most critical consideration for constraining the AMR. We also present the SGB chromosome map with age information. In the future, these stellar ages could be combined with chemical abundances to study age differences in subpopulations, and uncover the chemical evolution history of this massive nuclear star cluster., Comment: 23 pages, 11 figures

Variational models for image deblurring problems typically consist of a smooth term and a potentially non-smooth convex term. A common approach to solving these problems is using proximal gradient methods. To accelerate the convergence of these first-order iterative algorithms, strategies such as variable metric methods have been introduced in the literature. In this paper, we prove that, for image deblurring problems, the variable metric strategy can be reinterpreted as a right preconditioning method. Consequently, we explore an inexact left-preconditioned version of the same proximal gradient method. We prove the convergence of the new iteration to the minimum of a variational model where the norm of the data fidelity term depends on the preconditioner. The numerical results show that left and right preconditioning are comparable in terms of the number of iterations required to reach a prescribed tolerance, but left preconditioning needs much less CPU time, as it involves fewer evaluations of the preconditioner matrix compared to right preconditioning. The quality of the computed solutions with left and right preconditioning are comparable. Finally, we propose some non-stationary sequences of preconditioners that allow for fast and stable convergence to the solution of the variational problem with the classical $\ell^2$--norm on the fidelity term.

Theories of localised pattern formation are important to understand a broad range of natural patterns, but are less well-understood than more established mechanisms of domain-filling pattern formation. Here, we extend recent work on pattern localisation via slow spatial heterogeneity in reaction-diffusion systems to the Swift-Hohenberg equation. We use a WKB asymptotic approach to show that, in the limit of a large domain and slowly varying heterogeneity, conditions for Turing-type linear instability localise in a simple way, with the spatial variable playing the role of a parameter. For nonlinearities locally corresponding to supercritical bifurcations in the spatially homogeneous system, this analysis asymptotically predicts regions where patterned states are confined, which we confirm numerically. We resolve the inner region of this asymptotic approach, finding excellent agreement with the tails of these confined pattern regions. In the locally subcritical case, however, this theory is insufficient to fully predict such confined regions, and so we propose an approach based on numerical continuation of a local homogeneous analog system. Pattern localisation in the heterogeneous system can then be determined based on the Maxwell point of this system, with the spatial variable parameterizing this point. We compare this theory of localisation via spatial heterogeneity to localised patterns arising from homoclinic snaking, and suggest a way to distinguish between different localisation mechanisms in natural systems based on how these structures decay to the background state (i.e. how their tails decay). We also explore cases where both of these local theories of pattern formation fail to capture the interaction between spatial heterogeneity and underlying pattern-forming mechanisms, suggesting that more work needs to be done to fully disentangle exogenous and intrinsic heterogeneity., Comment: 36 pages, 17 figures

Through exposing items to users, implicit feedback recommender systems influence the logged interactions, and, ultimately, their own recommendations. This effect is called exposure bias and it can lead to issues such as filter bubbles and echo chambers. Previous research employed the multinomial logit model (MNL) with exposure information to reduce exposure bias on synthetic data. This extended abstract summarizes our previous study in which we investigated whether (i) these findings hold for human-generated choices, (ii) other discrete choice models mitigate bias better, and (iii) an item's estimated relevance can depend on the relevances of the other items that were presented with it. We collected a data set of biased and unbiased choices in a controlled online user study and measured the effects of overexposure and competition. We found that (i) the discrete choice models effectively mitigated exposure bias on human-generated choice data, (ii) there were no significant differences in robustness among the different discrete choice models, and (iii) only multivariate discrete choice models were robust to competition between items. We conclude that discrete choice models mitigate exposure bias effectively because they consider item-co-exposure. Moreover, exposing items alongside more or less popular items can bias future recommendations significantly and item exposure must be tracked for overcoming exposure bias. We consider our work vital for understanding what exposure bias it, how it forms, and how it can be mitigated., Comment: Accepted at the CONSEQUENCES '24 workshop, co-located with ACM RecSys '24

Statistical properties of LSS serve as powerful tools to constrain the cosmological properties of our Universe. Tracing the gas pressure, the tSZ effect is a biased probe of mass distribution and can be used to test the physics of feedback or cosmological models. Therefore, it is crucial to develop robust modeling of hot gas pressure for applications to tSZ surveys. Since gas collapses into bound structures, it is expected that most of the tSZ signal is within halos produced by cosmic accretion shocks. Hence, simple empirical halo models can be used to predict the tSZ power spectra. In this study, we employed the HMx halo model to compare the tSZ power spectra with those of several hydrodynamical simulations: the Horizon suite and the Magneticum simulation. We examined various contributions to the tSZ power spectrum across different redshifts, including the one- and two-halo term decomposition, the amount of bound gas, the importance of different masses and the electron pressure profiles. Our comparison of the tSZ power spectrum reveals discrepancies that increase with redshift. We find a 20% to 50% difference between the measured and predicted tSZ angular power spectrum over the multipole range $\ell=10^3-10^4$. Our analysis reveals that these differences are driven by the excess of power in the predicted two-halo term at low k and in the one-halo term at high k. At higher redshifts (z~3), simulations indicate that more power comes from outside the virial radius than from inside suggesting a limitation in the applicability of the halo model. We observe differences in the pressure profiles, despite the fair level of agreement on the tSZ power spectrum at low redshift with the default calibration of the halo model. In conclusion, our study suggests that the properties of the halo model need to be carefully controlled against real or mock data to be proven useful for cosmological purposes., Comment: 15 pages, 9 figures

We show that on a $\sigma$-finite measure preserving system $X = (X,\nu, T)$, the non-conventional ergodic averages $$ \mathbb{E}_{n \in [N]} \Lambda(n) f(T^n x) g(T^{P(n)} x)$$ converge pointwise almost everywhere for $f \in L^{p_1}(X)$, $g \in L^{p_2}(X)$, and $1/p_1 + 1/p_2 \leq 1$, where $P$ is a polynomial with integer coefficients of degree at least $2$. This had previously been established with the von Mangoldt weight $\Lambda$ replaced by the constant weight $1$ by the first and third authors with Mirek, and by the M\"obius weight $\mu$ by the fourth author. The proof is based on combining tools from both of these papers, together with several Gowers norm and polynomial averaging operator estimates on approximants to the von Mangoldt function of ''Cram\'er'' and ''Heath-Brown'' type., Comment: 37 pages

Learning uncertain dynamics models using Gaussian process~(GP) regression has been demonstrated to enable high-performance and safety-aware control strategies for challenging real-world applications. Yet, for computational tractability, most approaches for Gaussian process-based model predictive control (GP-MPC) are based on approximations of the reachable set that are either overly conservative or impede the controller's safety guarantees. To address these challenges, we propose a robust GP-MPC formulation that guarantees constraint satisfaction with high probability. For its tractable implementation, we propose a sampling-based GP-MPC approach that iteratively generates consistent dynamics samples from the GP within a sequential quadratic programming framework. We highlight the improved reachable set approximation compared to existing methods, as well as real-time feasible computation times, using two numerical examples., Comment: to be published in 63rd IEEE Conference on Decision and Control (CDC 2024)

We present the first detection of a cluster lensing signal with `Kinematic Lensing' (KL), a novel weak lensing method that combines photometry, spectroscopy, and the Tully-Fisher relation to enable shear measurements with individual source galaxies. This is the second paper in a two-part series aimed at measuring a KL signal from data. The first paper, arXiv:2209.11811, describes the inference pipeline, which jointly forward models galaxy imaging and spectroscopy, and demonstrates unbiased shear inference with simulated data. This paper presents measurements of the lensing signal from the galaxy cluster Abell 2261. We obtain spectroscopic observations of background disk galaxies in the cluster field selected from the CLASH Subaru catalog. The final sample consists of three source galaxies while the remaining are rejected due to insufficient signal-to-noise, spectroscopic failures, and inadequately sampled rotation curves. We apply the KL inference pipeline to the three sources and find the shear estimates to be in broad agreement with traditional weak lensing measurements. The typical shear measurement uncertainty for our sources is $\sigma(g_+)\approx 0.026$, which represents approximately a ten-fold improvement over the weak lensing shape noise. We identify target selection and observing strategy as the key avenues of improvement for future KL programs., Comment: v1: To be submitted to MNRAS. Comments are welcome!

Interfacial ferroelectricity, prevalent in various parallel-stacked layered materials, allows switching of out-of-plane ferroelectric order by in-plane sliding of adjacent layers. Its resilience against doping potentially enables next-generation storage and logic devices. However, studies have been limited to indirect sensing or visualization of ferroelectricity. For transition metal dichalcogenides, there is little knowledge about the influence of ferroelectric order on their intrinsic valley and excitonic properties. Here, we report direct probing of ferroelectricity in few-layer 3R-MoS$_2$ using reflectance contrast spectroscopy. Contrary to a simple electrostatic perception, layer-hybridized excitons with out-of-plane electric dipole moment remain decoupled from ferroelectric ordering, while intralayer excitons with in-plane dipole orientation are sensitive to it. Ab initio calculations identify stacking-specific interlayer hybridization leading to this asymmetric response. Exploiting this sensitivity, we demonstrate optical readout and control of multi-state polarization with hysteretic switching in a field-effect device. Time-resolved Kerr ellipticity reveals a direct correspondence between spin-valley dynamics and stacking order.

General conditions are established under which reaction-cross-diffusion systems can undergo spatiotemporal pattern-forming instabilities. Recent work has focused on designing systems theoretically and experimentally to exhibit patterns with specific features, but the case of non-diagonal diffusion matrices has yet to be analysed. Here, a framework is presented for the design of general $n$-component reaction-cross-diffusion systems that exhibit Turing and wave instabilities of a given wavelength. For a fixed set of reaction kinetics, it is shown how to choose diffusion matrices that produce each instability; conversely, for a given diffusion tensor, how to choose linearised kinetics. The theory is applied to several examples including a hyperbolic reaction-diffusion system, two different 3-component models, and a spatio-temporal version of the Ross-Macdonald model for the spread of malaria., Comment: 49 pages, 14 figures

We introduce a two-level trust-region method (TLTR) for solving unconstrained nonlinear optimization problems. Our method uses a composite iteration step, which is based on two distinct search directions. The first search direction is obtained through minimization in the full/high-resolution space, ensuring global convergence to a critical point. The second search direction is obtained through minimization in the randomly generated subspace, which, in turn, allows for convergence acceleration. The efficiency of the proposed TLTR method is demonstrated through numerical experiments in the field of machine learning

In landscape-aware algorithm selection problem, the effectiveness of feature-based predictive models strongly depends on the representativeness of training data for practical applications. In this work, we investigate the potential of randomly generated functions (RGF) for the model training, which cover a much more diverse set of optimization problem classes compared to the widely-used black-box optimization benchmarking (BBOB) suite. Correspondingly, we focus on automated algorithm configuration (AAC), that is, selecting the best suited algorithm and fine-tuning its hyperparameters based on the landscape features of problem instances. Precisely, we analyze the performance of dense neural network (NN) models in handling the multi-output mixed regression and classification tasks using different training data sets, such as RGF and many-affine BBOB (MA-BBOB) functions. Based on our results on the BBOB functions in 5d and 20d, near optimal configurations can be identified using the proposed approach, which can most of the time outperform the off-the-shelf default configuration considered by practitioners with limited knowledge about AAC. Furthermore, the predicted configurations are competitive against the single best solver in many cases. Overall, configurations with better performance can be best identified by using NN models trained on a combination of RGF and MA-BBOB functions.

Geological applications of phase-field methods for fracture are notably scarce. This work conducts a numerical examination of the applicability of standard phase-field models in reproducing jointing within sedimentary layers. We explore how the volumetric-deviatoric split alongside the AT1 and AT2 phase-field formulations have several advantages in simulating jointing, but also have intrinsic limitations that prevent a reliable quantitative analysis of rock fracture. The formulations qualitatively reproduce the process of joint saturation, along with the negative correlation between joint spacing and the height of the sedimentary layer. However, in quantitative comparison to alternative numerical methods and outcrop observations, the phase-field method overestimates joint spacings by a factor of 2 and induces unrealistic compressive fractures in the AT1 model, alongside premature shearing at layer interfaces for the AT2 model. The causes are identified to be intrinsic to the phase-field lengthscale and the unsuitable strength envelope arising from the Volumetric-Deviatoric split. Finally, our analysis elucidates on the phase-field lengthscale's distortion of the stress field around dilating fractures, causing the sedimentary layer to reach joint saturation prematurely, thereby stopping the nucleation of new fractures and leading to larger joint spacings than in natural examples. Decreasing the lengthscale results in gradual improvement but becomes prohibitively computationally expensive for very small lengthscales such that the limit of ``natural'' behavior was not reached in this study. Overall, our results motivate the development of constitutive phase-field models that are more suitable for geological applications and their benchmarking against geological observations., Comment: 24 pages

Decreasing costs and new technologies have led to an increase in the amount of data available to plant breeding programs. High-throughput phenotyping (HTP) platforms routinely generate high-dimensional datasets of secondary features that may be used to improve genomic prediction accuracy. However, integration of this data comes with challenges such as multicollinearity, parameter estimation in $p > n$ settings, and the computational complexity of many standard approaches. Several methods have emerged to analyze such data, but interpretation of model parameters often remains challenging. We propose genetic factor best linear unbiased prediction (gfBLUP), a seven-step prediction pipeline that reduces the dimensionality of the original secondary HTP data using generative factor analysis. In short, gfBLUP uses redundancy filtered and regularized genetic and residual correlation matrices to fit a maximum likelihood factor model and estimate genetic latent factor scores. These latent factors are subsequently used in multi-trait genomic prediction. Our approach performs on par or better than alternatives in extensive simulations and a real-world application, while producing easily interpretable and biologically relevant parameters. We discuss several possible extensions and highlight gfBLUP as the basis for a flexible and modular multi-trait genomic prediction framework.

Linear temporal logic (LTL) is a powerful language for task specification in reinforcement learning, as it allows describing objectives beyond the expressivity of conventional discounted return formulations. Nonetheless, recent works have shown that LTL formulas can be translated into a variable rewarding and discounting scheme, whose optimization produces a policy maximizing a lower bound on the probability of formula satisfaction. However, the synthesized reward signal remains fundamentally sparse, making exploration challenging. We aim to overcome this limitation, which can prevent current algorithms from scaling beyond low-dimensional, short-horizon problems. We show how better exploration can be achieved by further leveraging the LTL specification and casting its corresponding Limit Deterministic B\"uchi Automaton (LDBA) as a Markov reward process, thus enabling a form of high-level value estimation. By taking a Bayesian perspective over LDBA dynamics and proposing a suitable prior distribution, we show that the values estimated through this procedure can be treated as a shaping potential and mapped to informative intrinsic rewards. Empirically, we demonstrate applications of our method from tabular settings to high-dimensional continuous systems, which have so far represented a significant challenge for LTL-based reinforcement learning algorithms.