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This paper introduces a hierarchical framework for decision-making and optimization, leveraging Large Language Models (LLMs) for adaptive code generation. Instead of direct decision-making, LLMs generate and refine executable control policies through a meta-policy that guides task generation and a base policy for operational actions. Applied to a simplified microgrid scenario, the approach achieves up to 15 percent cost savings by iteratively improving battery control strategies. The proposed methodology lays a foundation for integrating LLM-based tools into planning and control tasks, offering adaptable and scalable solutions for complex systems while addressing challenges of uncertainty and reproducibility.

Understanding how brain networks recruit resources during cognitive tasks is key to explaining individual differences in task performance. Brain network parameters-including activity levels of regions and their connectivity-reflect the integration and segregation of functional subnetworks underlying task processing. However, the complexity and high dimensionality of these parameters pose a significant barrier to identifying functionally relevant individual differences. Here, we introduce stiff-sloppy analysis as a framework for uncovering the stiff parameter combinations that critically influence task-state brain dynamics, exemplified by working memory. Using the pairwise maximum entropy model (PMEM) calibrated to fMRI data and Fisher Information Matrix (FIM) analysis, we reveal that the stiff dimensions of the model parameters capture the most relevant integration and segregation processes of the default mode network and the working memory network. Individual differences along these stiff neural dimensions consistently correlate with working memory performance. Notably, stiff parameters robustly predicted working memory performance, even when the less sensitive ("sloppy") parameters were excluded. This study establishes stiff-sloppy analysis as a powerful approach to identify cognition-related brain networks, bridging neural dynamics and behavior and offering new avenues for personalized neuroscience including therapeutic innovation.

In train networks, carefully-chosen delays may be beneficial for certain passengers, who would otherwise miss some connection. Given a simple temporal graph and a set of passengers (each specifying a starting vertex, an ending vertex, and a desired arrival time), we ask whether it is possible to delay some of the edges of the temporal graph to realize all the passengers' demands. We call this problem DelayBetter (DB), and study it along with two variants: in $\delta$-DelayBetter, each delay must be of at most $\delta$; in Path DB, passengers fully specify the vertices they should visit on their journey. On the positive side, we give a polynomial-time algorithm for Path DB, and obtain as a corollary a polynomial-time algorithm for DB and $\delta$-DB on trees. We also provide an fpt algorithm for both problems parameterized by the size of the graph's Feedback Edge Set together with the number of passengers. On the negative side, we show NP-completeness of ($1$-)DB on bounded-degree temporal graphs even when the lifetime is $2$, and of ($10$-)DB on bounded-degree planar temporal graphs of lifetime $19$. Our results complement previous work studying reachability problems in temporal graphs with delaying operations. This is to our knowledge the first such problem in which the aim is to facilitate travel between specific points (as opposed to facilitating or impeding a broadcast from one or many sources)., Comment: 20 pages, 7 figures

The International Semantic Web Research School (ISWS) is a week-long intensive program designed to immerse participants in the field. This document reports a collaborative effort performed by ten teams of students, each guided by a senior researcher as their mentor, attending ISWS 2023. Each team provided a different perspective to the topic of creative AI, substantiated by a set of research questions as the main subject of their investigation. The 2023 edition of ISWS focuses on the intersection of Semantic Web technologies and Creative AI. ISWS 2023 explored various intersections between Semantic Web technologies and creative AI. A key area of focus was the potential of LLMs as support tools for knowledge engineering. Participants also delved into the multifaceted applications of LLMs, including legal aspects of creative content production, humans in the loop, decentralised approaches to multimodal generative AI models, nanopublications and AI for personal scientific knowledge graphs, commonsense knowledge in automatic story and narrative completion, generative AI for art critique, prompt engineering, automatic music composition, commonsense prototyping and conceptual blending, and elicitation of tacit knowledge. As Large Language Models and semantic technologies continue to evolve, new exciting prospects are emerging: a future where the boundaries between creative expression and factual knowledge become increasingly permeable and porous, leading to a world of knowledge that is both informative and inspiring., Comment: Technical Report

In recent years, significant advancements in computational methods have dramatically enhanced the precision in determining the energetic ranking of different phases of molecular crystals. The developments mainly focused on providing accurate dispersion corrected exchange correlation functionals and methods for describing the vibrational entropy contributions to the free energy at finite temperatures. Several molecular crystals phases were recently found to have of multi-minima character. For our investigations we highlight the multi-minima character in the example of the molecular crystal consisting of N-(4-Methylbenzylidene)-4-methylalanine. We explore its potential energy landscape on the full DFT level or with a machine learned potential that was fitted to DFT data. We calculate not only many local minima but also exact barriers along transformation pathways to demonstrate the multi-minima character of our system. Furthermore, we present a framework, based on the quantum superposition method, that includes both configurational and vibrational entropy. As an example, we show for our system that the transition temperature between two of its phases is afflicted by an error of about 200 K if the multi-minima character is not taken into account. This indicates that it is absolutely essential to consider configurational entropy to obtain reliable finite temperature free energy rankings for complex molecular crystals.

In semiconductor hole spin qubits, low magnetic field ($B$) operation extends the coherence time ($T_\mathrm{2}^*$) but proportionally reduces the gate speed. In contrast, singlet-triplet (ST) qubits are primarily controlled by the exchange interaction ($J$) and can thus maintain high gate speeds even at low $B$. However, a large $J$ introduces a significant charge component to the qubit, rendering ST qubits more vulnerable to charge noise when driven. Here, we demonstrate a highly coherent ST hole spin qubit in germanium, operating at both low $B$ and low $J$. By modulating $J$, we achieve resonant driving of the ST qubit, obtaining an average gate fidelity of $99.68\%$ and a coherence time of $T_\mathrm{2}^*=1.9\,\mu$s. Moreover, by applying the resonant drive continuously, we realize a dressed ST qubit with a tenfold increase in coherence time ($T_\mathrm{2\rho}^*=20.3\,\mu$s). Frequency modulation of the driving signal enables universal control, with an average gate fidelity of $99.64\%$. Our results demonstrate the potential for extending coherence times while preserving high-fidelity control of germanium-based ST qubits, paving the way for more efficient operations in semiconductor-based quantum processors.

A next-generation medium-energy gamma-ray telescope targeting the MeV range would address open questions in astrophysics regarding how extreme conditions accelerate cosmic-ray particles, produce relativistic jet outflows, and more. One concept, AMEGO-X, relies upon the mission-enabling CMOS Monolithic Active Pixel Sensor silicon chip AstroPix. AstroPix is designed for space-based use, featuring low noise, low power consumption, and high scalability. Desired performance of the device include an energy resolution of 5 keV (or 10% FWHM) at 122 keV and a dynamic range per-pixel of 25-700 keV, enabled by the addition of a high-voltage bias to each pixel which supports a depletion depth of 500 um. This work reports on the status of the AstroPix development process with emphasis on the current version under test, version three (v3), and highlights of version two (v2). Version 3 achieves energy resolution of 10.4 +\- 3.2 % at 59.5 keV and 94 +\- 6 um depletion in a low-resistivity test silicon substrate., Comment: 20 pages, 13 figures

Modern particle physics experiments, e.g. at the Large Hadron Collider (LHC) at CERN, crucially depend on the precise description of the scattering processes in terms of the known fundamental forces. This is limited by our current understanding of the strong nuclear force, as quantified by the strong coupling, $\alpha_s$, between quarks and gluons. Relating $\alpha_s$ to experiments poses a major challenge as the strong interactions lead to the confinement of quarks and gluons inside hadronic bound states. At high energies, however, the strong interactions become weaker ("asymptotic freedom") and thus amenable to an expansion in powers of the coupling. Attempts to relate both regimes usually rely on modeling of the bound state problem in one way or another. Using large scale numerical simulations of a first principles formulation of Quantum Chromodynamics on a space-time lattice, we have carried out a model-independent determination of $\alpha_s$ with unprecedented precision. The uncertainty, about half that of all other results combined, originates predominantly from the statistical Monte Carlo evaluation and has a clear probabilistic interpretation. The result for $\alpha_s$ describes a variety of physical phenomena over a wide range of energy scales. If used as input information, it will enable significantly improved analyses of many high energy experiments, by removing an important source of theoretical uncertainty. This will increase the likelihood to uncover small effects of yet unknown physics, and enable stringent precision tests of the Standard Model. In summary, this result boosts the discovery potential of the LHC and future colliders, and the methods developed in this work pave the way for even higher precision in the future., Comment: 49 pages, LaTeX

Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain, which has experienced great attention through the success of generative models and, in particular, diffusion models. However, current molecular diffusion models are tailored towards a specific downstream task and lack adaptability. We introduce UniGuide, a framework for controlled geometric guidance of unconditional diffusion models that allows flexible conditioning during inference without the requirement of extra training or networks. We show how applications such as structure-based, fragment-based, and ligand-based drug design are formulated in the UniGuide framework and demonstrate on-par or superior performance compared to specialised models. Offering a more versatile approach, UniGuide has the potential to streamline the development of molecular generative models, allowing them to be readily used in diverse application scenarios., Comment: 38th Conference on Neural Information Processing Systems (NeurIPS)

In this paper, we present a novel cascade control structure with formal guarantees of uniform almost global asymptotic stability for the state tracking error dynamics of a quadcopter. The proposed approach features a model predictive control strategy for the outer loop, explicitly accounting for the non-zero total thrust constraint. The outer-loop controller generates an acceleration reference, which is then converted into attitude, angular velocity and acceleration references, subsequently tracked by a nonlinear inner-loop controller. The proposed cascade control strategy is validated through numerical case studies, underlying high-fidelity models, demonstrating its ability to track fast trajectories with small error.

Epigenetic inheritance during cell division is essential for preserving cell identity by stabilizing the overall chromatin organisation. Heterochromatin,the condensed and transcriptionally silent fraction of chromatin,is marked by specific epigenetic modifications that are diluted during each cell division. Here we build a physical model,based on the formation of a biomolecular condensate,a liquid 'droplet',that promotes the restoration of epigenetic marks. Heterochromatin facilitates the droplet formation via polymer-assisted condensation(PAC). The resulting condensate serves as a reaction chamber to reconstruct the lost epigenetic marks. We incorporate the enzymatic reactions into a particle-based simulation and monitor the progress of the epigenetic markers through an in silico analogue of the cell cycle. We demonstrate that the proposed mechanism is robust and stabilizes the heterochromatin domains over many cell generations. This mechanism and variations thereof might be at work for other epigenetic marks as well.

Galaxy cluster masses estimated from parametric modeling of weak lensing shear observations are known to be biased by inaccuracies in observationally determined centers. It has recently been shown that such systematic effects can be non-isotropic when centers are derived from X-ray or Compton-Y (Sunyaev-Zeldovich effect) observations, which is often the case in practice. This fact challenges current methods of accurately correcting for weak lensing mass biases using simulations paired with isotropic empirical miscentering distributions, in particular as the effect on determined masses is currently a dominant source of systematic uncertainty. We use hydrodanamical cosmological simulations taken from the Magneticum Pathfinder simulations to show that the non-isotropic component of the mass bias can be reduced to within one percent of the mass when considering the center of mass, rather than the bottom of the gravitational potential, as the reference center of a galaxy cluster., Comment: 6 pages, 4 figures, MNRAS submitted

Decision trees are a classic model for summarizing and classifying data. To enhance interpretability and generalization properties, it has been proposed to favor small decision trees. Accordingly, in the minimum-size decision tree training problem (MSDT), the input is a set of training examples in $\mathbb{R}^d$ with class labels and we aim to find a decision tree that classifies all training examples correctly and has a minimum number of nodes. MSDT is NP-hard and therefore presumably not solvable in polynomial time. Nevertheless, Komusiewicz et al. [ICML '23] developed a promising algorithmic paradigm called witness trees which solves MSDT efficiently if the solution tree is small. In this work, we test this paradigm empirically. We provide an implementation, augment it with extensive heuristic improvements, and scrutinize it on standard benchmark instances. The augmentations achieve a mean 324-fold (median 84-fold) speedup over the naive implementation. Compared to the state of the art they achieve a mean 32-fold (median 7-fold) speedup over the dynamic programming based MurTree solver [Demirovi\'c et al., J. Mach. Learn. Res. '22] and a mean 61-fold (median 25-fold) speedup over SAT-based implementations [Janota and Morgado, SAT '20]. As a theoretical result we obtain an improved worst-case running-time bound for MSDT.

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

Our method proposes the efficient generation of samples from an unnormalized Boltzmann density by solving the underlying continuity equation in the low-rank tensor train (TT) format. It is based on the annealing path commonly used in MCMC literature, which is given by the linear interpolation in the space of energies. Inspired by Sequential Monte Carlo, we alternate between deterministic time steps from the TT representation of the flow field and stochastic steps, which include Langevin and resampling steps. These adjust the relative weights of the different modes of the target distribution and anneal to the correct path distribution. We showcase the efficiency of our method on multiple numerical examples.

Introduction Lifetime risks quantify health risks from radiation exposure and play an important role in radiation detriment and radon dose conversion. This study considers the lifetime risk of dying from lung cancer related to occupational radon exposure, focusing on lifetime excess absolute risk (LEAR), in addition to other lifetime risk measures. This article derives and discusses uncertainty intervals for these estimates. Methods Uncertainties in two components of lifetime risk calculations are modeled: risk model parameter estimates for excess relative risk of lung cancer and baseline mortality rates. Approximate normality assumption (ANA) methods and Bayesian techniques quantify risk model parameter uncertainty. The methods are applied to risk models from the German "Wismut" uranium miners cohort study (full cohort with follow-up 2018 and the 1960+ sub-cohort of miners hired in 1960 or later). Mortality rate uncertainty is assessed based on WHO data. Monte Carlo simulations yield uncertainty intervals, which are compared across different lifetime risk measures. Results Risk model parameter uncertainty is the largest contributor to lifetime risk uncertainty, with baseline mortality rate uncertainty also significant. For the 1960+ sub-cohort risk model, LEAR was 6.70% (95% uncertainty interval: [3.26, 12.28]) for an exposure of 2 Working Level Months from age 18-64, compared to 3.43% ([2.06, 4.84]) for the full cohort. Differences across lifetime risk measures are minor. Conclusion Here, risk model parameter uncertainty substantially drives lifetime risk uncertainty, supporting the use of ANA methods for practicality. Choice of lifetime risk measures has negligible impact. Derived uncertainty intervals align with the range of lifetime risk estimates from uranium miners studies in the literature and should inform radiation protection policies based on lifetime risks., Comment: 59 pages (25 main and 34 supplement),21 figures (3 in main and 18 in supplement)

For reasons of computational constraint, most global ocean circulation models used for Earth System Modeling still rely on parameterizations of sub-grid processes, and limitations in these parameterizations affect the modeled ocean circulation and impact on predictive skill. An increasingly popular approach is to leverage machine learning approaches for parameterizations, regressing for a map between the resolved state and missing feedbacks in a fluid system as a supervised learning task. However, the learning is often performed in an `offline' fashion, without involving the underlying fluid dynamical model during the training stage. Here, we explore the `online' approach that involves the fluid dynamical model during the training stage for the learning of baroclinic turbulence and its parameterization, with reference to ocean eddy parameterization. Two online approaches are considered: a full adjoint-based online approach, related to traditional adjoint optimization approaches that require a `differentiable' dynamical model, and an approximately online approach that approximates the adjoint calculation and does not require a differentiable dynamical model. The online approaches are found to be generally more skillful and numerically stable than offline approaches. Others details relating to online training, such as window size, machine learning model set up and designs of the loss functions are detailed to aid in further explorations of the online training methodology for Earth System Modeling., Comment: 25 pages, 1 table, 8 figures

The transport of molecules for chemical reactions is critically important in various cellular biological processes. Despite thermal diffusion being prevalent in many biochemical processes, it is unreliable for any sort of directed transport or preferential accumulation of molecules. In this paper we propose a strategy for directed motion in which the molecules are transported by active carriers via polymerization. This transport is facilitated by chemical/activity gradients which generate an effective drift of the polymers. By marginalizing out the active degrees of freedom of the system, we obtain an effective Fokker-Planck equation for the Rouse modes of such active-passive hybrid polymers. In particular, we solve for the steady state distribution of the center of mass and its mean first passage time to reach an intended destination. We focus on how the arrangement of active units within the polymer affect its steady-state and dynamic behaviour and how they can be optimized to achieve high accumulation or rapid motility.

We introduce a methodology for performing parameter inference in high-dimensional, non-linear diffusion processes. We illustrate its applicability for obtaining insights into the evolution of and relationships between species, including ancestral state reconstruction. Estimation is performed by utilising score matching to approximate diffusion bridges, which are subsequently used in an importance sampler to estimate log-likelihoods. The entire setup is differentiable, allowing gradient ascent on approximated log-likelihoods. This allows both parameter inference and diffusion mean estimation. This novel, numerically stable, score matching-based parameter inference framework is presented and demonstrated on biological two- and three-dimensional morphometry data.

Recent AI advances have enabled multi-modal systems to model and translate diverse information spaces. Extending beyond text and vision, we introduce OneProt, a multi-modal AI for proteins that integrates structural, sequence, alignment, and binding site data. Using the ImageBind framework, OneProt aligns the latent spaces of modality encoders along protein sequences. It demonstrates strong performance in retrieval tasks and surpasses state-of-the-art methods in various downstream tasks, including metal ion binding classification, gene-ontology annotation, and enzyme function prediction. This work expands multi-modal capabilities in protein models, paving the way for applications in drug discovery, biocatalytic reaction planning, and protein engineering., Comment: 28 pages, 15 figures, 7 tables

We review lattice results related to pion, kaon, $D$-meson, $B$-meson, and nucleon physics with the aim of making them easily accessible to the nuclear and particle physics communities. More specifically, we report on the determination of the light-quark masses, the form factor $f_+(0)$ arising in the semileptonic $K \to \pi$ transition at zero momentum transfer, as well as the decay-constant ratio $f_K/f_\pi$ and its consequences for the CKM matrix elements $V_{us}$ and $V_{ud}$. We review the determination of the $B_K$ parameter of neutral kaon mixing as well as the additional four $B$ parameters that arise in theories of physics beyond the Standard Model. For the heavy-quark sector, we provide results for $m_c$ and $m_b$ as well as those for the decay constants, form factors, and mixing parameters of charmed and bottom mesons and baryons. These are the heavy-quark quantities most relevant for the determination of CKM matrix elements and the global CKM unitarity-triangle fit. We review the status of lattice determinations of the strong coupling constant $\alpha_s$. We review the determinations of nucleon charges from the matrix elements of both isovector and flavour-diagonal axial, scalar and tensor local quark bilinears, and momentum fraction, helicity moment and the transversity moment from one-link quark bilinears. We also review determinations of scale-setting quantities. Finally, in this review we have added a new section on the general definition of the low-energy limit of the Standard Model., Comment: 435 pages, 53 Figures, 190 tables. arXiv admin note: substantial text overlap with arXiv:2111.09849, arXiv:1902.08191, some corrections and updated references

Galaxy cluster mergers are excellent laboratories for studying a wide variety of different physical phenomena. Such a unique system is the distant SPT-CLJ2228-5828 cluster merger located at $z\approx 0.77$. Previous analyses via Sunyaev-Zeldovich and weak lensing data suggested that the system potentially was a dissociative cluster post-merger. In this work, we use new, deep XMM-Newton data to study the hot gas in X-rays, spectroscopic Gemini data to precisely determine the redshift of the two mass concentrations, and new HST data to improve the total mass estimates of the two components. We find that SPT-CLJ2228-5828 constitutes a pre-merging, double cluster system, instead of a post-merger. The merging process of the two clusters has started with their outskirt gas colliding with a $\sim 22^{\circ}-27^{\circ}$ on the plane of the sky. We fully characterize the surface brightness, gas density, temperature, pressure, and entropy profiles of the two merging clusters. The two systems have very similar X-ray properties with a moderate cluster mass of $M_{\text{tot}}\sim (2.1-2.4)\times 10^{14}\ M_{\odot}$. A $\approx 333$ kpc long gas bridge connecting the two clusters is detected at a $5.8\sigma$ level. The baryon overdensity of the excess bridge gas is $\delta_{\text{b}}\sim (75-320)$ across the length of the bridge and its gas mass is $M_{\text{gas}}\sim 1.4\times 10^{12}\ M_{\odot}$. Gas density and temperature jumps are also found across the gas bridge, revealing the existence of a weak shock front with a Mach number $\mathcal{M}\sim 1.1$. The gas pressure and entropy are also increased at the position of the shock front. We estimate the age of the shock front to be $\lesssim 100$ Myr and its kinetic energy $\sim 2.4\times 10^{44}$ erg s$^{-1}$. SPT-CLJ2228-5828 is the first such high-$z$ pre-merger with a gas bridge and a shock front to be studied in X-rays., Comment: 21 pages, 9 figures. Submitted for publication in A&A

The number of type Ia supernova (SNe Ia) observations will grow significantly within the next decade, mainly thanks to the Legacy Survey of Space and Time (LSST) undertaken by the Vera Rubin Observatory in Chile. With this improvement, statistical uncertainties will decrease, and flux calibration will become the main uncertainty for the characterization of dark energy. Currently, the astronomical flux scale is anchored on the numerical models of white dwarf atmospheres from the CALSPEC catalog, and every error on the model can induce a bias over cosmological parameters inference. The StarDICE experiment proposes a new calibration reference that only relies on observations from the optical watt defined by the NIST towards the magnitude of standard stars. It is currently operating at l'Observatoire de Haute-Provence and has been collecting data since the beginning of 2023. To overcome the photometric calibration uncertainty and reach a sub-percent precision, the instrument throughput has been calibrated with a Collimated Beam Projector. It will be monitored on-site with a LED-based artificial star source calibrated with NIST photodiodes. In this proceeding, we will first illustrate how an error in the photometric calibration can impact the SNe Ia distance moduli and thus bias the measurement of cosmological parameters. Then we will present the StarDICE experiment and how we can recalibrate the CALSPEC catalog at the millimagnitude level on the NIST scale with photometric analysis., Comment: 4 pages, 3 figures, contribution to the 2024 Cosmology session of the 58th Rencontres de Moriond

The measurement of type Ia supernovae magnitudes provides cosmological distances, which can be used to constrain dark energy parameters. Large photometric surveys require a substantial improvement in the calibration precision of their photometry to reduce systematic uncertainties in cosmological constraints. The StarDICE experiment is designed to establish accurate broadband flux references for these surveys, aiming for sub-percent precision in magnitude measurements. This requires a precise measurement of the filter bandpasses of both the StarDICE and survey instruments with sub-nanometer accuracy. To that end, we have developed the Collimated Beam Projector (CBP), an optical device capable of calibrating the throughput of an astronomical telescope and of its filters. The CBP is built from a tunable laser source and a reversed telescope to emit a parallel monochromatic light beam that is continuously monitored in flux and wavelength. The CBP output light flux is measured using a large area photodiode, previously calibrated relative to a NIST photodiode. We derive the StarDICE telescope throughput and filter transmissions from the CBP measurements, anchoring it to the absolute calibration provided by the NIST. After analyzing the systematic uncertainties, we achieved sub-nanometer accuracy in determining filter central wavelengths, measured each filter transmission with a precision of 0.5% per 1nm bin, and detected out-of-band leakages at 0.01%. Furthermore, we have synthesized the equivalent transmission for full pupil illumination from four sample positions in the StarDICE telescope mirror, with an accuracy of approximately 0.2nm for central wavelengths and 7mmag for broadband fluxes. We demonstrated our ability to characterize a telescope throughput down to the mmag, and paved the way for future developments, such as a portable CBP version for in-situ transmission monitoring., Comment: 29 pages, 50 figures

The aggressiveness of prostate cancer, the most common cancer in men worldwide, is primarily assessed based on histopathological data using the Gleason scoring system. While artificial intelligence (AI) has shown promise in accurately predicting Gleason scores, these predictions often lack inherent explainability, potentially leading to distrust in human-machine interactions. To address this issue, we introduce a novel dataset of 1,015 tissue microarray core images, annotated by an international group of 54 pathologists. The annotations provide detailed localized pattern descriptions for Gleason grading in line with international guidelines. Utilizing this dataset, we develop an inherently explainable AI system based on a U-Net architecture that provides predictions leveraging pathologists' terminology. This approach circumvents post-hoc explainability methods while maintaining or exceeding the performance of methods trained directly for Gleason pattern segmentation (Dice score: 0.713 $\pm$ 0.003 trained on explanations vs. 0.691 $\pm$ 0.010 trained on Gleason patterns). By employing soft labels during training, we capture the intrinsic uncertainty in the data, yielding strong results in Gleason pattern segmentation even in the context of high interobserver variability. With the release of this dataset, we aim to encourage further research into segmentation in medical tasks with high levels of subjectivity and to advance the understanding of pathologists' reasoning processes., Comment: 58 pages, 15 figures (incl. supplementary)

Two-dimensional (2D) materials, such as transition metal dichalcogenides (TMDCs) in the 2H or 1T crystal phases, are promising (electro)catalyst candidates due to their high surface to volume ratio and composition of low-cost, abundant elements. While the edges of elemental TMDC nanoparticles, such as MoS$_2$, can show significant catalytic activity, the basal plane of the pristine materials are notoriously inert, which limits their normalized activity. Here we show that high densities of catalytically active sites can be formed on the TMDC basal plane by alloying elements that prefer the 2H (1T) phase into a 1T (2H) structure. The global stability of the alloy, in particular whether it crystallizes in the 2H or 1T phase, can be controlled by ensuring a majority of elements preferring the target phase. We further show that the mixing entropy plays a decisive role for stabilizing the alloy implying that high-entropy alloying becomes essential. Our calculations point to a number of interesting non-precious hydrogen evolution catalysts, including (CrHfTaVZr)S$_2$ and (CrNbTiVZr)S$_2$ in the T-phase and (MoNbTaVTi)S$_2$ in the H-phase. Our work opens new directions for designing catalytic sites via high-entropy alloy stabilization of locally unstable structures.

The generation and manipulation of non-classical light states is central to emerging quantum technologies. Color centers in insulating crystals have been extensively studied for single-photon generation, but organic molecules immobilized on substrates have gained attention due to their superior scalability, large oscillator strengths, and tunable emission frequency. Here, we use first principles calculations to investigate the photoemission spectrum of organic molecules adsorbed on various 2D materials. Machine learning interatomic potentials are combined with density functional theory to accelerate the search for stable adsorption configurations. The calculated zero phonon line (ZPL) energies and emission lineshapes show excellent agreement with experiments. Our results indicate that the 2D substrate couples weakly to the molecular transitions and that emission characteristics are almost universal across different substrates. The unique effect of the 2D substrate is to introduce a sharp sideband(s) near the ZPL as a fingerprint of hindered rotational and translational modes of the molecule.

Directed motion up a concentration gradient is crucial for the survival and maintenance of numerous biological systems, such as sperms moving towards an egg during fertilization or ciliates moving towards a food source. In these systems, chirality - manifested as a rotational torque - plays a vital role in facilitating directed motion. While systematic studies of active molecules in activity gradients exist, the effect of chirality remains little studied. In this study, we examine the simplest case of a chiral active particle connected to a passive particle in a spatially varying activity field. We demonstrate that this minimal setup can exhibit rich emergent tactic behaviors, with the chiral torque serving as the tuning parameter. Notably, when the chiral torque is sufficiently large, even a small passive particle enables the system to display the desired accumulation behavior. This study provides valuable insights into the design principles of hybrid bio-molecular devices of the future., Comment: 14 pages (6 main + 8 supplementary), 4 figures

All-optical helicity-independent switching (AO-HIS) is of interest for ultrafast and energy efficient magnetic switching in future magnetic data storage approaches. Yet, to achieve high bit density magnetic recording it is necessary to reduce the size of the magnetic bits addressed by laser pulses at well-controlled positions. Metallic nanostructures that support localized surface plasmons enable spatial electromagnetic confinement well below the diffraction limit and rare-earth transition metal alloys such as GdTbCo have demonstrated nanometre-sized stable domains. Here, we deposit plasmonic gold nanostructures on a GdTbCo film and probe the magnetic state using magnetic force microscopy. We observe localized AO-HIS down to a critical dimension of 240 nm after excitation of the gold nanostructures by a single 370 fs long laser pulse with a centre wavelength of 1030 nm. We demonstrate that the strong localization of optical fields through plasmonic nanostructures enables reproducible localized nanoscale AO-HIS at sub-wavelength length scales. We study the influence of the localized electromagnetic field enhancement by the plasmonic nanostructures on the required fluence to switch the magnetization., Comment: 10 pages, 4 figures

We present a recently established setup for laser spectroscopy at CRYRING@ESR at the GSI Helmholtz Centre for Heavy Ion Research. Here, laser spectroscopy can be performed on stored and cooled ion bunches and coasting beams. First spectra of $^{24,25}$Mg$^+$ ions are presented that were recorded by classical Doppler-limited fluorescence spectroscopy as well as $\Lambda$-spectroscopy using counter- and copropagating laser beams that are Doppler-shifted by several nm., Comment: 10 pages, 8 figures, submitted to conference proceedings of "1st Workshop on Polarized Beam and Target-Physics and Applications"

Mathematical models of measuring systems and processes play an essential role in metrology and practical measurements. They form the basis for understanding and evaluating measurements, their results and their trustworthiness. Classic analytical parametric modelling is based on largely complete knowledge of measurement technology and the measurement process. But due to digital transformation towards the Internet of Things (IIoT) with an increasing number of intensively and flexibly networked measurement systems and consequently ever larger amounts of data to be processed, data-based modelling approaches have gained enormous importance. This has led to new approaches in measurement technology and industry like Digital Twins, Self-X Approaches, Soft Sensor Technology and Data and Information Fusion. In the future, data-based modelling will be increasingly dominated by intelligent, cognitive systems. Evaluating of the accuracy, trustworthiness and the functional uncertainty of the corresponding models is required. This paper provides a concise overview of modelling in metrology from classical white box models to intelligent, cognitive data-driven solutions identifying advantages and limitations. Additionally, the approaches to merge trustworthiness and metrological uncertainty will be discussed., Comment: 8 pages, 13 figures, 1 table, XXIV IMEKO World Congress - Think Metrology, August 26 - 29, 2024, Hamburg, Germany

We express parallel transport for several common matrix Lie groups with a family of pseudo-Riemannian metrics in terms of matrix exponential and exponential actions. The expression for parallel transport is preserved by taking the quotient under certain scenarios. In particular, for a Stiefel manifold of orthogonal matrices of size $n\times d$, we give an expression for parallel transport along a geodesic from time zero to $t$, that could be computed with time complexity of $O(nd^2)$ for small $t$, and of $O(td^3)$ for large t, contributing a step in a long-standing open problem in matrix manifolds. A similar result holds for flag manifolds with the canonical metric. We also show the parallel transport formulas for the generalized linear group, and the special orthogonal group under these metrics.

Due to the dynamic nature of financial markets, maintaining models that produce precise predictions over time is difficult. Often the goal isn't just point prediction but determining uncertainty. Quantifying uncertainty, especially the aleatoric uncertainty due to the unpredictable nature of market drivers, helps investors understand varying risk levels. Recently, quantile regression forests (QRF) have emerged as a promising solution: Unlike most basic quantile regression methods that need separate models for each quantile, quantile regression forests estimate the entire conditional distribution of the target variable with a single model, while retaining all the salient features of a typical random forest. We introduce a novel approach to compute quantile regressions from random forests that leverages the proximity (i.e., distance metric) learned by the model and infers the conditional distribution of the target variable. We evaluate the proposed methodology using publicly available datasets and then apply it towards the problem of forecasting the average daily volume of corporate bonds. We show that using quantile regression using Random Forest proximities demonstrates superior performance in approximating conditional target distributions and prediction intervals to the original version of QRF. We also demonstrate that the proposed framework is significantly more computationally efficient than traditional approaches to quantile regressions., Comment: 9 pages, 5 figures, 3 tables

The trading ecosystem of the Municipal (muni) bond is complex and unique. With nearly 2\% of securities from over a million securities outstanding trading daily, determining the value or relative value of a bond among its peers is challenging. Traditionally, relative value calculation has been done using rule-based or heuristics-driven approaches, which may introduce human biases and often fail to account for complex relationships between the bond characteristics. We propose a data-driven model to develop a supervised similarity framework for the muni bond market based on CatBoost algorithm. This algorithm learns from a large-scale dataset to identify bonds that are similar to each other based on their risk profiles. This allows us to evaluate the price of a muni bond relative to a cohort of bonds with a similar risk profile. We propose and deploy a back-testing methodology to compare various benchmarks and the proposed methods and show that the similarity-based method outperforms both rule-based and heuristic-based methods., Comment: 9 pages, 7 tables, 8 figures

We propose a new algorithm for learning a bridged diffusion process using score-matching methods. Our method relies on reversing the dynamics of the forward process and using this to learn a score function, which, via Doob's $h$-transform, gives us a bridged diffusion process; that is, a process conditioned on an endpoint. In contrast to prior methods, ours learns the score term $\nabla_x \log p(t, x; T, y)$, for given $t, Y$ directly, completely avoiding the need for first learning a time reversal. We compare the performance of our algorithm with existing methods and see that it outperforms using the (learned) time-reversals to learn the score term. The code can be found at https://github.com/libbylbaker/forward_bridge.

We carried out long-term monitoring of the LIGO/Virgo/KAGRA binary black hole (BBH) merger candidate S230922g in search of electromagnetic emission from the interaction of the merger remnant with an embedding active galactic nuclei (AGN) accretion disk. Using a dataset primarily composed of wide-field imaging from the Dark Energy Camera (DECam) and supplemented by additional photometric and spectroscopic resources, we searched ~ 70% of the sky area probability for transient phenomena, and discovered 6 counterpart candidates. One especially promising candidate - AT 2023aagj - exhibited temporally varying asymmetric components in spectral broad line regions, a feature potentially indicative of an off-center event such as a BBH merger. This represents the first live search and multiwavelength, photometric, and spectroscopic monitoring of a GW BBH optical counterpart candidate in the disk of an AGN., Comment: 25 pages, 9 figures. Published in PRD

Category-level 3D pose estimation is a fundamentally important problem in computer vision and robotics, e.g. for embodied agents or to train 3D generative models. However, so far methods that estimate the category-level object pose require either large amounts of human annotations, CAD models or input from RGB-D sensors. In contrast, we tackle the problem of learning to estimate the category-level 3D pose only from casually taken object-centric videos without human supervision. We propose a two-step pipeline: First, we introduce a multi-view alignment procedure that determines canonical camera poses across videos with a novel and robust cyclic distance formulation for geometric and appearance matching using reconstructed coarse meshes and DINOv2 features. In a second step, the canonical poses and reconstructed meshes enable us to train a model for 3D pose estimation from a single image. In particular, our model learns to estimate dense correspondences between images and a prototypical 3D template by predicting, for each pixel in a 2D image, a feature vector of the corresponding vertex in the template mesh. We demonstrate that our method outperforms all baselines at the unsupervised alignment of object-centric videos by a large margin and provides faithful and robust predictions in-the-wild. Our code and data is available at https://github.com/GenIntel/uns-obj-pose3d.

We propose a normative model for spatial representation in the hippocampal formation that combines optimality principles, such as maximizing coding range and spatial information per neuron, with an algebraic framework for computing in distributed representation. Spatial position is encoded in a residue number system, with individual residues represented by high-dimensional, complex-valued vectors. These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation. Self-consistency between the representations of the overall position and of the individual residues is enforced by a modular attractor network whose modules correspond to the grid cell modules in entorhinal cortex. The vector binding operation can also associate different contexts to spatial representations, yielding a model for entorhinal cortex and hippocampus. We show that the model achieves normative desiderata including superlinear scaling of patterns with dimension, robust error correction, and hexagonal, carry-free encoding of spatial position. These properties in turn enable robust path integration and association with sensory inputs. More generally, the model formalizes how compositional computations could occur in the hippocampal formation and leads to testable experimental predictions., Comment: 23 pages, 12 figures

(abridged) We analyze the emission of axion quark nuggets in a large sample of 161 simulated galaxy clusters using the SLOW simulation. These clusters are divided into a sub-sample of 150 galaxy clusters, ordered in five mass bins ranging from $0.8$ to $31.7 \times 10^{14} \,M_\odot$, along with 11 cross-identified galaxy clusters from observations. We investigate dark matter-baryonic matter interactions in galaxy clusters in their present stage at redshift $z=0$ by assuming all dark matter consists of axion quark nuggets. The resulting electromagnetic signatures are compared to thermal Bremsstrahlung and non-thermal cosmic ray synchrotron emission in each galaxy cluster. We further investigate individual frequency bands imitating the observable range of the WMAP, Planck, Euclid, and XRISM telescopes for the most promising cross-identified galaxy clusters hosting detectable signatures of axion quark nugget emission. We propose that the Fornax and Virgo clusters represent the most promising candidates to search for axion quark nugget emission signatures., Comment: 22 pages, 14 figures

Axion quark nuggets (AQNs) are hypothetical objects with a mass greater than a few grams and sub-micrometer size, formed during the quark-hadron transition. Originating from the axion field, they offer a possible resolution of the similarity between visible and dark components of the Universe. These composite objects behave as cold dark matter, interacting with ordinary matter and resulting in pervasive electromagnetic radiation throughout the Universe. This work aims to predict the electromagnetic signature in large-scale structures from the AQN-baryon interaction, accounting for thermal and non-thermal radiations. We use Magneticum hydrodynamical simulations to describe the distribution and dynamics of gas and dark matter at cosmological scales. We calculate the electromagnetic signature from radio, starting at $\nu \sim$ 1 GHz, up to a few keV X-ray energies. We find that the AQNs signature is characterized by monopole and fluctuation signals. The amplitude of both signals strongly depends on the average AQN mass and the ionization level of the baryonic environment. We identify a most optimistic scenario with a signal often near the sensitivity limit of existing instruments, such as FIRAS and the South Pole Telescope for high-resolution. Fluctuations in the Extra-galactic Background Light caused by the AQN can be tested with space-based imagers Euclid and James Webb Space Telescope. We also identify a minimal configuration, still out of reach of existing instruments, but future experiments might be able to pose constraints on the AQN model. We conclude that this is a viable dark matter model, which does not violate the canons of cosmology, nor existing observations. The best chances for testing this model reside in 1) ultra-deep IR and optical surveys, 2) spectral distorsions of the CMB and 3) low-frequency (1 GHz < $\nu $ < 100 GHz) and high-resolution ($\ell > 10^4$) observations., Comment: 32 pages, 12 figures, Published in JCAP

We introduce a new framework for molecular graph generation with 3D molecular generative models. Our Synthetic Coordinate Embedding (SyCo) framework maps molecular graphs to Euclidean point clouds via synthetic conformer coordinates and learns the inverse map using an E(n)-Equivariant Graph Neural Network (EGNN). The induced point cloud-structured latent space is well-suited to apply existing 3D molecular generative models. This approach simplifies the graph generation problem - without relying on molecular fragments nor autoregressive decoding - into a point cloud generation problem followed by node and edge classification tasks. Further, we propose a novel similarity-constrained optimization scheme for 3D diffusion models based on inpainting and guidance. As a concrete implementation of our framework, we develop EDM-SyCo based on the E(3) Equivariant Diffusion Model (EDM). EDM-SyCo achieves state-of-the-art performance in distribution learning of molecular graphs, outperforming the best non-autoregressive methods by more than 30% on ZINC250K and 16% on the large-scale GuacaMol dataset while improving conditional generation by up to 3.9 times.