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Axions and other putative feebly interacting particles (FIPs) with a mass of tens to several hundreds of keVs can be produced in stellar cores with a Lorentz boost factor $E_a/m_a\lesssim 10$. Thus, starburst galaxies such as M82 are efficient factories of slow axions. Their decay $a\rightarrow\gamma\gamma$ would produce a large flux of X-ray photons, peaking around $100$ keV and spread around the galaxy by an angle that can be relatively large. We use observations of the Nuclear Spectroscopic Telescope Array (NuSTAR) mission to show that the absence of these features can constrain $30-500$ keV axion masses into uncharted regions for axion-photon coupling of $g_{a\gamma}\sim 10^{-10}-10^{-12}\,\rm GeV^{-1}$. Our argument can be applied to other heavy FIPs and astrophysical sources that are hot enough to produce them, yet cold enough to avoid large boost factors which slow down the decay., Comment: 4 pages, 3 figures, plus Supplemental Material

Pb on Si(111)-(7x7) shows surprising nucleation and mass transport dynamics at odds with standard theories. To create local imbalances on stable Pb islands we use the tip of a scanning force microscope. We enforce a short, local contact between the island and our tip. The subsequent island height growth and the local contact potential difference are studied via scanning force microscopy and Kelvin probe force microscopy. Though the island has a large volume increase after the contact, we observe that its surrounding wetting layer shows the same Pb density decrease as the global wetting layer. This indicates a collective density thinning of the wetting layer., Comment: 9 pages, 2 figures in main text and 5 figures in the Appendix; Paper accepted by Physical Review B: https://journals.aps.org/prb/accepted/5e078Yf5E3015a8088ba499754fdeefbd71da71aa

The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems. We introduce a shared standard for valid measurement that helps place many of the disparate-seeming evaluation practices in use today on a common footing. Our framework, grounded in measurement theory from the social sciences, extends the work of Adcock & Collier (2001) in which the authors formalized valid measurement of concepts in political science via three processes: systematizing background concepts, operationalizing systematized concepts via annotation procedures, and applying those procedures to instances. We argue that valid measurement of GenAI systems' capabilities, risks, and impacts, further requires systematizing, operationalizing, and applying not only the entailed concepts, but also the contexts of interest and the metrics used. This involves both descriptive reasoning about particular instances and inferential reasoning about underlying populations, which is the purview of statistics. By placing many disparate-seeming GenAI evaluation practices on a common footing, our framework enables individual evaluations to be better understood, interrogated for reliability and validity, and meaningfully compared. This is an important step in advancing GenAI evaluation practices toward more formalized and theoretically grounded processes -- i.e., toward a science of GenAI evaluations., Comment: NeurIPS 2024 Workshop on Statistical Foundations of LLMs and Foundation Models (SFLLM)

Fast entangling gate operations are a fundamental prerequisite for quantum simulation and computation. We propose an entangling scheme for arbitrary pairs of ions in a linear crystal, harnessing the high electric polarizability of highly excited Rydberg states. An all-to-all quantum gate connectivity is based on an initialization of a pair of ions to a superposition of ground- and Rydberg-states by laser excitation, followed by the entangling gate operation which relies on a state-dependent frequency shift of collective vibrational modes of the crystal. This gate operation requires applying an electric waveform to trap electrodes. Employing transverse collective modes of oscillation, we reveal order of $\mu s$ operation times within any of the qubit pairs in a small crystal. In our calculation, we are taking into account realistic experimental conditions and feasible electric field ramps. The proposed gate operation is ready to be combined with a scalable processor architecture to reconfigure the qubit register, either by shuttling ions or by dynamically controlling optical tweezer potentials., Comment: 9 pages, 4 figures

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.

In this work, we present a novel severe buffer-overflow vulnerability in the RPKI validator Fort, that allows an attacker to achieve Remote Code Execution (RCE) on the machine running the software. We discuss the unique impact of this RCE on networks that use RPKI, illustrating that RCE vulnerabilities are especially severe in the context of RPKI. The design of RPKI makes RCE easy to exploit on a large scale, allows compromise of RPKI validation integrity, and enables a powerful vector for additional attacks on other critical components of the network, like the border routers. We analyze the vulnerability exposing to this RCE and identify indications that the discovered vulnerability could constitute an intentional backdoor to compromise systems running the software over a benign coding mistake. We disclosed the vulnerability, which has been assigned a CVE rated 9.8 critical (CVE-2024-45237)., Comment: In Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS '24), October 14-18, 2024, Salt Lake City, UT, USA. ACM, New York, NY, USA, 3 pages

In recent years, intensity interferometry has been successfully applied to the Imaging Atmospheric Cherenkov Telescopes H.E.S.S. , MAGIC, and VERITAS. All three telescope systems have proven the feasibility and capability of this method. After our first campaign in 2022, when two of the H.E.S.S. telescopes in Namibia were equipped with our external setup and the angular diameter of two stars was measured, our setup was upgraded for a second campaign in 2023, where the goal is to perform simultaneous two colour measurements. The second campaign not only involves a third equipped telescope, but also each mechanical setup now includes two interference filters at two different wavelengths (375 nm and 470 nm) with a broader bandwidth of 10 nm. This enables having simultaneous two colour measurements, which yields information about the star's physical size at different wavelengths. This is the first time that simultaneous dual-waveband intensity interferometry measurements are performed. The angular diameter results of the 4 stars, Mimosa (beta Cru), Eta Centauri (eta Cen), Nunki (sigma Sgr) and Dschubba (delta Sco), are reported, where the effects of limb darkening are also taken into account.

The probability current is a vital quantity in the Fokker-Planck description of stochastic processes. It characterizes nonequilibrium stationary states, appears in linear response calculation, and has been related to the entropy production and the heat flux. We recover and review the probability current in the Onsager-Machlup functional approach to Markov processes. We derive a self contained expression for the stationary probability current and the non-equilibrium fluctuation-dissipation theorem using field theoretical methods. The derived formulas are explicitly evaluated in the Ornstein-Uhlenbeck process of a harmonically bound particle in shear flow as exemplary analytic expressions. Our work closes a gap since it removes a missing link, i.e.~the probability current, in the supposed equivalence of the Fokker-Planck and the path-integral approach., Comment: 17 pages

BabyIAXO is the intermediate stage of the International Axion Observatory (IAXO) to be hosted at DESY. Its primary goal is the detection of solar axions following the axion helioscope technique. Axions are converted into photons in a large magnet that is pointing to the sun. The resulting X-rays are focused by appropriate X-ray optics and detected by sensitive low-background detectors placed at the focal spot. The aim of this article is to provide an accurate quantitative description of the different components (such as the magnet, optics, and X-ray detectors) involved in the detection of axions. Our efforts have focused on developing robust and integrated software tools to model these helioscope components, enabling future assessments of modifications or upgrades to any part of the IAXO axion helioscope and evaluating the potential impact on the experiment's sensitivity. In this manuscript, we demonstrate the application of these tools by presenting a precise signal calculation and response analysis of BabyIAXO's sensitivity to the axion-photon coupling. Though focusing on the Primakoff solar flux component, our virtual helioscope model can be used to test different production mechanisms, allowing for direct comparisons within a unified framework., Comment: 36 pages, 18 figures, 4 tables, Submitted to JHEP

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research., Comment: 98 pages

Graphs are essential representations in the professions and education concerning the science, technology, engineering, and mathematics (STEM) disciplines. Beyond their academic relevance, graphs find extensive utility in everyday scenarios, ranging from news media to educational materials. This underscores the importance of people's being able to understand graphs. However, the ability to understand graphs is connected to the ability to create graphs. Therefore, in school education, particularly in STEM subjects, not only the understanding but also the skill of constructing graphs from numerical data is emphasized. Although constructing graphs is a skill that most people do not require in their everyday lives and professions, it is a well-established student activity that has been empirically studied several times. Therefore, since a synthesis of the research findings on this topic has not yet been conducted, a summary of the studies investigating graphing via various viewpoints and differing methods could be a valuable contribution. To provide an overview of the empirical literature on this important topic, our systematic review identifies how the construction of convention-based graphical representations of numerical data, referred to as graphing, has been studied in previous research, how effective graphing is, and which types of difficulties are encountered by students. Based on these aspects, we defined inclusion criteria that led to 50 peer-reviewed empirical studies on graphing in K-12 STEM education found in SCOPUS, ERIC, and PsychInfo. Graphing instruction seemed to be beneficial for student learning, not only improving graph construction but also graph interpretation skills. However, the students experienced various difficulties during graphing, both during graph construction and the interpretation and usage of data., Comment: 63 pages, 9 figures

Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, impacts, opportunities, and risks of GenAI systems. The framework distinguishes between four levels: the background concept, the systematized concept, the measurement instrument(s), and the instance-level measurements themselves. This four-level approach differs from the way measurement is typically done in ML, where researchers and practitioners appear to jump straight from background concepts to measurement instruments, with little to no explicit systematization in between. As well as surfacing assumptions, thereby making it easier to understand exactly what the resulting measurements do and do not mean, this framework has two important implications for evaluating evaluations: First, it can enable stakeholders from different worlds to participate in conceptual debates, broadening the expertise involved in evaluating GenAI systems. Second, it brings rigor to operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their resulting measurements., Comment: NeurIPS 2024 Workshop on Evaluating Evaluations (EvalEval)

Logs are crucial for analyzing large-scale software systems, offering insights into system health, performance, security threats, potential bugs, etc. However, their chaotic nature$\unicode{x2013}$characterized by sheer volume, lack of standards, and variability$\unicode{x2013}$makes manual analysis complex. The use of clustering algorithms can assist by grouping logs into a smaller set of templates, but lose the temporal and relational context in doing so. On the contrary, Large Language Models (LLMs) can provide meaningful explanations but struggle with processing large collections efficiently. Moreover, representation techniques for both approaches are typically limited to either plain text or traditional charting, especially when dealing with large-scale systems. In this paper, we combine clustering and LLM summarization with event detection and Multidimensional Scaling through the use of Time Curves to produce a holistic pipeline that enables efficient and automatic summarization of vast collections of software system logs. The core of our approach is the proposal of a semimetric distance that effectively measures similarity between events, thus enabling a meaningful representation. We show that our method can explain the main events of logs collected from different applications without prior knowledge. We also show how the approach can be used to detect general trends as well as outliers in parallel and distributed systems by overlapping multiple projections. As a result, we expect a significant reduction of the time required to analyze and resolve system-wide issues, identify performance bottlenecks and security risks, debug applications, etc.

Holes in planar germanium (Ge) heterostructures show promise for quantum applications, particularly in superconducting and spin qubits, due to strong spin-orbit interaction, low effective mass, and absence of valley degeneracies. However, charge traps cause issues such as gate hysteresis and charge noise. This study examines the effect of surface treatments on the accumulation behaviour and transport properties of Ge-based two dimensional hole gases (2DHGs). Oxygen plasma treatment reduces conduction in a setting without applied top-gate voltage and improves the mobility and lowers the percolation density, while hydrofluoric acid (HF) etching provides no benefit. The results suggest that interface traps from the partially oxidised silicon (Si) cap pin the Fermi level, and that oxygen plasma reduces the trap density by fully oxidising the Si cap. Therefore, optimising surface treatments is crucial for minimising the charge traps and thereby enhancing the device performance., Comment: 4 pages, 3 figures, additional supplementary information

We propose an entropy-related function (non-repeatability) that describes dynamical behaviors in complex systems. A normalized version of this function (mutability) has been previously used in statistical physics. To illustrate their characteristics, we apply these functions to different systems: (a) magnetic moments on a square lattice and (b) real seismic data extracted from the IPOC-2007-2014 catalog. These systems are well-established in the literature, making them suitable benchmarks for testing the new approach. Shannon entropy is used as a reference to facilitate comparison, enabling us to highlight similarities, differences, and the potential benefits of the new measure. Notably, non-repeatability and mutability are sensitive to the order in which the data sequence is collected, distinguishing them from traditional entropy measures.

The rapid expansion of Internet of Things (IoT), edge, and embedded devices in the past decade has introduced numerous challenges in terms of security and configuration management. Simultaneously, advances in cloud-native development practices have greatly enhanced the development experience and facilitated quicker updates, thereby enhancing application security. However, applying these advances to IoT, edge, and embedded devices remains a complex task, primarily due to the heterogeneous environments and the need to support devices with extended lifespans. WebAssembly and the WebAssembly System Interface (WASI) has emerged as a promising technology to bridge this gap. As WebAssembly becomes more popular on IoT, edge, and embedded devices, there is a growing demand for hardware interface support in WebAssembly programs. This work presents WASI proposals and proof-of-concept implementations to enable hardware interaction with I2C and USB, which are two commonly used protocols in IoT, directly from WebAssembly applications. This is achieved by running the device drivers within WebAssembly as well. A thorough evaluation of the proof of concepts shows that WASI-USB introduces a minimal overhead of at most 8% compared to native operating system USB APIs. However, the results show that runtime initialization overhead can be significant in low-latency applications., Comment: Preprint of article submitted to IEEE/IFIP Network Operations and Management Symposium 2025

We give an explicit algebraic characterisation of all definable henselian valuations on a dp-minimal real field. Additionally we characterise all dp-minimal real fields that admit a definable henselian valuation with real closed residue field. We do so by first proving this for the more general setting of almost real closed fields., Comment: 12 pages

The purpose of this note is to review some recent results concerning the pseudospectra and the eigenvalues asymptotics of non-selfadjoint semiclassical pseudo-differential operators subject to small random perturbations., Comment: A short review article

The demand for innovative synthetic polymers with improved properties is high, but their structural complexity and vast design space hinder rapid discovery. Machine learning-guided molecular design is a promising approach to accelerate polymer discovery. However, the scarcity of labeled polymer data and the complex hierarchical structure of synthetic polymers make generative design particularly challenging. We advance the current state-of-the-art approaches to generate not only repeating units, but monomer ensembles including their stoichiometry and chain architecture. We build upon a recent polymer representation that includes stoichiometries and chain architectures of monomer ensembles and develop a novel variational autoencoder (VAE) architecture encoding a graph and decoding a string. Using a semi-supervised setup, we enable the handling of partly labelled datasets which can be benefitial for domains with a small corpus of labelled data. Our model learns a continuous, well organized latent space (LS) that enables de-novo generation of copolymer structures including different monomer stoichiometries and chain architectures. In an inverse design case study, we demonstrate our model for in-silico discovery of novel conjugated copolymer photocatalysts for hydrogen production using optimization of the polymer's electron affinity and ionization potential in the latent space., Comment: 24 pages, 20 figures

Quadruped robots are proliferating in industrial environments where they carry sensor suites and serve as autonomous inspection platforms. Despite the advantages of legged robots over their wheeled counterparts on rough and uneven terrain, they are still yet to be able to reliably negotiate ubiquitous features of industrial infrastructure: ladders. Inability to traverse ladders prevents quadrupeds from inspecting dangerous locations, puts humans in harm's way, and reduces industrial site productivity. In this paper, we learn quadrupedal ladder climbing via a reinforcement learning-based control policy and a complementary hooked end-effector. We evaluate the robustness in simulation across different ladder inclinations, rung geometries, and inter-rung spacings. On hardware, we demonstrate zero-shot transfer with an overall 90% success rate at ladder angles ranging from 70{\deg} to 90{\deg}, consistent climbing performance during unmodeled perturbations, and climbing speeds 232x faster than the state of the art. This work expands the scope of industrial quadruped robot applications beyond inspection on nominal terrains to challenging infrastructural features in the environment, highlighting synergies between robot morphology and control policy when performing complex skills. More information can be found at the project website: https://sites.google.com/leggedrobotics.com/climbingladders., Comment: Project website: https://sites.google.com/leggedrobotics.com/climbingladders

The Resource Public Key Infrastructure (RPKI) protocol was standardized to add cryptographic security to Internet routing. With over 50% of Internet resources protected with RPKI today, the protocol already impacts significant parts of Internet traffic. In addition to its growing adoption, there is also increasing political interest in RPKI. The White House indicated in its Roadmap to Enhance Internet Routing Security, on 4 September 2024, that RPKI is a mature and readily available technology for securing inter-domain routing. The Roadmap attributes the main obstacles towards wide adoption of RPKI to a lack of understanding, lack of prioritization, and administrative barriers. This work presents the first comprehensive study of the maturity of RPKI as a viable production-grade technology. We find that current RPKI implementations still lack production-grade resilience and are plagued by software vulnerabilities, inconsistent specifications, and operational challenges, raising significant security concerns. The deployments lack experience with full-fledged strict RPKI-validation in production environments and operate in fail-open test mode. We provide recommendations to improve RPKI resilience and guide stakeholders in securing their deployments against emerging threats. The numerous issues we have discovered with the current RPKI specifications and implementations inevitably lead to the question: Is RPKI sufficiently stable to align with the expectations outlined in the White House roadmap? Certainly, it is not perfect, but is it good enough? The answer, as we will explore, varies depending on one's viewpoint.

Many socio-economic systems require positive economic growth rates to function properly. Given uncertainty about future growth rates and increasing evidence that economic growth is a driver of social and environmental crises, these growth dependencies pose serious societal challenges. In recent years, more and more researchers have thus tried to identify growth-dependent systems and develop policies to reduce their growth dependence. However, the concept of 'growth dependence' still lacks a consistent definition and operationalization, which impedes more systematic empirical and theoretical research. This article proposes a simple but powerful framework for defining and operationalizing the concept of 'growth dependence' across socio-economic systems. We provide a general definition consisting of four components that can be specified for different empirical cases: (1) the system under investigation, (2) the unit of measurement of growth, (3) the level of growth and (4) the relevant functions or properties of the system under investigation. According to our general definition, a socio-economic system is growth-dependent if it requires a long-term positive growth rate in terms of a unit of economic measurement to maintain all its functions or properties that are relevant within the chosen normative framework. To illustrate the usefulness of our scheme, we apply it to three areas at the heart of the existing literature on growth dependence: employment, social insurance systems and public finance. These case studies demonstrate that whether or not a system is growth-dependent hinges not only on the empirical properties of the system itself but also on the specification of the concept of growth dependence. Our framework enables coherent, robust and effective definitions and research questions, fostering comparability of findings across different cases and disciplines., Comment: 24 pages, 2 figures, 2 tables

Ellipsoidal particles confined at liquid interfaces exhibit complex self-assembly behaviour due to quadrupolar capillary interactions induced by meniscus deformation. These interactions cause particles to attract each other in either tip-to-tip or side-to-side configurations. However, controlling their interfacial self-assembly is challenging because it is difficult to predict which of these two states will be preferred. In this study, we demonstrate that introducing a soft shell around hard ellipsoidal particles provides a means to control the self-assembly process, allowing us to switch the preferred configuration between these states. We study their interfacial self-assembly and find that pure ellipsoids without a shell consistently form a "chain-like" side-to-side assembly, regardless of aspect ratio. In contrast, core-shell ellipsoids transition from "flower-like" tip-to-tip to "chain-like" side-to-side arrangements as their aspect ratios increase. The critical aspect ratio for transitioning between these structures increases with shell-to-core ratios. Our experimental findings are corroborated by theoretical calculations and Monte Carlo simulations, which map out the phase diagram of thermodynamically preferred self-assembly structures for core-shell ellipsoids as a function of aspect ratio and shell-to-core ratios. This study shows how to program the self-assembly of anisotropic particles by tuning their physicochemical properties, allowing the deterministic realization of distinct structural configurations.

We study the influence of airborne CO2 on the charge state of carboxylate stabilized polymer latex particles suspended in aqueous electrolytes. We combine conductometric experiments interpreted in terms of Hessinger's conductivity model with Poisson-Boltzmann cell (PBC) model calculations with charge regulation boundary conditions. Without CO2, a minority of the weakly acidic surface groups are dissociated and only a fraction of the total number of counter-ions actually contribute to conductivity. The remaining counter-ions exchange freely with added other ions like Na+, K+ or Cs+. From the PBC-calculations we infer a corresponding pKa of 4.26 as well as a renormalized charge in reasonably good agreement with the number of freely mobile counter-ions. Equilibration of salt- and CO2-free suspensions against ambient air leads to a drastic de-charging, which exceeds by far the expected effects of to dissolved CO2 and its dissociation products. Further, no counter-ion-exchange is observed. To reproduce the experimental findings, we have to assume an effective pKa of 6.48. This direct influence of CO2 on the state of surface group dissociation explains our recent finding of a CO2-induced decrease of the {\zeta}-potential and supports the suggestion of an additional charge regulation caused by molecular CO2. Given the importance of charged surfaces in contact with aqueous electrolytes, we anticipate that our observations bear substantial theoretical challenges and important implications for applications ranging from desalination to bio-membranes., Comment: Submitted to Soft Matter, regular article, 22 pages, 6 figures, 65 references, supplementary material of 8 pages with 7 figures

Clusters of colloidal particles, often termed supraparticles, can provide more functionality than the individual particles they consist of. Since these functionalities are determined by the arrangement of the primary particles within a supraparticle, controlling the structure formation process is of fundamental importance. Here, we show how buckling is determined by particle-surfactant interactions and how the final morphology of the formed supraparticles can be controlled by manipulating these interactions in time. We use water/oil emulsions to template supraparticle formation and tailor the interactions of negatively charged colloidal particles with the surfactants stabilizing the water/oil-interface via the local pH within the aqueous droplet. At low pH, protonation of the anionic headgroup of the surfactant decreases electrostatic repulsion of the particles, facilitates interfacial adsorption, and subsequently causes buckling. We show that the local pH of the aqueous phase continuously changes during the assembly process. We gain control over the formation pathway by determining the point in time when interfacial adsorption is enabled, which we control via the initial pH. As a consequence, the final supraparticle morphology can be tailored at will, from fully buckled structures, via undulated surface morphologies to spherically rough and spherically smooth supraparticles and crystalline colloidal clusters.

In this work, we tackle the task of point cloud denoising through a novel framework that adapts Diffusion Schr\"odinger bridges to points clouds. Unlike previous approaches that predict point-wise displacements from point features or learned noise distributions, our method learns an optimal transport plan between paired point clouds. Experiments on object datasets like PU-Net and real-world datasets such as ScanNet++ and ARKitScenes show that P2P-Bridge achieves significant improvements over existing methods. While our approach demonstrates strong results using only point coordinates, we also show that incorporating additional features, such as color information or point-wise DINOv2 features, further enhances the performance. Code and pretrained models are available at https://p2p-bridge.github.io., Comment: ECCV 2024 Project page: https://p2p-bridge.github.io

We say of an isolated macroscopic quantum system in a pure state $\psi$ that it is in macroscopic thermal equilibrium if $\psi$ lies in or close to a suitable subspace $\mathcal{H}_{eq}$ of Hilbert space. It is known that every initial state $\psi_0$ will eventually reach macroscopic thermal equilibrium and stay there most of the time ("thermalize") if the Hamiltonian is non-degenerate and satisfies the appropriate version of the eigenstate thermalization hypothesis (ETH), i.e., that every eigenvector is in macroscopic thermal equilibrium. Shiraishi and Tasaki recently proved the ETH for a certain perturbation $H_\theta$ of the Hamiltonian $H_0$ of $N\gg 1$ free fermions on a one-dimensional lattice. The perturbation is needed to remove the high degeneracies of $H_0$. Here, we point out that also for degenerate Hamiltonians, all $\psi_0$ thermalize if the ETH holds for every eigenbasis, and we prove that this is the case for $H_0$. On top of that and more generally, we develop another strategy of proving thermalization, inspired by the fact that there is one eigenbasis of $H_0$ for which ETH can be proven more easily and with smaller error bounds than for the others. This strategy applies to arbitrarily small generic perturbations $H$ of $H_0$ and to arbitrary spatial dimensions. In fact, we consider any given $H_0$, suppose that the ETH holds for some but not necessarily every eigenbasis of $H_0$, and add a small generic perturbation, $H=H_0+\lambda V$ with $\lambda\ll 1$. Then, although $H$ (which is non-degenerate) may still not satisfy the ETH, we show that nevertheless (i) every $\psi_0$ thermalizes for most perturbations $V$, and more generally, (ii) for any subspace $\mathcal{H}_\nu$ (such as corresponding to a non-equilibrium macro state), most perturbations $V$ are such that most $\psi_0$ from $\mathcal{H}_\nu$ thermalize., Comment: 42 pages LaTeX, no figures; v2 minor improvements throughout the paper, further references added

Photonic microring resonators are used in a variety of chip-integrated sensing applications where they allow to measure transmission intensity changes upon external signals with a sensitivity that scales linearly with the Q-factor. In this work, we suggest to exploit the nonlinear self-phase-modulation effect to increase the overall sensitivity by an additional gain factor appearing when the operational point of the nonlinear resonator is chosen just at the cross-over from the mono- to the bistable regime. We present the theoretical idea together with a first proof of concept experiment displaying a gain factor of 22 on a chip-integrated silicon-nitride resonator.

We consider the non-interacting Bose gas of $N$ bosons in dimension $d\geq 3$ in a trap in a mean-field setting with a vanishing factor $a_N$ in front of the kinetic energy. The choice $a_N=N^{-2/d}$ is the semi-classical setting and was analysed in great detail in a special, interacting case in Deuchert and Seiringer (2021). Using a version of the well-known Feynman--Kac representation and a further representation in terms of a Poisson point process, we derive precise asymptotics for the reduced one-particle density matrix, implying off-diagonal long-range order (ODLRO, a well-known criterion for Bose--Einstein condensation) for $a_N$ above a certain threshold and non-occurrence of ODLRO for $a_N$ below that threshold. In particular, we relate the condensate and its total mass to the amount of particles in long loops in the Feynman--Kac formula, the order parameter that Feynman suggested in Feynman (1953). For $a_N\ll N^{-2/d}$, we prove that all loops have the minimal length one, and for $a_N\gg N^{-2/d}$ we prove 100 percent condensation and identify the distribution of the long-loop lengths as the Poisson--Dirichlet distribution., Comment: 24 pages, comments welcome

We provide a comprehensive technical analysis of the data acquisition process with oscillating delay lines for Terahertz-time domain spectroscopy. The utilization of these rapid stages, particularly in high-repetition-rate systems, is known to enable an effective reduction of noise content through averaging. However, caution must be exercised to optimize the data averaging process, with the goal of significantly optimizing the dynamic range (DR) and signal-to-noise ratio (SNR). Here we discuss some pitfalls to avoid and the effect of improper data handling on the dynamic range obtainable. A free and open-source program, called parrot (Processing All Rapidly & Reliably Obtained THz-traces), is provided alongside this publication to overcome the discussed pitfalls and facilitate the acceleration of experimental setups and data analysis, thereby enhancing signal fidelity and reproducibility., Comment: Published in the Journal of Infrared, Millimeter, and Terahertz Waves. 13 pages, 6 figures

In this study, high pressure synchrotron powder X-ray diffraction is used to investigate the compression of two high pressure polymorphs of \ce{CrSb2}. The first is the \ce{CuAl2}-type polymorph with an eight-fold coordinated Cr, which can be quenched to ambient conditions from high-pressure high-temperature conditions. The second is the recently discovered \ce{MoP2}-type polymorph, which is induced by compression at room temperature, with a seven-fold coordinated Cr. Here, the assigned structure is unambiguously confirmed by solving it from single-crystal X-ray diffraction. Furthermore, the electrical properties of the \ce{MoP2}-type polymorph were investigated theoretically and the resistance calculations under pressure were accompanied by resistance measurements under high pressure on a single crystal of \ce{CrSb2}. The calculated electronic band structure for the \ce{MoP2}-type phase is discussed and we show that the polymorph is semimetallic and possesses type-I Weyl points. No further phase transitions were observed for the \ce{CuAl2}-type structure up to 50 GPa and 40 GPa for the \ce{MoP2}-type structure. Even though the \ce{CuAl2}-phase has the highest coordination number of Cr, it was found to be less compressible than the \ce{MoP2}-phase having a seven-fold coordinated Cr, which was attributed to the longer Cr-Sb distance in the \ce{CuAl2}-type phase. The discovery of a type-I Weyl semimetallic phase in \ce{CrSb2} opens up for discovering other Weyl semimetals in the transition metal di-pnictides under high pressure., Comment: 20 pages, 4 tables, 6 figures

Topic modeling is frequently being used for analysing large text corpora such as news articles or social media data. BERTopic, consisting of sentence embedding, dimension reduction, clustering, and topic extraction, is the newest and currently the SOTA topic modeling method. However, current topic modeling methods have room for improvement because, as unsupervised methods, they require careful tuning and selection of hyperparameters, e.g., for dimension reduction and clustering. This paper aims to analyse the technical application of BERTopic in practice. For this purpose, it compares and selects different methods and hyperparameters for each stage of BERTopic through density based clustering validation and six different topic coherence measures. Moreover, it also aims to analyse the results of topic modeling on real world data as a use case. For this purpose, the German fake news dataset (GermanFakeNCovid) on Covid-19 was created by us and in order to experiment with topic modeling in a multilingual (English and German) setting combined with the FakeCovid dataset. With the final results, we were able to determine thematic similarities between the United States and Germany. Whereas, distinguishing the topics of fake news from India proved to be more challenging., Comment: Accepted at the Workshop on Representation Learning and Clustering (RLC) at the 17th ACM International WSDM Conference in 2024

The nature of dark matter in the Universe is still an open question in astrophysics and cosmology. Axions and axion-like particles (ALPs) offer a compelling solution, and traditionally ground-based experiments have eagerly, but to date unsuccessfully, searched for these hypothetical low-mass particles that are expected to be produced in large quantities in the strong electromagnetic fields in the interior of stars. This work offers a fresh look at axions and ALPs by leveraging their conversion into X-rays in the magnetic field of the Sun's atmosphere rather than a laboratory magnetic field. Unique data acquired with the Nuclear Spectroscopic Telescope Array (NuSTAR) during the solar minimum in 2020 allows us to set stringent limits on the coupling of axions to photons using state-of-the-art magnetic field models of the solar atmosphere. We report pioneering limits on the axion-photon coupling strength of $6.9\times 10^{-12}$ GeV$^{-1}$ at 95\% confidence level for axion masses $m_a \lesssim 2\times 10^{-7}$ eV, surpassing current ground-based searches and further probing unexplored regions of the axion-photon coupling parameter space up to axion masses of $m_a \lesssim 5\times 10^{-4}$ eV., Comment: 23 pages, 12 figures

We study the cohomology of $G$-representation varieties and $G$-character stacks by means of a topological quantum field theory (TQFT). This TQFT is constructed as the composite of a so-called field theory and the 6-functor formalism of sheaves on topological stacks. We apply this framework to compute the cohomology of various $G$-representation varieties and $G$-character stacks of closed surfaces for $G = \text{SU}(2), \text{SO}(3)$ and $\text{U}(2)$. This work can be seen as a categorification of earlier work, in which such a TQFT was constructed on the level of Grothendieck groups to compute the corresponding Euler characteristics., Comment: 27 pages, comments are welcome!

Hypothetical axions provide a compelling explanation for dark matter and could be emitted from the hot solar interior. The CERN Axion Solar Telescope (CAST) has been searching for solar axions via their back conversion to X-ray photons in a 9-T 10-m long magnet directed towards the Sun. We report on an extended run with the IAXO (International Axion Observatory) pathfinder detector, doubling the previous exposure time. The detector was operated with a xenon-based gas mixture for part of the new run, providing technical insights for future detector configurations in IAXO. No counts are detected in the 95% signal-encircling region during the new run, while 0.75 are expected. The new data improve the axion-photon coupling limit to 5.8$\times 10^{-11}\,$GeV$^{-1}$ at 95% C.L. (for $m_a \lesssim 0.02$ eV), the most restrictive experimental limit to date., Comment: 9 pages, 4 figures (plus 8 pages, 10 figures of supplemental material) Corresponding authors: C. Margalejo (cmargalejo@unizar.es) and J. Ruz (Jaime.Ruz@cern.ch)

In this paper, we introduce the Extreme Metal Vocals Dataset, which comprises a collection of recordings of extreme vocal techniques performed within the realm of heavy metal music. The dataset consists of 760 audio excerpts of 1 second to 30 seconds long, totaling about 100 min of audio material, roughly composed of 60 minutes of distorted voices and 40 minutes of clear voice recordings. These vocal recordings are from 27 different singers and are provided without accompanying musical instruments or post-processing effects. The distortion taxonomy within this dataset encompasses four distinct distortion techniques and three vocal effects, all performed in different pitch ranges. Performance of a state-of-the-art deep learning model is evaluated for two different classification tasks related to vocal techniques, demonstrating the potential of this resource for the audio processing community.

The properties of functional oxide heterostructures are strongly influenced by the physics governing their interfaces. Modern deposition techniques allow us to accurately engineer the interface physics through the growth of atomically precise heterostructures. This enables minute control over the electronic, magnetic, and structural characteristics. Here, we investigate the magnetic properties of tailor-made superlattices employing the ferromagnetic and insulating double perovskites RE$_2$NiMnO$_6$ (RE = La, Nd), featuring distinct Curie temperatures. Adjusting the superlattice periodicity at the unit cell level allows us to engineer their magnetic phase diagram. Large periodicity superlattices conserve the individual para- to ferromagnetic transitions of the La$_2$NiMnO$_6$ and Nd$_2$NiMnO$_6$ parent compounds. As the superlattice periodicity is reduced, the Curie temperatures of the superlattice constituents converge and, finally, collapse into one single transition for the lowest period samples. This is a consequence of the magnetic order parameter propagating across the superlattice interfaces, as supported by a minimal Landau theory model. Further, we find that the Nd-Ni/Mn exchange interaction can be enhanced by the superlattice interfaces. This leads to a field-induced reversal of the Nd magnetic moments, as confirmed by synchrotron X-ray magnetic circular dichroism measurements and supported by first-principles calculations. Our work demonstrates how superlattice engineering can be employed to fine-tune the magnetic properties in oxide heterostructures and broadens our understanding of magnetic interfacial effects.

Scene coordinate regression (SCR) methods are a family of visual localization methods that directly regress 2D-3D matches for camera pose estimation. They are effective in small-scale scenes but face significant challenges in large-scale scenes that are further amplified in the absence of ground truth 3D point clouds for supervision. Here, the model can only rely on reprojection constraints and needs to implicitly triangulate the points. The challenges stem from a fundamental dilemma: The network has to be invariant to observations of the same landmark at different viewpoints and lighting conditions, etc., but at the same time discriminate unrelated but similar observations. The latter becomes more relevant and severe in larger scenes. In this work, we tackle this problem by introducing the concept of co-visibility to the network. We propose GLACE, which integrates pre-trained global and local encodings and enables SCR to scale to large scenes with only a single small-sized network. Specifically, we propose a novel feature diffusion technique that implicitly groups the reprojection constraints with co-visibility and avoids overfitting to trivial solutions. Additionally, our position decoder parameterizes the output positions for large-scale scenes more effectively. Without using 3D models or depth maps for supervision, our method achieves state-of-the-art results on large-scale scenes with a low-map-size model. On Cambridge landmarks, with a single model, we achieve 17% lower median position error than Poker, the ensemble variant of the state-of-the-art SCR method ACE. Code is available at: https://github.com/cvg/glace., Comment: Large-scale visual localization with a single optimizable MLP. CVPR 2024. Code: https://github.com/cvg/glace. Project page: https://xjiangan.github.io/glace