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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 one is expected. The new data improve the axion-photon coupling limit to 5.7$\times 10^{-11}\,$GeV$^{-1}$ at 95\% C.L., the most restrictive experimental limit to date., Comment: 8 pages, 4 figures (plus 6 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

We present, to the best of our knowledge, the highest average power from a laser-driven single-cycle THz source demonstrated so far, using optical rectification in the titled pulse-front geometry in cryogenically cooled lithium niobate, pumped by a commercially available 500 W ultrafast thin-disk Yb-amplifier. We study repetition rate dependent effects in our setup at 100 kHz and 40 kHz at this high average power, revealing different optimal fluence conditions for efficient conversion. The demonstrated sources with multi-100 mW average power at these high repetition rates combine high THz pulse energies and high repetition rate and is thus ideally suited for nonlinear THz spectroscopy experiments with significantly reduced measurement times. The presented result is a first benchmark for high average power THz time domain spectroscopy systems for nonlinear spectroscopy, driven by very high average power ultrafast Yb lasers., Comment: Published in Optics Letters 4 pages, 4 figures, Data set can be found under https://zenodo.org/doi/10.5281/zenodo.11484003

Availability is a major concern in the design of DNSSEC. To ensure availability, DNSSEC follows Postel's Law [RFC1123]: "Be liberal in what you accept, and conservative in what you send." Hence, nameservers should send not just one matching key for a record set, but all the relevant cryptographic material, e.g., all the keys for all the ciphers that they support and all the corresponding signatures. This ensures that validation succeeds, and hence availability, even if some of the DNSSEC keys are misconfigured, incorrect or correspond to unsupported ciphers. We show that this design of DNSSEC is flawed. Exploiting vulnerable recommendations in the DNSSEC standards, we develop a new class of DNSSEC-based algorithmic complexity attacks on DNS, we dub KeyTrap attacks. All popular DNS implementations and services are vulnerable. With just a single DNS packet, the KeyTrap attacks lead to a 2.000.000x spike in CPU instruction count in vulnerable DNS resolvers, stalling some for as long as 16 hours. This devastating effect prompted major DNS vendors to refer to KeyTrap as the worst attack on DNS ever discovered. Exploiting KeyTrap, an attacker could effectively disable Internet access in any system utilizing a DNSSEC-validating resolver. We disclosed KeyTrap to vendors and operators on November 2, 2023, confidentially reporting the vulnerabilities to a closed group of DNS experts, operators and developers from the industry. Since then we have been working with all major vendors to mitigate KeyTrap, repeatedly discovering and assisting in closing weaknesses in proposed patches. Following our disclosure, the industry-wide umbrella CVE-2023-50387 has been assigned, covering the DNSSEC protocol vulnerabilities we present in this work., Comment: Accepted to ACM CCS 2024

Traditional manufacturing faces challenges adapting to dynamic environments and quickly responding to manufacturing changes. The use of multi-agent systems has improved adaptability and coordination but requires further advancements in rapid human instruction comprehension, operational adaptability, and coordination through natural language integration. Large language models like GPT-3.5 and GPT-4 enhance multi-agent manufacturing systems by enabling agents to communicate in natural language and interpret human instructions for decision-making. This research introduces a novel framework where large language models enhance the capabilities of agents in manufacturing, making them more adaptable, and capable of processing context-specific instructions. A case study demonstrates the practical application of this framework, showing how agents can effectively communicate, understand tasks, and execute manufacturing processes, including precise G-code allocation among agents. The findings highlight the importance of continuous large language model integration into multi-agent manufacturing systems and the development of sophisticated agent communication protocols for a more flexible manufacturing system.

We revisit the flux of chameleons (light scalar particles which could play a role in the dark energy phenomenon) produced in the interior of the Sun. Our novel analysis incorporates various important details and new processes that have previously been overlooked, including the impact of the bulk magnetic field profile, as well as Primakoff production of chameleons in the electric fields of electrons and ions. In this paper we consider only the contributions of transverse photons. The production of chameleons from longitudinal electromagnetic excitations will be presented in a dedicated follow-up work. Demanding that the total flux of chameleons does not exceed 3% of the solar luminosity leads to the stringent upper limit on the chameleon-photon conformal coupling $\beta_\gamma \lesssim 10^{10}$, assuming that the height of the chameleon potential is set to the dark energy scale $\Lambda = 2.4$ meV, and independently of other couplings to matter. Although this bound is tighter than current upper limits on $\beta_{\gamma}$ from the CAST helioscope, these limits will have to be reassessed in terms of the updated solar chameleon flux we have computed. We argue that solar chameleons, potentially detectable in next-generation helioscopes such as IAXO, can be used to probe a region of chameleon parameter space that has yet to be covered., Comment: 11 + 7 pages, 6 figures. v2: minor changes to the text and to the title. Matches the published version. Code available at https://github.com/tomasoshea/chameleon

We prove a large deviation principle for deep neural networks with Gaussian weights and (at most linearly growing) activation functions. This generalises earlier work, in which bounded and continuous activation functions were considered. In practice, linearly growing activation functions such as ReLU are most commonly used. We furthermore simplify previous expressions for the rate function and a give power-series expansions for the ReLU case., Comment: 13 pages, 2 figures

In this work, we study the magnetocaloric effect applied to a magnetic working substance corresponding to a square lattice of spins with $Q$ possible orientations known as the ``$Q$-state clock model" where for $Q\geq 5$, the systems present the famous Berezinskii-Kosterlitz-Thouless phase (BKT). Thermodynamic quantities are obtained in exact form for a small lattice size of $L \times L$ with $L=3$ and by the mean-field approximation and Monte Carlo simulations for $Q$ pairs between 2 and 8 with $L = 3, 8, 16, 32$ with free boundary conditions, and magnetic fields varying between $B = 0$ and $1$ in natural units of the system. By obtaining the entropy, it is possible to quantify the caloric effect through an isothermal process in which the external magnetic field on the spin system is varied. In particular, we find the values of $Q$ that maximize the effect depending on the lattice size and the magnetic phase transitions related to maximizing the caloric phenomena. These indicate that in a small lattice (up to $\sim 7\times 7$), when $Q\geq 5$, the transition that maximizes the effect is related to ferromagnetic to BKT type. In contrast, transitioning from BKT to paramagnetic type increases the system's caloric response when we work with a larger lattice size.

Parallel computing is very important to accelerate the performance of software systems. Additionally, considering that a recurring challenge is to process high data volumes continuously, stream processing emerged as a paradigm and software architectural style. Several software systems rely on stream processing to deliver scalable performance, whereas open-source frameworks provide coding abstraction and high-level parallel computing. Although stream processing's performance is being extensively studied, the measurement of fault tolerance--a key abstraction offered by stream processing frameworks--has still not been adequately measured with comprehensive testbeds. In this work, we extend the previous fault recovery measurements with an exploratory analysis of the configuration space, additional experimental measurements, and analysis of improvement opportunities. We focus on robust deployment setups inspired by requirements for near real-time analytics of a large cloud observability platform. The results indicate significant potential for improving fault recovery and performance. However, these improvements entail grappling with configuration complexities, particularly in identifying and selecting the configurations to be fine-tuned and determining the appropriate values for them. Therefore, new abstractions for transparent configuration tuning are also needed for large-scale industry setups. We believe that more software engineering efforts are needed to provide insights into potential abstractions and how to achieve them. The stream processing community and industry practitioners could also benefit from more interactions with the high-level parallel programming community, whose expertise and insights on making parallel programming more productive and efficient could be extended., Comment: Extended paper version. arXiv admin note: substantial text overlap with arXiv:2404.06203

We study amorphous solids with strong elastic disorder and find an un-jamming instability that exists, inter alia, in an harmonic model built using Euclidean random matrices (ERM). Employing the Zwanzig-Mori projection operator formalism and Gaussian factorization approximations, we develop a first-principles, self-consistent theory of transverse momentum correlations in athermal disordered materials, extending beyond the standard Born approximation. The vibrational anomalies in glass at low temperatures are recovered in the stable solid limit, and floppy modes lacking restoring forces are predicted in unstable states below the jamming transition. Near the un-jamming transition, the speed of sound $v_0^\perp$ vanishes with $ \propto \sqrt{\epsilon}$, where $\epsilon$ denotes the distance from the critical point. Additionally, the density of states develops a plateau, independent of $\epsilon$ above a frequency $\omega_*$ which vanishes at the transition, $\omega_*\propto |\epsilon|$. We identify a characteristic length scale in the un-jammed phase, $\lambda_-^\perp\propto1/\sqrt{\epsilon}$, indicating the distance over which injected momentum remains correlated. We confirm the theoretical predictions with numerical solutions of a scalar ERM model, demonstrating overall good qualitative and partly quantitative agreement., Comment: 12 pages, 7 appendices, and 6 figures

It has been previously advocated that the presence of the daily and annual modulations of the axion flux on the Earth's surface may dramatically change the strategy of the axion searches. The arguments were based on the so-called Axion Quark Nugget (AQN) dark matter model which was originally put forward to explain the similarity of the dark and visible cosmological matter densities $\Omega_{\rm dark}\sim \Omega_{\rm visible}$. In this framework, the population of galactic axions with mass $ 10^{-6} {\rm eV}\lesssim m_a\lesssim 10^{-3}{\rm eV}$ and velocity $\langle v_a\rangle\sim 10^{-3} c$ will be accompanied by axions with typical velocities $\langle v_a\rangle\sim 0.6 c$ emitted by AQNs. Furthermore, in this framework, it has also been argued that the AQN-induced axion daily modulation (in contrast with the conventional WIMP paradigm) could be as large as $(10-20)\%$, which represents the main motivation for the present investigation. We argue that the daily modulations along with the broadband detection strategy can be very useful tools for the discovery of such relativistic axions. The data from the CAST-CAPP detector have been used following such arguments. Unfortunately, due to the dependence of the amplifier chain on temperature-dependent gain drifts and other factors, we could not conclusively show the presence or absence of a dark sector-originated daily modulation. However, this proof of principle analysis procedure can serve as a reference for future studies., Comment: 18 pages, 8 figures

Silicon-Germanium heterostructures are a promising quantum circuit platform, but crucial aspects as the long-term charge dynamics and cooldown-to-cooldown variations are still widely unexplored quantitatively. In this letter we present the results of an extensive bias cooling study performed on gated silicon-germanium quantum dots with an Al2O3-dielectric. Over 80 cooldowns were performed in the course of our investigations. The performance of the devices is assessed by low-frequency charge noise measurements in the band of 200 micro Hertz to 10 milli Hertz. We measure the total noise power as a function of the applied voltage during cooldown in four different devices and find a minimum in noise at 0.7V bias cooling voltage for all observed samples. We manage to decrease the total noise power median by a factor of 6 and compute a reduced tunneling current density using Schr\"odinger-Poisson simulations. Furthermore, we show the variation in noise from the same device in the course of eleven different cooldowns performed under the nominally same conditions., Comment: 11 pages, 8 figures, silicon, SiGe, heterostructure, bias cooling, experiment, simulation

We propose a novel pipeline for unknown object grasping in shared robotic autonomy scenarios. State-of-the-art methods for fully autonomous scenarios are typically learning-based approaches optimised for a specific end-effector, that generate grasp poses directly from sensor input. In the domain of assistive robotics, we seek instead to utilise the user's cognitive abilities for enhanced satisfaction, grasping performance, and alignment with their high level task-specific goals. Given a pair of stereo images, we perform unknown object instance segmentation and generate a 3D reconstruction of the object of interest. In shared control, the user then guides the robot end-effector across a virtual hemisphere centered around the object to their desired approach direction. A physics-based grasp planner finds the most stable local grasp on the reconstruction, and finally the user is guided by shared control to this grasp. In experiments on the DLR EDAN platform, we report a grasp success rate of 87% for 10 unknown objects, and demonstrate the method's capability to grasp objects in structured clutter and from shelves., Comment: 7 pages, 9 figures

Gas time projection chambers (TPCs) with Micromegas pixelated readouts are being used in dark matter searches and other rare event searches, due to their potential in terms of low background levels, energy and spatial resolution, gain, and operational stability. Moreover, these detectors can provide precious features,such as topological information, allowing for event directionality and powerful signal-background discrimination. The Micromegas technology of the microbulk type is particularly suited to low-background applications and is being exploited by detectors for CAST and IAXO (solar axions) and TREX-DM (low-mass WIMPs) experiments. Challenges for the future include reducing intrinsic background levels, reaching lower energy detection levels, and technical issues such as robustness of detector, new design choices, novel gas mixtures and operation points, scaling up to larger detector sizes, handling large readout granularity, etc. We report on the status and prospects of the development ongoing in the context of IAXO and TREX-DM experiments, pointing to promising perspectives for the use of Micromegas detectors in directdark matter searches

This paper presents an open-source miniature car-like robot with low-cost sensing and a pipeline for optimization-based system identification, state estimation, and control. The overall robotics platform comes at a cost of less than $700 and thus significantly simplifies the verification of advanced algorithms in a realistic setting. We present a modified bicycle model with Pacejka tire forces to model the dynamics of the considered all-wheel drive vehicle and to prevent singularities of the model at low velocities. Furthermore, we provide an optimization-based system identification approach and a moving horizon estimation (MHE) scheme. In extensive hardware experiments, we show that the presented system identification approach results in a model with high prediction accuracy, while the MHE results in accurate state estimates. Finally, the overall closed-loop system is shown to perform well even in the presence of sensor failure for limited time intervals. All hardware, firmware, and control and estimation software is released under a BSD 2-clause license to promote widespread adoption and collaboration within the community.

Nowadays, several software systems rely on stream processing architectures to deliver scalable performance and handle large volumes of data in near real-time. Stream processing frameworks facilitate scalable computing by distributing the application's execution across multiple machines. Despite performance being extensively studied, the measurement of fault tolerance-a key feature offered by stream processing frameworks-has still not been measured properly with updated and comprehensive testbeds. Moreover, the impact that fault recovery can have on performance is mostly ignored. This paper provides a comprehensive analysis of fault recovery performance, stability, and recovery time in a cloud-native environment with modern open-source frameworks, namely Flink, Kafka Streams, and Spark Structured Streaming. Our benchmarking analysis is inspired by chaos engineering to inject failures. Generally, our results indicate that much has changed compared to previous studies on fault recovery in distributed stream processing. In particular, the results indicate that Flink is the most stable and has one of the best fault recovery. Moreover, Kafka Streams shows performance instabilities after failures, which is due to its current rebalancing strategy that can be suboptimal in terms of load balancing. Spark Structured Streaming shows suitable fault recovery performance and stability, but with higher event latency. Our study intends to (i) help industry practitioners in choosing the most suitable stream processing framework for efficient and reliable executions of data-intensive applications; (ii) support researchers in applying and extending our research method as well as our benchmark; (iii) identify, prevent, and assist in solving potential issues in production deployments., Comment: Accepted for publication in the 18th ACM International Conference on Distributed and Event-Based Systems (DEBS'24), June 24-28, 2024, Villeurbanne, France, 12 pages

Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) and established to investigate the use of synthetic data for training face recognition models. The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones. In the first task, the face recognition backbone was fixed and the dataset size was limited, while the second task provided almost complete freedom on the model backbone, the dataset, and the training pipeline. The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data. The submissions were evaluated and ranked on a diverse set of seven benchmarking datasets. The paper gives an overview of the submitted face recognition models and reports achieved performance compared to baseline models trained on real and synthetic datasets. Furthermore, the evaluation of submissions is extended to bias assessment across different demography groups. Lastly, an outlook on the current state of the research in training face recognition models using synthetic data is presented, and existing problems as well as potential future directions are also discussed., Comment: The 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024)

Feelings of something belonging to someone is called "psychological ownership." A common assumption is that writing with generative AI lowers psychological ownership, but the extent to which this occurs is unclear. We report on two experiments to examine the relationship between psychological ownership and prompt length. Participants wrote short stories either completely by themselves or wrote prompts of varying lengths, enforced through word limits. Results show that when participants wrote longer prompts, they had higher levels of psychological ownership. Their comments suggest they thought more about their prompts, often adding more details. However, benefits plateaued when prompt length was 75-100% of the target story length. To work in current interfaces, we propose creating a "soft constraint" in the interface to nudge behaviour. Results show that a submit button that must be held down a long time if the prompt is short is effective at increasing prompt length.