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This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

To detect ultra-high-energy neutrinos, experiments such as ARA and RNO-G target the radio emission these particles induce when cascading in the ice, using deep antennas in South Pole or in Greenland. One of the main backgrounds for such signals is the radio emission generated by cosmic-ray showers, either directly in the ice, or in the air and transmitted to the ice, which can both reach the deep antennas. The first detection of cosmic rays with deep antennas would thus validate this detection principle and allow us to calibrate the detectors. FAERIE, the Framework for the simulation of Air shower Emission of Radio for in-Ice Experiments, is a numerical tool that couples both CoREAS and GEANT4 Monte-Carlo codes to simulate the radio emission from cosmic-ray showers deep in the ice. Using this code, we will investigate cosmic-ray radio signatures and the possible implications on the design of a cosmic-ray veto., Comment: 8 pages, 5 figures. Proceeding of the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA)

Mass segregation is seen in many star clusters, but whether massive stars form in the center of a cluster or migrate there dynamically is still debated. N-body simulations have shown that early dynamical mass segregation is possible when sub-clusters merge to form a dense core with a small crossing time. However, the effect of gas dynamics on both the formation and dynamics of the stars could inhibit the formation of the dense core. We aim to study the dynamical mass segregation of star cluster models that include gas dynamics and self-consistently form stars from the dense substructure in the gas. Our models use the Torch framework, which is based on AMUSE and includes stellar and magnetized gas dynamics, as well as stellar evolution and feedback from radiation, stellar winds, and supernovae. Our models consist of three star clusters forming from initial turbulent spherical clouds of mass $10^{4,5,6}\rm~M_\odot$ and radius $11.7\rm~pc$ that have final stellar masses of $3.6\times10^3\rm~M_\odot$, $6.5\times10^4\rm~M_\odot$, and $8.9\times10^5\rm~M_\odot$, respectively. There is no primordial mass segregation in the model by construction. All three clusters become dynamically mass segregated at early times via collapse confirming that this mechanism occurs within sub-clusters forming directly out of the dense substructure in the gas. The dynamics of the embedded gas and stellar feedback do not inhibit the collapse of the cluster. We find that each model cluster becomes mass segregated within $2~$Myr of the onset of star formation, reaching the levels observed in young clusters in the Milky Way. However, we note that the exact values are highly time-variable during these early phases of evolution. Massive stars that segregate to the center during core collapse are likely to be dynamically ejected, a process that can decrease the overall level of mass segregation again., Comment: Submitted to A&A. 8 pages, 4 figures

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses., Comment: 11 pages, 9 figures, plus appendices

The covalent functionalization of single-walled carbon nanotubes (SWNTs) with luminescent oxygen defects increases their brightness and enables their application as optical biosensors or fluorescent probes for in-vivo imaging in the second-biological window (NIR-II). However, obtaining luminescent defects with high brightness is challenging with the current functionalization methods due to a restricted window of reaction conditions or the necessity for controlled irradiation with ultraviolet light. Here we report a method for introducing luminescent oxygen defects via a Fenton-like reaction that uses benign and inexpensive chemicals without light irradiation. (6,5) SWNTs in aqueous dispersion functionalized with this method show bright $E_{11}$* emission (1105 nm) with 3.2-times higher peak intensities than the pristine $E_{11}$ emission and a reproducible photoluminescence quantum yield of 3%. The functionalization can be performed within a wide range of reaction parameters and even with unsorted nanotube raw material at high concentrations (100 mg/L), giving access to large amounts of brightly luminescent SWNTs. We further find that the introduced oxygen defects rearrange under light irradiation, which gives additional insights into the structure and dynamics of oxygen defects. Finally, the functionalization of ultra-short SWNTs with oxygen defects also enables high photoluminescence quantum yields and their excellent emission properties are retained after surfactant exchange with biocompatible pegylated phospholipids or single-stranded DNA to make them suitable for in-vivo NIR-II imaging and dopamine sensing.

In dynamic programming and reinforcement learning, the policy for the sequential decision making of an agent in a stochastic environment is usually determined by expressing the goal as a scalar reward function and seeking a policy that maximizes the expected total reward. However, many goals that humans care about naturally concern multiple aspects of the world, and it may not be obvious how to condense those into a single reward function. Furthermore, maximization suffers from specification gaming, where the obtained policy achieves a high expected total reward in an unintended way, often taking extreme or nonsensical actions. Here we consider finite acyclic Markov Decision Processes with multiple distinct evaluation metrics, which do not necessarily represent quantities that the user wants to be maximized. We assume the task of the agent is to ensure that the vector of expected totals of the evaluation metrics falls into some given convex set, called the aspiration set. Our algorithm guarantees that this task is fulfilled by using simplices to approximate feasibility sets and propagate aspirations forward while ensuring they remain feasible. It has complexity linear in the number of possible state-action-successor triples and polynomial in the number of evaluation metrics. Moreover, the explicitly non-maximizing nature of the chosen policy and goals yields additional degrees of freedom, which can be used to apply heuristic safety criteria to the choice of actions. We discuss several such safety criteria that aim to steer the agent towards more conservative behavior., Comment: 16 pages main text + 4 pages supplement. Accepted for Algorithmic Decision Theory 2024

We demonstrate a qubit-readout architecture where the dispersive coupling is entirely mediated by a kinetic inductance. This allows us to engineer the dispersive shift of the readout resonator independent of the qubit and resonator capacitances. We validate the pure kinetic coupling concept and demonstrate various generalized flux qubit regimes from plasmon to fluxon, with dispersive shifts ranging from 60 kHz to 2 MHz at the half-flux quantum sweet spot. We achieve readout performances comparable to conventional architectures with quantum state preparation fidelities of 99.7 % and 92.7 % for the ground and excited states, respectively, and below 0.1 % leakage to non-computational states., Comment: 20 pages, 10 figures

The mechanism of antiproton-nucleus annihilation at rest is not fully understood, despite substantial previous experimental and theoretical work. In this study we used slow extracted, sub-keV antiprotons from the ASACUSA apparatus at CERN to measure the charged particle multiplicities and their energy deposits from antiproton annihilations at rest on three different nuclei: carbon, molybdenum and gold. The results are compared with predictions from different models in the simulation tools Geant4 and FLUKA. A model that accounts for all the observed features is still missing, as well as measurements at low energies, to validate such models., Comment: 25 pages + 2 cover pages, 16 figures

Where and under what conditions the transfer of energy between electromagnetic fields and particles takes place in the solar wind remains an open question. We investigate the conditions that promote the growth of kinetic instabilities predicted by linear theory, to infer how turbulence and temperature-anisotropy-driven instabilities are interrelated. Using a large dataset from Solar Orbiter, we introduce the radial rate of strain, a novel measure computed from single-spacecraft data, that we interpret as a proxy for the double-adiabatic strain rate. The solar wind exhibits high absolute values of the radial rate of strain at locations with large temperature anisotropy. We measure the kurtosis and skewness of the radial rate of strain from the statistical moments to show that it is non-Gaussian for unstable intervals and increasingly intermittent at smaller scales with a power-law scaling. We conclude that the velocity field fluctuations in the solar wind contribute to the presence of temperature anisotropy sufficient to create potentially unstable conditions., Comment: Accepted for publication in the Journal of Plasma Physics

This paper presents a novel approach to evaluating blue-chip art as a viable asset class for portfolio diversification. We present the Arte-Blue Chip Index, an index that tracks 100 top-performing artists based on 81,891 public transactions from 157 artists across 584 auction houses over the period 1990 to 2024. By comparing blue-chip art price trends with stock market fluctuations, our index provides insights into the risk and return profile of blue-chip art investments. Our analysis demonstrates that a 20% allocation of blue-chip art in a diversified portfolio enhances risk-adjusted returns by around 20%, while maintaining volatility levels similar to the S&P 500., Comment: 15 pages, 1798 words, 3 figures, 3 tables

This paper presents a large scale dataset from a meticulous 360-degree LiDAR (Light Detection and Ranging) scan conducted on Kronborg Castle, a renowned Renaissance fortress located in Elsinore (Helsing{\o}r), Denmark, famously associated with Shakespeare's "Hamlet." Utilising a vertical mounted, gimbal stabilised, 16 channel, 360-degree Velodyne VLP-16 LiDAR scanner, paired with an Intel RealSense L515 depth camera. This research offers an unparalleled digital representation of the castle's intricate architectural details and structural nuances, enabling fellow researchers to conduct experiments utilising the data for SLAM (Simultaneous Localisation and Mapping) as well as floorplan generation., Comment: 4 pages, 4 figures, 3 tables

We study computational problems in financial networks of banks connected by debt contracts and credit default swaps (CDSs). A main problem is to determine \emph{clearing} payments, for instance right after some banks have been exposed to a financial shock. Previous works have shown the $\varepsilon$-approximate version of the problem to be $\mathrm{PPAD}$-complete and the exact problem $\mathrm{FIXP}$-complete. We show that $\mathrm{PPAD}$-hardness hold when $\varepsilon \approx 0.101$, improving the previously best bound significantly. Due to the fact that the clearing problem typically does not have a unique solution, or that it may not have a solution at all in the presence of default costs, several natural decision problems are also of great interest. We show two such problems to be $\exists\mathbb{R}$-complete, complementing previous $\mathrm{NP}$-hardness results for the approximate setting.

Distributed Asynchronous Object Store (DAOS) is a novel software-defined object store leveraging Non-Volatile Memory (NVM) devices, designed for high performance. It provides a number of interfaces for applications to undertake I/O, ranging from a native object storage API to a DAOS FUSE module for seamless compatibility with existing applications using POSIX file system APIs. In this paper we discuss these interfaces and the options they provide, exercise DAOS through them with various I/O benchmarks, and analyse the observed performance. We also briefly compare the performance with a distributed file system and another object storage system deployed on the same hardware, and showcase DAOS' potential and increased flexibility to support high-performance I/O., Comment: 9 pages, 38 figures, PDSW'24

We measure human capital using the self-reported skill sets of 8.75 million U.S. college graduates from professional profiles on the online platform LinkedIn. We establish that these skills are systematically related to human capital investments such as different types of schooling and work experience. The average profile of the number of reported skills by age looks remarkably similar to the well-established concave age-earnings profiles. More experienced workers and those with higher educational degrees have larger shares of occupation-specific skills, consistent with their acquisition through professional-degree programs and on-the-job experience. Workers who report more, and particularly more specific and managerial, skills are more likely to hold highly paid jobs. Skill differences across workers can account for more earnings variation than detailed vectors of education and experience. We also document a substantial gender gap in reported skills, which starts to manifest when young women reach typical ages of first motherhood. Gender differences in skill profiles can rationalize a substantial proportion of the gender gap in the propensity to work in highly paid jobs. Overall, the results are consistent with an important role of multidimensional skills in accounting for several well-known basic labor-market patterns.

A $k$-rainbow dominating function ($k$RDF) of $G$ is a function that assigns subsets of $ \{1,2,...,k\}$ to the vertices of $G$ such that for vertices $v$ with $f(v)=\emptyset $ we have $\bigcup\nolimits_{u\in N(v)}f(u)=\{1,2,...,k\}$. The weight $w(f)$ of a $k$RDF $f$ is defined as $w(f)=\sum_{v\in V(G)}\left\vert f(v)\right\vert $. The minimum weight of a $k$RDF of $G$ is called the $k$-rainbow domination number of $G$, which is denoted by $\gamma_{rk}(G)$. In this paper, we study the 2-rainbow domination number of the Cartesian product of two cycles. Exact values are given for a number of infinite families and we prove lower and upper bounds for all other cases.

In hidden-sector models, axion-like particles (ALPs) can couple to heavy neutral leptons (HNLs), leading to rich phenomenologies.We study ALPs produced from $D$- and $B$-meson decays via quark-flavor-violating couplings, and decaying exclusively into a pair of HNLs which mix with active neutrinos. The ALP can be either short- or long-lived, depending on the masses of the ALP and the HNL, as well as the corresponding coupling strength.Such GeV-scale HNLs are necessarily long-lived given the current bounds on their mixing parameters.We assess the sensitivities of the LHC far detectors and SHiP, to the long-lived HNLs in such theoretical scenarios.We find that for currently allowed values of the ALP couplings, most of these experiments can probe the active-sterile-neutrino mixing parameters beyond the parameter regions targeted with the type-I seesaw mechanism in the kinematically allowed mass range. Compared to the case of a promptly decaying ALP, considering an ALP of longer lifetimes weakens the sensitivities of the considered experiments to the long-lived HNLs., Comment: 16 pages+refs, 6 figures, 1 table

Experiments in the field of quantum optics often require very low concentrations of dust particles in the laboratory, but the complexity of working routines precludes operation within a proper clean room. Research teams have established a multitude of different approaches, precaution measures, and habits to keep the delicate optics setups free of contamination. Here, we systematically quantify dust particle concentration during day-to-day operation of a quantum optics lab, assess the effectiveness of various measures, and give practical recommendations., Comment: 4 pages, 4 figures, comments welcome!

State of the art reinforcement learning methods sometimes encounter unsafe situations. Identifying when these situations occur is of interest both for post-hoc analysis and during deployment, where it might be advantageous to call out to a human overseer for help. Efforts to gauge the criticality of different points in time have been developed, but their accuracy is not well established due to a lack of ground truth, and they are not designed to be easily interpretable by end users. Therefore, we seek to define a criticality framework with both a quantifiable ground truth and a clear significance to users. We introduce true criticality as the expected drop in reward when an agent deviates from its policy for n consecutive random actions. We also introduce the concept of proxy criticality, a low-overhead metric that has a statistically monotonic relationship to true criticality. Safety margins make these interpretable, when defined as the number of random actions for which performance loss will not exceed some tolerance with high confidence. We demonstrate this approach in several environment-agent combinations; for an A3C agent in an Atari Beamrider environment, the lowest 5% of safety margins contain 47% of agent losses; i.e., supervising only 5% of decisions could potentially prevent roughly half of an agent's errors. This criticality framework measures the potential impacts of bad decisions, even before those decisions are made, allowing for more effective debugging and oversight of autonomous agents., Comment: 17 pages, 10 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Rolling origin forecast instability refers to variability in forecasts for a specific period induced by updating the forecast when new data points become available. Recently, an extension to the N-BEATS model for univariate time series point forecasting was proposed to include forecast stability as an additional optimization objective, next to accuracy. It was shown that more stable forecasts can be obtained without harming accuracy by minimizing a composite loss function that contains both a forecast error and a forecast instability component, with a static hyperparameter to control the impact of stability. In this paper, we empirically investigate whether further improvements in stability can be obtained without compromising accuracy by applying dynamic loss weighting algorithms, which change the loss weights during training. We show that some existing dynamic loss weighting methods achieve this objective. However, our proposed extension to the Random Weighting approach -- Task-Aware Random Weighting -- shows the best performance.

Nonlinear metasurfaces offer a new paradigm to realize optical nonlinear devices with new and unparalleled behavior compared to nonlinear crystals, due to the interplay between photonic resonances and materials properties. The complicated interdependency between efficiency and emission directionality of the nonlinear optical signal on the existence, localization, and lifetimes of photonic resonances, as well as on the nonlinear susceptibility, makes it extremely difficult to design optimal metasurfaces using conventional materials and geometries. Inverse design using topology optimization is a powerful design tool for photonic structures, but traditional approaches developed for linear photonics are not suitable for such high dimensional nonlinear problems. Here, we use a topology optimization approach to inverse-design a fabrication-robust nonlinear metasurface that includes quantum-engineered resonant nonlinearities in semiconductor heterostructures for efficient and directional second harmonic generation. Furthermore, we also demonstrate that under practical constraints, among all the parameters, the nonlinear modal overlap emerges as the dominant parameter that enhances conversion efficiency, a finding that contrasts with intuition-driven studies that often emphasize Purcell enhancement. Our results open new opportunities for optimizing nonlinear processes in nanophotonic structures for novel light sources, quantum information applications, and communication.

Contextualized embeddings based on large language models (LLMs) are available for various languages, but their coverage is often limited for lower resourced languages. Training LLMs for such languages is often difficult due to insufficient data and high computational cost. Especially for very low resource languages, static word embeddings thus still offer a viable alternative. There is, however, a notable lack of comprehensive repositories with such embeddings for diverse languages. To address this, we present LowREm, a centralized repository of static embeddings for 87 low-resource languages. We also propose a novel method to enhance GloVe-based embeddings by integrating multilingual graph knowledge, utilizing another source of knowledge. We demonstrate the superior performance of our enhanced embeddings as compared to contextualized embeddings extracted from XLM-R on sentiment analysis. Our code and data are publicly available under https://huggingface.co/DFKI., Comment: Short paper, preview

Adaptivity and local mesh refinement are crucial for the efficient numerical simulation of wave phenomena in complex geometry. Local mesh refinement, however, can impose a tiny time-step across the entire computational domain when using explicit time integration. By taking smaller time-steps yet only inside locally refined regions, local time-stepping methods overcome the stringent CFL stability restriction imposed on the global time-step by a small fraction of the elements without sacrificing explicitness. In [21], a leapfrog based local time-stepping method was proposed for the inhomogeneous wave equation, which applies standard leapfrog time-marching with a smaller time-step inside the refined region. Here, to remove potential instability at certain time-steps, a stabilized version is proposed which leads to optimal L2-error estimates under a CFL condition independent of the coarse-to-fine mesh ratio. Moreover, a weighted transition is introduced to restore optimal H1-convergence when the source is nonzero across the coarse-to-fine mesh interface. Numerical experiments corroborate the theoretical error estimates and illustrate the usefulness of these improvements.

The primary objective of this work is to establish pointwise gradient estimates for solutions to a class of parabolic nonlinear nonlocal measure data problems, expressed in terms of caloric Riesz potentials of the data. As a consequence of our pointwise estimates, we obtain that the first-order regularity properties of solutions to such general parabolic nonlinear nonlocal equations, both in terms of size and oscillations of the spatial gradient, closely resemble the ones of the fractional heat equation even at highly refined scales. Along the way, we show that solutions to homogeneous parabolic nonlinear nonlocal equations have H\"older continuous spatial gradients under optimal assumptions on the nonlocal tails., Comment: 76 pages

We introduce the alchemical harmonic approximation (AHA) of the absolute electronic energy for all charge-neutral iso-electronic diatomics at some fixed interatomic distance $d_0$. To account for variations in this distance, we combine AHA with the following Ansatz for the electronic binding potential, $E(d)=(E_{u}-E_s)\left(\frac{E_c-E_s}{E_u-E_s} \right)^{\sqrt{d/d_0}}+E_s$, where $E_u$, $E_c$, and $E_s$ correspond to the energies of the united atom, calibration at $d_0$, and sum of infinitely separated atoms, respectively. For any number of electrons, our model covers the entire two-dimensional electronic potential energy surface spanned by distance and difference in nuclear charge. Using data from pbe0/ccpvdz as reference, we present numerical evidence for all neutral diatomics with 8, 10, 12, 14 electrons. We assess the validity of our model by comparison to legacy potentials (Harm. osc., Lennard-Jones, Morse) within the most relevant range of binding (0.7 - 2.5 A), and find comparable accuracy if restricted to one diatomic, and significantly better when extrapolating to the entire iso-electronic series. We have also investigated $\Delta$-learning with our model as baseline. For any given iso-electronic charge neutral diatomic surface, this baseline results in a systematic improvement, effectively reducing training data for reaching chemical accuracy by up to an order of magnitude from $\sim1000$ to $\sim100$. By contrast and with respect to direct learning, using AHA+Morse as a baseline hardly leads to any improvement, and sometimes even deteriorates predictive power. Direct KRR-based extrapolation throughout chemical space converges to an error of $\sim$0.1 Ha when inferring the energy of unseen CO after training on all other iso-electronic diatomics. Our model as a baseline (calibrated to the energy of BF at $d_0 = 1.2$ A) lowers the error to $\sim$0.04 Ha.

We study the problem of approximating and estimating classification functions that have their decision boundary in the $RBV^2$ space. Functions of $RBV^2$ type arise naturally as solutions of regularized neural network learning problems and neural networks can approximate these functions without the curse of dimensionality. We modify existing results to show that every $RBV^2$ function can be approximated by a neural network with bounded weights. Thereafter, we prove the existence of a neural network with bounded weights approximating a classification function. And we leverage these bounds to quantify the estimation rates. Finally, we present a numerical study that analyzes the effect of different regularity conditions on the decision boundaries.

Over the past decades, near-Earth spacecraft observations have provided insights into the physics of the bow shock, suggesting that solar wind intrinsic turbulence influences the bow shock dynamics. On the other hand, theoretical studies, based on global numerical simulations, have not yet investigated the global 3D interaction between a turbulent solar wind and a planetary magnetosphere. This paper addresses this gap for the first time by investigating the global dynamics of this interaction, providing new perspectives on the underlying physical processes. We examine how the turbulent nature of the solar wind influences the 3D structure and dynamics of magnetized planetary environments or magnetized Earth-like exoplanets, using the newly developed numerical code Menura. We use the hybrid PIC model Menura to conduct 3D simulations of the turbulent solar wind and its interaction with an Earth-like magnetized planet through global numerical simulations of the magnetosphere and its surroundings. We show that solar wind turbulence globally influences the shape and dynamics of the bow shock, the magnetosheath structures, and the ion foreshock dynamics. We show that a turbulent solar wind disrupts the coherence of foreshock fluctuations, induces large fluctuations on the quasi-perpendicular surface of the bow shock, facilitates the formation of bubble-like structures near the bow shock's nose, and modifies the properties of the magnetosheath region. None of these phenomena occur when comparing with the case in which the solar wind is laminar. The turbulent nature of the solar wind impacts the 3D shape and dynamics of the bow shock, magnetosheath, and ion foreshock region. This influence should be considered when studying solar wind-planet interactions in observations and simulations. We discuss the relevance of our findings for current and future missions launched into the heliosphere.

We consider actions of Taft algebras on noetherian graded down-up algebras. We classify all such actions and determine properties of the corresponding invariant rings $A^T$. We identify precisely when $A^T$ is commutative, when it is Artin-Schelter regular, and give sufficient conditions for it to be Artin-Schelter Gorenstein. Our results show that many results and conjectures in the literature concerning actions of semisimple Hopf algebras on Artin-Schelter regular algebras can fail when the semisimple hypothesis is omitted., Comment: 29 pages

The Finite Element Method (FEM) is a widely used technique for simulating crash scenarios with high accuracy and reliability. To reduce the significant computational costs associated with FEM, the Finite Element Method Integrated Networks (FEMIN) framework integrates neural networks (NNs) with FEM solvers. However, this integration can introduce errors and deviations from full-FEM simulations, highlighting the need for an additional metric to assess prediction confidence, especially when no ground truth data is available. In this study, we adapt the Deep Variational Bayes Filter (DVBF) to the FEMIN framework, incorporating a probabilistic approach to provide qualitative insights into prediction confidence during FEMIN simulations. The adaptation involves using the learned transition model for a predictive decoding step, generating a preliminary force prediction. This predictive force is used alongside the displacement and the velocity data from the FEM solver as input for the encoder model. The decoder reconstructs the likelihood distribution based on the posterior. The mean force of this distribution is applied to the FEM solver, while the predicted standard deviation can be used for uncertainty estimation. Our findings demonstrate that the DVBF outperforms deterministic NN architectures in terms of accuracy. Furthermore, the standard deviation derived from the decoder serves as a valuable qualitative metric for assessing the confidence in FEMIN simulations. This approach enhances the robustness of FEMIN by providing a measure of reliability alongside the simulation results.

Most of the tracking detectors for high energy particle experiments are filled with silicon detectors since they are radiation hard, they can give very small spatial resolution and they can take advantage of the silicon electronics foundries developments and production lines. Strip detectors are very useful to cover large areas for tracking purposes, while consuming less power per area compared to pixel sensors. The majority of particle physics experiments use conventional silicon strip detectors fabricated in foundries that do not use stitching, relying on a very small number of foundries worldwide that can provide large amounts of strip detectors. Fabricating strip detectors in a CMOS foundry opens the possibility to use more foundries and to include active elements in the strips for future productions. For the passive CMOS strip detectors project we fabricated strip detectors in a CMOS foundry using two 1 cm2 reticles that are stitched together along the wafer. The fabricated strips stitched the reticles three and five times, and it was shown that the performance of those strips is not affected by the stitching. This paper shows 3D TCAD simulations of the stitching area to investigate the possible effects stitching can have on the performance of the strip detectors, considering different stitching mismatches. We will show that the mismatch of stitched structures up to 1 um does not impact the performance with TCAD simulations which agrees with the results obtained from the measurements., Comment: 8 pages, 7 figures, IWORID2024 workshop

Entropy scaling is a powerful technique that has been used successfully for predicting transport properties of pure components over a wide range of states. However, modeling mixture diffusion coefficients by entropy scaling is an unresolved task. We have tackled this issue and present an entropy scaling framework for predicting mixture self-diffusion as well as mutual diffusion in a thermodynamically consistent way. The predictions of the mixture diffusion coefficients are made based on information on the self-diffusion coefficients of the pure components and the infinite dilution diffusion coefficients. This is accomplished using information on the entropy of the mixture, which is taken here from molecular-based equations of state. The framework is flexible and can be applied together with any equation of state. Examples for the application of the new entropy scaling framework for the prediction of diffusion coefficients in mixtures illustrate its impressive performance. It enables predictions a wide range of temperatures and pressures including gaseous, liquid, supercritical, and metastable states - also for strongly non-ideal mixtures.

The scaling of superconducting nanowire-based devices to larger arrays is often limited by the cabling required to interface with each device. Cryogenic integrated circuits constructed from nanowire cryotrons, or nanocryotrons, can address this limitation by performing signal processing on chip. In this study, we characterize key performance metrics of the nanocryotron to elucidate its potential as a logical element in cryogenic integrated circuits and develop an electro-thermal model to connect material parameters with device performance. We find that the performance of the nanocryotron depends significantly on the device geometry, and trade-offs are associated with optimizing the gain, jitter, and energy dissipation. We demonstrate that nanocryotrons fabricated on niobium nitride can achieve a grey zone less than 210 nA wide for a 5 ns long input pulse corresponding to a maximum achievable gain of 48 dB, an energy dissipation of less than 20 aJ per operation, and a jitter of less than 60 ps., Comment: 5 pages, 5 figures

Structure in the Universe is believed to have evolved out of quantum fluctuations seeded by inflation in the early Universe. These fluctuations lead to density perturbations that grow via gravitational instability into large cosmological structures. In the linear regime, the growth of structure is directly coupled to the velocity field since perturbations are amplified by attracting (and accelerating) matter. Surveys of galaxy redshifts and distances allow one to infer the underlying density and velocity fields. Here, assuming the LCDM standard model of cosmology and applying a Hamiltonian Monte-Carlo algorithm to the grouped Cosmicflows-4 (CF4) compilation of 38,000 groups of galaxies, the large scale structure of the Universe is reconstructed out to a redshift corresponding to about 30, 000 km/s. Our method provides a probabilistic assessment of the domains of gravitational potential minima: basins of attraction (BoA). Earlier Cosmicflows catalogs suggested the Milky Way Galaxy was associated with a BoA called Laniakea. Now with the newer CF4 data, there is a slight probabilistic preference for Laniakea to be part of the much larger Shapley BoA. The largest BoA recovered from the CF4 data is associated with the Sloan Great Wall with a volume within the sample of 15.5 10^6(Mpc/h)^3, which is more than twice the size of the second largest Shapley BoA., Comment: 16 pages, 6 Figures, contact the authors for a nature formatted version accessible via SharedIt

Strongly lensed supernovae (SNe) are a rare class of transient that can offer tight cosmological constraints that are complementary to methods from other astronomical events. We present a follow-up study of one recently-discovered strongly lensed SN, the quadruply-imaged Type Ia SN 2022qmx (aka, "SN Zwicky") at z = 0.3544. We measure updated, template-subtracted photometry for SN Zwicky and derive improved time delays and magnifications. This is possible because SNe are transient, fading away after reaching their peak brightness. Specifically, we measure point spread function (PSF) photometry for all four images of SN Zwicky in three Hubble Space Telescope WFC3/UVIS passbands (F475W, F625W, F814W) and one WFC3/IR passband (F160W), with template images taken $\sim 11$ months after the epoch in which the SN images appear. We find consistency to within $2\sigma$ between lens model predicted time delays ($\lesssim1$ day), and measured time delays with HST colors ($\lesssim2$ days), including the uncertainty from chromatic microlensing that may arise from stars in the lensing galaxy. The standardizable nature of SNe Ia allows us to estimate absolute magnifications for the four images, with images A and C being elevated in magnification compared to lens model predictions by about $6\sigma$ and $3\sigma$ respectively, confirming previous work. We show that millilensing or differential dust extinction is unable to explain these discrepancies and find evidence for the existence of microlensing in images A, C, and potentially D, that may contribute to the anomalous magnification., Comment: Submitted to ApJ

For general random tensor network states at large bond dimension, we prove that the integer R\'enyi reflected entropies (away from phase transitions) are determined by minimal triway cuts through the network. This generalizes the minimal cut description of bipartite entanglement for these states. A natural extrapolation away from integer R\'enyi parameters, suggested by the triway cut problem, implies the holographic conjecture $S_R=2EW$, where $S_R$ is the reflected entropy and $EW$ is the entanglement wedge cross-section. Minimal triway cuts can be formulated as integer programs which cannot be relaxed to find a dual maximal flow/bit-thread description. This sheds light on the gap between the existence of tripartite entanglement in holographic states and the bipartite entanglement structure motivated by bit-threads. In particular, we prove that the Markov gap that measures tripartite entanglement is lower bounded by the integrality gap of the integer program that computes the triway cut., Comment: 48 Pages + Appendices, many figures

Autonomous exploration of unknown space is an essential component for the deployment of mobile robots in the real world. Safe navigation is crucial for all robotics applications and requires accurate and consistent maps of the robot's surroundings. To achieve full autonomy and allow deployment in a wide variety of environments, the robot must rely on on-board state estimation which is prone to drift over time. We propose a Micro Aerial Vehicle (MAV) exploration framework based on local submaps to allow retaining global consistency by applying loop-closure corrections to the relative submap poses. To enable large-scale exploration we efficiently compute global, environment-wide frontiers from the local submap frontiers and use a sampling-based next-best-view exploration planner. Our method seamlessly supports using either a LiDAR sensor or a depth camera, making it suitable for different kinds of MAV platforms. We perform comparative evaluations in simulation against a state-of-the-art submap-based exploration framework to showcase the efficiency and reconstruction quality of our approach. Finally, we demonstrate the applicability of our method to real-world MAVs, one equipped with a LiDAR and the other with a depth camera. Video available at https://youtu.be/Uf5fwmYcuq4 ., Comment: 7 pages, 8 figures, for the accompanying video see https://youtu.be/Uf5fwmYcuq4

We aim to constrain the equation of state of nuclear matter by comparing calculations with the SMASH transport model to directed and elliptic flow measurements for protons and deuterons performed by the HADES collaboration in a systematic way. A momentum-dependent term is included in the potential for which we show that it is needed to describe flow data. We further incorporate a simple symmetry potential in the transport model and present constraints on the stiffness of the equation of state of nuclear matter at saturation density and on the symmetry potential. The constraints are obtained by performing a Bayesian analysis such that we can also provide an uncertainty for the estimated parameters. The posterior distribution is obtained by Markov chain Monte Carlo sampling for which we emulate the transport model with a Gaussian process to lower computational costs. We find that a relatively stiff equation of state is favoured in our analysis with a small uncertainty whereas the constraints obtained for the symmetry potential are rather loose., Comment: 11 pages, 11 figures

If $\mathscr A$ is a set of natural numbers of exponential density $\delta$, then the exponential density of all numbers of the form $x^3+a$ with $x\in\mathbb N$ and $a\in\mathscr A$ is at least $\min(1, \frac 13+\frac 56 \delta)$. This is a considerable improvement on the previous best lower bounds for this problem, obtained by Davenport more than 80 years ago. The result is the best possible for $\delta\ge \frac 45$., Comment: 27 pages, 16 references

The realization of fault-tolerant quantum computing requires the execution of quantum error-correction (QEC) schemes, to mitigate the fragile nature of qubits. In this context, to ensure the success of QEC, a protocol capable of implementing both qubit reset and leakage reduction is highly desirable. We demonstrate such a protocol in an architecture consisting of fixed-frequency transmon qubits pair-wise coupled via tunable couplers -- an architecture that is compatible with the surface code. We use tunable couplers to transfer any undesired qubit excitation to the readout resonator of the qubit, from which this excitation decays into the feedline. In total, the combination of qubit reset, leakage reduction, and coupler reset takes only 83ns to complete. Our reset scheme is fast, unconditional, and achieves fidelities well above 99%, thus enabling fixed-frequency qubit architectures as future implementations of fault-tolerant quantum computers. Our protocol also provides a means to both reduce QEC cycle runtime and improve algorithmic fidelity on quantum computers.

This paper presents a novel approach for robust global localisation and 6DoF pose estimation of ground robots in forest environments by leveraging cross-view factor graph optimisation and deep-learned re-localisation. The proposed method addresses the challenges of aligning aerial and ground data for pose estimation, which is crucial for accurate point-to-point navigation in GPS-denied environments. By integrating information from both perspectives into a factor graph framework, our approach effectively estimates the robot's global position and orientation. We validate the performance of our method through extensive experiments in diverse forest scenarios, demonstrating its superiority over existing baselines in terms of accuracy and robustness in these challenging environments. Experimental results show that our proposed localisation system can achieve drift-free localisation with bounded positioning errors, ensuring reliable and safe robot navigation under canopies., Comment: 7 pages, 4 figures, Submitted to ICRA2025

In this article, we develop tools for computing $G$-crossed extensions of braided tensor categories, including all coherence data (associator, braiding, tensor structures). Their equivariantisations describe representation categories of fixed-point vertex operator algebras, also called orbifolds. The seminal work of Etingof, Nikshych and Ostrik asserts the existence and uniqueness of the $G$-crossed extensions we construct. In particular, for the group $G=\mathbb{Z}_2$ acting as inversion on a discriminant form or metric group, we explicitly construct a braided $\mathbb{Z}_2$-crossed tensor category with a nondegenerate braiding that generalises the Tambara-Yamagami category, now with more than one simple object in the twisted sector. Its equivariantisation yields the modular tensor category of the orbifold of a lattice vertex operator algebra under a lift of $-\mathrm{id}$. In order to derive the above result, we show that $G$-crossed extensions and condensations by commutative algebras commute appropriately, leading to an effective method for constructing new $G$-crossed extensions. Based on this, we also outline a strategy for addressing the general problem of lattice orbifolds., Comment: 85 pages, LaTeX

Hot Jupiters are giant planets subject to intense stellar radiation. The physical and chemical properties of their atmosphere makes them the most amenable targets for the atmospheric characterization. In this paper we analyze the photometry collected during the secondary eclipses of the hot Jupiter WASP-3 b by CHEOPS, TESS and Spitzer. Our aim is to characterize the atmosphere of the planet by measuring the secondary eclipse depth in several passbands and constrain the planetary dayside spectrum. Our update of the stellar and planetary properties is consistent with previous works. The analysis of the occultations returns an eclipse depth of 92+-21 ppm in the CHEOPS passband, 83+-27 ppm for TESS and >2000 ppm in the IRAC 1-2-4 Spitzer passbands. Using the eclipse depths in the Spitzer bands we propose a set of likely emission spectra which constrain the emission contribution in the \cheops and TESS passbands to approximately a few dozens of parts per million. This allowed us to measure a geometric albedo of 0.21+-0.07 in the CHEOPS passband, while the TESS data lead to a 95\% upper limit of $\sim$0.2. WASP-3 b belongs to the group of ultra-hot Jupiters which are characterized by low Bond albedo (<0.3+-0.1), as predicted by different atmospheric models. On the other hand, it unexpectedly seems to efficiently recirculate the absorbed stellar energy, unlike similar highly irradiated planets. To explain this inconsistency, we propose that other energy recirculation mechanisms may be at play other than advection (for example, dissociation and recombination of H_2). Another possibility is that the observations in different bandpasses probe different atmospheric layers, making the atmospheric analysis difficult without an appropriate modeling of the thermal emission spectrum of WASP-3 b, which is not feasible with the limited spectroscopic data available to date.

Infrared-faint white dwarfs are cool white dwarfs exhibiting significant infrared flux deficits, most often attributed to collision-induced absorption (CIA) from H$_2$-He in mixed hydrogen-helium atmospheres. We present James Webb Space Telescope (JWST) near- and mid-infrared spectra of three such objects using NIRSpec (0.6-5.3 $\mu$m) and MIRI (5-14 $\mu$m): LHS 3250, WD J1922+0233, and LHS 1126. Surprisingly, for LHS 3250, we detect no H$_2$-He CIA absorption at 2.4 $\mu$m, instead observing an unexpected small flux bump at this wavelength. WD J1922+0233 exhibits the anticipated strong absorption feature centered at 2.4 $\mu$m, but with an unexpected narrow emission-like feature inside this absorption band. LHS 1126 shows no CIA features and follows a $\lambda^{-2}$ power law in the mid-infrared. LHS 1126's lack of CIA features suggests a very low hydrogen abundance, with its infrared flux depletion likely caused by He-He-He CIA. For LHS 3250 and WD J1922+0233, the absence of a 1.2 $\mu$m CIA feature in both stars argues against ultracool temperatures, supporting recent suggestions that infrared-faint white dwarfs are warmer and more massive than previously thought. This conclusion is further solidified by Keck near-infrared spectroscopy of seven additional objects. We explore possible explanations for the unexpected emission-like features in both stars, and temperature inversions above the photosphere emerge as a promising hypothesis. Such inversions may be common among the infrared-faint population, and since they significantly affect the infrared spectral energy distribution, this would impact their photometric fits. Further JWST observations are needed to confirm the prevalence of this phenomenon and guide the development of improved atmospheric models., Comment: Submitted to ApJ on 2024-09-07

The human visual system is well-tuned to detect faces of all shapes and sizes. While this brings obvious survival advantages, such as a better chance of spotting unknown predators in the bush, it also leads to spurious face detections. ``Face pareidolia'' describes the perception of face-like structure among otherwise random stimuli: seeing faces in coffee stains or clouds in the sky. In this paper, we study face pareidolia from a computer vision perspective. We present an image dataset of ``Faces in Things'', consisting of five thousand web images with human-annotated pareidolic faces. Using this dataset, we examine the extent to which a state-of-the-art human face detector exhibits pareidolia, and find a significant behavioral gap between humans and machines. We find that the evolutionary need for humans to detect animal faces, as well as human faces, may explain some of this gap. Finally, we propose a simple statistical model of pareidolia in images. Through studies on human subjects and our pareidolic face detectors we confirm a key prediction of our model regarding what image conditions are most likely to induce pareidolia. Dataset and Website: https://aka.ms/faces-in-things