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In molecular systems, the ultrafast motion of electrons initiates the process of chemical change. Tracking this electronic motion across molecules requires coupling attosecond time resolution to atomic-scale spatial sensitivity. In this work, we employ a pair of attosecond x-ray pulses from an x-ray free-electron laser to follow electron motion resulting from the sudden removal of an electron from a prototypical aromatic system, para-aminophenol. X-ray absorption enables tracking this motion with atomic-site specificity. Our measurements are compared with state-of-the-art computational modeling, reproducing the observed response across multiple timescales. Sub-femtosecond dynamics are assigned to states undergoing non-radiative decay, while few-femtosecond oscillatory motion is associated with electronic wavepacket motion in stable cation states, that will eventually couple to nuclear motion. Our work provides insight on the ultrafast charge motion preceding and initiating chemical transformations in moderately complex systems, and provides a powerful benchmark for computational models of ultrafast charge motion in matter.

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

We present results for single axial-vector and scalar meson pole contributions to the hadronic light-by-light scattering (HLbL) part of the muon's anomalous magnetic moment. In the dispersive approach to these quantities (in narrow width approximation) the central inputs are the corresponding space-like electromagnetic transition form factors. We determine these directly using a functional approach to QCD by Dyson-Schwinger and Bethe-Salpeter equations in the very same setup we used previously to determine pseudo-scalar meson exchange ($\pi$, $\eta$ and $\eta'$) as well as meson ($\pi$ and $K$) box contributions. Particular care is taken to preserve gauge invariance and to comply with short distance constraints in both the form factors and the HLbL tensor. Our result for the contributions from a tower of axial-vector states including short distance constraints is $a_\mu^{\text{HLbL}}[\text{AV-tower+SDC}] = 27.5 \,(3.2) \times 10^{-11}$. For the combined contributions from $f_0(980), a_0(980), f_0(1370)$ and $a_0(1450)$ we find $a_\mu^{\text{HLbL}}[\text{scalar}] = -1.6 \,(5) \times 10^{-11}$., Comment: 14 pages, 10 figures

Fibrillar structures are ubiquitous in the solar chromosphere and their potential for mediating the mass and energy transport in the solar atmosphere is undeniable. An accurate determination of their properties requires the use of advanced high-resolution observations which are now becoming broadly available from different observatories. We exploit the capabilities of multi-atom, multi-line spectropolarimetric inversions using the Stockholm Inversion Code (STiC). Non-local thermodynamic equilibrium inversions of a fibril-rich area are performed using spectropolarimetric observations in the Ca II 854.2 nm line obtained with the CRISP imaging spectropolarimeter and spectroscopic observations in the Ca II H line obtained with the CHROMospheric Imaging Spectrometer (CHROMIS) at the Swedish 1-meter Solar Telescope (SST). Additionally, coobservations in the Mg II h & k lines obtained with the Interface Region Imaging Spectrograph (IRIS) are used in the inversions to better constrain the thermodynamic properties of the fibrils. The incorporation of multiple atomic species and spectral lines proves to better constrain the properties of the plasma constituting the fibrils. In particular, the tracing of a large number of fibrils allowed for the study of the variation of the temperature and magnetic field along their projected length over the field of view. The results provide a view of fibrils possessing hot footpoints of about 5 900 K. The temperature drop away from the footpoints is on average 250 K, with a larger drop of around 500 K for the longer fibrils. The magnetic field is also reported to be larger at the footpoints, being almost twice as large as the minimum value reported at the middle point of the fibrils.

Misclassification in binary outcomes is not uncommon and statistical methods to investigate its impact on policy-driving study results are lacking. While misclassifying binary outcomes is a statistically ubiquitous phenomena, we focus on misclassification in a public health application: vaccinations. One such study design in public health that addresses policy is the cluster controlled randomized trial (CCRT). A CCRT that measures the impact of a novel behavioral intervention on increasing vaccine uptake can be severely biased when the supporting data are incomplete vaccination records. In particular, these vaccine records more often may be prone to negative misclassification, that is, a clinic's record of an individual patient's vaccination status may be unvaccinated when, in reality, this patient was vaccinated outside of the clinic. With large nation-wide endeavors to encourage vaccinations without a gold-standard vaccine record system, sensitivity analyses that incorporate misclassification rates are promising for robust inference. In this work we introduce a novel extension of Bayesian logistic regression where we perturb the clinic size and vaccination count with random draws from expert-elicited prior distributions. These prior distributions represent the misclassification rates for each clinic that stochastically add unvaccinated counts to the observed vaccinated counts. These prior distributions are assigned for each clinic (the first level in a group-level randomized trial). We demonstrate this method with a data application from a CCRT evaluating the influence of a behavioral intervention on vaccination uptake among U.S. veterans. A simulation study is carried out demonstrating its estimation properties.

WallGo is an open source software for the computation of the bubble wall velocity in first-order cosmological phase transitions. It also computes the energy budget available for the generation of gravitational waves. The main part of WallGo, built in Python, determines the wall velocity by solving the scalar-field(s) equation of motion, the Boltzmann equations and energy-momentum conservation for the fluid velocity and temperature. WallGo also includes two auxiliary modules: WallGoMatrix, which computes matrix elements for out-of-equilibrium particles, and WallGoCollision, which performs higher-dimensional integrals for Boltzmann collision terms. Users can implement custom models by defining an effective potential and specifying a list of out-of-equilibrium particles and their interactions. As the first public software to compute the wall velocity including out-of-equilibrium contributions, WallGo improves the precision of the computation compared to common assumptions in earlier computations. It utilises a spectral method for the deviation from equilibrium and collision terms that provides exponential convergence in basis polynomials, and supports multiple out-of-equilibrium particles, allowing for Boltzmann mixing terms. WallGo is tailored for non-runaway wall scenarios where leading-order coupling effects dominate friction. While this work introduces the software and the underlying theory, a more detailed documentation can be found in https://wallgo.readthedocs.io., Comment: 69 pages, 8 figures

This paper extends doubly robust censoring unbiased transformations to a broad class of censored data structures under the assumption of coarsening at random and positivity. This includes the classic survival and competing risks setting, but also encompasses multiple events. A doubly robust representation for the conditional bias of the transformed data is derived. This leads to rate double robustness and oracle efficiency properties for estimating conditional expectations when combined with cross-fitting and linear smoothers. Simulation studies demonstrate favourable performance of the proposed method relative to existing approaches. An application of the methods to a regression discontinuity design with censored data illustrates its practical utility.

Microfluidic Live-Cell Imaging (MLCI) generates high-quality data that allows biotechnologists to study cellular growth dynamics in detail. However, obtaining these continuous data over extended periods is challenging, particularly in achieving accurate and consistent real-time event classification at the intersection of imaging and stochastic biology. To address this issue, we introduce the Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis (EAP4EMSIG). In particular, we present initial zero-shot results from the real-time segmentation module of our approach. Our findings indicate that among four State-Of-The- Art (SOTA) segmentation methods evaluated, Omnipose delivers the highest Panoptic Quality (PQ) score of 0.9336, while Contour Proposal Network (CPN) achieves the fastest inference time of 185 ms with the second-highest PQ score of 0.8575. Furthermore, we observed that the vision foundation model Segment Anything is unsuitable for this particular use case., Comment: Proceedings - 34. Workshop Computational Intelligence

Much of the research on the interpretability of deep neural networks has focused on studying the visual features that maximally activate individual neurons. However, recent work has cast doubts on the usefulness of such local representations for understanding the behavior of deep neural networks because individual neurons tend to respond to multiple unrelated visual patterns, a phenomenon referred to as "superposition". A promising alternative to disentangle these complex patterns is learning sparsely distributed vector representations from entire network layers, as the resulting basis vectors seemingly encode single identifiable visual patterns consistently. Thus, one would expect the resulting code to align better with human perceivable visual patterns, but supporting evidence remains, at best, anecdotal. To fill this gap, we conducted three large-scale psychophysics experiments collected from a pool of 560 participants. Our findings provide (i) strong evidence that features obtained from sparse distributed representations are easier to interpret by human observers and (ii) that this effect is more pronounced in the deepest layers of a neural network. Complementary analyses also reveal that (iii) features derived from sparse distributed representations contribute more to the model's decision. Overall, our results highlight that distributed representations constitute a superior basis for interpretability, underscoring a need for the field to move beyond the interpretation of local neural codes in favor of sparsely distributed ones.

Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative to existing techniques for identifying ground states of quantum problems. A significant advancement toward improving the practical scalability of NQS has been the incorporation of autoregressive models, most recently transformers, as variational ansatze. Transformers learn sequence information with greater expressiveness than recurrent models, but at the cost of increased time complexity with respect to sequence length. We explore the use of the retentive network (RetNet), a recurrent alternative to transformers, as an ansatz for solving electronic ground state problems in $\textit{ab initio}$ quantum chemistry. Unlike transformers, RetNets overcome this time complexity bottleneck by processing data in parallel during training, and recurrently during inference. We give a simple computational cost estimate of the RetNet and directly compare it with similar estimates for transformers, establishing a clear threshold ratio of problem-to-model size past which the RetNet's time complexity outperforms that of the transformer. Though this efficiency can comes at the expense of decreased expressiveness relative to the transformer, we overcome this gap through training strategies that leverage the autoregressive structure of the model -- namely, variational neural annealing. Our findings support the RetNet as a means of improving the time complexity of NQS without sacrificing accuracy. We provide further evidence that the ablative improvements of neural annealing extend beyond the RetNet architecture, suggesting it would serve as an effective general training strategy for autoregressive NQS., Comment: 16 pages, 1 figure, to be submitted for peer-reviewed publication

Social contact surveys are an important tool to assess infection risks within populations, and the effect of non-pharmaceutical interventions on social behaviour during disease outbreaks, epidemics, and pandemics. Numerous longitudinal social contact surveys were conducted during the COVID-19 era, however data analysis is plagued by reporting fatigue, a phenomenon whereby the average number of social contacts reported declines with the number of repeat participations and as participants' engagement decreases over time. Using data from the German COVIMOD Study between April 2020 to December 2021, we demonstrate that reporting fatigue varied considerably by sociodemographic factors and was consistently strongest among parents reporting children contacts (parental proxy reporting), students, middle-aged individuals, those in full-time employment and those self-employed. We find further that, when using data from first-time participants as gold standard, statistical models incorporating a simple logistic function to control for reporting fatigue were associated with substantially improved estimation accuracy relative to models with no reporting fatigue adjustments, and that no cap on the number of repeat participations was required. These results indicate that existing longitudinal contact survey data can be meaningfully interpreted under an easy-to-implement statistical approach adressing reporting fatigue confounding, and that longitudinal designs including repeat participants are a viable option for future social contact survey designs.

Planets form from the same cloud of molecular gas and dust as their host stars. Confirming if planetary bodies acquire the same refractory element composition as their natal disc during formation, and how efficiently volatile elements are incorporated into growing planets, is key to linking the poorly constrained interior composition of rocky exoplanets to the observationally-constrained composition of their host star. Such comparisons also afford insight into the planet formation process. This work compares planetary composition with host-star composition using observations of a white dwarf that has accreted planetary material and its F-type star wide binary companion as a reference for the composition of the natal molecular gas and dust. Spectroscopic analysis reveals abundances of Fe, Mg, Si, Ca, and Ti in both stars. We use the white dwarf measurements to estimate the composition of the exoplanetary material and the F-type companion to constrain the composition of the material the planet formed from. Comparing planetary material to the composition of its natal cloud, our results reveal that the planetary material is depleted in moderate refractories (Mg, Si, Fe) relative to the refractory material (Ca, Ti). Grouping elements based on their condensation temperatures is key to linking stellar and planetary compositions. Fractionation during formation or subsequent planetary evolution leads to the depletion of moderate refractories from the planetary material accreted by the white dwarf. This signature, as seen for bulk Earth, will likely be present in the composition of many exoplanets relative to their host-stars., Comment: 14 pages (without including appendix). 12 Figures. Accepted for publication in A&A

The time until black hole formation in a binary neutron-star (NS) merger contains invaluable information about the nuclear equation of state (EoS) but has thus far been difficult to measure. We propose a new way to constrain the merger remnant's NS lifetime, which is based on the tendency of the NS remnant neutrino-driven winds to enrich the ejected material with helium. Based on the He I $\lambda 1083.3$ nm line, we show that the feature around 800-1200 nm in AT2017gfo at 4.4 days seems inconsistent with a helium mass fraction of $X_{\mathrm{He}} \gtrsim 0.05$ in the polar ejecta. Recent neutrino-hydrodynamic simulations of merger remnants are only compatible with this limit if the NS remnant collapses within 20-30 ms. Such a short lifetime implies that the total binary mass of GW170817, $M_\mathrm{\rm tot}$, lay close to the threshold binary mass for direct gravitational collapse, $M_\mathrm{thres}$, for which we estimate $M_{\mathrm{thres}}\lesssim 2.93 M_\odot$. This upper bound on $M_\mathrm{thres}$ yields upper limits on the radii and maximum mass of cold, non-rotating NSs, which rule out simultaneously large values for both quantities. In combination with causality arguments, this result implies a maximum NS mass of $M_\mathrm{max}\lesssim2.3 M_\odot$. The combination of all limits constrains the radii of 1.6 M$_\odot$ NSs to about 12$\pm$1 km for $M_\mathrm{max}$ = 2.0 M$_\odot$ and 11.5$\pm$1 km for $M_\mathrm{max}$ = 2.15 M$_\odot$. This $\sim2$ km allowable range then tightens significantly for $M_\mathrm{max}$ above $\approx2.15$ M$_\odot$. This rules out a significant number of current EoS models. The short NS lifetime also implies that a black-hole torus, not a highly magnetized NS, was the central engine powering the relativistic jet of GRB170817A. Our work motivates future developments... [abridged], Comment: 30 pages, 16 figures, 2 tables, submitted to PRX

Large language models (LLMs) often fail to synthesize information from their context to generate an accurate response. This renders them unreliable in knowledge intensive settings where reliability of the output is key. A critical component for reliable LLMs is the integration of a robust fact-checking system that can detect hallucinations across various formats. While several open-access fact-checking models are available, their functionality is often limited to specific tasks, such as grounded question-answering or entailment verification, and they perform less effectively in conversational settings. On the other hand, closed-access models like GPT-4 and Claude offer greater flexibility across different contexts, including grounded dialogue verification, but are hindered by high costs and latency. In this work, we introduce VERITAS, a family of hallucination detection models designed to operate flexibly across diverse contexts while minimizing latency and costs. VERITAS achieves state-of-the-art results considering average performance on all major hallucination detection benchmarks, with $10\%$ increase in average performance when compared to similar-sized models and get close to the performance of GPT4 turbo with LLM-as-a-judge setting.

We demonstrate how the composition of two unsupervised clustering algorithms, $\texttt{AstroLink}$ and $\texttt{FuzzyCat}$, makes for a powerful tool when studying galaxy formation and evolution. $\texttt{AstroLink}$ is a general-purpose astrophysical clustering algorithm built for extracting meaningful hierarchical structure from point-cloud data defined over any feature space, while $\texttt{FuzzyCat}$ is a generalised soft-clustering algorithm that propagates the dynamical effects of underlying data processes into a fuzzy hierarchy of stable fuzzy clusters. Their composition, $\texttt{FuzzyCat}$ $\circ$ $\texttt{AstroLink}$, can therefore identify a fuzzy hierarchy of astrophysically- and statistically-significant fuzzy clusters within any point-based data set whose representation is subject to changes caused by some underlying process. Furthermore, the pipeline achieves this without relying upon strong assumptions about the data, the change process, the number/importance of specific structure types, or much user input -- thereby making itself applicable to a wide range of fields in the physical sciences. We find that for the task of structurally decomposing simulated galaxies into their constituents, our context-agnostic approach has a substantial impact on the diversity and completeness of the structures extracted as well as on their relationship within the broader galactic structural hierarchy -- revealing dwarf galaxies, infalling groups, stellar streams (and their progenitors), stellar shells, galactic bulges, and star-forming regions., Comment: Accepted to the NeurIPS 2024 Machine Learning and the Physical Sciences workshop. 9 pages, 2 figures

This paper is concerned with the theoretical modeling and analysis of uplink connection performance of a radiosonde network deployed in a typhoon. Similar to existing works, the stochastic geometry theory is leveraged to derive the expression of the uplink connection probability (CP) of a radiosonde. Nevertheless, existing works assume that network nodes are spherically or uniformly distributed. Different from the existing works, this paper investigates two particular motion patterns of radiosondes in a typhoon, which significantly challenges the theoretical analysis. According to their particular motion patterns, this paper first separately models the distributions of horizontal and vertical distances from a radiosonde to its receiver. Secondly, this paper derives the closed-form expressions of cumulative distribution function (CDF) and probability density function (PDF) of a radiosonde's three-dimensional (3D) propagation distance to its receiver. Thirdly, this paper derives the analytical expression of the uplink CP for any radiosonde in the network. Finally, extensive numerical simulations are conducted to validate the theoretical analysis, and the influence of various network design parameters are comprehensively discussed. Simulation results show that when the signal-to-interference-noise ratio (SINR) threshold is below -35 dB, and the density of radiosondes remains under 0.01/km^3, the uplink CP approaches 26%, 39%, and 50% in three patterns.

We define two $L$-functions associated to a common vector valued eigenform $f$ transforming with the ``finite'' Weil representation. The first one can be seen as a standard zeta function defined by the eigenvalues of $f$. The second one can be interpreted as standard $L$-function defined as an Euler product where each $p$-factor is a rational function in terms of two unramified characters of the $p$-adic field $\Q_p$. We show that both $L$-functions are related and prove further that they both can be continued meromorphically to the whole complex $s$-plane., Comment: This is the second part of a series of two articles

Critical open source software systems undergo significant validation in the form of lengthy fuzz campaigns. The fuzz campaigns typically conduct a biased random search over the domain of program inputs, to find inputs which crash the software system. Such fuzzing is useful to enhance the security of software systems in general since even closed source software may use open source components. Hence testing open source software is of paramount importance. Currently OSS-Fuzz is the most significant and widely used infrastructure for continuous validation of open source systems. Unfortunately even though OSS-Fuzz has identified more than 10,000 vulnerabilities across 1000 or more software projects, the detected vulnerabilities may remain unpatched, as vulnerability fixing is often manual in practice. In this work, we rely on the recent progress in Large Language Model (LLM) agents for autonomous program improvement including bug fixing. We customise the well-known AutoCodeRover agent for fixing security vulnerabilities. This is because LLM agents like AutoCodeRover fix bugs from issue descriptions via code search. Instead for security patching, we rely on the test execution of the exploit input to extract code elements relevant to the fix. Our experience with OSS-Fuzz vulnerability data shows that LLM agent autonomy is useful for successful security patching, as opposed to approaches like Agentless where the control flow is fixed. More importantly our findings show that we cannot measure quality of patches by code similarity of the patch with reference codes (as in CodeBLEU scores used in VulMaster), since patches with high CodeBLEU scores still fail to pass given the given exploit input. Our findings indicate that security patch correctness needs to consider dynamic attributes like test executions as opposed to relying of standard text/code similarity metrics.

We introduce forward-backward stochastic differential equations, highlighting the connection between solutions of these and solutions of partial differential equations, related by the Feynman-Kac theorem. We review the technique of approximating solutions to high dimensional partial differential equations using neural networks, and similarly approximate solutions of stochastic differential equations using multilevel Monte Carlo. Connecting the multilevel Monte Carlo method with the neural network framework using the setup established by E et al. and Raissi, we replicate many of their empirical results, and provide novel numerical analyses to produce strong error bounds for the specific framework of Raissi. Our results bound the overall strong error in terms of the maximum of the discretisation error and the neural network's approximation error. Our analyses are pivotal for applications of multilevel Monte Carlo, for which we propose suitable frameworks to exploit the variance structures of the multilevel estimators we elucidate. Also, focusing on the loss function advocated by Raissi, we expose the limitations of this, highlighting and quantifying its bias and variance. Lastly, we propose various avenues of further research which we anticipate should offer significant insight and speed improvements., Comment: 31 pages, 4 figures

Motivated by recently observed anomalies in the flavour sector, we analyse the potential of measurements of top quarks at the Large Hadron Collider (LHC) to provide complementary constraints on interactions that shape low-energy precision investigations in the $B$ sector. The measurement of top quark properties, such as the top width and the abundant top pair production channels, are already reaching the percent level at this relatively early stage of the LHC phenomenology program. A targeted analysis of four-fermion interactions employing effective field theory and RGE effects connecting $B$ scales with representative top measurements shows that the LHC is becoming competitive with and complementary to flavour physics constraints. Our results indicate that the LHC top physics program will provide a complementary tool in the search for physics beyond the Standard Model in the flavour sector., Comment: 8 pages, 3 figures, 1 table

Osteoporosis is a common condition that increases fracture risk, especially in older adults. Early diagnosis is vital for preventing fractures, reducing treatment costs, and preserving mobility. However, healthcare providers face challenges like limited labeled data and difficulties in processing medical images. This study presents a novel multi-modal learning framework that integrates clinical and imaging data to improve diagnostic accuracy and model interpretability. The model utilizes three pre-trained networks-VGG19, InceptionV3, and ResNet50-to extract deep features from X-ray images. These features are transformed using PCA to reduce dimensionality and focus on the most relevant components. A clustering-based selection process identifies the most representative components, which are then combined with preprocessed clinical data and processed through a fully connected network (FCN) for final classification. A feature importance plot highlights key variables, showing that Medical History, BMI, and Height were the main contributors, emphasizing the significance of patient-specific data. While imaging features were valuable, they had lower importance, indicating that clinical data are crucial for accurate predictions. This framework promotes precise and interpretable predictions, enhancing transparency and building trust in AI-driven diagnoses for clinical integration.

We demonstrate a cryogenic electro-optic sampling (EOS) setup that allows for the measurement of microwave signals at arbitrary positions on a cryogenic chip-scale device. We use a Josephson Arbitrary Waveform Synthesizer (JAWS) to generate quantum-accurate voltage signals and measure them with the EOS setup, allowing for the calibration of its response, yielding traceability of the microwave measurements to a quantum standard. We use the EOS setup to determine the time-domain response of ultrafast cryogenic photodiodes and the electrical reflection coefficient, i.e., the S11 scattering parameter, in a superconducting transmission line. Finally, we introduce an optical femtosecond pulse source which can be used to study the fidelity of superconducting transmission lines and terminations, as well as reflections from elements like Josephson junction arrays imbedded in them., Comment: 16 pages

Holographic displays hold the promise of providing authentic depth cues, resulting in enhanced immersive visual experiences for near-eye applications. However, current holographic displays are hindered by speckle noise, which limits accurate reproduction of color and texture in displayed images. We present HoloChrome, a polychromatic holographic display framework designed to mitigate these limitations. HoloChrome utilizes an ultrafast, wavelength-adjustable laser and a dual-Spatial Light Modulator (SLM) architecture, enabling the multiplexing of a large set of discrete wavelengths across the visible spectrum. By leveraging spatial separation in our dual-SLM setup, we independently manipulate speckle patterns across multiple wavelengths. This novel approach effectively reduces speckle noise through incoherent averaging achieved by wavelength multiplexing. Our method is complementary to existing speckle reduction techniques, offering a new pathway to address this challenge. Furthermore, the use of polychromatic illumination broadens the achievable color gamut compared to traditional three-color primary holographic displays. Our simulations and tabletop experiments validate that HoloChrome significantly reduces speckle noise and expands the color gamut. These advancements enhance the performance of holographic near-eye displays, moving us closer to practical, immersive next-generation visual experiences.

The KM3NeT Collaboration has tackled a common challenge faced by the astroparticle physics community, namely adapting the experiment-specific simulation software to work with the CORSIKA air shower simulation output. The proposed solution is an extension of the open-source code gSeaGen, allowing for the transport of muons generated by CORSIKA to a detector of any size at an arbitrary depth. The gSeaGen code was not only extended in terms of functionalities but also underwent a thorough redesign of the muon propagation routine, resulting in a more accurate and efficient simulation. This paper presents the capabilities of the new gSeaGen code as well as prospects for further developments., Comment: 27 pages, 13 figures

State conversion is a fundamental task in quantum information processing. Quantum resource theories allow to analyze and bound conversions that use restricted sets of operations. In the context of continuous-variable systems, state conversions restricted to Gaussian operations are crucial for both fundamental and practical reasons -- particularly in state preparation and quantum computing with bosonic codes. However, previous analysis did not consider the relevant case of approximate state conversion. In this work, we introduce a framework for assessing approximate Gaussian state conversion by extending the stellar rank to the approximate stellar rank, which serves as an operational measure of non-Gaussianity. We derive bounds for Gaussian state conversion under both approximate and probabilistic conditions, yielding new no-go results for non-Gaussian state preparation and enabling a reliable assessment of the performance of generic Gaussian conversion protocols.

Semi-supervised anomaly detection is based on the principle that potential anomalies are those records that look different from normal training data. However, in some cases we are specifically interested in anomalies that correspond to high attribute values (or low, but not both). We present two asymmetrical distance measures that take this directionality into account: ramp distance and signed distance. Through experiments on synthetic and real-life datasets we show that ramp distance performs as well or better than the absolute distance traditionally used in anomaly detection. While signed distance also performs well on synthetic data, it performs substantially poorer on real-life datasets. We argue that this reflects the fact that in practice, good scores on some attributes should not be allowed to compensate for bad scores on others.

We show how the Gelfand spectrum of certain commutative operator algebras can be studied based on the theorem of Stone and von Neumann. The method presented is a natural addition to the tools of quantum spectral synthesis, which were recently used to characterize certain commutative Toeplitz algebras on the Fock space. Our method applies to this setting and also to more general abelian phase spaces. Besides characterizing Gelfand spectra of such commutative operator algebras, we also prove an extension of this result to the operator-valued case., Comment: 14 pages

Plasmons can drive chemical reactions by directly exciting intramolecular transitions. However, strong coupling of plasmons to single molecules remains a challenge as ultra-small mode volumes are required. In the presented work, we propose Cu-Pd plasmonic network metamaterials as a scalable platform for plasmon-assisted catalysis. Due to the absence of translational symmetry, these networks provide a unique plasmonic environment featuring a large local density of optical states and an unparalleled density of hotspots that effectively localizes light in mode volumes $V<8\cdot10^{-24}$ m$^3$. Catalytic performance tests during CO$_2$ conversion reveal production rates of up to 4.3$\cdot$10$^2$ mmol g$^{-1}$h$^{-1}$ and altered reaction selectivity under light illumination. Importantly, we show that the selectivity of the catalytic process can be tuned by modifying the network's chemical composition, offering a versatile approach to optimize reaction pathways.

A promising approach to detect high-energy tau neutrinos is through the measurement of impulsive radio emission from horizontal air showers initiated in the Earth's atmosphere. Observations at frequencies between 30 and 80 MHz seem particularly promising -- if high-gain antennas focused at the horizon and blocking out as much as possible of the noisy sky are employed. Due to the large wavelengths, however, designing an antenna with the required properties is highly non-trivial at such low frequencies. In this article, we explore suitable antenna designs that provide the desired high gain, possess a smooth beam, are insensitive to ground conditions, are easily impedance-matched over the wide band, and are mechanically simple for deployment in large numbers in inaccessible terrain. In particular, we consider the "rhombus" antenna design for both horizontally and vertically polarized radiation a very attractive option for tau neutrino detection efforts in the atmosphere with the radio technique., Comment: 15 pages, to be submitted to JINST

Molybdenum (Mo) due to its optimal structural, physical, and acoustic properties find application as electrode material in aluminum scandium nitride (AlScN) and aluminum nitride (AlN) based bulk acoustic wave (BAW) resonators. Epitaxial Mo thin films exhibiting low resistivity can improve the performance of the BAW resonator by enhancing both the electromechanical coupling coefficient and quality factor. In this study, we systematically vary the growth temperature of Mo grown on fiber-textured and epitaxial wurtzite-aluminum nitride (AlN) to study the changes in structural and electrical properties of the Mo films. Results show that Mo grown at 700{\deg}C on epitaxial AlN exhibit low surface roughness, large average grain diameter, low resistivity, and high crystal quality. XRD pole figure and phi-scan reveal that irrespective of the growth temperature, Mo is fiber textured on fiber-textured AlN, and has three rotational domains on epitaxial AlN. The study shows that the resistivity of Mo reduces with increasing growth temperature, which we relate to increasing average grain diameter. Additionally, we show that fiber-textured Mo has more high angle grain boundaries resulting in consistently higher resistivity than its epitaxial equivalent., Comment: 13 pages, 7 figures, 1 supplementary file

We discuss boson stars and black holes with scalar hair in a model where the complex scalar field forming the boson star and the hair on the black hole, respectively, interacts with a real scalar field via a H\'enon-Heiles-type potential. We demonstrate that black holes and boson stars carrying only a real scalar field with cubic self-interaction are possible and that black holes with both real and complex scalar field branch off from these solutions for sufficiently large interaction between the two fields and/or sufficiently large horizon radius $r_h$. The latter possess lower mass for the same choice of coupling constants than the former, however seem to be thermodynamically preferred only for high enough temperature., Comment: 16 pages including 13 figures

The advent of large language models (LLMs), such as GPT-4, has enabled significant advancements in generating code across various domains. However, these models face unique challenges when generating IEC 61131-3 Structured Text (ST) code due to limited data in public training datasets and the complexity of ST language syntax. This paper proposes a novel approach to training LLMs that emphasizes improving the quality of learning data through an online process involving compiler feedback and evaluation from a secondary LLM. In this framework, the primary LLM generates new training samples, which are subsequently evaluated by a compiler for syntactical correctness and by a specialized LLM that excels at assessing semantic accuracy, though it is not optimized for code generation itself. Through iterative refinement of the training data, this approach results in marked improvements for the trained LLM, leading to higher compilation success rates and better semantic precision. As a result, the framework proves highly suitable for industrial automation applications and outperforms state-of-the-art models., Comment: This work has been submitted to the IEEE for possible publication

We consider the relationship between modal representations obtained from data-driven decomposition methods and Lagrangian Coherent Structures (LCSs). Mode sensitivity is used to describe this analysis as an extension of the model sensitivity framework developed by Kasz\'as and Haller (2020). The method, based on the computation of the finite-time-Lyapunov exponent, uses modes from fluid data to compute the amplitude perturbations experienced by fluid particle trajectories along with their sensitivity to initial conditions. Demonstrations of the method are presented with both periodic and turbulent flows, including a kinematic flow model, experimental data from the wake past an oscillating foil, numerical data of the classical cylinder wake flow, and a direct numerical simulation (DNS) of a turbulent channel flow. Mode sensitivity fields reveal both quantitatively and qualitatively how the finite-time Lyapunov exponent fields used to visualize LCSs change due to the influence of modes or external perturbations.

Aluminum scandium nitride (Al$_{1-x}$Sc$_x$N) is a promising material for ferroelectric devices due to its large remanent polarization, scalability, and compatibility with semiconductor technology. By doping AlN with Sc, the bonds in the polar AlN structure are weakened, which enables ferroelectric switching below the dielectric breakdown field. However, one disadvantage of Sc doping is that it increases the material's tendency towards oxidation. In the present study, the oxidation process of tungsten-capped and uncapped Al$_{0.83}$Sc$_{0.17}$N thin films is investigated by hard X-ray photoelectron spectroscopy (HAXPES). The samples had been exposed to air for either two weeks or 6 months. HAXPES spectra indicate the replacement of nitrogen by oxygen, and the tendency of oxygen to favor oxidation with Sc rather than Al. The appearance of an N$_2$ spectral feature thus can be directly related to the oxidation process. We present an oxidation model that mimics these spectroscopic results of the element-specific oxidation processes within Al$_{1-x}$Sc$_x$N. Finally, in operando HAXPES data of uncapped and capped AlScN-capacitor stacks are interpreted using the proposed model.

Synchrotron X-ray emission has been detected from nearly a dozen young supernova remnants (SNRs). X-rays of synchrotron origin exhibit linear polarization in a regular, non-randomly oriented magnetic field. The significant polarized X-ray emission from four such SNRs has already been reported on the basis of observations with the Imaging X-ray Polarimetry Explorer (IXPE). The magnetic-field structure as derived from IXPE observations is radial for Cassiopeia A, Tycho's SNR, and SN 1006, and tangential for RX J1713.7-3946. The latter together with the recent detection of a tangential magnetic field in SNR 1E 0102.2-7219 by the Australia Telescope Compact Array in the radio band shows that tangential magnetic fields can also be present in young SNRs. Thus, the dichotomy in polarization between young and middle-aged SNRs (radial magnetic fields in young SNRs, but tangential magnetic fields in middle-aged SNRs), previously noticed in the radio band, deserves additional attention. The present analysis of IXPE observations determines, for the first time, a magnetic-field structure in the northwestern rim of Vela Jr, also known as RX J0852.0-4622, and provides a new example of a young SNR with a tangential magnetic field., Comment: Accepted for publication in Astronomy and Astrophysics

X-ray polarization is a unique new probe of the particle acceleration in astrophysical jets made possible through the Imaging X-ray Polarimetry Explorer. Here we report on the first dense X-ray polarization monitoring campaign on the blazar Mrk 421. Our observations were accompanied by an even denser radio and optical polarization campaign. We find significant short-timescale variability in both X-ray polarization degree and angle, including a $\sim90^\circ$ angle rotation about the jet axis. We attribute this to random variations of the magnetic field, consistent with the presence of turbulence but also unlikely to be explained by turbulence alone. At the same time, the degree of lower-energy polarization is significantly lower and shows no more than mild variability. Our campaign provides further evidence for a scenario in which energy-stratified shock-acceleration of relativistic electrons, combined with a turbulent magnetic field, is responsible for optical to X-ray synchrotron emission in blazar jets., Comment: 23 pages, including 8 pages of appendices. 12 figures, 3 tables. Submitted to ApJ

Seismic and geodynamic studies indicate that the boundary between the Earth's liquid outer core and solid mantle is not spherical, but is likely characterized by topography in the form of inverted mountains and valleys that have typical amplitudes of several kilometers. One of the dynamical consequences of these deformations is that turbulent flow in the core can exert pressure torques on the mantle, thereby resulting in a transfer of angular momentum between the outer core and the mantle. Understanding this transfer of angular momentum is important for explaining variations in the Earth's rotation rate, or length of day. Whether kilometer-sized topography can explain observed variations in length of day is a longstanding question in geophysics. Here we use physical scaling arguments and a suite of state-of-the-art numerical simulations of convection in a rotating spherical shell and show that topographic torques exhibit a linear dependence on topographic amplitude and a nearly quadratic dependence on flow speeds. These results imply that topographic torques are of sufficient magnitude to explain length of day variations on annual to decadal time scales., Comment: 17 pages, 5 figures

Loop amplitudes in string theories reduce to those of gauge theories and (super)gravity in their worldline description as the inverse string tension $\alpha'$ tends to zero. The appearance of reducible diagrams in these $\alpha' \rightarrow 0$ limits is determined through so-called pinching rules in the worldline literature. In this work, we extend these pinching rules to the chiral-splitting description of one-loop superstring amplitudes where left- and right-moving degrees of freedom decouple at fixed loop momentum. Starting from six points, the Kronecker-Eisenstein integrands of chiral amplitudes introduce subtleties into the pinching rules and integration-by-parts simplifications. Resolutions of these subtleties are presented and applied to produce a new superspace representation of the six-point one-loop amplitude of type IIA/B supergravity. The worldline computations and their subtleties are compared with the ambitwistor-string approach to one-loop field-theory amplitudes where integration-by-parts manipulations are shown to be more flexible. Throughout this work, the homology invariance of loop-momentum dependent correlation functions on the torus is highlighted as a consistency condition of $\alpha' \rightarrow 0$ limits and their comparison with ambitwistor methods., Comment: 56 + 20 pages; 1 Mathematica notebook added as an ancillary file which refers to lengthy data files available from https://doi.org/10.5281/zenodo.13992743

The fabrication of well-defined, low-dimensional diamondoid-based materials is a promising approach for tailoring diamond properties such as superconductivity. On-surface self-assembly of halogenated diamondoids under ultrahigh vacuum conditions represents an effective strategy in this direction, enabling reactivity exploration and on-surface synthesis approaches. Here we demonstrate through scanning probe microscopy, time-of-flight mass spectrometry and photoelectron spectroscopy, that self-assembled layers of dibromodiamantanes on gold can be debrominated at atomic wavelengths (Al K$\alpha$ at 8.87 $\mathring{A}$ and Mg K$\alpha$ at 9.89 $\mathring{A}$) and low temperatures without affecting their well-defined arrangement. The resulting 'in-architecture' debromination enables the fabrication of diamantane chains from self-assembled precursors in close proximity, which is otherwise inaccessible through annealing on metal surfaces. Our work introduces a novel approach for the fabrication of nanodiamond chains, with significant implications for in-architecture and layer-by-layer synthesis.

Holographic displays are a promising technology for immersive visual experiences, and their potential for compact form factor makes them a strong candidate for head-mounted displays. However, at the short propagation distances needed for a compact, head-mounted architecture, image contrast is low when using a traditional phase-only spatial light modulator (SLM). Although a complex SLM could restore contrast, these modulators require bulky lenses to optically co-locate the amplitude and phase components, making them poorly suited for a compact head-mounted design. In this work, we introduce a novel architecture to improve contrast: by adding a low resolution amplitude SLM a short distance away from the phase modulator, we demonstrate peak signal-to-noise ratio improvement up to 31 dB in simulation compared to phase-only, even when the amplitude modulator is 60$\times$ lower resolution than its phase counterpart. We analyze the relationship between diffraction angle and amplitude modulator pixel size, and validate the concept with a benchtop experimental prototype. By showing that low resolution modulation is sufficient to improve contrast, we pave the way towards practical high-contrast holography in a compact form factor., Comment: 19 pages, 17 figures

The spinterface mechanism was suggested as a possible origin for the chirality induced spin-selectivity (CISS) effect, and was used to explain and reproduce, with remarkable accuracy, experimental data from transport experiments showing the CISS effect. Here, we apply the spinterface mechanism to explain the appearance of magnetization at the interface between non-magnetic metals and chiral molecules, through the stabilization of other-wise fluctuating magnetic moments. We show that the stabilization of surface magnetic moments occurs for a wide range of realistic parameters and is robust against dephasing. Importantly, we show that the direction of the surface magnetic moments is determined by the chiral axis of the chiral molecules. Armed with the concept of stable surface magnetic moments, we then formulated a theory for the photoemission CISS effect. The theory, based on spin-dependent scattering, leads to direct predictions regarding the relation between the photoemission CISS effect, the chiral axis direction, the spinterface "size", and the tilt angle of the detector with respect to the surface. These predictions are within reach of current experimental capabilities, and may shed new light on the origin of the CISS effect., Comment: 32 pages

Optical-model potentials (OMPs) are critical ingredients for basic and applied nuclear physics. Present-day computational capabilities allow us to generate data-driven nucleon-nucleus OMPs that are non-local and exactly dispersive (as theoretically required to be), include statistically-sound uncertainty quantification, and are trained on both scattering and bound-state data from a wide area of the nuclear chart. Combined together, these features allow for significant improvement in fidelity and extrapolative power of the model. Here, we present preliminary work toward the development and training of such an OMP. The capability of the model to describe data at this first stage is encouraging.

When training neural networks with custom objectives, such as ranking losses and shortest-path losses, a common problem is that they are, per se, non-differentiable. A popular approach is to continuously relax the objectives to provide gradients, enabling learning. However, such differentiable relaxations are often non-convex and can exhibit vanishing and exploding gradients, making them (already in isolation) hard to optimize. Here, the loss function poses the bottleneck when training a deep neural network. We present Newton Losses, a method for improving the performance of existing hard to optimize losses by exploiting their second-order information via their empirical Fisher and Hessian matrices. Instead of training the neural network with second-order techniques, we only utilize the loss function's second-order information to replace it by a Newton Loss, while training the network with gradient descent. This makes our method computationally efficient. We apply Newton Losses to eight differentiable algorithms for sorting and shortest-paths, achieving significant improvements for less-optimized differentiable algorithms, and consistent improvements, even for well-optimized differentiable algorithms., Comment: Published at NeurIPS 2024

Accurate time series forecasting is a highly valuable endeavour with applications across many industries. Despite recent deep learning advancements, increased model complexity, and larger model sizes, many state-of-the-art models often perform worse or on par with simpler models. One of those cases is a recently proposed model, FITS, claiming competitive performance with significantly reduced parameter counts. By training a one-layer neural network in the complex frequency domain, we are able to replicate these results. Our experiments on a wide range of real-world datasets further reveal that FITS especially excels at capturing periodic and seasonal patterns, but struggles with trending, non-periodic, or random-resembling behavior. With our two novel hybrid approaches, where we attempt to remedy the weaknesses of FITS by combining it with DLinear, we achieve the best results of any known open-source model on multivariate regression and promising results in multiple/linear regression on price datasets, on top of vastly improving upon what FITS achieves as a standalone model., Comment: arXiv:2307.03756v3 45 pages, 36 figures

At millimeter wavelengths, the atmospheric emission is circularly polarized owing to the Zeeman splitting of molecular oxygen by the Earth's magnetic field. We report a measurement of the signal in the 150 GHz band using 3 years of observations of the \textsc{Polarbear} project. Although the detectors are sensitive to linear polarization, we can measure the circular polarization because a continuously rotating half-wave plate in the optics converts part of circular polarization into linear polarization. The atmospheric circular polarization signal appears as a modulated signal at twice the frequency of rotation of the half-wave plate. We reconstruct the azimuthal gradient of the circular polarization signal and measure the dependencies on the scanning azimuth and the detector bandpass. We compare the signal with a simulation based on atmospheric emission theory, the detector bandpass, and the half-wave plate leakage spectrum model. We find the ratio of the observed azimuthal slope to the simulated slope is $0.92 \pm 0.01\rm{(stat)} \pm 0.07\rm{(sys)}$, which demonstrates that our measurement is consistent with theoretical prediction. This result validates our understanding of the instrument and reinforces the feasibility of measuring the circular polarization using the imperfection of the half-wave plate. Quantifying atmospheric circular polarization is the first step toward conducting a search for cosmological circular polarization at these wavelengths., Comment: 22 pages, 11 figures

Configuration Optimization Problems (COPs), which involve minimizing a loss function over a set of discrete points $\boldsymbol{\gamma} \subset P$, are common in areas like Model Order Reduction, Active Learning, and Optimal Experimental Design. While exact solutions are often infeasible, heuristic methods such as the Greedy Sampling Method (GSM) provide practical alternatives, particularly for low-dimensional cases. GSM recursively updates $\boldsymbol{\gamma}$ by solving a continuous optimization problem, which is typically approximated by a search over a discrete sample set $S \subset P$. However, as the dimensionality grows, the sample size suffers from the curse of dimensionality. To address this, we introduce the Polytope Division Method (PDM), a scalable greedy-type approach that adaptively partitions the parameter space and targets regions of high loss. PDM achieves linear scaling with problem dimensionality and offers an efficient solution approach for high-dimensional COPs, overcoming the limitations of traditional methods., Comment: 22 pages, 14 figures

Research software is an import output of research and must be published according to the FAIR Principles for Research Software. This can be achieved by publishing software with metadata under a persistent identifier. HERMES is a tool that leverages continuous integration to automate the publication of software with rich metadata. In this work, we describe the HERMES workflow itself, and how to extend it to meet the needs of specific research software metadata or infrastructure. We introduce the HERMES plugin architecture and provide the example of creating a new HERMES plugin that harvests metadata from a metadata source in source code repositories. We show how to use HERMES as an end user, both via the command line interface, and as a step in a continuous integration pipeline. Finally, we report three informal case studies whose results provide a preliminary evaluation of the feasibility and applicability of HERMES workflows, and the extensibility of the hermes software package., Comment: 17 pages, 2 figures, 2 tables, submitted to a special issue of Electronic Communications of the EASST collecting submissions of deRSE24, Conference for Research Software Engineers

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

Thousands of metabolomic papers are published each year, creating challenges for scientists to combine results and yield conclusions that span across studies. Literature databases such as the Human Metabolome Database provide summaries of metabolite detections and relevance, but it does not focus on specific processes, such as aging. Another database, MetaboAge, focuses specifically on how metabolite concentrations change with age. However, both databases can only search metabolites individually. Both databases lack an easily displayed overview of all the information provided, and they do not combine datasets to validate results across studies. This project aims to design a database from data extracted from the literature that cover studies across species, include query options, and combine data to get statistically sound results. The Aging Metabolite Database summarizes aging-related information for almost 2,700 non-unique metabolites and was created from 104 publications. A sub-database was created to focus on metabolite plasma levels in human aging and, including metadata from 27 papers. This resulted in 6,500 data points from 1,167 metabolites. 12 of these papers were selected for the summary results section of the database. Across these 12 studies, 53 common metabolites were reported and can be queried in 163 summary statistics, for example by age and sex. Summary statistics are provided for all aging and sex combinations. This information assists users to compare the results of their own age-related studies to published literature. Combining multiple datasets results in a more solid understanding of validated metabolic changes during the aging process.

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures