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The GPU-based High-order adaptive OpticS Testbench (GHOST) at the European Southern Observatory (ESO) is a new 2-stage extreme adaptive optics (XAO) testbench at ESO. The GHOST is designed to investigate and evaluate new control methods (machine learning, predictive control) for XAO which will be required for instruments such as the Planetary Camera and Spectrograph of ESOs Extremely Large Telescope. The first stage corrections are performed in simulation, with the residual wavefront error at each iteration saved. The residual wavefront errors from the first stage are then injected into the GHOST using a spatial light modulator. The second stage correction is made with a Boston Michromachines Corporation 492 actuator deformable mirror and a pyramid wavefront sensor. The flexibility of the bench also opens it up to other applications, one such application is investigating the flip-flop modulation method for the pyramid wavefront sensor., Comment: spie astronomical telescopes + instrumentation 2022

One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

We proposed and tested a cryogenic setup comprising dielectric waveguides for mm-wave frequencies in the range of 75-110 GHz and temperatures down to 10 mK. The targeted applications are quantum technologies at millimeter-wave frequencies, which require measurements at low photon numbers and noise. We show that the high density polyethylene waveguides combine a frequency independent low photon loss with a very low heat conductance. Black high density polyethylene shows a higher attenuation, which is useful to block thermal photons in a cryogenic environment. The dielectric waveguides are thermally anchored and attenuated at several stages of the cryostat. They are individually protected by additional metallic shields to suppress mutual cross-talk and external interference. We have measured a Fabry-P\'erot cavity with superconducting mirrors at 10 mK and found out that the quality of a signal transmitted through the dielectric waveguides is sufficient to measure resonator quality factors over one million at 110 GHz., Comment: 8 pages, 10 figures

This document compiles results obtained from the test campaign of the European Moon Rover System (EMRS) project. The test campaign, conducted at the Planetary Exploration Lab of DLR in Wessling, aimed to understand the scope of the EMRS breadboard design, its strengths, and the benefits of the modular design. The discussion of test results is based on rover traversal analyses, robustness assessments, wheel deflection analyses, and the overall transportation cost of the rover. This not only enables the comparison of locomotion modes on lunar regolith but also facilitates critical decision-making in the design of future lunar missions., Comment: 6 pages, 5 figures, conference International Conference on Space Robotics

Smart grids feature a bidirectional flow of electricity and data, enhancing flexibility, efficiency, and reliability in increasingly volatile energy grids. However, data from smart meters can reveal sensitive private information. Consequently, the adoption of smart meters is often restricted via legal means and hampered by limited user acceptance. Since metering data is beneficial for fault-free grid operation, power management, and resource allocation, applying privacy-preserving techniques to smart metering data is an important research problem. This work addresses this by using secure multi-party computation (SMPC), allowing multiple parties to jointly evaluate functions of their private inputs without revealing the latter. Concretely, we show how to perform power flow analysis on cryptographically hidden prosumer data. More precisely, we present a tailored solution to the power flow problem building on an SMPC implementation of Newtons method. We analyze the security of our approach in the universal composability framework and provide benchmarks for various grid types, threat models, and solvers. Our results indicate that secure multi-party computation can be able to alleviate privacy issues in smart grids in certain applications.

We investigate the controlled K-type breakdown of a flat-plate boundary-layer with highly non-ideal supercritical fluid at a reduced pressure of $p_{r,\infty}=1.10$. Direct numerical simulations are performed at a Mach number of $M_\infty=0.2$ for one subcritical (liquid-like regime) temperature profile and one strongly-stratified transcritical (pseudo-boiling) temperature profile with slightly heated wall. In the subcritical case, the formation of aligned $\Lambda$-vortices is delayed compared to the reference ideal-gas case of Sayadi et al. (J. Fluid Mech., vol. 724, 2013, pp. 480-509), with steady longitudinal modes dominating the late-transitional stage. When the wall temperature exceeds the pseudo-boiling temperature, streak secondary instabilities lead to the simultaneous development of additional hairpin vortices and near-wall streaky structures near the legs of the primary aligned $\Lambda$-vortices. Nonetheless, transition to turbulence is not violent and is significantly delayed compared to the subcritical regime., Comment: To appear in the Proceedings of the 10th IUTAM Symposium on Laminar-Turbulent Transition (Nagano, 2024)

We perform the first dedicated comparison of five hadronic codes (AM$^3$, ATHE$\nu$A, B13, LeHa-Paris, and LeHaMoC) that have been extensively used in modeling of the spectral energy distribution (SED) of jetted active galactic nuclei. The purpose of this comparison is to identify the sources of systematic errors (e.g., implementation method of proton-photon interactions) and to quantify the expected dispersion in numerical SED models computed with the five codes. The outputs from the codes are first tested in synchrotron self-Compton scenarios that are the simplest blazar emission models used in the literature. We then compare the injection rates and spectra of secondary particles produced in pure hadronic cases with monoenergetic and power-law protons interacting on black-body and power-law photon fields. We finally compare the photon SEDs and the neutrino spectra for realistic proton-synchrotron and leptohadronic blazar models. We find that the codes are in excellent agreement with respect to the spectral shape of the photons and neutrinos. There is a remaining spread in the overall normalization that we quantify, at its maximum, at the level of $\pm 40\%$. This value should be used as an additional, conservative, systematic uncertainty term when comparing numerical simulations and observations., Comment: 32 pages; prepared for submission to AAS journals

The flow of fluids within porous rocks is an important process with numerous applications in Earth sciences. Modeling the compaction-driven fluid flow requires the solution of coupled nonlinear partial differential equations that account for the fluid flow and the solid deformation within the porous medium. Despite the nonlinear relation of porosity and permeability that is commonly encountered, natural data show evidence of channelized fluid flow in rocks that have an overall layered structure. Layers of different rock types routinely have discontinuous hydraulic and mechanical properties. We present numerical results obtained by a novel space-time method which can handle discontinuous initial porosity (and permeability) distributions. The space-time method enables a straightforward coupling to models of mass transport for trace elements. Our results show the influence of different kinds of layering in the development of fluid-rich channels and mass transport., Comment: 12 pages, 9 figures

Benzophenone serves as a prototype chromophore for studying the photochemistry of aromatic ketones, with applications ranging from biochemistry to organic light-emitting diodes. In particular, its intersystem crossing from the first singlet excited state to triplet states has been extensively studied, but experimental or theoretical studies on the preceding internal conversion within the singlet manifold are very rare. This relaxation mechanism is particularly important because direct population transfer of the first singlet excited state from the ground state is inefficient due to its low oscillator strength. In this work, we aim to fill this gap by employing mixed quantum classical and full quantum dynamics simulations and time-resolved photoelectron spectroscopy for gas-phase benzophenone and meta-methyl benzophenone. Our results show that nonadiabatic relaxation via conical intersections leads to a linear increase in the population of the first singlet excited state. This population transfer due to conical intersections can be directly detected by a bifurcation of the photoelectron signal. In addition, we are able to clarify the role of the third singlet excited state degenerate to the second excited state - a topic that remains largely unexplored in the existing literature on benzophenone.

Internet-wide studies provide extremely valuable insight into how operators manage their Internet of Things (IoT) deployments in reality and often reveal grievances, e.g., significant security issues. However, while IoT devices often use IPv6, past studies resorted to comprehensively scan the IPv4 address space. To fully understand how the IoT and all its services and devices is operated, including IPv6-reachable deployments is inevitable-although scanning the entire IPv6 address space is infeasible. In this paper, we close this gap and examine how to best discover IPv6-reachable IoT deployments. To this end, we propose a methodology that allows combining various IPv6 scan direction approaches to understand the findability and prevalence of IPv6-reachable IoT deployments. Using three sources of active IPv6 addresses and eleven address generators, we discovered 6658 IoT deployments. We derive that the available address sources are a good starting point for finding IoT deployments. Additionally, we show that using two address generators is sufficient to cover most found deployments and save time as well as resources. Assessing the security of the deployments, we surprisingly find similar issues as in the IPv4 Internet, although IPv6 deployments might be newer and generally more up-to-date: Only 39% of deployments have access control in place and only 6.2% make use of TLS inviting attackers, e.g., to eavesdrop sensitive data., Comment: 8 pages, 2 figures

Molecular featurisation refers to the transformation of molecular data into numerical feature vectors. It is one of the key research areas in molecular machine learning and computational drug discovery. Recently, message-passing graph neural networks (GNNs) have emerged as a novel method to learn differentiable features directly from molecular graphs. While such techniques hold great promise, further investigations are needed to clarify if and when they indeed manage to definitively outcompete classical molecular featurisations such as extended-connectivity fingerprints (ECFPs) and physicochemical-descriptor vectors (PDVs). We systematically explore and further develop classical and graph-based molecular featurisation methods for two important tasks: molecular property prediction, in particular, quantitative structure-activity relationship (QSAR) prediction, and the largely unexplored challenge of activity-cliff (AC) prediction. We first give a technical description and critical analysis of PDVs, ECFPs and message-passing GNNs, with a focus on graph isomorphism networks (GINs). We then conduct a rigorous computational study to compare the performance of PDVs, ECFPs and GINs for QSAR and AC-prediction. Following this, we mathematically describe and computationally evaluate a novel twin neural network model for AC-prediction. We further introduce an operation called substructure pooling for the vectorisation of structural fingerprints as a natural counterpart to graph pooling in GNN architectures. We go on to propose Sort & Slice, a simple substructure-pooling technique for ECFPs that robustly outperforms hash-based folding at molecular property prediction. Finally, we outline two ideas for future research: (i) a graph-based self-supervised learning strategy to make classical molecular featurisations trainable, and (ii) trainable substructure-pooling via differentiable self-attention., Comment: Doctoral Thesis (Mathematical Institute, University of Oxford)

Characterizing the nonclassicality of quantum systems under minimal assumptions is an important challenge for quantum foundations and technology. Here we introduce a theory-independent method of process tomography and perform it on a superconducting qubit. We demonstrate its decoherence without assuming quantum theory or trusting the devices by modelling the system as a general probabilistic theory. We show that the superconducting system is initially well-described as a quantum bit, but that its realized state space contracts over time, which in quantum terminology indicates its loss of coherence. The system is initially nonclassical in the sense of generalized contextuality: it does not admit of a hidden-variable model where statistically indistinguishable preparations are represented by identical hidden-variable distributions. In finite time, the system becomes noncontextual and hence loses its nonclassicality. Moreover, we demonstrate in a theory-independent way that the system undergoes non-Markovian evolution at late times. Our results extend theory-independent tomography to time-evolving systems, and show how important dynamical physical phenomena can be experimentally monitored without assuming quantum theory., Comment: 15 pages, 9 figures

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

The double detonation is a widely discussed explosion mechanism for Type Ia supernovae, whereby a helium shell detonation ignites a secondary detonation in the carbon/oxygen core of a white dwarf. Even for modern models that invoke relatively small He shell masses, many previous studies have found that the products of the helium shell detonation lead to discrepancies with normal Type Ia supernovae, such as strong Ti II absorption features, extremely red light curves and too large a variation with viewing direction. It has been suggested that non local thermodynamic equilibrium (non-LTE) effects may help to reduce these discrepancies with observations. Here we carry out full non-LTE radiative transfer simulations for a recent double detonation model with a relatively small helium shell mass of 0.05 M$_\odot$. We construct 1D models representative of directions in a 3D explosion model to give an indication of viewing angle dependence. The full non-LTE treatment leads to improved agreement between the models and observations. The light curves become less red, due to reduced absorption by the helium shell detonation products, since these species are more highly ionised. Additionally, the expected variation with observer direction is reduced. The full non-LTE treatment shows promising improvements, and reduces the discrepancies between the double detonation models and observations of normal Type Ia supernovae., Comment: Submitted to MNRAS

Spin-polarized particle beams are of interest for applications like deep-inelastic scattering, e.g. to gain further understanding of the proton's nuclear structure. With the advent of high-intensity laser facilities, laser-plasma-based accelerators offer a promising alternative to common radiofrequency-based accelerators, as they can shorten the required acceleration length significantly. However, in the scope of spin-polarized particles, they bring unique challenges. This paper reviews the developments in the field of spin-polarized particles on the basis of the interaction of laser pulses and high-energy particle beams with plasma. The relevant scaling laws for spin-dependent effects in laser-plasma interaction, as well as acceleration schemes for polarized leptons, ions and gamma quanta are discussed., Comment: 42 pages, 14 figures, submitted to Reports on Progress in Physics

Monocular geometric scene understanding combines panoptic segmentation and self-supervised depth estimation, focusing on real-time application in autonomous vehicles. We introduce MGNiceNet, a unified approach that uses a linked kernel formulation for panoptic segmentation and self-supervised depth estimation. MGNiceNet is based on the state-of-the-art real-time panoptic segmentation method RT-K-Net and extends the architecture to cover both panoptic segmentation and self-supervised monocular depth estimation. To this end, we introduce a tightly coupled self-supervised depth estimation predictor that explicitly uses information from the panoptic path for depth prediction. Furthermore, we introduce a panoptic-guided motion masking method to improve depth estimation without relying on video panoptic segmentation annotations. We evaluate our method on two popular autonomous driving datasets, Cityscapes and KITTI. Our model shows state-of-the-art results compared to other real-time methods and closes the gap to computationally more demanding methods. Source code and trained models are available at https://github.com/markusschoen/MGNiceNet., Comment: Accepted for ACCV 2024

Depth estimation is an essential task toward full scene understanding since it allows the projection of rich semantic information captured by cameras into 3D space. While the field has gained much attention recently, datasets for depth estimation lack scene diversity or sensor modalities. This work presents the ADUULM-360 dataset, a novel multi-modal dataset for depth estimation. The ADUULM-360 dataset covers all established autonomous driving sensor modalities, cameras, lidars, and radars. It covers a frontal-facing stereo setup, six surround cameras covering the full 360-degree, two high-resolution long-range lidar sensors, and five long-range radar sensors. It is also the first depth estimation dataset that contains diverse scenes in good and adverse weather conditions. We conduct extensive experiments using state-of-the-art self-supervised depth estimation methods under different training tasks, such as monocular training, stereo training, and full surround training. Discussing these results, we demonstrate common limitations of state-of-the-art methods, especially in adverse weather conditions, which hopefully will inspire future research in this area. Our dataset, development kit, and trained baselines are available at https://github.com/uulm-mrm/aduulm_360_dataset., Comment: 2024 IEEE International Conference on Intelligent Transportation Systems (ITSC)

With over 3 billion users globally, mobile instant messaging apps have become indispensable for both personal and professional communication. Besides plain messaging, many services implement additional features such as delivery and read receipts informing a user when a message has successfully reached its target. This paper highlights that delivery receipts can pose significant privacy risks to users. We use specifically crafted messages that trigger delivery receipts allowing any user to be pinged without their knowledge or consent. By using this technique at high frequency, we demonstrate how an attacker could extract private information such as the online and activity status of a victim, e.g., screen on/off. Moreover, we can infer the number of currently active user devices and their operating system, as well as launch resource exhaustion attacks, such as draining a user's battery or data allowance, all without generating any notification on the target side. Due to the widespread adoption of vulnerable messengers (WhatsApp and Signal) and the fact that any user can be targeted simply by knowing their phone number, we argue for a design change to address this issue.

Recent progress in interactive point prompt based Image Segmentation allows to significantly reduce the manual effort to obtain high quality semantic labels. State-of-the-art unsupervised methods use self-supervised pre-trained models to obtain pseudo-labels which are used in training a prompt-based segmentation model. In this paper, we propose a novel unsupervised and training-free approach based solely on the self-attention of Stable Diffusion. We interpret the self-attention tensor as a Markov transition operator, which enables us to iteratively construct a Markov chain. Pixel-wise counting of the required number of iterations along the Markov-chain to reach a relative probability threshold yields a Markov-iteration-map, which we simply call a Markov-map. Compared to the raw attention maps, we show that our proposed Markov-map has less noise, sharper semantic boundaries and more uniform values within semantically similar regions. We integrate the Markov-map in a simple yet effective truncated nearest neighbor framework to obtain interactive point prompt based segmentation. Despite being training-free, we experimentally show that our approach yields excellent results in terms of Number of Clicks (NoC), even outperforming state-of-the-art training based unsupervised methods in most of the datasets.

Guidance is an error-correcting technique used to improve the perceptual quality of images generated by diffusion models. Typically, the correction is achieved by linear extrapolation, using an auxiliary diffusion model that has lower performance than the primary model. Using a 2D toy example, we show that it is highly beneficial when the auxiliary model exhibits similar errors as the primary one but stronger. We verify this finding in higher dimensions, where we show that competitive generative performance to state-of-the-art guidance methods can be achieved when the auxiliary model differs from the primary one only by having stronger weight regularization. As an independent contribution, we investigate whether upweighting long-range spatial dependencies improves visual fidelity. The result is a novel guidance method, which we call sliding window guidance (SWG), that guides the primary model with itself by constraining its receptive field. Intriguingly, SWG aligns better with human preferences than state-of-the-art guidance methods while requiring neither training, architectural modifications, nor class conditioning. The code will be released., Comment: Preprint. 19 pages, 14 figures in total, including references and appendix

Comparing the mean vectors across different groups is a cornerstone in the realm of multivariate statistics, with quadratic forms commonly serving as test statistics. However, when the overall hypothesis is rejected, identifying specific vector components or determining the groups among which differences exist requires additional investigations. Conversely, employing multiple contrast tests (MCT) allows conclusions about which components or groups contribute to these differences. However, they come with a trade-off, as MCT lose some benefits inherent to quadratic forms. In this paper, we combine both approaches to get a quadratic form based multiple contrast test that leverages the advantages of both. To understand its theoretical properties, we investigate its asymptotic distribution in a semiparametric model. We thereby focus on two common quadratic forms - the Wald-type statistic and the Anova-type statistic - although our findings are applicable to any quadratic form. Furthermore, we employ Monte-Carlo and resampling techniques to enhance the test's performance in small sample scenarios. Through an extensive simulation study, we assess the performance of our proposed tests against existing alternatives, highlighting their advantages.

Gender bias in artificial intelligence has become an important issue, particularly in the context of language models used in communication-oriented applications. This study examines the extent to which Large Language Models (LLMs) exhibit gender bias in pronoun selection in occupational contexts. The analysis evaluates the models GPT-4, GPT-4o, PaLM 2 Text Bison and Gemini 1.0 Pro using a self-generated dataset. The jobs considered include a range of occupations, from those with a significant male presence to those with a notable female concentration, as well as jobs with a relatively equal gender distribution. Three different sentence processing methods were used to assess potential gender bias: masked tokens, unmasked sentences, and sentence completion. In addition, the LLMs suggested names of individuals in specific occupations, which were then examined for gender distribution. The results show a positive correlation between the models' pronoun choices and the gender distribution present in U.S. labor force data. Female pronouns were more often associated with female-dominated occupations, while male pronouns were more often associated with male-dominated occupations. Sentence completion showed the strongest correlation with actual gender distribution, while name generation resulted in a more balanced 'politically correct' gender distribution, albeit with notable variations in predominantly male or female occupations. Overall, the prompting method had a greater impact on gender distribution than the model selection itself, highlighting the complexity of addressing gender bias in LLMs. The findings highlight the importance of prompting in gender mapping., Comment: 13 pages, 12 figures, 1 table

We consider the generalization of the Araki-Uhlmann formula for relative entropy to Petz-R\'enyi relative entropy. We compute this entropy for a free scalar field in the Minkowski wedge between the vacuum and a coherent state, as well as for a thermal state. In contrast to the relative entropy which in these cases only depends on the sympletic form and thus reduces to the classical entropy of a wave packet, the Petz-R\'enyi relative entropy also depends on the symmetric part of the two-point function and is thus genuinely quantum., Comment: 15 pages, comments welcome

Technical carbon dioxide removal through bioenergy with carbon capture or direct air capture plays a role in virtually all climate mitigation scenarios. Both of these technologies rely on the use of chemical solvents or sorbents in order to capture CO$_2$. Lately, concerns have surfaced about the cost and energy implications of producing solvents and sorbents at scale. Here, we show that the production of chemical sorbents could have significant implications on system cost, energy use and material use depending on how much they are consumed. Among the three chemical sorbents investigated, namely monoethanolamine (MEA) for post-combustion carbon capture, potassium hydroxide for liquid direct air capture and polyethylenimine-silica (PEI) for solid sorbent direct air capture, we found that the production of the compound for solid sorbent direct air capture represent the highest uncertainties for the system. At the high range of solid sorbent consumption, total energy system cost increased by up to 6.5\%, while effects for other options were small to negligible. Scale-up of material production capacities was also substantial for MEA and PEI. Implications of sorbent consumption for carbon capture technologies should be considered more thoroughly in scenarios relying on direct air capture using a solid sorbent.

The effect of replacing individual contributions to an empirical energy function are assessed for halogenated benzenes (X-Bz, X = H, F, Cl, Br) and chlorinated phenols (Cl-PhOH). Introducing electrostatic models based on distributed charges (MDCM) instead of usual atom-centered point charges yields overestimated hydration free energies unless the van der Waals parameters are reparametrized. Scaling van der Waals ranges by 10 \% to 20 \% for three Cl-PhOH and most X-Bz yield results within experimental error bars, which is encouraging, whereas for benzene (H-Bz) point charge-based models are sufficient. Replacing the bonded terms by a neural network-trained energy function with either fluctuating charges or MDCM electrostatics also yields qualitatively correct hydration free energies which still require adaptation of the van der Waals parameters. The infrared spectroscopy of Cl-PhOH is rather well predicted by all models although the ML-based energy function performs somewhat better in the region of the framework modes. It is concluded that refinements of empirical energy functions for targeted applications is a meaningful way towards more quantitative simulations.

We arrange the orders in an algebraic number field in a tree. This tree can be used to enumerate all orders of bounded index in the maximal order as well as the orders over some given order.

Meningeal lymphatic vessels (MLVs) are responsible for the drainage of waste products from the human brain. An impairment in their functionality has been associated with aging as well as brain disorders like multiple sclerosis and Alzheimer's disease. However, MLVs have only recently been described for the first time in magnetic resonance imaging (MRI), and their ramified structure renders manual segmentation particularly difficult. Further, as there is no consistent notion of their appearance, human-annotated MLV structures contain a high inter-rater variability that most automatic segmentation methods cannot take into account. In this work, we propose a new rater-aware training scheme for the popular nnU-Net model, and we explore rater-based ensembling strategies for accurate and consistent segmentation of MLVs. This enables us to boost nnU-Net's performance while obtaining explicit predictions in different annotation styles and a rater-based uncertainty estimation. Our final model, MLV$^2$-Net, achieves a Dice similarity coefficient of 0.806 with respect to the human reference standard. The model further matches the human inter-rater reliability and replicates age-related associations with MLV volume., Comment: ML4H 2024

Energy efficient, safe and reliable Li-ion batteries (LIBs) are required for a wide range of applications. Charging capabilities of thick electrodes still holding their stored high-energy is a most desirable characteristic in future advanced LIBs. The introduction of ultra-thick graphite anode meets limitations in internal electrode transport properties, leading to Li-ion gradients with detrimental consequences for battery cell performance and lifetime. Yet, there is a lack of experimental tools capable of providing a complete view of local processes and evolving gradients within such thick electrodes. Here, we introduce a multi-modal operando measurement approach, enabling quantitative spatio-temporal observations of Li concentrations and intercalation phases in ultra-thick, graphite electrodes. Neutron imaging and diffraction concurrently provide correlated information from the macroscopic scale of the cell and electrode down to the crystallographic scale portraying the intercalation and deintercalation processes. In particular, the evolving formation of the solid electrolyte interphase (SEI), observation of gradients in total lithium content, as well as in the formation of ordered LixC6 phases and trapped lithium have been mapped throughout the first charge-discharge cycle of the cell. Different lithiation stages co-exist during charging and discharging of an ultra-thick composite graphite-based electrode; delayed lithiation and delithiation processes are observed at the central region of the electrode, while the SEI formation, potential plating and dead lithium are predominantly found closer to the interface with the separator. The study furthermore emphasizes the potential of the method to study Li ion diffusion and the kinetics of lithiation phase formation in advanced ultra-thick electrodes.

Spectroscopy of Earth-like exoplanets and ultra-faint galaxies are priority science cases for the coming decades. Here, broadband source flux rates are measured in photons per square meter per hour, imposing extreme demands on detector performance, including dark currents lower than \mbox{1 e-/pixel/kilosecond}, read noise less than \mbox{1 e-/pixel/frame}, and large formats. There are currently no infrared detectors that meet these requirements. The University of Hawai'i and industrial partners are developing one promising technology, linear mode avalanche photodiodes (LmAPDs), which is on track to meet the above-mentioned requirements. We present progress towards developing a science-grade, megapixel format linear-mode avalanche photodiode array for low background shortwave (1 - 2.4 um) infrared astronomy. Our latest results show outstanding performance, with dark current \textless 1e-4 electrons/pixel/second and read noise reducing by 30\% per volt of bias, reaching less than 1e-/pixel/frame in correlated double-sampling, and able to average down to $\sim$0.3 e-/pixel/frame when using multiple non-destructive reads. We present some on-sky data as well as comment on prospects for photon number resolving capability., Comment: SPIE Astronomical Telescopes + Instrumentation 2024, PROC#13103-25. arXiv admin note: text overlap with arXiv:2208.11834

X-ray radioscopy was used to measure the 2D projected dynamic void fraction in a zero/narrow gap alkaline water electrolyzer at a spatial resolution of 15 $\mu$m, for narrow gap sizes up to 300 $\mu$m and current densities up to 0.54 A/cm$^2$. As expected, the void fraction in the bulk was found to increase along the cell height and with increasing current density. The void fraction measured in the gap region (the space between the diaphragm and the electrode and its holes) was always larger than in the bulk. It hardly depended on the gap size at current densities below 0.3 A/cm$^2$. The lowest cell potential was measured for zero gap. No evidence of isolating gas pockets/films in the gaps was found. Liquid crossover and oxygen void fraction exceeding the hydrogen void fraction occurred for porous plate electrodes, but these phenomena were suppressed for perforated foil electrodes.

One-dimensional structures such as nanowires (NWs) show great promise in tailoring the rates of hot carrier thermalization in semiconductors with important implications for the design of efficient hot carrier absorbers. However, fabrication of high-quality, phase-pure crystal structures and control of their intrinsic electronic properties can be challenging, raising concerns about the role of competing radiative and non-radiative recombination mechanisms that govern hot carrier effects. Here, we elucidate the impact of crystal purity and altered electronic properties on the hot carrier properties by comparing two classes of III-V semiconductor NW arrays with similar band-gap energies and geometries, yet different crystal quality: one composed of GaAsSb NWs, free of planar stacking defects, and the other InGaAs NWs with a high density of stacking defects. Photoluminescence spectroscopy demonstrates distinct hot carrier effects in both NW arrays; however, the InGaAs NWs with lower crystal quality exhibit stronger hot carrier effects, as evidenced by increased carrier temperature under identical photoabsorptivity. This difference arises from higher rates of Auger recombination in the InGaAs NWs due to their increased n-type conductivity, as confirmed by excitation power-dependent measurements. Our findings suggest that while enhancing material properties is crucial for improving the performance of hot carrier absorbers, optimizing conditions to increase the rates of Auger recombination will further boost the efficiency of these devices., Comment: 13 pages, 4 figures

Non-transiting terrestrial planets will be accessible by upcoming observatories of which LIFE is an example. Planet b orbiting Teegarden's Star is one of the optimal targets for such missions. We use a one-dimensional atmospheric model with real-gas radiation, a multi-species pseudo-adiabatic convection-condensation scheme, and a water cloud scheme, to estimate the impact of the cloud coverage on the emission spectrum of the target, as well as to assess how sensitive LIFE could be to changes in outgoing flux caused by these clouds. Though the emergent flux decreases with a higher cloud coverage, it does not decrease by more than one order of magnitude as the coverage increases from 0% to 90%. This allows LIFE to retain a high sensitivity to the cloud cover fraction for wavelengths longer than 7 microns. In this spectral range, with at least 1 bar of N2, LIFE is able to distinguish cloud cover fractions as small as 10% given an integration time of 24 hours, and yields much better precision with a week-long integration. An integration time of one week also allows the resolution of local variations in spectral flux, which can lead to an easier molecule identification. This ability remains if the planet is a CO2-dominated Venus analog. Partial pressures of N2 lower than 1 bar may create a degeneracy with the cloud cover fraction. LIFE shows promising potential for detecting and characterizing atmospheres even with a high cloud coverage, and retaining a fine sensitivity to relatively small differences in cloud cover fractions.

Entanglement can improve the measurement precision of quantum sensors beyond the shot noise limit. Neutral atoms, the basis of some of the most precise and accurate optical clocks and interferometers, do not naturally exhibit all-to-all interactions that are traditionally used to generate such entangled states. Instead, we take advantage of the magnetic dipole-dipole interaction native to most neutral atoms to realize spin-squeezed states. We achieve 7.1 dB of metrologically useful squeezing using the finite-range spin exchange interactions in an erbium quantum gas microscope. We further propose and demonstrate that introducing atomic motion protects the spin sector coherence at low fillings, significantly improving the achievable spin squeezing in a 2D dipolar system. This work's protocol can be implemented with most neutral atoms, opening the door to quantum-enhanced metrology in other itinerant dipolar systems, such as molecules or optical lattice clocks, and serves as a novel method for studying itinerant quantum magnetism with long-range interactions., Comment: 15 pages, 10 figures

Direct imaging observations are biased towards wide-separation, massive companions that have degenerate formation histories. Although the majority of exoplanets are expected to form via core accretion, most directly imaged exoplanets have not been convincingly demonstrated to follow this formation pathway. We obtained new interferometric observations of the directly imaged giant planet AF Lep b with the VLTI/GRAVITY instrument. We present three epochs of 50$\mu$as relative astrometry and the K-band spectrum of the planet for the first time at a resolution of R=500. Using only these measurements, spanning less than two months, and the Hipparcos-Gaia Catalogue of Accelerations, we are able to significantly constrain the planet's orbit; this bodes well for interferometric observations of planets discovered by Gaia DR4. Including all available measurements of the planet, we infer an effectively circular orbit ($e<0.02, 0.07, 0.13$ at $1, 2, 3 \sigma$) in spin-orbit alignment with the host, and a measure a dynamical mass of $M_\mathrm{p}=3.75\pm0.5\,M_\mathrm{Jup}$. Models of the spectrum of the planet show that it is metal rich ([M/H]$=0.75\pm0.25$), with a C/O ratio encompassing the solar value. This ensemble of results show that the planet is consistent with core accretion formation., Comment: Accepted to the Astronomical Journal. 12 figures, 4 tables

The increasing importance of supply chain security for digital devices -- from consumer electronics to critical infrastructure -- has created a high demand for skilled cybersecurity experts. These experts use Hardware Reverse Engineering (HRE) as a crucial technique to ensure trust in digital semiconductors. Recently, the US and EU have provided substantial funding to educate this cybersecurity-ready semiconductor workforce, but success depends on the widespread availability of academic training programs. In this paper, we investigate the current state of education in hardware security and HRE to identify efficient approaches for establishing effective HRE training programs. Through a systematic literature review, we uncover 13 relevant courses, including eight with accompanying academic publications. We identify common topics, threat models, key pedagogical features, and course evaluation methods. We find that most hardware security courses do not prioritize HRE, making HRE training scarce. While the predominant course structure of lectures paired with hands-on projects appears to be largely effective, we observe a lack of standardized evaluation methods and limited reliability of student self-assessment surveys. Our results suggest several possible improvements to HRE education and offer recommendations for developing new training courses. We advocate for the integration of HRE education into curriculum guidelines to meet the growing societal and industry demand for HRE experts., Comment: 7 pages, 1 figure, 2 tables. To be published in Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1. For supplementary materials, see https://osf.io/dt8ne/

Mastering the challenge of predicting properties of unknown materials with multiple principal elements (high entropy alloys/compositionally complex solid solutions) is crucial for the speedup in materials discovery. We show and discuss three models, using property data from two ternary systems (Ag-Pd-Ru; Ag-Pd-Pt), to predict material performance in the shared quaternary system (Ag-Pd-Pt-Ru). First, we apply Gaussian Process Regression (GPR) based on composition, which includes both Ag and Pd, achieving an initial correlation coefficient for the prediction ($r$) of 0.63 and a determination coefficient ($r^2$) of 0.08. Second, we present a version of the GPR model using word embedding-derived materials vectors as representations. Using materials-specific embedding vectors significantly improves the predictive capability, evident from an improved $r^2$ of 0.65. The third model is based on a `standard vector method' which synthesizes weighted vector representations of material properties, then creating a reference vector that results in a very good correlation with the quaternary system's material performance (resulting $r$ of 0.89). Our approach demonstrates that existing experimental data combined with latent knowledge of word embedding-based representations of materials can be used effectively for materials discovery where data is typically sparse., Comment: 17 pages, 12 figures, pre-print

Peptides are ubiquitous and important biologically derived molecules, that have been found to self-assemble to form a wide array of structures. Extensive research has explored the impacts of both internal chemical composition and external environmental stimuli on the self-assembly behaviour of these systems. However, there is yet to be a systematic study that gathers this rich literature data and collectively examines these experimental factors to provide a global picture of the fundamental rules that govern protein self-assembly behavior. In this work, we curate a peptide assembly database through a combination of manual processing by human experts and literature mining facilitated by a large language model. As a result, we collect more than 1,000 experimental data entries with information about peptide sequence, experimental conditions and corresponding self-assembly phases. Utilizing the collected data, ML models are trained and evaluated, demonstrating excellent accuracy (>80\%) and efficiency in peptide assembly phase classification. Moreover, we fine-tune our GPT model for peptide literature mining with the developed dataset, which exhibits markedly superior performance in extracting information from academic publications relative to the pre-trained model. We find that this workflow can substantially improve efficiency when exploring potential self-assembling peptide candidates, through guiding experimental work, while also deepening our understanding of the mechanisms governing peptide self-assembly. In doing so, novel structures can be accessed for a range of applications including sensing, catalysis and biomaterials.

Cracks are among the earliest indicators of deterioration in concrete structures. Early automatic detection of these cracks can significantly extend the lifespan of critical infrastructures, such as bridges, buildings, and tunnels, while simultaneously reducing maintenance costs and facilitating efficient structural health monitoring. This study investigates whether leveraging multi-temporal data for crack segmentation can enhance segmentation quality. Therefore, we compare a Swin UNETR trained on multi-temporal data with a U-Net trained on mono-temporal data to assess the effect of temporal information compared with conventional single-epoch approaches. To this end, a multi-temporal dataset comprising 1356 images, each with 32 sequential crack propagation images, was created. After training the models, experiments were conducted to analyze their generalization ability, temporal consistency, and segmentation quality. The multi-temporal approach consistently outperformed its mono-temporal counterpart, achieving an IoU of $82.72\%$ and a F1-score of $90.54\%$, representing a significant improvement over the mono-temporal model's IoU of $76.69\%$ and F1-score of $86.18\%$, despite requiring only half of the trainable parameters. The multi-temporal model also displayed a more consistent segmentation quality, with reduced noise and fewer errors. These results suggest that temporal information significantly enhances the performance of segmentation models, offering a promising solution for improved crack detection and the long-term monitoring of concrete structures, even with limited sequential data.

We investigate the performance of state-of-the-art spintronic THz emitters (W or Ta)/CoFeB/Pt with non-magnetic underlayer deposited using oblique angle deposition. The THz emission amplitude in the presence or absence of an external magnetic field remains the same and remarkably stable over time. This stability is attributed to the enhanced uniaxial magnetic anisotropy in the ferromagnetic layer, achieved by oblique angle deposition of the underlying non-magnetic layer. Our findings could be used for the development of practical field-free emitters of linearly polarized THz radiation, potentially enabling novel applications in future THz technologies., Comment: 6 pages including references and 5 figures

We study lattice paths in the cube, starting at $(n,n,n)$ and ending at $(0,0,0)$, with unit steps $(-1,0,0)$, $(0,-1,0)$, $(0,0,-1)$. Our main interest is the number of times the diagonal $x=y=z$ is visited during the random walk. We derive the corresponding generating function of such lattice paths. We also turn to the cuboid, enumerating lattice paths starting at $(n_1,n_2,n_3)$ and ending at $(0,0,0)$ according to the visits to the cube's diagonal. Furthermore, we provide for the cube a refined enumeration according to visits after a detour of a certain length. These enumerations allow us to obtain distributional results for the corresponding random variables. Extensions to hypercube are discussed, as well as a summary of known results for the square. We collect applications to the sampling without replacement urn and a card guessing game. Finally, we also show how to recover (and extend) a very recent result of Li and Starr on Dyck bridges using generating functions and composition schemes., Comment: 20 pages and five figures

Chiral phonons are desirable for applications in spintronics but their generation and control remains a challenge. Here we demonstrate the emergence of truly chiral phonons from selective magnon-phonon coupling in inversion-symmetric magnetic systems. Considering bcc Fe as example, we quantitatively calculate hybridized magnon-phonon quasiparticle states across the entire Brillouin zone utilizing first-principles calculations. Our findings challenge conventional magneto-elastic interpretations and reveal finite zero-point phonon angular momentum and strong anomalous Hall responses linked to finite (spin) Berry curvatures. Our results further establish that the existence of chiral phonons, particularly along high-symmetry directions, is common in many magnetic materials, offering promising avenues for novel spintronic and phononic devices.

The effective visualization of genomic data is crucial for exploring and interpreting complex relationships within and across genes and genomes. Despite advances in developing dedicated bioinformatics software, common visualization tools often fail to efficiently integrate the diverse datasets produced in comparative genomics, lack intuitive interfaces to construct complex plots and are missing functionalities to inspect the underlying data iteratively and at scale. Here, we introduce gggenomes, a versatile R package designed to overcome these challenges by extending the widely used ggplot2 framework for comparative genomics. gggenomes is available from CRAN and GitHub, accompanied by detailed and user-friendly documentation (https://thackl.github.io/gggenomes)., Comment: TH and MA contributed equally to this work. Corresponding author: Thomas Hackl

While Minimum Bayes Risk (MBR) decoding using metrics such as COMET or MetricX has outperformed traditional decoding methods such as greedy or beam search, it introduces a challenge we refer to as metric bias. As MBR decoding aims to produce translations that score highly according to a specific utility metric, this very process makes it impossible to use the same metric for both decoding and evaluation, as improvements might simply be due to reward hacking rather than reflecting real quality improvements. In this work we find that compared to human ratings, neural metrics not only overestimate the quality of MBR decoding when the same metric is used as the utility metric, but they also overestimate the quality of MBR/QE decoding with other neural utility metrics as well. We also show that the metric bias issue can be mitigated by using an ensemble of utility metrics during MBR decoding: human evaluations show that MBR decoding using an ensemble of utility metrics outperforms a single utility metric., Comment: To appear at WMT2024

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

LiDAR Semantic Segmentation is a fundamental task in autonomous driving perception consisting of associating each LiDAR point to a semantic label. Fully-supervised models have widely tackled this task, but they require labels for each scan, which either limits their domain or requires impractical amounts of expensive annotations. Camera images, which are generally recorded alongside LiDAR pointclouds, can be processed by the widely available 2D foundation models, which are generic and dataset-agnostic. However, distilling knowledge from 2D data to improve LiDAR perception raises domain adaptation challenges. For example, the classical perspective projection suffers from the parallax effect produced by the position shift between both sensors at their respective capture times. We propose a Semi-Supervised Learning setup to leverage unlabeled LiDAR pointclouds alongside distilled knowledge from the camera images. To self-supervise our model on the unlabeled scans, we add an auxiliary NeRF head and cast rays from the camera viewpoint over the unlabeled voxel features. The NeRF head predicts densities and semantic logits at each sampled ray location which are used for rendering pixel semantics. Concurrently, we query the Segment-Anything (SAM) foundation model with the camera image to generate a set of unlabeled generic masks. We fuse the masks with the rendered pixel semantics from LiDAR to produce pseudo-labels that supervise the pixel predictions. During inference, we drop the NeRF head and run our model with only LiDAR. We show the effectiveness of our approach in three public LiDAR Semantic Segmentation benchmarks: nuScenes, SemanticKITTI and ScribbleKITTI., Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024

Rare earth intermetallic compounds crystallized in AlB$_2$-type and its low-symmetry derivative CeCu$_2$-type structures potentially host diverse frustrated magnetic structures and rich magnetotransport phenomena. We report the film growth of CeCu$_2$-type EuZn$_2$ by molecular beam epitaxy and the observation of topological Hall responses highly contrastive to isostructural EuCd$_2$. While their magnetization curves are rather similar, the topological Hall effect observed in EuZn$_2$ is simpler, with the only one component enhanced at the magnetic transition field. EuZn$_2$ may be a unique system for studying the magnetic domain boundary effect on topological Hall responses among the CeCu$_2$-type rare-earth intermetallic compounds., Comment: 12 pages, 4 figures

Digital technologies have long been explored as a complement to standard procedure in mental health research and practice, ranging from the management of electronic health records to app-based interventions. The recent emergence of large language models (LLMs), both proprietary and open-source ones, represents a major new opportunity on that front. Yet there is still a divide between the community developing LLMs and the one which may benefit from them, thus hindering the beneficial translation of the technology into clinical use. This divide largely stems from the lack of a common language and understanding regarding the technology's inner workings, capabilities, and risks. Our narrative review attempts to bridge this gap by providing intuitive explanations behind the basic concepts related to contemporary LLMs.

The measurement and description of the charge-carrier lifetime (tauc) is crucial for the wide-ranging applications of lead-halide perovskites. We present time-resolved microwave-detected photoconductivity decay (TRMCD) measurements and a detailed analysis of the possible recombination mechanisms including trap-assisted, radiative, and Auger recombination. We prove that performing injection-dependent measurement is crucial in identifying the recombination mechanism. We present temperature and injection level dependent measurements in CsPbBr_3, which is an inorganic lead-halide perovskite. In this material, we observe the dominance of charge-carrier trapping, which results in ultra-long charge-carrier lifetimes. Although charge trapping can limit the effectiveness of materials in photovoltaic applications, it also offers significant advantages for various alternative uses, including delayed and persistent photodetection, charge-trap memory, afterglow light-emitting diodes, quantum information storage, and photocatalytic activity.

We investigate what kind of images lie in the high-density regions of diffusion models. We introduce a theoretical mode-tracking process capable of pinpointing the exact mode of the denoising distribution, and we propose a practical high-probability sampler that consistently generates images of higher likelihood than usual samplers. Our empirical findings reveal the existence of significantly higher likelihood samples that typical samplers do not produce, often manifesting as cartoon-like drawings or blurry images depending on the noise level. Curiously, these patterns emerge in datasets devoid of such examples. We also present a novel approach to track sample likelihoods in diffusion SDEs, which remarkably incurs no additional computational cost.