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The teacher workforce in science, technology, engineering, and math (STEM) has been a perpetual weak spot in public schools' teaching rosters. Prior reports show the pipeline of new STEM teachers into the profession is weak while demand for instruction in STEM fields continues to grow. This paper seeks to document whether and how the STEM teacher workforce in high-need settings has been impacted by these pressures. It analyzes successive waves of nationally representative teacher survey data to explore demographics and qualifications among the secondary STEM teacher workforce in high-need settings has fared over time. Results show the STEM teacher workforce in high-need schools is consistently less likely to be experienced, less likely to hold any degree in a STEM field, less likely to hold a master's degree, and less likely to be fully certified than STEM teachers in more advantaged settings. Yet, surprisingly, the observed qualifications gaps across high- versus low-need settings are either stable or slightly narrowing over time. Certain STEM fields--namely, physical sciences and computer science--rely on a less qualified workforce than those in math or biology, with low levels of teacher qualifications observed across both high- and low-need settings. Though even when considering field-specific alignment between teachers' background qualifications and their teaching assignments, the qualifications gap between high- and low-need settings has been slowly shrinking in three of four STEM fields analyzed here. In addition to high-need schools, small schools (based on enrollment size) rely more heavily on underqualified STEM teachers.

The chemical abundances of elements such as barium and the lanthanides are essential to understand the nucleosynthesis of heavy elements in the early Universe as well as the contribution of different neutron capture processes (for example slow versus rapid) at different epochs. The Chemical Evolution of R-process Elements in Stars (CERES) project aims to provide a homogeneous analysis of a sample of metal-poor stars ( [Fe/H]\<-1.5) to improve our understanding of the nucleosynthesis of neutron capture elements, in particular the r-process elements, in the early Galaxy. Our data consist of a sample of high resolution and high signal-to-noise ratio UVES spectra. The chemical abundances were derived through spectrum synthesis, using the same model atmospheres and stellar parameters as derived in the first paper of the CERES series. We measured chemical abundances or upper limits of seven heavy neutron capture elements (Ba, La, Ce, Pr, Nd, Sm, and Eu) for a sample of 52 metal-poor giant stars. We estimated through the mean shift clustering algorithm that at Ba/H =-2.4 and Fe/H =-2.4 a variation in the trend of X/Ba with X=La,Nd,Sm,Eu, versus Ba/H occurs. This result suggests that, for Ba/H $\<$$-2.4$, Ba nucleosynthesis in the Milky Way halo is primarily due to the $r$-process, while for Ba/H \<-2.4 the effect of the s-process contribution begins to be visible. In our sample, stars with Ba/Eu compatible with a Solar System pure r-process value (hereafter, r-pure) do not show any particular trend compared to other stars, suggesting r-pure stars may form in similar environments to stars with less pure r-process enrichments. Homogeneous investigations of high resolution and signal-to-noise ratio spectra are crucial for studying the heavy elements formation, as they provide abundances that can be used to test nucleosynthesis models as well as Galactic chemical evolution models., Comment: Astronomy & Astrophysics - A&A, In press

We present a theoretical framework allowing to make an explicit connection between the phenomenology of QCD, namely the properties of the gluon correlator and Wilson loops, and a particular relativistic model for the description of nuclear matter and neutron stars, the chiral confining model. Starting with the Field Correlator Method, which incorporates explicitly and simultaneously confinement and chiral symmetry breaking, we describe how to obtain the response of the composite nucleon to the nuclear scalar field, the relative role of confinement and chiral symmetry breaking in the in-medium nucleon mass evolution, generating the three-body forces needed for the saturation mechanism., Comment: 55 pages, 5 figures

Visual tokenization via auto-encoding empowers state-of-the-art image and video generative models by compressing pixels into a latent space. Although scaling Transformer-based generators has been central to recent advances, the tokenizer component itself is rarely scaled, leaving open questions about how auto-encoder design choices influence both its objective of reconstruction and downstream generative performance. Our work aims to conduct an exploration of scaling in auto-encoders to fill in this blank. To facilitate this exploration, we replace the typical convolutional backbone with an enhanced Vision Transformer architecture for Tokenization (ViTok). We train ViTok on large-scale image and video datasets far exceeding ImageNet-1K, removing data constraints on tokenizer scaling. We first study how scaling the auto-encoder bottleneck affects both reconstruction and generation -- and find that while it is highly correlated with reconstruction, its relationship with generation is more complex. We next explored the effect of separately scaling the auto-encoders' encoder and decoder on reconstruction and generation performance. Crucially, we find that scaling the encoder yields minimal gains for either reconstruction or generation, while scaling the decoder boosts reconstruction but the benefits for generation are mixed. Building on our exploration, we design ViTok as a lightweight auto-encoder that achieves competitive performance with state-of-the-art auto-encoders on ImageNet-1K and COCO reconstruction tasks (256p and 512p) while outperforming existing auto-encoders on 16-frame 128p video reconstruction for UCF-101, all with 2-5x fewer FLOPs. When integrated with Diffusion Transformers, ViTok demonstrates competitive performance on image generation for ImageNet-1K and sets new state-of-the-art benchmarks for class-conditional video generation on UCF-101., Comment: 28 pages, 25 figures, 7 Tables

Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these models. Counterfactual explanations aim to explain predictions by finding the ''nearest'' in-distribution alternative input whose prediction changes in a pre-specified way. However, it remains an open question how to define this nearest alternative input, whose solution depends on both the domain (e.g. images, graphs, tabular data, etc.) and the specific application considered. For graphs, this problem is complicated i) by their discrete nature, as opposed to the continuous nature of state-of-the-art graph classifiers; and ii) by the node permutation group acting on the graphs. We propose a method to generate counterfactual explanations for any differentiable black-box graph classifier, utilizing a case-specific permutation equivariant graph variational autoencoder. We generate counterfactual explanations in a continuous fashion by traversing the latent space of the autoencoder across the classification boundary of the classifier, allowing for seamless integration of discrete graph structure and continuous graph attributes. We empirically validate the approach on three graph datasets, showing that our model is consistently high-performing and more robust than the baselines., Comment: Published at Northern Lights Deep Learning Conference 2025

Background Advanced adaptive randomised clinical trials are increasingly used. Compared to their conventional counterparts, their flexibility may make them more efficient, increase the probability of obtaining conclusive results without larger samples than necessary, and increase the probability that individual participants are allocated to more promising interventions. However, limited guidance is available on designing and evaluating the performance of advanced adaptive trials. Methods We summarise the methodological considerations and provide practical guidance on the entire workflow of planning and evaluating advanced adaptive trials using adaptive stopping, adaptive arm dropping, and response-adaptive randomisation within a Bayesian statistical framework. Results This comprehensive practical guide covers the key methodological decisions for advanced adaptive trials and their specification and evaluation using statistical simulation. These considerations include interventions and common control use; outcome type and generation; analysis timing and outcome-data lag; allocation rules; analysis model; adaptation rules for stopping and arm dropping; clinical scenarios assessed; performance metrics; calibration; sensitivity analyses; and reporting. The considerations are covered in the context of realistic examples, along with simulation code using the adaptr R package. Conclusions This practical guide will help clinical trialists, methodologists, and biostatisticians design and evaluate advanced adaptive trials., Comment: 63 pages (30 without appendices), 5 figures (3 without appendices), 3 tables

We present a novel finite-volume QED action designed to improve the infinite-volume extrapolation of hadronic observables in precision lattice QCD+QED calculations. The new action, which we call QED$_\text{r}$, is designed to remove kinematics-independent finite-volume corrections that appear at $\mathrm{O}(1/L^3)$ in the commonly used QED$_\text{L}$ formulation, where $L$ is the spatial extent of the physical volume. For a number of key observables, these effects depend on the internal structure of the hadrons and are difficult to evaluate non-perturbatively, making an analytical subtraction of the finite-volume effects impractical. We show that the QED$_\text{r}$ action proposed here corresponds to a particular subset of the infrared-improved QED actions presented by Davoudi et al. in 2019. We explicitly study the QED$_\text{r}$ electromagnetic finite-size effects on hadron masses and leptonic decay rates, relevant for Standard Model precision tests using the Cabibbo-Kobayashi-Maskawa matrix elements. In addition, we propose methods to remove the kinematics-dependent $\mathrm{O}(1/L^3)$ effects in leptonic decays. The removal of such contributions, shifting the leading contamination to $\mathrm{O}(1/L^4)$, will help to reduce the systematic uncertainties associated with finite-volume effects in future lattice QCD+QED calculations.

Understanding the movement of the left ventricle myocardium (LVmyo) during the cardiac cycle is essential for assessing cardiac function. One way to model this movement is through a series of deformable image registrations (DIRs) of the LVmyo. Traditional deep learning methods for DIRs, such as those based on convolutional neural networks, often require substantial memory and computational resources. In contrast, implicit neural representations (INRs) offer an efficient approach by operating on any number of continuous points. This study extends the use of INRs for DIR to cardiac computed tomography (CT), focusing on LVmyo registration. To enhance the precision of the registration around the LVmyo, we incorporate the signed distance field of the LVmyo with the Hounsfield Unit values from the CT frames. This guides the registration of the LVmyo, while keeping the tissue information from the CT frames. Our framework demonstrates high registration accuracy and provides a robust method for temporal registration that facilitates further analysis of LVmyo motion., Comment: 9 pages, 5 figures, STACOM 2024

Being a form of biometric identification, the security of the speaker identification (SID) system is of utmost importance. To better understand the robustness of SID systems, we aim to perform more realistic attacks in SID, which are challenging for both humans and machines to detect. In this study, we propose DiffAttack, a novel timbre-reserved adversarial attack approach that exploits the capability of a diffusion-based voice conversion (DiffVC) model to generate adversarial fake audio with distinct target speaker attribution. By introducing adversarial constraints into the generative process of the diffusion-based voice conversion model, we craft fake samples that effectively mislead target models while preserving speaker-wise characteristics. Specifically, inspired by the use of randomly sampled Gaussian noise in conventional adversarial attacks and diffusion processes, we incorporate adversarial constraints into the reverse diffusion process. These constraints subtly guide the reverse diffusion process toward aligning with the target speaker distribution. Our experiments on the LibriTTS dataset indicate that DiffAttack significantly improves the attack success rate compared to vanilla DiffVC and other methods. Moreover, objective and subjective evaluations demonstrate that introducing adversarial constraints does not compromise the speech quality generated by the DiffVC model., Comment: 5 pages,4 figures, accepted by ICASSP 2025

Accurate and efficient circuit models are necessary to control the power electronic circuits found on plasma physics experiments. Tuning and controlling the behavior of these circuits is inextricably linked to plasma performance. Linear models are greatly preferred for control applications due to their well-established performance guarantees, but they typically fail to capture nonlinear dynamics and changes in experimental parameters. Data-driven system identification can help mitigate these shortcomings by learning interpretable and accurate reduced-order models of a complex system, in this case the injector circuits of the Helicity Injected Torus - Steady Inductive Upgrade (HIT-SIU) experiment. Specifically, the Bagging Optimized Dynamic Mode Decomposition (BOP-DMD), is leveraged to learn stable, reduced order models of the interaction between the spheromak plasma formed in the confinement volume, and the injector circuits of the device. BOP-DMD is trained and evaluated on an analytic model of the vacuum dynamics of the injector circuits of HIT-SIU, as well as an analytic linear reduced-order model for the injector dynamics when a plasma is present. BOP-DMD is then fit on experimental data, both on shots with and without a plasma in the confinement volume. In doing so, we demonstrate the capability of data-driven methods to produce stable, linear models for control and uncertainty quantification in plasma experiments., Comment: Corrected a missing figure reference

It is possible to formulate immiscible and incompressible two-phase flow in porous media in a mathematical framework resembling thermodynamics based on the Jaynes generalization of statistical mechanics. We review this approach and discuss the meaning of the emergent variables that appear, agiture, flow derivative and flow pressure, which are conjugate to the configurational entropy, the saturation and the porosity respectively. We conjecture that the agiture, the temperature-like variable, is directly related to the pressure gradient. This has as a consequence that the configurational entropy, a measure of how the fluids are distributed within the porous media and the accompanying velocity field, and the mobility of the fluids are related. We also develop elements of another version of the thermodynamics-like formalism where fractional flow rather than saturation is the control variable, since this is typically the natural control variable in experiments., Comment: 17 pages, 4 figures

The Great Salt Lake (Utah, USA) is reducing in size, which raises several ecological concerns, including the effect of an increasing area of dry playa exposed by the retreating lake. This study focuses solely on concerns about the toxicity of metals in the dust blowing off the playa. Although considerable efforts have been made to understand aeolian dust in urban areas along the Wasatch Front, located just east and south of the Great Salt Lake, there is still a need to consolidate existing research and to conduct a compositional analysis of the dust found in these urban areas. We investigated the dust reaching urban monitoring sites around the Great Salt Lake that are managed by the Utah Division of Air Quality. By analyzing historical data, we found that the decrease in the Great Salt Lake's surface area has not led to a statistically significant increase in dust events in urban areas. Windrose plots align with prior research, indicating that heightened dust levels in urban areas coincide with winds originating from the south or west, passing over identified playas and deserts such as the Milford Flats, Sevier Dry Lake, Tule Dry Lake, Great Salt Lake Desert, Dugway Proving Grounds, and the West Desert of Utah. Metallic compositional analysis of urban dust was used to evaluate potential health risks associated with the dust using the hazard quotient, air regional screening levels, and cancer risk methods. This analysis revealed no significant increase in concentrations of toxic metals. However, this is not to preclude a risk of dust-related health concerns, especially due to pre-existing arsenic and lead levels.

We created new reddening maps and derived new extinction laws from visual to near-infrared passbands using improved RR~Lyrae period-absolute magnitude-metallicity relations, thus enabling distance estimates for individual bulge RR~Lyrae variables. The extinction law is most uniform in RIK and RJK and the distances to individual RR~Lyrae based on these colors are determined with an accuracy six and four percent, respectively. Using only the near-infrared passbands for distance estimation we inferred the distance to the Galactic center equal to djk = 8.2 +- 0.001(stat) +- 0.53(sys)pc after geometrical correction. We show that variations in the extinction law toward the Galactic bulge can mimic a barred spatial distribution in the bulge RR~Lyrae star population in visual passbands. This arises from a gradient in extinction differences along Galactic longitudes and latitudes, which can create the perception of the Galactic bar, particularly when using visual passband-based distances. A barred angle in the RR~Lyrae spatial distribution disappears when near-infrared passband-based distances are used, as well as when reddening law variations are incorporated in visual passband-based distances. The prominence of the bar, as traced by RR~Lyrae stars, depends on their metallicity, with metal-poor RR~Lyrae stars ([Fe/H]<-1.0dex) showing little to no tilt with respect to the bar. Metal-rich ([Fe/H]>-1.0dex) RR~Lyrae stars do show a barred/bulge signature in spatial properties derived using near-infrared distances, with an angle {\iota} = 18 +- 5deg, consistent with previous bar measurements from the literature. The 5D kinematic analysis, primarily based on transverse velocities, indicates a rotational lag in RR~Lyrae stars compared to red clump giants. Despite variations in the extinction law, our kinematic conclusions are robust across different distance estimation methods., Comment: Accepted for publication in A&A

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

A bicycle node network is a wayfinding system targeted at recreational cyclists, consisting of numbered signposts placed alongside already existing infrastructure. Bicycle node networks are becoming increasingly popular as they encourage sustainable tourism and rural cycling, while also being flexible and cost-effective to implement. However, the lack of a formalized methodology and data-driven tools for the planning of such networks is a hindrance to their adaptation on a larger scale. To address this need, we present the BikeNodePlanner: a fully open-source decision support tool, consisting of modular Python scripts to be run in the free and open-source geographic information system QGIS. The BikeNodePlanner allows the user to evaluate and compare bicycle node network plans through a wide range of metrics, such as land use, proximity to points of interest, and elevation across the network. The BikeNodePlanner provides data-driven decision support for bicycle node network planning, and can hence be of great use for regional planning, cycling tourism, and the promotion of rural cycling., Comment: 8 pages, 3 figures; SI: 7 pages, 5 figures

Complex Langevin simulations are an attempt to solve the sign (or complex-action) problem encountered in various physical systems of interest. The method is based on a complexification of the underlying degrees of freedom and an evolution in an auxiliary time dimension. The complexification, however, does not come without drawbacks, the most severe of which is the infamous 'wrong convergence' problem, stating that complex Langevin simulations sometimes fail to produce correct answers despite their apparent convergence. It has long been realized that wrong convergence may - in principle - be fixed by the introduction of a suitable kernel into the complex Langevin equation, such that the conventional correctness criteria are met. However, as we discuss in this work, complex Langevin results may - especially in the presence of a kernel - still be affected by unwanted so-called integration cycles of the theory spoiling them. Indeed, we confirm numerically that in the absence of boundary terms the complex Langevin results are given by a linear combination of such integration cycles, as put forward by Salcedo & Seiler. In particular, we shed light on the way different choices of kernel affect which integration cycles are being sampled in a simulation and how this knowledge can be used to ensure correct convergence in simple toy models., Comment: 19 pages + 5 pages appendix, 19 figures

This work investigates the current wall-modeled large-eddy simulation (WMLES) capabilities of the open-source computational fluid dynamics solver OpenFOAM, which is used widely in academia and industry. This is achieved by a simulation campaign that covers both attached and smooth body separation cases. The campaign includes simulations using four different wall models and aims to investigate the sensitivity of the results to changes in numerics, mesh resolution, and subgrid-scale modeling. The results demonstrate that two main factors largely determine OpenFOAM-based WMLES performance. These are the discretization of the convective term and wall modeling. For the former, the best performance in the attached case is achieved with low-dissipation numerics, however, for the smooth body separation case, more dissipative numerics give the best performance. For the latter, we find that both equilibrium and non-equilibrium wall models perform well in the attached case but that the non-equilibrium models significantly improve the prediction of smooth body separation. Still, the non-equilibrium wall model results do not show a uniform improvement over equilibrium models. This is explained by an inconsistent accounting of non-equilibrium physics in these models, i.e., including the pressure gradient term without also including the convective term. This highlights the potential for future performance improvements by using non-equilibrium wall models that consistently account for both the convective and pressure gradient terms., Comment: 27 pages, 26 figures

Low-light image enhancement (LLIE) is a fundamental task in computational photography, aiming to improve illumination, reduce noise, and enhance the image quality of low-light images. While recent advancements primarily focus on customizing complex neural network models, we have observed significant redundancy in these models, limiting further performance improvement. In this paper, we investigate and rethink the model redundancy for LLIE, identifying parameter harmfulness and parameter uselessness. Inspired by the rethinking, we propose two innovative techniques to mitigate model redundancy while improving the LLIE performance: Attention Dynamic Reallocation (ADR) and Parameter Orthogonal Generation (POG). ADR dynamically reallocates appropriate attention based on original attention, thereby mitigating parameter harmfulness. POG learns orthogonal basis embeddings of parameters and prevents degradation to static parameters, thereby mitigating parameter uselessness. Experiments validate the effectiveness of our techniques. We will release the code to the public.

In this paper we present a new thin-wall eddy current modeling code, ThinCurr, for studying inductively-coupled currents in 3D conducting structures -- with primary application focused on the interaction between currents flowing in coils, plasma, and conducting structures of magnetically-confined plasma devices. The code utilizes a boundary finite element method on an unstructured, triangular grid to accurately capture device structures. The new code, part of the broader Open FUSION Toolkit, is open-source and designed for ease of use without sacrificing capability and speed through a combination of Python, Fortran, and C/C++ components. Scalability to large models is enabled through use of hierarchical off-diagonal low-rank compression of the inductance matrix, which is otherwise dense. Ease of handling large models of complicated geometry is further supported by automatic determination of supplemental elements through a greedy homology approach. A detailed description of the numerical methods of the code and verification of the implementation of those methods using cross-code comparisons against the VALEN code and Ansys commercial analysis software is shown.

A remarkable characteristic of quantum computing is the potential for reliable computation despite faulty qubits. This can be achieved through quantum error correction, which is typically implemented by repeatedly applying static syndrome checks, permitting correction of logical information. Recently, the development of time-dynamic approaches to error correction has uncovered new codes and new code implementations. In this work, we experimentally demonstrate three time-dynamic implementations of the surface code, each offering a unique solution to hardware design challenges and introducing flexibility in surface code realization. First, we embed the surface code on a hexagonal lattice, reducing the necessary couplings per qubit from four to three. Second, we walk a surface code, swapping the role of data and measure qubits each round, achieving error correction with built-in removal of accumulated non-computational errors. Finally, we realize the surface code using iSWAP gates instead of the traditional CNOT, extending the set of viable gates for error correction without additional overhead. We measure the error suppression factor when scaling from distance-3 to distance-5 codes of $\Lambda_{35,\text{hex}} = 2.15(2)$, $\Lambda_{35,\text{walk}} = 1.69(6)$, and $\Lambda_{35,\text{iSWAP}} = 1.56(2)$, achieving state-of-the-art error suppression for each. With detailed error budgeting, we explore their performance trade-offs and implications for hardware design. This work demonstrates that dynamic circuit approaches satisfy the demands for fault-tolerance and opens new alternative avenues for scalable hardware design., Comment: 11 pages, 5 figures, Supplementary Information

Quantum error correction is essential for bridging the gap between the error rates of physical devices and the extremely low logical error rates required for quantum algorithms. Recent error-correction demonstrations on superconducting processors have focused primarily on the surface code, which offers a high error threshold but poses limitations for logical operations. In contrast, the color code enables much more efficient logic, although it requires more complex stabilizer measurements and decoding techniques. Measuring these stabilizers in planar architectures such as superconducting qubits is challenging, and so far, realizations of color codes have not addressed performance scaling with code size on any platform. Here, we present a comprehensive demonstration of the color code on a superconducting processor, achieving logical error suppression and performing logical operations. Scaling the code distance from three to five suppresses logical errors by a factor of $\Lambda_{3/5}$ = 1.56(4). Simulations indicate this performance is below the threshold of the color code, and furthermore that the color code may be more efficient than the surface code with modest device improvements. Using logical randomized benchmarking, we find that transversal Clifford gates add an error of only 0.0027(3), which is substantially less than the error of an idling error correction cycle. We inject magic states, a key resource for universal computation, achieving fidelities exceeding 99% with post-selection (retaining about 75% of the data). Finally, we successfully teleport logical states between distance-three color codes using lattice surgery, with teleported state fidelities between 86.5(1)% and 90.7(1)%. This work establishes the color code as a compelling research direction to realize fault-tolerant quantum computation on superconducting processors in the near future.

This paper shows that the semantics of programs with aggregates implemented by the solvers clingo and dlv can be characterized as extended First-Order formulas with intensional functions in the logic of Here-and-There. Furthermore, this characterization can be used to study the strong equivalence of programs with aggregates under either semantics. We also present a transformation that reduces the task of checking strong equivalence to reasoning in classical First-Order logic, which serves as a foundation for automating this procedure., Comment: Accepted for publication in the Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence

Despite the rapid integration of video perception capabilities into Large Multimodal Models (LMMs), the underlying mechanisms driving their video understanding remain poorly understood. Consequently, many design decisions in this domain are made without proper justification or analysis. The high computational cost of training and evaluating such models, coupled with limited open research, hinders the development of video-LMMs. To address this, we present a comprehensive study that helps uncover what effectively drives video understanding in LMMs. We begin by critically examining the primary contributors to the high computational requirements associated with video-LMM research and discover Scaling Consistency, wherein design and training decisions made on smaller models and datasets (up to a critical size) effectively transfer to larger models. Leveraging these insights, we explored many video-specific aspects of video-LMMs, including video sampling, architectures, data composition, training schedules, and more. For example, we demonstrated that fps sampling during training is vastly preferable to uniform frame sampling and which vision encoders are the best for video representation. Guided by these findings, we introduce Apollo, a state-of-the-art family of LMMs that achieve superior performance across different model sizes. Our models can perceive hour-long videos efficiently, with Apollo-3B outperforming most existing $7$B models with an impressive 55.1 on LongVideoBench. Apollo-7B is state-of-the-art compared to 7B LMMs with a 70.9 on MLVU, and 63.3 on Video-MME., Comment: https://apollo-lmms.github.io

We consider a fully discretized numerical scheme for parabolic stochastic partial differential equations with multiplicative noise. Our abstract framework can be applied to formulate a non-iterative domain decomposition approach. Such methods can help to parallelize the code and therefore lead to a more efficient implementation. The domain decomposition is integrated through the Douglas-Rachford splitting scheme, where one split operator acts on one part of the domain. For an efficient space discretization of the underlying equation, we chose the discontinuous Galerkin method as this suits the parallelization strategy well. For this fully discretized scheme, we provide a strong space-time convergence result. We conclude the manuscript with numerical experiments validating our theoretical findings., Comment: 38 pages, 3 figures

We consider tree level scattering of gravitons in type IIA string theory on $AdS_4\times \mathbb{CP}^3$ to all orders in $\alpha'$, which is dual to the stress tensor correlator in $U(N)_k\times U(N)_{-k}$ ABJM theory in the planar large $N$ limit and to all orders in large $\lambda\sim N/k$. The small curvature expansion of this correlator, defined via a Borel transform, is given by the flat space Virasoro-Shapiro amplitude plus AdS curvature corrections. We fix curvature corrections by demanding that their resonances are consistent with the superconformal block expansion of the correlator and with a worldsheet ansatz in terms of single-valued multiple polylogarithms. The first correction is fully fixed in this way, and matches independent results from integrability, as well as the $R^4$ correction at finite AdS curvature that was previously fixed using supersymmetric localization. We are also able to fix the second curvature correction by using a few additional assumptions, and find that it also satisfies various non-trivial consistency checks. We use our results to fix the tree level $D^4R^4$ correction at finite AdS curvature, and to give many predictions for future integrability studies., Comment: 35 pages, 1 figure

The emergence of dielectric bowtie cavities enable optical confinement with ultrahigh quality factor and ultra-small optical mode volumes with perspectives for enhanced light-matter interaction. Experimental work has so far emphasized the realization of these nanocavities. Here, we experimentally investigate the ultrafast dynamics of a topology-optimized dielectric (silicon) bowtie nanocavity, with device dimensions down to 12 nm, that localizes light to a mode volume deep below the so-called diffraction limit given by the half-wavelength cubed. This strong spatial light concentration is shown to significantly enhance the carrier generation rate through two-photon absorption, as well as reducing the time it takes for the carriers to recover. A diffusion time below 1 ps is achieved for the bowtie cavity, which is more than an order of magnitude smaller than for a conventional microcavity. Additionally, parametric effects due to coherent interactions between pump and probe signals are also enhanced in the bowtie cavity, leading to an improved extinction ratio. These results demonstrate important fundamental advantages of dielectric bowtie cavities compared to conventional point-defect cavities, laying a foundation for novel low-power and ultrafast optical devices, including switches and modulators., Comment: 32 pages, 14 figures

Conventional silicon photomultipliers (SiPMs) are well established as light detectors with single-photon-detection capability and used throughout high energy physics, medical, and commercial applications. The possibility to produce single photon avalanche diodes (SPADs) in commercial CMOS processes creates the opportunity to combine a matrix of SPADs and an application-specific integrated circuit in the same die. The potential of such digital SiPMs (dSiPMs) is still being explored, while it already is an established technology in certain applications, like light detection and ranging (LiDAR). A prototype dSiPM, produced in the LFoundry 150-nm CMOS technology, was designed and tested at DESY. The dSiPM central part is a matrix of 32 by 32 pixels. Each pixel contains four SPADs, a digital front-end, and has an area of 69.6 $\times$ 76 um$^2$. The chip has four time-to-digital converters and includes further circuitry for data serialization and data links. This work focuses on the characterization of the prototype in an electron beam at the DESY II Test Beam facility, to study its capability as a tracking and timing detector for minimum ionizing particles (MIPs). The MIP detection efficiency is found to be dominated by the fill factor and on the order of 31 %. The position of the impinging MIPs can be measured with a precision of about 20 um, and the time of the interaction can be measured with a precision better than 50 ps for about 85 % of the detected events. In addition, laboratory studies on the breakdown voltage, dark count rate, and crosstalk probability, as well as the experimental methods required for the characterization of such a sensor type in a particle beam are presented., Comment: 32 pages, 18 figures

We consider tree-level scattering amplitudes for four string tachyons on $AdS_3 \times {\cal N}$ with pure NSNS fluxes. We show that in a small curvature expansion, properly defined, the amplitudes take the form of a genus zero integral given by the Virasoro-Shapiro integrand with the extra insertion of single valued multiple polylogarithms. This is the same structure as the one found for the AdS Virasoro-Shapiro amplitude in higher dimensions., Comment: 22 pages

Time-resolved X-ray absorption spectroscopy (tr-XAS) has been shown to be a versatile measurement technique for investigating non-equilibrium dynamics. Novel X-ray free electron laser (XFEL) facilities like the European XFEL offer increased repetition rates for stroboscopic XAS experiments through a burst operation mode, which enables measurements with up to 4.5 MHz. These higher repetition rates lead to higher data acquisition rates but can also introduce long-lived excitations that persist and thus build up during each burst. Here, we report on such long-lived effects in Ni and NiO thin film samples that were measured at the European XFEL. We disentangle the long-lived excitations from the initial pump-induced change and perform a detailed modelling-based analysis of how they modify transient X-ray spectra. As a result, we link the long-lived effects in Ni to a local temperature increase, as well as the effects in NiO to excited charge carrier trapping through polaron formation. In addition, we present possible correction methods, as well as discuss ways in which the effects of these long-lived excitations could be minimized for future time-resolved X-ray absorption spectroscopy measurements.

We present a numerical investigation of the relativistic-field-theoretic (RFT) formalism, used to predict the discrete energy spectrum of three pions in a finite volume. Applying the previously derived generalization, we extract results for all non-maximal isospin values ($I_{\pi\pi\pi} = 2,1,$ and $0$), for different total momenta $\boldsymbol{P}$, and for various irreducible representations of the finite-volume symmetry group. We restrict attention to the unphysical scenario in which the three-particle interactions are set to zero. This set-up thus serves as a baseline for future lattice QCD calculations that will aim to extract such three-body interactions., Comment: 12 pages, 4 figures, Contribution to Lattice 2024

The Wasatch Front in Utah, USA, is currently a non-attainment area for ozone according to the Environmental Protection Agency's (EPA) National Ambient Air Quality Standards (NAAQS). Nitrogen oxides ($\mathrm{NO_x = NO_2 + NO}$) and volatile organic compounds (VOCs), in the presence of sunlight, lead to ozone formation in the troposphere. When the rate of oxidant production, defined as the sum of $\mathrm{O_3}$ and $\mathrm{NO_2}$, is faster than the rate of $\mathrm{NO_x}$ production, a region is said to be $\mathrm{NO_x}$limited, and ozone formation will be limited by the concentration of $\mathrm{NO_x}$ species in the region. The inverse of this situation makes the region VOC-limited. Knowing whether a region is $\mathrm{NO_x}$-limited or VOC-limited can aid in generating effective mitigation strategies. Understanding the background or regional contributions to ozone in a region, whether from the transport of precursors or of ozone, provides information about the lower limit for ozone concentrations that a region can achieve through regulation of local precursors. In this paper, measured oxidant and $\mathrm{NO_x}$ concentrations are analyzed from 14 counties in the state of Utah to calculate the regional and local contributions to ozone for each region. This analysis is used to determine the nature of the atmosphere in each county by identifying whether the region is VOC or $\mathrm{NO_x}$-limited. Furthermore, this analysis is performed for each county for the years 2012 and 2022 to assess changes in the oxidative nature and quantify regional and local contributions to ozone over a 10-year period. All studied counties--except for Washington County--in Utah were found to be VOC-limited in 2012. This shifted in 2022, with most counties being either in a transitional state or $\mathrm{NO_x}$-limited. Local contributions to ozone increased in two major counties, Cache and Salt Lake Counties, but decreased in Carbon, Davis, Duchesne, Uinta, Utah, Washington, and Weber Counties. Generally, the regional contributions to oxidant concentrations decreased across the state. A summertime spike in both regional and local contributions to oxidants was observed. Smoke from wildfires was found to increase regional contributions to oxidants and shift the local regime to be more $\mathrm{NO_x}$-limited.

We report on experimental investigation of potential high-performance cavity length stabilization using odd-indexed higher-order spatial modes. Schemes based on higher-order modes are particularly useful for micro-cavities that are used for enhanced fluorescence detection of a few emitters, which need to minimize photons leaking from a stabilization beam. We describe the design and construction of an assembly for a microcavity setup with tunable high passive stability. In addition, different types of active stabilization techniques based on higher-order modes, are then implemented and characterized based on their performance. We achieved a stability of about 0.5 pm RMS, while the error photons leaking from the continuous locking beam to a fluorescence detector are suppressed by more than 100-fold. We expect these results to be important for quantum technology implementations of various emitter-cavity setups, where these techniques provide a useful tool to meet the highly challenging demands.

The third r-process peak (Os, Ir, Pt) is poorly understood due to observational challenges, with spectral lines located in the blue or near-ultraviolet region of stellar spectra. These challenges need to be overcome for a better understanding of the r-process in a broader context. To understand how the abundances of the third r-process peak are synthesised and evolve in the Universe, a homogeneous chemical analysis of metal-poor stars using high quality data observed in the blue region of the electromagnetic spectrum (< 400 nm) is necessary. We provide a homogeneous set of abundances for the third r-process peak (Os, Ir, Pt) and Hf, increasing by up to one order of magnitude their availability in the literature. A classical 1D, local thermodynamic equilibrium (LTE) analysis of four elements (Hf, Os, Ir, Pt) is performed, using ATLAS model atmospheres to fit synthetic spectra in high resolution (> 40,000), high signal-to-noise ratio, of 52 red giants observed with UVES/VLT. Due to the heavy line blending involved, a careful determination of upper limits and uncertainties is done. The observational results are compared with state-of-the-art nucleosynthesis models. Our sample displays larger abundances of Ir (Z=77) in comparison to Os (Z=76), which have been measured in a few stars in the past. The results also suggest decoupling between abundances of third r-process peak elements with respect to Eu (rare earth element) in Eu-poor stars. This seems to contradict a co-production scenario of Eu and the third r-process peak elements Os, Ir, and Pt in the progenitors of these objects. Our results are challenging to explain from the nucleosynthetic point of view: the observationally derived abundances indicate the need for an additional early, primary formation channel (or a non-robust r-process)., Comment: 15 pages, 11 figures, Accepted for publication in A&A

Wastewater treatment plants face unique challenges for process control due to their complex dynamics, slow time constants, and stochastic delays in observations and actions. These characteristics make conventional control methods, such as Proportional-Integral-Derivative controllers, suboptimal for achieving efficient phosphorus removal, a critical component of wastewater treatment to ensure environmental sustainability. This study addresses these challenges using a novel deep reinforcement learning approach based on the Soft Actor-Critic algorithm, integrated with a custom simulator designed to model the delayed feedback inherent in wastewater treatment plants. The simulator incorporates Long Short-Term Memory networks for accurate multi-step state predictions, enabling realistic training scenarios. To account for the stochastic nature of delays, agents were trained under three delay scenarios: no delay, constant delay, and random delay. The results demonstrate that incorporating random delays into the reinforcement learning framework significantly improves phosphorus removal efficiency while reducing operational costs. Specifically, the delay-aware agent achieved 36% reduction in phosphorus emissions, 55% higher reward, 77% lower target deviation from the regulatory limit, and 9% lower total costs than traditional control methods in the simulated environment. These findings underscore the potential of reinforcement learning to overcome the limitations of conventional control strategies in wastewater treatment, providing an adaptive and cost-effective solution for phosphorus removal.

Air quality is a prevalent concern due to its imposing health risks. The state of Utah, USA, has, at times over the last 20 years, experienced some of the worst air quality in the nation. The propensity for Utah to experience elevated concentrations of particulate matter ($\mathrm{PM_{2.5}}$) and ozone ($\mathrm{O_3}$) can, in part, be attributed to its unique geography, which features dry, mountainous terrain. Valleys in Utah create ideal environments for extended cold-pool events. In this review, we summarize air quality research conducted in Utah over the past 20 years (2002-2022) by dividing the state into six regions: Utah Valley, Summit County, Southern Utah (regions south of Utah Valley), Cache Valley, Uinta Basin, and Salt Lake Valley. We review the published literature chronologically and provide a summary for each region, identifying areas where additional research is warranted. We found that research efforts are heavily weighted toward the Uinta Basin and Salt Lake Valley, with the remaining regions collectively accounting for only 20% of studies. We identified the need for more source apportionment studies, speciated volatile organic compound (VOC) analyses, and ozone isopleths. Where ozone isopleths cannot be created, measurements of glyoxal ($\mathrm{CHOCHO}$) and formaldehyde ($\mathrm{HCHO}$) concentrations could serve as cost-effective surrogates to inform ozone mitigation policies.

Air quality has important climate and health effects. There is a need, therefore, to monitor air quality both indoors and outdoors. Methods of measuring air quality should be cost-effective if they are to be used widely, and one such method is low-cost sensors (LCS). This study reports on the use of LCSs in Ulaanbaatar, Mongolia, to measure $\mathrm{PM_{2.5}}$ concentrations inside yurts or "gers." Some of these gers were part of a non-government agency (NGO) initiative to improve the insulating properties of these housing structures. The goal of the NGO was to decrease particulate emissions inside the gers; a secondary result was to lower the use of coal and other biomass material. LCSs were installed in gers heated primarily by coal, and interior air quality was measured. Gers that were modified by increasing their insulating capacities showed a 17.5% reduction in $\mathrm{PM_{2.5}}$ concentrations, but these concentrations remained higher than levels recommended by health organizations. Gers that were insulated and used a combination of both coal and electricity showed a 19.1% reduction in $\mathrm{PM_{2.5}}$ concentrations. Insulated gers that used electricity for both heating and cooking showed a 48% reduction in $\mathrm{PM_{2.5}}$, though concentrations were still 6.4 times higher than those recommended by the World Health Organization (WHO). Nighttime and daytime trends followed similar patterns in $\mathrm{PM_{2.5}}$ concentrations with slight variations. It was found that, at nighttime, the outside $\mathrm{PM_{2.5}}$ concentrations were generally higher than the inside concentrations of the gers in this study. This suggests that $\mathrm{PM_{2.5}}$ would flow into the gers whenever the doors were opened, causing spikes in $\mathrm{PM_{2.5}}$ concentrations.

Glyoxal CHOCHO is a trace gas in the atmosphere, often used as an indicator of biogenic emissions. It is frequently compared to formaldehyde concentrations, which serve as indicators of anthropogenic emissions, to gain insights into the characteristics of the environmental source. This study employed broadband cavity-enhanced absorption spectroscopy to detect gaseous CHOCHO, methylglyoxal, and $\mathrm{NO_2}$. Two different detection methods are compared. Spectrograph and CCD Detection: This approach involves coupling the system to a spectrograph with a charge-coupled device (CCD) detector. It achieved a 1 min 1-$\sigma$ detection limit of $2.5 \times 10^8$ molecules/cm$^3$, or 10 parts per trillion (ppt). Methylglyoxal and $\mathrm{NO_2}$ achieved 1 min 1-$\sigma$ detection limits of 34 ppt and 22 ppt, respectively. Interferometer and PMT Detection: In this method, an interferometer is used in conjunction with a photomultiplier tube (PMT) detector. It resulted in a 2 min 1-$\sigma$ detection limit of $1.5 \times 10^{10}$ molecules/cm$^3$, or 600 ppt. The $\mathrm{NO_2}$ 2 min 1-$\sigma$ detection limit was determined to be 900 ppt. Concentrations of methylglyoxal were difficult to determine using this method, as they appeared to be below the detection limit of the instrument. This study discusses the advantages and limitations of each of these detection methods.

As part of the PIP-II project at Fermilab, a pre-production cryomodule featuring 325 MHz Single Spoke Resonator type 2 (SSR2) superconducting RF cavities is under construction. These SSR2 cavities are fabricated by industry partners and undergo initial cold testing at our collaborating institution, IJCLab in France, utilizing low-power coupler. Subsequently, the cavities are subjected to final qualification at Fermilab, complete with tuner and high-power coupler assemblies. This paper provides an overview of the ongoing efforts dedicated to high-power testing of jacketed SSR2 cavities in the Spoke Test Cryostat (STC) at Fermilab. Performance parameters obtained from these tests are presented, offering valuable insights into the cavities operational characteristics and readiness for integration into the PIP-II cryomodule., Comment: 32nd Linear Accelerator Conference (LINAC 2024)

As global life expectancy increases, so does the burden of chronic diseases, yet individuals exhibit considerable variability in the rate at which they age. Identifying biomarkers that distinguish fast from slow ageing is crucial for understanding the biology of ageing, enabling early disease detection, and improving prevention strategies. Using contrastive deep learning, we show that skin biopsy images alone are sufficient to determine an individual's age. We then use visual features in histopathology slides of the skin biopsies to construct a novel biomarker of ageing. By linking with comprehensive health registers in Denmark, we demonstrate that visual features in histopathology slides of skin biopsies predict mortality and the prevalence of chronic age-related diseases. Our work highlights how routinely collected health data can provide additional value when used together with deep learning, by creating a new biomarker for ageing which can be actively used to determine mortality over time., Comment: 20 pages, 5 tables, 5 figures Under review: npj Digital Medicine

Sulfur dioxide ($\mathrm{SO}_2$) is an important precursor for the formation of atmospheric sulfate aerosol and acid rain. We present an instrument using Broadband Cavity-Enhanced Absorption Spectroscopy (BBCEAS) for the measurement of $\mathrm{SO}_2$ with a minimum limit of detection of 0.75 ppbv (3-$\sigma$) using the spectral range 305.5--312 nm and an averaging time of 5 min. The instrument consists of high-reflectivity mirrors (0.9985 at 310 nm) and a deep UV light source (Light Emitting Diode). The effective absorption path length of the instrument is 610 m with a 0.966 m base length. Published reference absorption cross sections were used to fit and retrieve the $\mathrm{SO}_2$ concentrations and were compared to fluorescence standard measurements for $\mathrm{SO}_2$. The comparison was well correlated ($R^2 = 0.9998$) with a correlation slope of 1.04. Interferences for fluorescence measurements were tested, and the BBCEAS showed no interference, while ambient measurements responded similarly to standard measurement techniques.

We confirm at the $5.7\sigma$ level previous studies reporting Cosmic Microwave Background (CMB) temperatures being significantly lower around nearby spiral galaxies than expected in the $\Lambda$CDM model. The significance reported in our earlier work was disputed by Addison 2024, who reported lower signficances when including pixels at distances far beyond the galactic halos while disregarding pixels close to the galaxies where the main signal is seen. Here we limit the study to pixels well within the galactic halos, focus on galaxies in dense cosmic filaments and improve on signal-to-noise compared to previous studies. The average CMB temperature in discs around these galaxies is always much lower in Planck data than in any of the 10.000 Planck-like CMB simulations. Even when correcting for the look-elsewhere-effect, the detection is still at the $3-4\sigma$ level. We further show that the largest scales ($\ell<16$) of the Planck CMB fluctuations are more correlated with the distribution of nearby galaxies than $99.99\%$ of simulated CMB maps. We argue that the existence of a new CMB foreground cannot be ignored and a physical interaction mechanism, possibly involving dark matter, as well as linked to intergalactic magnetic fields, should be sought., Comment: Submitted to A&A

Vibrational wave packets are created in the lowest triplet state \triplet of $\mathrm{K_2}$ and $\mathrm{Rb_2}$ residing on the surface of helium nanodroplets, through non-resonant stimulated impulsive Raman scattering induced by a moderately intense near-infrared laser pulse. A delayed, intense 50-fs laser pulse doubly ionizes the alkali dimers via multiphoton absorption and thereby causes them to Coulomb explode into a pair of alkali ions $\mathrm{Ak^+}$. From the kinetic energy distribution $P(E_\mathrm{kin})$ of the $\mathrm{Ak^+}$ fragment ions, measured at a large number of delays, we determine the time-dependent internuclear distribution $P(R,t)$, which represents the modulus square of the wave packet within the accuracy of the experiment. For both $\mathrm{K_2}$ and $\mathrm{Rb_2}$, $P(R,t)$ exhibits a periodic oscillatory structure throughout the respective 300 ps and 100 ps observation times. The oscillatory structure is reflected in the time-dependent mean value of $R$, $\langle R \rangle(t)$. Fourier transformation of $\langle R \rangle(t)$ shows that the wave packets are composed mainly of the vibrational ground state and the first excited vibrational state, in agreement with numerical simulations. In the case of $\mathrm{K_2}$, the oscillations are observed for 300 ps corresponding to more than 180 vibrational periods with an amplitude that decreases gradually from 0.035 {\AA} to 0.020 {\AA}. Using time-resolved spectral analysis, we find that the decay time of the amplitude is $\sim$ 260 ps. The decrease is ascribed to the weak coupling between the vibrating dimers and the droplet.

Absorption cross-sections for the 5th (6 $\leftarrow$ 0) and 6th (7 $\leftarrow$ 0) OH overtones for gas-phase methanol, ethanol, and isopropanol were measured using a slow flow cell and Incoherent Broadband Cavity-Enhanced Absorption Spectroscopy (IBBCEAS). Measurements were performed in two wavelength regions, 447-457 nm, and 508-518 nm, using two different instruments. The experimental results are consistent with previous computational predictions of the excitation energies for these transitions. Treating the OH stretch as a local mode allowed for calculation of the fundamental vibrational frequency ($\omega_e$), anharmonicity constant ($\omega_e x_e$), and the vertical dissociation energy (VDE) for each alcohol studied. The fundamental vibrational frequency is $3848 \pm 18 \, \text{cm}^{-1}$, $3807 \pm 55 \,\text{cm}^{-1}$, and $3813 \pm 63 \, \text{cm}^{-1}$ for methanol, ethanol, and isopropanol, respectively. The anharmonicity constant was measured to be $84.8 \pm 2.1 \, \text{cm}^{-1}$, $80.2 \pm 5.9 \, \text{cm}^{-1}$, and $84.4 \pm 6.8 \, \text{cm}^{-1}$ for methanol, ethanol, and isopropanol, respectively. The OH vertical dissociation energy was measured to be $499.4 \pm 18.4$ kJ/mol, $518.0 \pm 56.7$ kJ/mol, and $492.7 \pm 59.9$ kJ/mol. The spectroscopically measured values are compared to thermodynamically measured OH bond dissociation energies. The observed differences in previous measurements of the bond dissociation energies compared to the values reported herein can be explained due to the difference between vertical dissociation energies and bond dissociation energies. If the OH overtone stretching mode is excited in methanol to either the 5th or 6th overtone, the bimolecular reaction between methanol and O$_2$ becomes thermodynamically feasible and could contribute to formation of methoxy and HO$_2$ radicals under the proper combination of pressure and temperature.

Semantic segmentation is a crucial task in medical imaging. Although supervised learning techniques have proven to be effective in performing this task, they heavily depend on large amounts of annotated training data. The recently introduced Segment Anything Model (SAM) enables prompt-based segmentation and offers zero-shot generalization to unfamiliar objects. In our work, we leverage SAM's abstract object understanding for medical image segmentation to provide pseudo labels for semi-supervised learning, thereby mitigating the need for extensive annotated training data. Our approach refines initial segmentations that are derived from a limited amount of annotated data (comprising up to 43 cases) by extracting bounding boxes and seed points as prompts forwarded to SAM. Thus, it enables the generation of dense segmentation masks as pseudo labels for unlabelled data. The results show that training with our pseudo labels yields an improvement in Dice score from $74.29\,\%$ to $84.17\,\%$ and from $66.63\,\%$ to $74.87\,\%$ for the segmentation of bones of the paediatric wrist and teeth in dental radiographs, respectively. As a result, our method outperforms intensity-based post-processing methods, state-of-the-art supervised learning for segmentation (nnU-Net), and the semi-supervised mean teacher approach. Our Code is available on GitHub., Comment: Presented at MICCAI Workshop on Advancing Data Solutions in Medical Imaging AI 2024; Code and data: https://github.com/multimodallearning/SamCarriesTheBurden

As software systems grow in complexity, data and tools that provide valuable insights for easier program comprehension become increasingly important. OpenTelemetry has become a standard for the collection of monitoring data. In this work we present our experiences with different ways how OpenTelemetry can be leveraged to automatically instrument software systems for the purpose of software visualization. Particularly, we explore automatic instrumentation with the OpenTelemetry SDKs, and both application and unit test instrumentation with the Java agent inspectIT Ocelot. The collected data is exported to our live trace visualization tool ExplorViz., Comment: submitted to SSP 2024, see https://www.performance-symposium.org/ssp-2024/program/

In this paper, we introduce the dotted pattern-avoiding map $s_{\dot{\tau}}$, which avoids the dotted pattern $\dot{\tau}$ instead of descents as West's stack-sorting map $s$ does. We also extend the pattern-avoiding machine, which is composed of a $\sigma$ avoiding map and West's stack-sorting map $s$, to the dotted pattern-avoiding machine. In this paper, we prove the analogs of the classical results on West's stack sorting map for the length $2$ dotted pattern-avoiding maps. We end with several conjectures.

False data injection attacks (FDIAs) on smart inverters are a growing concern linked to increased renewable energy production. While data-based FDIA detection methods are also actively developed, we show that they remain vulnerable to impactful and stealthy adversarial examples that can be crafted using Reinforcement Learning (RL). We propose to include such adversarial examples in data-based detection training procedure via a continual adversarial RL (CARL) approach. This way, one can pinpoint the deficiencies of data-based detection, thereby offering explainability during their incremental improvement. We show that a continual learning implementation is subject to catastrophic forgetting, and additionally show that forgetting can be addressed by employing a joint training strategy on all generated FDIA scenarios.

We formulate fermionic versions, for any number of spatial dimensions, of the van der Waals and Casimir-Polder interactions, and study their properties. In both cases, the systems we introduce contain localized `atoms': two-level systems, coupled to a vacuum Dirac field. This Dirac field plays here a role akin to the electromagnetic field in the van der Waals case. In this context, bag-model conditions for the Dirac field serve as the analog of the 'mirror' in the Casimir-Polder effect. We found that, in this case, the resulting interaction is repulsive., Comment: References added; minor text changes