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The MUon Scattering Experiment (MUSE) was motivated by the proton radius puzzle arising from the discrepancy between muonic hydrogen spectroscopy and electron-proton measurements. The MUSE physics goals also include testing lepton universality, precisely measuring two-photon exchange contribution, and testing radiative corrections. MUSE addresses these physics goals through simultaneous measurement of high precision cross sections for electron-proton and muon-proton scattering using a mixed-species beam. The experiment will run at both positive and negative beam polarities. Measuring precise cross sections requires understanding both the incident beam energy and the radiative corrections. For this purpose, a lead-glass calorimeter was installed at the end of the beam line in the MUSE detector system. In this article we discuss the detector specifications, calibration and performance. We demonstrate that the detector performance is well reproduced by simulation, and meets experimental requirements.

(abridged)Photometric redshifts for AGN (galaxies hosting an accreting supermassive black hole in their center) are notoriously challenging and currently better computed via SED fitting, assuming that deep photometry for many wavelengths is available. However, for AGN detected all-sky, the photometry is limited and provided by different projects. This makes the task of homogenising the data challenging and is a dramatic drawback for the millions of AGN that wide surveys like SRG/eROSITA will detect. This work aims to compute reliable photometric redshifts for X-ray-detected AGN using only one dataset that covers a large area: the 10th Data Release of the Imaging Legacy Survey (LS10) for DESI. LS10 provides deep grizW1-W4 forced photometry within various apertures, thus avoids issues related to the cross-calibration of surveys. We present the results from CircleZ, a machine-learning algorithm based on a Fully Connected Neural Network. CircleZ uses training sample of 14,000 X-ray-detected AGN and utilizes multi-aperture photometry. The accuracy and the fraction of outliers reached in a test sample of 2913 AGN are 0.067 and 11.6%, respectively. The results are comparable to or better than those obtained previously for the same field but with much less effort. We further tested the stability of the results by computing the photometric redshifts for the sources detected in CSC2 and Chandra-COSMOS Legacy, reaching comparable accuracy as in eFEDS when limiting the magnitude of the counterparts with respect to the depth of LS10. The method applies to fainter samples of AGN using deeper optical data from future surveys (e.g., LSST, Euclid), granted LS10-like information on the light distribution beyond a morphological type is provided. With the paper, we release an updated version of the photometric redshifts (including errors and probability distribution function) for eROSITA/eFEDS., Comment: A&A, paper accepted

Integration of Samarium Cobalt hard magnets on silicon requires buffer/protective layers that can enhance the magnetic properties of the magnet while preserving its structure and chemical composition after post-annealing treatments needed for the formation of the magnetically hard phase. In this work, a comparison of Samarium-Cobalt films for five different buffer/protective layers, namely Ti, W, TiW, Ta, Cr and two different annealing temperatures, 650{\deg}C and 750{\deg}C, is presented. Depending on materials and annealing temperatures, magnetic properties such as saturation and coercivity of the SmCo film can be finely tuned. We show that coercivity up to 3.65 T or saturation magnetization up to 0.95 T can be reached by proper choice of the relevant process parameters: deposition temperature, material for the buffer/protective layer and annealing temperature. Such value of coercivity is among the highest found in literature for thin films of SmCo.

We conducted a scoping review for active learning in the domain of natural language processing (NLP), which we summarize in accordance with the PRISMA-ScR guidelines as follows: Objective: Identify active learning strategies that were proposed for entity recognition and their evaluation environments (datasets, metrics, hardware, execution time). Design: We used Scopus and ACM as our search engines. We compared the results with two literature surveys to assess the search quality. We included peer-reviewed English publications introducing or comparing active learning strategies for entity recognition. Results: We analyzed 62 relevant papers and identified 106 active learning strategies. We grouped them into three categories: exploitation-based (60x), exploration-based (14x), and hybrid strategies (32x). We found that all studies used the F1-score as an evaluation metric. Information about hardware (6x) and execution time (13x) was only occasionally included. The 62 papers used 57 different datasets to evaluate their respective strategies. Most datasets contained newspaper articles or biomedical/medical data. Our analysis revealed that 26 out of 57 datasets are publicly accessible. Conclusion: Numerous active learning strategies have been identified, along with significant open questions that still need to be addressed. Researchers and practitioners face difficulties when making data-driven decisions about which active learning strategy to adopt. Conducting comprehensive empirical comparisons using the evaluation environment proposed in this study could help establish best practices in the domain., Comment: The Version of Record of this contribution is published in Deep Learning Theory and Applications 5th International Conference, DeLTA 2024 Proceedings, and will be available after the conference

One of the most appealing features of magnonics is the easy tunability of spin-waves propagation via external magnetic fields. Usually this requires bulky and power-hungry electromagnets which are not compatible with device miniaturization. Here we propose a different approach, exploiting the stray field from permanent micromagnets integrated on the same chip of a magnonic wave-guide. In our monolithic device, we employ two SmCo square micromagnets (10x10 $\mu$m$^2$) flanking a CoFeB conduit at different distances from its axis, to produce a tunable transverse bias field between 7.5 and 3.0 mT in the conduit region between the magnets. Spin waves excited by an antenna just outside the region between the magnets enter a region with a variable higher (lower) effective field when an external bias field is applied parallel (antiparallel) to that from the micromagnets. Consequently, the attenuation length and phase shift of Damon-Eshbach spin waves can be tuned in a wide range by playing with the parallel-antiparallel configuration of the external bias and the distance between SmCo micromagnets and the CoFeB conduit. This work demonstrates the potential of permanent micro-magnets for the realization of low-power, integrated magnonic devices with tunable functionalities.

The differential cross section for $\gamma p\rightarrow K^+\Lambda(1520)$ was measured from threshold to a centre-of-mass energy of 2090\,MeV at forward angles at the BGOOD experiment. The high statistical precision and resolution in centre-of-mass energy and angle allows a detailed characterisation of this low-momentum transfer kinematic region. The data agree with a previous LEPS measurement and support effective Lagrangian models that indicate that the contact term dominates the cross section near threshold., Comment: 6 pages, 7 figures

The coherent reaction, $\gamma d \rightarrow \pi^0\eta d$ was studied with the BGOOD experiment at ELSA from threshold to a centre-of-mass energy of 3200\,MeV. A full kinematic reconstruction was made, with final state deuterons identified in the forward spectrometer and $\pi^0$ and $\eta$ decays in the central BGO Rugby Ball. The strength of the differential cross section exceeds what can be described by models of coherent photoproduction at forward angles by orders of magnitude. The distribution of the differential cross section has an excellent agreement with a model including quasi-free $\Delta \pi$ photoproduction, pion re-scattering and $N(1535)$ formation and subsequent nucleon coalescence to the deuteron. This also gives a reasonable description of the two-body invariant mass distributions and naturally explains the similar magnitudes of this channel and $\pi^0\pi^0 d$ coherent photoproduction., Comment: 7 pages, 5 figures

This paper introduces a code generator designed for node-level optimized, extreme-scalable, matrix-free finite element operators on hybrid tetrahedral grids. It optimizes the local evaluation of bilinear forms through various techniques including tabulation, relocation of loop invariants, and inter-element vectorization - implemented as transformations of an abstract syntax tree. A key contribution is the development, analysis, and generation of efficient loop patterns that leverage the local structure of the underlying tetrahedral grid. These significantly enhance cache locality and arithmetic intensity, mitigating bandwidth-pressure associated with compute-sparse, low-order operators. The paper demonstrates the generator's capabilities through a comprehensive educational cycle of performance analysis, bottleneck identification, and emission of dedicated optimizations. For three differential operators ($-\Delta$, $-\nabla \cdot (k(\mathbf{x})\, \nabla\,)$, $\alpha(\mathbf{x})\, \mathbf{curl}\ \mathbf{curl} + \beta(\mathbf{x}) $), we determine the set of most effective optimizations. Applied by the generator, they result in speed-ups of up to 58$\times$ compared to reference implementations. Detailed node-level performance analysis yields matrix-free operators with a throughput of 1.3 to 2.1 GDoF/s, achieving up to 62% peak performance on a 36-core Intel Ice Lake socket. Finally, the solution of the curl-curl problem with more than a trillion ($ 10^{12}$) degrees of freedom on 21504 processes in less than 50 seconds demonstrates the generated operators' performance and extreme-scalability as part of a full multigrid solver., Comment: 22 pages

Providing adequate indoor environmental quality is crucial in educational settings. In this paper, we implemented and tested a framework that collects occupant feedback and investigated correlations between teachers comfort and the operational characteristics of an Austrian school building in September and October 2022. Initial results show that the measured average temperatures (23.1 t-25.1 deg C) in all rooms are on the upper limit of various recommendations, such as comfort guidelines for building operation or workplace regulations. This assessment is in line with the feedback we received from the teachers. A literature review demonstrated that childrens comfort temperatures are lower compared to adults. Hence, it is reasonable to conclude that indoor temperatures during the survey period were inadequate for the pupils either, even without direct feedback. An analysis of the CO2 measurements showed that, during school hours, approximately 20% of all measurement values were above 1000 ppm, with 2% above 1500 ppm. CO2 levels above 1000 ppm are considered hygienically critical, with the latest research proposing to lower the limits below 800 ppm to ensure a healthy and effective learning environment. While we only assessed the challenges of providing a healthy indoor environment for an educational building in Austria, our literature review shows similar challenges and research efforts worldwide. Our analysis demonstrates the need for adapting design requirements, especially for school buildings, acknowledging the different comfort needs of adults and children and the importance of high indoor air quality for providing an optimum learning environment. Future research should focus on testing adapted indoor environmental quality requirements for schools, especially in urban areas, and how to integrate real-time occupant feedback in the heating, ventilation and air conditioning systems.

Our evolving understanding of the heterocellular cardiac environment demands innovative tools for its study. While murine models are lauded for their versatility and accessibility, they are constrained by scale; tools designed for larger subjects are unsuitable in mice. Advanced techniques, such as optical trans-membrane potential mapping and three-dimensional microscopy may fill this void. To enhance the utility of these methods, we developed a novel cross-correlation technique grounded in quaternion mathematics. The successful integration of multimodal observations represents a significant advancement in our ability to study cardiac electrophysiology in murine models, providing insight into mechanisms previously unexplored due to technical constraints., Comment: 3 pages; 1 figure; methods paper

We propose a lean and functional transaction scheme to establish a secure delivery-versus-payment across two blockchains, where a) no intermediary is required and b) the operator of the payment chain/payment system has a small overhead and does not need to store state. The main idea comes with two requirements: First, the payment chain operator hosts a stateless decryption service that allows decrypting messages with his secret key. Second, a "Payment Contract" is deployed on the payment chain that implements a function transferAndDecrypt(uint id, address from, address to, string keyEncryptedSuccess, string keyEncryptedFail) that processes the (trigger-based) payment and emits the decrypted key depending on the success or failure of the transaction. The respective key can then trigger an associated transaction, e.g. claiming delivery by the buyer or re-claiming the locked asset by the seller.

The ratio of the transverse and longitudinal component of polarization transfer to protons in quasi-elastic $(\vec{e}, e^{\prime} \vec{p}\,)$ reaction, $P^{\prime}_x/P^{\prime}_z$, is sensitive to the proton's electromagnetic form factor ratio, $G_E/G_M$. To explore density-dependent in-medium modifications, a comparison of polarization transfer ratios involving protons from distinct nuclear shells, each with different local nuclear densities, has been proposed. In this study, we present such comparisons between four shells, $1s_{1/2}$, $1p_{3/2}$ in $^{12}\mathrm{C}$ and $1d_{3/2}$, $2s_{1/2}$ in $^{40}\mathrm{Ca}$. In an effort to account for other many-body effects that may differ between shells, we use state-of-the-art relativistic distorted-wave impulse-approximation (RDWIA) calculation and present the double ratios, $(P^{\prime}_x/P^{\prime}_z)_{\rm Data}/(P^{\prime}_x/P^{\prime}_z)_{\rm RDWIA}$ as well as the super ratios, $\left[(P^{\prime}_x/P^{\prime}_z)_{\rm A}/(P^{\prime}_x/P^{\prime}_z)_{\rm B}\right]_{\rm Data}/\left[(P^{\prime}_x/P^{\prime}_z)_{\rm A}/(P^{\prime}_x/P^{\prime}_z)_{\rm B}\right]_{\rm RDWIA}$, for chosen shells A and B, as a function of effective local nuclear densities. We find that double ratios for individual shells show a dependence on the probed effective nuclear densities. Studying the ratios, we observed a systematic variation between pairs of higher- and lower-density shells.

Human bias is capable of changing the analysis of measured data sufficiently to alter the results of an experiment. It is incumbent upon modern experiments, especially those investigating quantities considered contentious in the broader community, to blind their analysis in an effort to minimize bias. The choice of a blinding model is experiment specific, but should also aim to prevent accidental release of results before an analysis is finalized. In this paper, we discuss common threats to an unbiased analysis, as well as common quantities that can be blinded in different types of nuclear physics experiments. We use the Muon Scattering Experiment as an example, and detail the blinding scheme used therein., Comment: 6 pages, 3 figures

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we cannot only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications., Comment: Accepted at 2024 Winter Conference on Applications of Computer Vision (WACV)

Simulating turbulent flows is crucial for a wide range of applications, and machine learning-based solvers are gaining increasing relevance. However, achieving temporal stability when generalizing to longer rollout horizons remains a persistent challenge for learned PDE solvers. In this work, we analyze if fully data-driven fluid solvers that utilize an autoregressive rollout based on conditional diffusion models are a viable option to address this challenge. We investigate accuracy, posterior sampling, spectral behavior, and temporal stability, while requiring that methods generalize to flow parameters beyond the training regime. To quantitatively and qualitatively benchmark the performance of a range of flow prediction approaches, three challenging scenarios including incompressible and transonic flows, as well as isotropic turbulence are employed. We find that even simple diffusion-based approaches can outperform multiple established flow prediction methods in terms of accuracy and temporal stability, while being on par with state-of-the-art stabilization techniques like unrolling at training time. Such traditional architectures are superior in terms of inference speed, however, the probabilistic nature of diffusion approaches allows for inferring multiple predictions that align with the statistics of the underlying physics. Overall, our benchmark contains three carefully chosen data sets that are suitable for probabilistic evaluation alongside various established flow prediction architectures., Comment: Source code available at https://github.com/tum-pbs/autoreg-pde-diffusion and further information and videos at https://ge.in.tum.de/publications/2023-acdm-kohl

In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions. We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models., Comment: Accepted at DeLTA 2023: Deep Learning Theory and Applications conference

This paper presents efficient data structures for the implementation of matrix-free finite element methods on block-structured, hybrid tetrahedral grids. It provides a complete categorization of all geometric sub-objects that emerge from the regular refinement of the unstructured, tetrahedral coarse grid and describes efficient iteration patterns and analytical linearization functions for the mapping of coefficients to memory addresses. This foundation enables the implementation of fast, extreme-scalable, matrix-free, iterative solvers, and in particular geometric multigrid methods by design. Their application to the variable-coefficient Stokes system subject to an enriched Galerkin discretization and to the curl-curl problem discretized with N\'ed\'elec edge elements showcases the flexibility of the implementation. Eventually, the solution of a curl-curl problem with $1.6 \cdot 10^{11}$ (more than one hundred billion) unknowns on more than $32000$ processes with a matrix-free full multigrid solver demonstrates its extreme-scalability., Comment: 21 pages