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Time series data from a variety of sensors and IoT devices need effective compression to reduce storage and I/O bandwidth requirements. While most time series databases and systems rely on lossless compression, lossy techniques offer even greater space-saving with a small loss in precision. However, the unknown impact on downstream analytics applications requires a semi-manual trial-and-error exploration. We initiate work on lossy compression that provides guarantees on complex statistical features (which are strongly correlated with the accuracy of the downstream analytics). Specifically, we propose a new lossy compression method that provides guarantees on the autocorrelation and partial-autocorrelation functions (ACF/PACF) of a time series. Our method leverages line simplification techniques as well as incremental maintenance of aggregates, blocking, and parallelization strategies for effective and efficient compression. The results show that our method improves compression ratios by 2x on average and up to 54x on selected datasets, compared to previous lossy and lossless compression methods. Moreover, we maintain -- and sometimes even improve -- the forecasting accuracy by preserving the autocorrelation properties of the time series. Our framework is extensible to multivariate time series and other statistical features of the time series., Comment: 14 pages, 13 figures

Interfaces play a substantial role for the functional properties of structured magnetic materials and magnetic multilayers. Modeling the functional behavior of magnetic materials requires the treatment of the relevant phenomena at the device level. Properties predicted from the electronic structure and spin dynamics at the atomistic level have to be properly transferred into a continuum level treatment. In this work we show how Co/Ru/Co three layers can be simulated with the continuum theory of micromagnetism, with interface coupling energies and bulk intrinsic properties properly derived from the results of \emph{ab initio} and spin dynamics simulations at different temperatures.

This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field.

Solving optimization problems is a highly demanding workload requiring high-performance computing systems. Optimization solvers are usually difficult to parallelize in conventional digital architectures, particularly when stochastic decisions are involved. Recently, analog computing architectures for accelerating stochastic optimization solvers have been presented, but they were limited to academic problems in quadratic polynomial format. Here we present KLIMA, a k-Local In-Memory Accelerator with resistive Content Addressable Memories (CAMs) and Dot-Product Engines (DPEs) to accelerate the solution of high-order industry-relevant optimization problems, in particular Boolean Satisfiability. By co-designing the optimization heuristics and circuit architecture we improve the speed and energy to solution up to 182x compared to the digital state of the art.

Geodetic and astrometric VLBI has entered a new era with the implementation of the VLBI Global Observing System (VGOS). These broadband and dual linear polarization observations aim at an accuracy of station coordinates of 1 mm and a reference frame stability of 0.1 mm/year. Although the extended brightness distribution of many of the radio-loud active galactic nuclei observed during geodetic VLBI sessions is resolved by the interferometer, the established processing chain still treats these objects as point sources. We investigate the impact of source structure on the visibility level and develop tools to remove the structure from the visibility data, right after correlation. Here we present our approach and show results obtained from observational VGOS data., Comment: 4 pages, 6 figures, published in Proceedings of the 16th EVN Symposium, Ed. E. Ros, P. Benke, S.A. Dzib, I. Rottmann, & J.A. Zensus, Bonn: Max-Planck-Institut f\"ur Radioastronomie, 2024, pages 145-148

Drones have revolutionized various domains, including agriculture. Recent advances in deep learning have propelled among other things object detection in computer vision. This study utilized YOLO, a real-time object detector, to identify and count coconut palm trees in Ghanaian farm drone footage. The farm presented has lost track of its trees due to different planting phases. While manual counting would be very tedious and error-prone, accurately determining the number of trees is crucial for efficient planning and management of agricultural processes, especially for optimizing yields and predicting production. We assessed YOLO for palm detection within a semi-automated framework, evaluated accuracy augmentations, and pondered its potential for farmers. Data was captured in September 2022 via drones. To optimize YOLO with scarce data, synthetic images were created for model training and validation. The YOLOv7 model, pretrained on the COCO dataset (excluding coconut palms), was adapted using tailored data. Trees from footage were repositioned on synthetic images, with testing on distinct authentic images. In our experiments, we adjusted hyperparameters, improving YOLO's mean average precision (mAP). We also tested various altitudes to determine the best drone height. From an initial mAP@.5 of $0.65$, we achieved 0.88, highlighting the value of synthetic images in agricultural scenarios., Comment: 9 pages

The formation of iron oxide nanoparticles (NPs) presents challenges such as efficiency losses and fine dust emissions in practical iron combustion systems, highlighting the need for deeper understanding of the formation mechanisms and thermochemical conditions. This study combines experiments and multi-scale simulations to analyze NP clouds generated by single iron particles burning in high-temperature oxidizing environments. The ambient gas conditions were provided by a laminar flat flame burner, with post-flame oxygen mole fractions varied between 20, 30, and 40 vol% at a constant temperature of ~1800K. High-speed in-situ diagnostics were used to measure particle size, NP initiation, NP cloud evolution, and microparticle surface temperature history. The experimental setup utilized three 10kHz imaging systems: one for two-color pyrometry and two for diffusive-backlight illumination (DBI), targeting particle size and NP measurements. The findings showcase the powerful capabilities of multi-physics diagnostics in quantifying NP initiation time and temperature, which depend on particle size and ambient oxygen concentration. CFD simulations revealed enhanced convection velocity driven by increased Stefan flow, which transported NPs toward parent iron particles under high-oxygen conditions. This delayed the detection of NP clouds, leading to higher microparticle temperatures at NP initiation. Molecular dynamics (MD) simulations uncovered FeO2(g) as a key NP precursor, forming when Fe atoms dissociate from the liquid phase. The initial temperature significantly influenced the resulting nanocluster composition, with Fe(II) dominating at higher temperatures and Fe(III) at lower temperatures. This integrated approach enhances understanding of NP formation in iron combustion, offering insights into the conditions affecting nanoparticle characteristics.

Particle velocimetry is essential in solid fuel combustion studies, however, the accurate detection and tracking of particles in high Particle Number Density (PND) combustion scenario remain challenging. The current study advances the machine-learning approaches for precise velocity measurements of solid particles. For this, laser imaging experiments were performed for high-volatile bituminous coal particles burning in a laminar flow reactor. Particle positions were imaged using time-resolved Mie scattering. Various detection methods, including conventional blob detection and Machine Learning (ML) based You Only Look Once (YOLO) and Realtime Detection Transformer (RT-DETR) were employed and bench marked.~Particle tracking was performed using the Simple Online Realtime Tracking (SORT) algorithm. The results demonstrated the capability of machine learning models trained on low-PND data for prediction of high-PND data. Slicing Aided Hyper Inference (SAHI) algorithm is important for the better performance of the used models. By evaluating the velocity statistics, it is found that the mean particle velocity decreases with increasing PND, primarily due to stronger particle interactions. The particle dynamics are closely related to the position of combustion zone observed in the previous study. Thus, PND is considered as the dominant factor for the particle group combustion behavior of high-volatile solid fuels.

Augmented Reality (AR) is an emerging technology that ranks among the top innovations in interactive media. With the emergence of new technologies, the question about the factors influencing user acceptance arises. Many research models on the user acceptance of technologies were developed and extended to answer this question in the last decades. This research paper provides an overview of the current state in the scientific literature on user acceptance factors of AR in training and education. We conducted a systematic literature review, identifying 45 scientific papers on technology acceptance of augmented reality. Twenty-two papers refer more specifically to the field of training and education. Overall, 33 different technology acceptance models and 34 acceptance variables were identified. Based on the results, there is a great potential for further research., Comment: 8 pages, 7 figures, The Fifteenth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services CENTRIC 2022

Questionnaires are a widely used tool for measuring the user experience (UX) of products. There exists a huge number of such questionnaires that contain different items (questions) and scales representing distinct aspects of UX, such as efficiency, learnability, fun of use, or aesthetics. These items and scales are not independent; they often have semantic overlap. However, due to the large number of available items and scales in the UX f ield, analyzing and understanding these semantic dependencies can be challenging. Large language models (LLM) are powerful tools to categorize texts, including UX items. We explore how ChatGPT-4 can be utilized to analyze the semantic structure of sets of UX items. This paper investigates three different use cases. In the first investigation, ChatGPT-4 is used to generate a semantic classification of UX items extracted from 40 UX questionnaires. The results demonstrate that ChatGPT-4 can effectively classify items into meaningful topics. The second investigation demonstrates ChatGPT-4's ability to filter items related to a predefined UX concept from a pool of UX items. In the third investigation, a second set of more abstract items is used to describe another classification task. The outcome of this investigation helps to determine semantic similarities between common UX concepts and enhances our understanding of the concept of UX. Overall, it is considered useful to apply GenAI in UX research, Comment: 16 pages, 3 figures, International Journal on Advances in Systems and Measurements, vol 17 no 1 & 2, year 2024, http://www.iariajournals.org/systems_and_measurements/. arXiv admin note: substantial text overlap with arXiv:2411.13118

Measuring User Experience (UX) with standardized questionnaires is a widely used method. A questionnaire is based on different scales that represent UX factors and items. However, the questionnaires have no common ground concerning naming different factors and the items used to measure them. This study aims to identify general UX factors based on the formulation of the measurement items. Items from a set of 40 established UX questionnaires were analyzed by Generative AI (GenAI) to identify semantically similar items and to cluster similar topics. We used the LLM ChatGPT-4 for this analysis. Results show that ChatGPT-4 can classify items into meaningful topics and thus help to create a deeper understanding of the structure of the UX research field. In addition, we show that ChatGPT-4 can filter items related to a predefined UX concept out of a pool of UX items., Comment: 10 pages, 1 figure, The Sixteenth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services CENTRIC 2023

Due to technological development, Augmented Reality (AR) can be applied in different domains. However, innovative technologies refer to new interaction paradigms, thus creating a new experience for the user. This so-called User Experience (UX) is essential for developing and designing interactive products. Moreover, UX must be measured to get insights into the user's perception and, thus, to improve innovative technologies. We conducted a Systematic Literature Review (SLR) to provide an overview of the current research concerning UX evaluation of AR. In particular, we aim to identify (1) research referring to UX evaluation of AR and (2) articles containing AR-specific UX models or frameworks concerning the theoretical foundation. The SLR is a five-step approach including five scopes. From a total of 498 records based on eight search terms referring to two databases, 30 relevant articles were identified and further analyzed. Results show that most approaches concerning UX evaluation of AR are quantitative. In summary, five UX models/frameworks were identified. Concerning the UX evaluation results of AR in Training and Education, the UX was consistently positive. Negative aspects refer to errors and deficiencies concerning the AR system and its functionality. No specific metric for UX evaluation of AR in the field of Training and Education exists. Only three AR-specific standardized UX questionnaires could be found. However, the questionnaires do not refer to the field of Training and Education. Thus, there is a lack of research in the field of UX evaluation of AR in Training and Education., Comment: 18 pages, 11 figures, CENTRIC 2024 : The Seventeenth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services

Measuring User Experience (UX) with questionnaires is essential for developing and improving products. However, no domain-specific standardized UX questionnaire exists for Augmented Reality (AR) in Corporate Training (CT). Thus, this study introduces the UXAR-CT questionnaire - an AR-specific UX questionnaire for CT environments. We describe the construction procedure and the evaluation process of the questionnaire. A set of candidate items was constructed, and a larger sample of participants evaluated several AR-based learning scenarios with these items. Based on the results, we performed a Principal Component Analysis (PCA) to identify relevant measurement items for each scale. The three best-fitting items were selected based on the results to form the final questionnaire. The first results regarding scale quality indicate a high level of internal consistency. The final version of the UXAR-CT questionnaire is provided and will be evaluated in further research., Comment: 9 pages, 9 figures, CENTRIC 2024 : The Seventeenth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services

User Experience (UX) Research covers various methods for gathering the users' subjective impressions of a product. For this, practitioners face different activities and tasks related to the research process. This includes processing a large amount of data based on qualitative and quantitative data. However, this can be very laborious in practice. Thus, the application of GenAI can support UX research activities. This paper provides a practical perspective on this topic. Based on previous studies, we present different use cases indicating the potential of GenAI in UX research. Moreover, we provide insights into an exploratory study using GenAI along an entire UX research process. Results show that Large Language Models (LLMs) are useful for various tasks. Thus, the research activities can be carried out more efficiently. However, the researcher should always review results to ensure quality. In summary, we want to express the potential of GenAI enhancing UX research, Comment: 13 pages, 4 figures, CENTRIC 2024 : The Seventeenth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services

The paper presents a Graph Attention Convolutional Network (GACN) for flow reconstruction from very sparse data in time-varying geometries. The model incorporates a feature propagation algorithm as a preprocessing step to handle extremely sparse inputs, leveraging information from neighboring nodes to initialize missing features. In addition, a binary indicator is introduced as a validity mask to distinguish between the original and propagated data points, enabling more effective learning from sparse inputs. Trained on a unique data set of Direct Numerical Simulations (DNS) of a motored engine at a technically relevant operating condition, the GACN shows robust performance across different resolutions and domain sizes and can effectively handle unstructured data and variable input sizes. The model is tested on previously unseen DNS data as well as on an experimental data set from Particle Image Velocimetry (PIV) measurements that were not considered during training. A comparative analysis shows that the GACN consistently outperforms both a conventional Convolutional Neural Network (CNN) and cubic interpolation methods on the DNS and PIV test sets by achieving lower reconstruction errors and better capturing fine-scale turbulent structures. In particular, the GACN effectively reconstructs flow fields from domains up to 14 times larger than those observed during training, with the performance advantage increasing for larger domains.

Governments are increasingly employing funding for open source software (OSS) development as a policy lever to support the security of software supply chains, digital sovereignty, economic growth, and national competitiveness in science and innovation, among others. However, the impacts of public funding on OSS development remain poorly understood, with a lack of consensus on how to meaningfully measure them. This gap hampers assessments of the return on public investment and impedes the optimisation of public-interest funding strategies. We address this gap with a toolkit of methodological considerations that may inform such measurements, drawing on prior work on OSS valuations and community health metrics by the Community Health Analytics Open Source Software (CHAOSS) project as well as our first-hand learnings as practitioners tasked with evaluating funding programmes by the Next Generation Internet initiative and the Sovereign Tech Agency. We discuss salient considerations, including the importance of accounting for funding objectives, project life stage and social structure, and regional and organisational cost factors. Next, we present a taxonomy of potential social, economic, and technological impacts that can be both positive and negative, direct and indirect, internal (i.e. within a project) and external (i.e. among a project's ecosystem of dependents and users), and manifest over various time horizons. Furthermore, we discuss the merits and limitations of qualitative, quantitative, and mixed-methods approaches, as well as options for and hazards of estimating multiplier effects. With this toolkit, we contribute to the multi-stakeholder conversation about the value and impacts of funding on OSS developers and society at large., Comment: 23 pages, 3 tables

This paper introduces a novel measure of software innovation based on open source software (OSS) development activity on GitHub. We examine the dependency growth and release complexity among $\sim$200,000 unique releases from 28,000 unique packages across the JavaScript, Python, and Ruby ecosystems over two years post-release. We find that major versions show differential, strong prediction of one-year lagged log change in dependencies. In addition, semantic versioning of OSS releases is correlated with their complexity and predict downstream adoption. We conclude that major releases of OSS packages count as a unit of innovation complementary to scientific publications, patents, and standards, offering applications for policymakers, managers, and researchers.

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

Ultraviolet wavelengths offer unique insights into aerosols in exoplanetary atmospheres. However, only a handful of exoplanets have been observed in the ultraviolet to date. Here, we present the ultraviolet-visible transmission spectrum of the inflated hot Jupiter WASP-127b. We observed one transit of WASP-127b with WFC3/UVIS G280 as part of the Hubble Ultraviolet-optical Survey of Transiting Legacy Exoplanets (HUSTLE), obtaining a transmission spectrum from 200-800 nm. Our reductions yielded a broad-band transit depth precision of 91 ppm and a median precision of 240 ppm across 59 spectral channels. Our observations are suggestive of a high-altitude cloud layer with forward modeling showing they are composed of sub-micron particles and retrievals indicating a high opacity patchy cloud. While our UVIS/G280 data only offers weak evidence for Na, adding archival HST WFC3/IR and STIS observations raises the overall Na detection significance to 4.1-sigma. Our work demonstrates the capabilities of HST WFC3/UVIS G280 observations to probe the aerosols and atmospheric composition of transiting hot Jupiters with comparable precision to HST STIS., Comment: 25 pages, 8 figures, 6 tables

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

Crowdsourced annotations of data play a substantial role in the development of Artificial Intelligence (AI). It is broadly recognised that annotations of text data can contain annotator bias, where systematic disagreement in annotations can be traced back to differences in the annotators' backgrounds. Being unaware of such annotator bias can lead to representational bias against minority group perspectives and therefore several methods have been proposed for recognising bias or preserving perspectives. These methods typically require either a substantial number of annotators or annotations per data instance. In this study, we propose a simple method for handling bias in annotations without requirements on the number of annotators or instances. Instead, we ask annotators about their beliefs of other annotators' judgements of an instance, under the hypothesis that these beliefs may provide more representative and less biased labels than judgements. The method was examined in two controlled, survey-based experiments involving Democrats and Republicans (n=1,590) asked to judge statements as arguments and then report beliefs about others' judgements. The results indicate that bias, defined as systematic differences between the two groups of annotators, is consistently reduced when asking for beliefs instead of judgements. Our proposed method therefore has the potential to reduce the risk of annotator bias, thereby improving the generalisability of AI systems and preventing harm to unrepresented socio-demographic groups, and we highlight the need for further studies of this potential in other tasks and downstream applications.

Berry phases offer a geometric perspective on wave propagation and are key to designing materials with topological wave transport. However, controlling Berry phases is challenging due to their dependence on global integrals over the Brillouin zone, making differentiation difficult. We present an adjoint-based method for efficiently computing the gradient of the Berry phase with respect to system parameters, involving only one forward and one adjoint calculation. This approach enables the use of advanced optimization techniques, such as topology optimization, to design new materials with tailored topological wave properties., Comment: 9 pages, 1 figure

Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE (Structure-preserving High-dimensional Analysis with Density-based Exploration), the first deep clustering algorithm that incorporates density-connectivity into its loss function. Similar to existing deep clustering algorithms, SHADE supports high-dimensional and large data sets with the expressive power of a deep autoencoder. In contrast to most existing deep clustering methods that rely on a centroid-based clustering objective, SHADE incorporates a novel loss function that captures density-connectivity. SHADE thereby learns a representation that enhances the separation of density-connected clusters. SHADE detects a stable clustering and noise points fully automatically without any user input. It outperforms existing methods in clustering quality, especially on data that contain non-Gaussian clusters, such as video data. Moreover, the embedded space of SHADE is suitable for visualization and interpretation of the clustering results as the individual shapes of the clusters are preserved., Comment: Short version accepted at ICDM 2024

We present a novel deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next generation multi-ton scale liquid Xenon-based direct detection experiment, DARWIN. We train an anomaly detector comprising a variational autoencoder and a classifier on extensive, high-dimensional simulated detector response data and construct a one-dimensional anomaly score optimised to reject the background only hypothesis in the presence of an excess of non-background-like events. We benchmark the procedure with a sensitivity study that determines its power to reject the background-only hypothesis in the presence of an injected WIMP dark matter signal, outperforming the classical, likelihood-based background rejection test. We show that our neural networks learn relevant energy features of the events from low-level, high-dimensional detector outputs, without the need to compress this data into lower-dimensional observables, thus reducing computational effort and information loss. For the future, our approach lays the foundation for an efficient end-to-end pipeline that eliminates the need for many of the corrections and cuts that are traditionally part of the analysis chain, with the potential of achieving higher accuracy and significant reduction of analysis time., Comment: 10 Figures, 3 Tables, 23 Pages (incl. references)

In this paper, we use hydrodynamic zoom-in simulations of Milky Way-type haloes to explore using dust as an observational tracer to discriminate between cold and warm dark matter (WDM) universes. Comparing a cold and 3.5 keV WDM particle model, we tune the efficiency of galaxy formation in our simulations using a variable supernova rate to create Milky Way systems with similar satellite galaxy populations while keeping all other simulation parameters the same. Cold dark matter (CDM), having more substructure, requires a higher supernova efficiency than WDM to achieve the same satellite galaxy number. These different supernova efficiencies create different dust distributions around their host galaxies, which we generate by post-processing the simulation output with the \powderday{} codebase. Analysing the resulting dust in each simulation, we find $\sim$4.5 times more dust in our CDM Milky Way haloes compared with WDM. The distribution of dust out to $R_{200\text{c}}$ is then explored, revealing that the WDM simulations are noticeably less concentrated than their CDM counterparts, although differences in substructure complicate the comparison. Our results indicate that dust is a possible unique probe to test theories of dark matter., Comment: Accepted for publication to MNRAS, 12 pages, 5 figures

In-memory computing (IMC) has been shown to be a promising approach for solving binary optimization problems while significantly reducing energy and latency. Building on the advantages of parallel computation, we propose an IMC-compatible parallelism framework inspired by parallel tempering (PT), enabling cross-replica communication to improve the performance of IMC solvers. This framework enables an IMC solver not only to improve performance beyond what can be achieved through parallelization, but also affords greater flexibility for the search process with low hardware overhead. We justify that the framework can be applied to almost any IMC solver. We demonstrate the effectiveness of the framework for the Boolean satisfiability (SAT) problem, using the WalkSAT heuristic as a proxy for existing IMC solvers. The resulting PT-inspired cooperative WalkSAT (PTIC-WalkSAT) algorithm outperforms the traditional WalkSAT heuristic in terms of the iterations-to-solution in 76.3% of the tested problem instances and its na\"ive parallel variant (PA-WalkSAT) does so in 68.4% of the instances. An estimate of the energy overhead of the PTIC framework for two hardware accelerator architectures indicates that in both cases the overhead of running the PTIC framework would be less than 1% of the total energy required to run each accelerator., Comment: 21 pages, 9 figures

Current ton-scale dark matter direct detection experiments have reached an important milestone with the detection of solar neutrinos. In this paper, we show that these data can be used to determine a critical parameter of the Standard Model in particle physics, across an energy regime that has never been probed before. In particular, we show that the value of the weak mixing angle ($\theta_W$) which relates the mass of the $W$ and $Z$ bosons can be derived from 1) the recent measurements of coherent neutrino-nucleus scattering by PandaX-4T and XENONnT in the sub-GeV energy range -- a regime which is usually only probed by low energy neutrino experiments -- and from 2) XENONnT electron recoil data through neutrino-electron scattering at energy scale $\simeq 0.1 ~ \rm{ MeV}$, corresponding to a momentum transfer region over an order of magnitude smaller than that explored by atomic parity violation experiments. Future observation of neutrinos in the next generation of dark matter direct detection experiments have therefore the potential to provide accurate tests of the Standard Model weak interactions in the keV-MeV regime., Comment: 8 pages, 2 figures. Included XENONnT $^8$B data. Improved analysis - the error bars on our results have increased

Detecting and segmenting cracks in infrastructure, such as roads and buildings, is crucial for safety and cost-effective maintenance. In spite of the potential of deep learning, there are challenges in achieving precise results and handling diverse crack types. With the proposed dataset and model, we aim to enhance crack detection and infrastructure maintenance. We introduce Hybrid-Segmentor, an encoder-decoder based approach that is capable of extracting both fine-grained local and global crack features. This allows the model to improve its generalization capabilities in distinguish various type of shapes, surfaces and sizes of cracks. To keep the computational performances low for practical purposes, while maintaining the high the generalization capabilities of the model, we incorporate a self-attention model at the encoder level, while reducing the complexity of the decoder component. The proposed model outperforms existing benchmark models across 5 quantitative metrics (accuracy 0.971, precision 0.804, recall 0.744, F1-score 0.770, and IoU score 0.630), achieving state-of-the-art status., Comment: 25 pages, 6 figures