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1. International comparison of optical frequencies with transportable optical lattice clocks

Author: Clock, International, Networking, Oscillator, Collaboration, Amy-Klein, Anne, Benkler, Erik, Blondé, Pascal, Bongs, Kai, Cantin, Etienne, Chardonnet, Christian, Denker, Heiner, Dörscher, Sören, Feng, Chen-Hao, Gaudron, Jacques-Olivier, Gill, Patrick, Hill, Ian R, Huang, Wei, Johnson, Matthew Y H, Kale, Yogeshwar B, Katori, Hidetoshi, Klose, Joshua, Kronjäger, Jochen, Kuhl, Alexander, Targat, Rodolphe Le, Lisdat, Christian, Lopez, Olivier, Lücke, Tim, Mazouth, Maxime, Mukherjee, Shambo, Nosske, Ingo, Pointard, Benjamin, Pottie, Paul-Eric, Schioppo, Marco, Singh, Yeshpal, Stahl, Kilian, Takamoto, Masao, Tønnes, Mads, Tunesi, Jacob, Ushijima, Ichiro, and Vishwakarma, Chetan
Subjects: Physics - Atomic Physics
Abstract: Optical clocks have improved their frequency stability and estimated accuracy by more than two orders of magnitude over the best caesium microwave clocks that realise the SI second. Accordingly, an optical redefinition of the second has been widely discussed, prompting a need for the consistency of optical clocks to be verified worldwide. While satellite frequency links are sufficient to compare microwave clocks, a suitable method for comparing high-performance optical clocks over intercontinental distances is missing. Furthermore, remote comparisons over frequency links face fractional uncertainties of a few $10^{-18}$ due to imprecise knowledge of each clock's relativistic redshift, which stems from uncertainty in the geopotential determined at each distant location. Here, we report a landmark campaign towards the era of optical clocks, where, for the first time, state-of-the-art transportable optical clocks from Japan and Europe are brought together to demonstrate international comparisons that require neither a high-performance frequency link nor information on the geopotential difference between remote sites. Conversely, the reproducibility of the clocks after being transported between countries was sufficient to determine geopotential height offsets at the level of 4 cm. Our campaign paves the way for redefining the SI second and has a significant impact on various applications, including tests of general relativity, geodetic sensing for geosciences, precise navigation, and future timing networks., Comment: 29 pages, 5 figures
Published: 2024

2. Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling

Author: Holthusen, Hagen, Brepols, Tim, Linka, Kevin, and Kuhl, Ellen
Subjects: Computer Science - Machine Learning, Condensed Matter - Materials Science, Computer Science - Computational Engineering, Finance, and Science, 65, 74, I.6, J.2
Abstract: Soft biological tissues exhibit a tendency to maintain a preferred state of tensile stress, known as tensional homeostasis, which is restored even after external mechanical stimuli. This macroscopic behavior can be described using the theory of kinematic growth, where the deformation gradient is multiplicatively decomposed into an elastic part and a part related to growth and remodeling. Recently, the concept of homeostatic surfaces was introduced to define the state of homeostasis and the evolution equations for inelastic deformations. However, identifying the optimal model and material parameters to accurately capture the macroscopic behavior of inelastic materials can only be accomplished with significant expertise, is often time-consuming, and prone to error, regardless of the specific inelastic phenomenon. To address this challenge, built-in physics machine learning algorithms offer significant potential. In this work, we extend our inelastic Constitutive Artificial Neural Networks (iCANNs) by incorporating kinematic growth and homeostatic surfaces to discover the scalar model equations, namely the Helmholtz free energy and the pseudo potential. The latter describes the state of homeostasis in a smeared sense. We evaluate the ability of the proposed network to learn from experimentally obtained tissue equivalent data at the material point level, assess its predictive accuracy beyond the training regime, and discuss its current limitations when applied at the structural level. Our source code, data, examples, and an implementation of the corresponding material subroutine are made accessible to the public at https://doi.org/10.5281/zenodo.13946282., Comment: 46 pages, 12 figures, 5 tables
Published: 2024

3. Spin resonance without a spin: A microwave analog

Author: Hofmann, Tobias, Schmidt, Finn, Stöckmann, Hans-Jürgen, and Kuhl, Ulrich
Subjects: Physics - Applied Physics, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: An analog of nuclear magnetic resonance is realized in a microwave network with symplectic symmetry. The network consists of two identical subgraphs coupled by a pair of bonds with a length difference corresponding to a phase difference of $\pi$ for the waves traveling through the bonds. As a consequence all eigenvalues appear as Kramers doublets. Detuning the length difference from the $\pi$ condition Kramers degeneracy is lifted, which may be interpreted as a Zeeman splitting of a spin 1/2 in a magnetic field. The lengths of another pair of bonds are modulated periodically with frequencies of some 10 MHz by means of diodes, thus emulating a magnetic radiofrequency field. Features well-known from NMR such as the transition from the laboratory to the rotating frame, and Lorentzian shaped resonance curves can thus be realized., Comment: 5 pages, 4 figures. arXiv admin note: text overlap with arXiv:2410.07031
Published: 2024

4. Realization of an NMR analog in a microwave network with symplectic symmetry

Author: Schmidt, Finn, Hofmann, Tobias, Stöckmann, H. -J., and Kuhl, Ulrich
Subjects: Physics - Classical Physics
Abstract: In a previous paper, we realized a microwave network with symplectic symmetry simulating a spin 1/2 (Rehemanjiang et al. [Phys. Rev. Lett. 117, 064101 (2016)]), following a suggestion by Joyner et al. [Europhys. Lett. 107, 50004(2014))]. The network consisted of two identical sub-units coupled by a pair of bonds with a length difference corresponding to a phase difference of $\pi$ for the waves traveling through the bonds. In such a symmetry each eigenvalue appears as a two-fold degenerate Kramers doublet. Distorting the symmetry the degeneracy is lifted which may be interpreted in terms of the Zeeman splitting of a spin 1/2 in an external magnetic field. In the present work, a microwave analog of a spin resonance is realized. To this end, two magnetic fields have to be emulated, a static and a radio-frequency one. The static one is realized by detuning the length difference from the $\pi$ condition by means of phase shifters, the radio-frequency field by modulating the length difference of another pair of bonds by means of diodes with frequencies up to 125 MHz. Features well-known from magnetic resonance such as the transition from the laboratory to the rotating frame, and Lorentzian shaped resonance curves can thus be realized., Comment: 13 pages, 8 figures
Published: 2024

5. The Impact of Surface Co-location and Eye-tracking on Mixed Reality Typing

Author: Schmitz, Cecilia, Reynolds, Joshua, Kuhl, Scott, and Vertanen, Keith
Subjects: Computer Science - Human-Computer Interaction
Abstract: Accuracy and speed are pivotal when typing. We hypothesized that the lack of tactile feedback on midair mixed reality keyboards may adversely impact performance. Our first experiment assessed the potential to provide tactile feedback to users typing in mixed reality by co-locating the virtual keyboard on a table or a wall. The keyboard was deterministic (without auto-correct), relied only on the headset's egocentric cameras for sensing, and included symbol keys. Users preferred and had the highest entry rate of 12 words-per-minute using a midair keyboard. Error rates were similar in all conditions. Based on user feedback, our second experiment explored ten-finger typing. We used a novel eye-tracking technique to mitigate accidental key presses. The technique halved the number of times backspace was pressed and was preferred by users. However, participants were faster using only their index fingers without eye-tracking at 11 words-per-minute.
Published: 2024

6. Atomically Flat Dielectric Patterns for Band Gap Engineering and Lateral Junction Formation in MoSe$_2$ Monolayers

Author: Moser, Philipp, Wolz, Lukas M., Henning, Alex, Thurn, Andreas, Kuhl, Matthias, Ji, Peirui, Soubelet, Pedro, Schalk, Martin, Eichhorn, Johanna, Sharp, Ian D., Stier, Andreas V., and Finley, Jonathan J.
Subjects: Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: Combining a precise sputter etching method with subsequent AlO$_x$ growth within an atomic layer deposition chamber enables fabrication of atomically flat lateral patterns of SiO$_2$ and AlO$_x$. The transfer of MoSe$_2$ monolayers onto these dielectrically modulated substrates results in formation of lateral heterojunctions, with the flat substrate topography leading to minimal strain across the junction. Kelvin probe force microscopy (KPFM) measurements show significant variations in the contact potential difference (CPD) across the interface, with AlO$_x$ regions inducing a 230~mV increase in CPD. Spatially resolved photoluminescence spectroscopy reveals shifts in spectral weight of neutral and charged exciton species across the different dielectric regions. On the AlO$_x$ side, the Fermi energy moves closer to the conduction band, leading to a higher trion-to-exciton ratio, indicating a bandgap shift consistent with CPD changes. In addition, transient reflection spectroscopy highlights the influence of the dielectric environment on carrier dynamics, with the SiO$_2$ side exhibiting rapid carrier decay typical of neutral exciton recombination. In contrast, the AlO$_x$ side shows slower, mixed decay behavior consistent with conversion of trions back into excitons. These results demonstrate how dielectric substrate engineering can tune the electronic and optical characteristics of proximal two-dimensional materials, allowing scalable fabrication of advanced junctions for novel (opto)electronics applications., Comment: 24 pages, 5 figures. SI: 2 pages, 3 figures. We welcome your feedback!
Published: 2024

7. Working Memory Sensitive Math Intervention in Students with Learning Disabilities -- A Single Case Study

Author: Sarah Schulze and Jan Kuhl
Abstract: This study examines the effects of a working memory (WM) sensitive math intervention in students with learning disabilities (LD). The intervention aims to improve early mathematical competencies while accounting for weak cognitive resources through a reduced instructional design. These principles are considered to be effective for learning. Ten students with mild to moderate intelligence impairment participated in our study. We applied an AB single-case intervention study across participants to evaluate the effects of the intervention. The students take part in at least 10 30-minute intervention sessions. Non-overlap indices, as well as regression analysis, support positive effects on students' mathematical competencies. Nevertheless, there are still students for whom the effects were unstable or failed to materialize. Besides the effectiveness of the WM-sensitive math intervention, the results support the assumption that the mathematical learning of students with LD is similar to that of students without LD but delayed.
Published: 2024

8. In situ counter-diffusion crystallization and long-term crystal preservation in microfluidic fixed targets for serial crystallography

Author: Liu, Zhongrui, Gu, Kevin, Shelby, Megan, Roy, Debdyuti, Muniyappan, Srinivasan, Schmidt, Marius, Narayanasamy, Sankar Raju, Coleman, Matthew, Frank, Matthias, and Kuhl, Tonya L
Subjects: Inorganic Chemistry, Chemical Sciences, Physical Chemistry, Physical Sciences, Condensed Matter Physics, Bioengineering, Biotechnology, X-ray scattering, XFELs, counter diffusion, in situ, long-term crystal preservation, microfluidic fixed targets, protein diffraction, serial crystallography, vacuum operation, Mathematical Sciences, Engineering, Inorganic & Nuclear Chemistry, Inorganic chemistry, Physical chemistry, Condensed matter physics
Abstract: Compared with batch and vapor diffusion methods, counter diffusion can generate larger and higher-quality protein crystals yielding improved diffraction data and higher-resolution structures. Typically, counter-diffusion experiments are conducted in elongated chambers, such as glass capillaries, and the crystals are either directly measured in the capillary or extracted and mounted at the X-ray beamline. Despite the advantages of counter-diffusion protein crystallization, there are few fixed-target devices that utilize counter diffusion for crystallization. In this article, different designs of user-friendly counter-diffusion chambers are presented which can be used to grow large protein crystals in a 2D polymer microfluidic fixed-target chip. Methods for rapid chip fabrication using commercially available thin-film materials such as Mylar, propyl-ene and Kapton are also detailed. Rules of thumb are provided to tune the nucleation and crystal growth to meet users' needs while minimizing sample consumption. These designs provide a reliable approach to forming large crystals and maintaining their hydration for weeks and even months. This allows ample time to grow, select and preserve the best crystal batches before X-ray beam time. Importantly, the fixed-target microfluidic chip has a low background scatter and can be directly used at beamlines without any crystal handling, enabling crystal quality to be preserved. The approach is demonstrated with serial diffraction of photoactive yellow protein, yielding 1.32 Å resolution at room temperature. Fabrication of this standard microfluidic chip with commercially available thin films greatly simplifies fabrication and provides enhanced stability under vacuum. These advances will further broaden microfluidic fixed-target utilization by crystallographers.
Published: 2024

9. RadioRAG: Factual Large Language Models for Enhanced Diagnostics in Radiology Using Dynamic Retrieval Augmented Generation

Author: Arasteh, Soroosh Tayebi, Lotfinia, Mahshad, Bressem, Keno, Siepmann, Robert, Ferber, Dyke, Kuhl, Christiane, Kather, Jakob Nikolas, Nebelung, Sven, and Truhn, Daniel
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Large language models (LLMs) have advanced the field of artificial intelligence (AI) in medicine. However LLMs often generate outdated or inaccurate information based on static training datasets. Retrieval augmented generation (RAG) mitigates this by integrating outside data sources. While previous RAG systems used pre-assembled, fixed databases with limited flexibility, we have developed Radiology RAG (RadioRAG) as an end-to-end framework that retrieves data from authoritative radiologic online sources in real-time. RadioRAG is evaluated using a dedicated radiologic question-and-answer dataset (RadioQA). We evaluate the diagnostic accuracy of various LLMs when answering radiology-specific questions with and without access to additional online information via RAG. Using 80 questions from RSNA Case Collection across radiologic subspecialties and 24 additional expert-curated questions, for which the correct gold-standard answers were available, LLMs (GPT-3.5-turbo, GPT-4, Mistral-7B, Mixtral-8x7B, and Llama3 [8B and 70B]) were prompted with and without RadioRAG. RadioRAG retrieved context-specific information from www.radiopaedia.org in real-time and incorporated them into its reply. RadioRAG consistently improved diagnostic accuracy across all LLMs, with relative improvements ranging from 2% to 54%. It matched or exceeded question answering without RAG across radiologic subspecialties, particularly in breast imaging and emergency radiology. However, degree of improvement varied among models; GPT-3.5-turbo and Mixtral-8x7B-instruct-v0.1 saw notable gains, while Mistral-7B-instruct-v0.2 showed no improvement, highlighting variability in its effectiveness. LLMs benefit when provided access to domain-specific data beyond their training data. For radiology, RadioRAG establishes a robust framework that substantially improves diagnostic accuracy and factuality in radiological question answering.
Published: 2024

10. Non-Weyl Behavior Induced by Superradiance: A Microwave Graph Study

Author: Lu, Junjie, Hofmann, Tobias, Stöckmann, Hans-Jürgen, and Kuhl, Ulrich
Subjects: Physics - Classical Physics
Abstract: We study experimentally the manifestation of non-Weyl graph behavior in open systems using microwave networks. For this a coupling variation to the network is necessary, which was out of reach till now. The coupling to the environment is changed by indirectly varying the boundary condition at the coupling vertex from Dirichlet to Neumann using a dangling bond with variable length attached the coupling vertex. A transformation of equal length spectra to equal reflection phase spectra of the dangling bond allows to create spectra with different fixed coupling strength. This allows to follow the resonances in the complex plane as a function of the coupling. While going from closed (Dirichlet) to fully open (Neumann) graph we see resonances escaping via a superradiant transition leading to non-Weyl behavior if the coupling to the outside is balanced. The open tetrahedral graph displays a rich parametric dynamic of the resonances in the complex plane presenting loops, regions of connected resonances and resonances approaching infinite imaginary parts., Comment: 6 pages, 3 figures
Published: 2024

11. Teachers' Use of Data from Digital Learning Platforms for Instructional Design: A Systematic Review

Author: Alina Hase and Poldi Kuhl
Abstract: Data-based decision-making is a well-established field of research in education. In particular, the potential of data use for addressing heterogeneous learning needs is emphasized. With data collected during the learning process of students, teachers gain insight into the performance, strengths, and weaknesses of their students and are potentially able to adjust their teaching accordingly. Digital media are becoming increasingly important for the use of learning data. Students can use digital learning platforms to work on exercises and receive direct feedback, while teachers gain data on the students' learning processes. Although both data-based decision-making and the use of digital media in schools are already widely studied, there is little evidence on the combination of the two issues. This systematic review aims to answer to what extent the connection between data-based decision-making and the use of digital learning platforms has already been researched in terms of using digital learning data for further instructional design. The analysis of n = 11 studies revealed that the use of data from digital learning platforms for instructional design has so far been researched exploratively. Nevertheless, we gained initial insights into which digital learning platforms teachers use, which data they can obtain from them, and how they further use these data.
Published: 2024
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12. A universal material model subroutine for soft matter systems

Author: Peirlinck, Mathias, Hurtado, Juan A., Rausch, Manuel K., Tepole, Adrian Buganza, and Kuhl, Ellen
Subjects: Computer Science - Computational Engineering, Finance, and Science, Condensed Matter - Materials Science, Condensed Matter - Soft Condensed Matter
Abstract: Soft materials play an integral part in many aspects of modern life including autonomy, sustainability, and human health, and their accurate modeling is critical to understand their unique properties and functions. Today's finite element analysis packages come with a set of pre-programmed material models, which may exhibit restricted validity in capturing the intricate mechanical behavior of these materials. Regrettably, incorporating a modified or novel material model in a finite element analysis package requires non-trivial in-depth knowledge of tensor algebra, continuum mechanics, and computer programming, making it a complex task that is prone to human error. Here we design a universal material subroutine, which automates the integration of novel constitutive models of varying complexity in non-linear finite element packages, with no additional analytical derivations and algorithmic implementations. We demonstrate the versatility of our approach to seamlessly integrate innovative constituent models from the material point to the structural level through a variety of soft matter case studies: a frontal impact to the brain; reconstructive surgery of the scalp; diastolic loading of arteries and the human heart; and the dynamic closing of the tricuspid valve. Our universal material subroutine empowers all users, not solely experts, to conduct reliable engineering analysis of soft matter systems. We envision that this framework will become an indispensable instrument for continued innovation and discovery within the soft matter community at large.
Published: 2024
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13. ChimpVLM: Ethogram-Enhanced Chimpanzee Behaviour Recognition

Author: Brookes, Otto, Mirmehdi, Majid, Kuhl, Hjalmar, and Burghardt, Tilo
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: We show that chimpanzee behaviour understanding from camera traps can be enhanced by providing visual architectures with access to an embedding of text descriptions that detail species behaviours. In particular, we present a vision-language model which employs multi-modal decoding of visual features extracted directly from camera trap videos to process query tokens representing behaviours and output class predictions. Query tokens are initialised using a standardised ethogram of chimpanzee behaviour, rather than using random or name-based initialisations. In addition, the effect of initialising query tokens using a masked language model fine-tuned on a text corpus of known behavioural patterns is explored. We evaluate our system on the PanAf500 and PanAf20K datasets and demonstrate the performance benefits of our multi-modal decoding approach and query initialisation strategy on multi-class and multi-label recognition tasks, respectively. Results and ablations corroborate performance improvements. We achieve state-of-the-art performance over vision and vision-language models in top-1 accuracy (+6.34%) on PanAf500 and overall (+1.1%) and tail-class (+2.26%) mean average precision on PanAf20K. We share complete source code and network weights for full reproducibility of results and easy utilisation.
Published: 2024

14. Best-in-class modeling: A novel strategy to discover constitutive models for soft matter systems

Author: Linka, Kevin and Kuhl, Ellen
Subjects: Condensed Matter - Soft Condensed Matter, 68T07, 74-05, 74B20, J.2
Abstract: The ability to automatically discover interpretable mathematical models from data could forever change how we model soft matter systems. For convex discovery problems with a unique global minimum, model discovery is well-established. It uses a classical top-down approach that first calculates a dense parameter vector, and then sparsifies the vector by gradually removing terms. For non-convex discovery problems with multiple local minima, this strategy is infeasible since the initial parameter vector is generally non-unique. Here we propose a novel bottom-up approach that starts with a sparse single-term vector, and then densifies the vector by systematically adding terms. Along the way, we discover models of gradually increasing complexity, a strategy that we call best-in-class modeling. To identify successful candidate terms, we reverse-engineer a library of sixteen functional building blocks that integrate a century of knowledge in material modeling with recent trends in machine learning and artificial intelligence. Yet, instead of solving the discrete combinatorial problem with 65,536 possible combinations of terms, best-in-class modeling starts with the best one-term model and iteratively repeats adding terms, until the objective function meets a user-defined convergence criterion. Strikingly, we achieve good convergence with only one or two terms. We illustrate the best-in-class one- and two-term models for a variety of soft matter systems including rubber, brain, artificial meat, skin, and arteries. Our discovered models display distinct and unexpected features for each family of materials, and suggest that best-in-class modeling is an efficient, robust, and easy-to-use strategy to discover the mechanical signatures of traditional and unconventional soft materials. We anticipate that our technology will generalize naturally to other classes of natural and man-made soft matter., Comment: 10 pages, 8 figures
Published: 2024

15. Nonlinearity-induced Scattering Zero Degeneracies for Spectral Management of Coherent Perfect Absorption in Complex Systems

Author: Wang, Cheng-Zhen, Guillamon, John, Tuxbury, William, Kuhl, Ulrich, and Kottos, Tsampikos
Subjects: Physics - Optics, Condensed Matter - Disordered Systems and Neural Networks, Nonlinear Sciences - Chaotic Dynamics, Physics - Applied Physics
Abstract: We develop a Coherent Perfect Absorption (CPA) protocol for cases where scale invariance is violated due to the presence of nonlinear mechanisms. We demonstrate, using a microwave setting that lacks geometrical symmetries, that the nonlinearity offers new reconfigurable modalities: the destruction or formation of nonlinear CPAs (NL-CPAs), and their frequency positioning and bandwidth management using the incident power as a control knob. The latter occurs via the formation of exceptional point degeneracies of the zeroes of nonlinear scattering processes. Our results establish NL-CPA protocols as a versatile scheme for the creation of reconfigurable hot/cold-spots in complicated enclosures (e.g. buildings or vessels) with applications to next-generation telecommunications, long-range wireless power transfer, and electromagnetic warfare., Comment: 16 pages, 9 figures
Published: 2024

16. Minimal activation with maximal reach: Reachability clouds of bio-inspired slender manipulators

Author: Kaczmarski, Bartosz, Moulton, Derek E., Goriely, Alain, and Kuhl, Ellen
Subjects: Computer Science - Robotics
Abstract: In the field of soft robotics, flexibility, adaptability, and functionality define a new era of robotic systems that can safely deform, reach, and grasp. To optimize the design of soft robotic systems, it is critical to understand their configuration space and full range of motion across a wide variety of design parameters. Here we integrate extreme mechanics and soft robotics to provide quantitative insights into the design of bio-inspired soft slender manipulators using the concept of reachability clouds. For a minimal three-actuator design inspired by the elephant trunk, we establish an efficient and robust reduced-order method to generate reachability clouds of almost half a million points each to visualize the accessible workspace of a wide variety of manipulator designs. We generate an atlas of 256 reachability clouds by systematically varying the key design parameters including the fiber count, revolution, tapering angle, and activation magnitude. Our results demonstrate that reachability clouds not only offer an immediately clear perspective into the inverse problem of control, but also introduce powerful metrics to characterize reachable volumes, unreachable regions, and actuator redundancy to quantify the performance of soft slender robots. Our study provides new insights into the design of soft robotic systems with minimal activation and maximal reach with potential applications in medical robotics, flexible manufacturing, and the autonomous exploration of space., Comment: 13 pages, 4 figures
Published: 2024

17. The influence of global governance on the sustainable performance of countries

Author: Massuga, Flavia, Larson, Marcos Aurélio, Kuhl, Marcos Roberto, and Doliveira, Sérgio Luis Dias
Published: 2024
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18. Revolution or risk?—Assessing the potential and challenges of GPT-4V in radiologic image interpretation

Author: Huppertz, Marc Sebastian, Siepmann, Robert, Topp, David, Nikoubashman, Omid, Yüksel, Can, Kuhl, Christiane Katharina, Truhn, Daniel, and Nebelung, Sven
Published: 2024
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19. A machine learning approach to fill gaps in dendrometer data

Author: Kuhl, Eileen, Ziaco, Emanuele, Esper, Jan, Konter, Oliver, and Martinez del Castillo, Edurne
Published: 2024
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20. The virtual reference radiologist: comprehensive AI assistance for clinical image reading and interpretation

Author: Siepmann, Robert, Huppertz, Marc, Rastkhiz, Annika, Reen, Matthias, Corban, Eric, Schmidt, Christian, Wilke, Stephan, Schad, Philipp, Yüksel, Can, Kuhl, Christiane, Truhn, Daniel, and Nebelung, Sven
Published: 2024
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21. Comparison of Complications in Patients with Ductal Cholangiocarcinoma (CCC) and Patients with Colorectal Liver Metastases (CRLMs) After Portal Vein Embolization (PVE): A Matched Cohort Study

Author: De Beukelaer, F., Van den Bosch, V., Kuhl, C., Pedersoli, F., and Bruners, P.
Published: 2024
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22. Minimal design of the elephant trunk as an active filament

Author: Kaczmarski, Bartosz, Leanza, Sophie, Zhao, Renee, Moulton, Derek E., Kuhl, Ellen, and Goriely, Alain
Subjects: Condensed Matter - Soft Condensed Matter, Physics - Applied Physics
Abstract: One of the key problems in active materials is the control of shape through actuation. A fascinating example of such control is the elephant trunk, a long, muscular, and extremely dexterous organ with multiple vital functions. The elephant trunk is an object of fascination for biologists, physicists, and children alike. Its versatility relies on the intricate interplay of multiple unique physical mechanisms and biological design principles. Here we explore these principles using the theory of active filaments and build, theoretically, computationally, and experimentally, a minimal model that explains and accomplishes some of the spectacular features of the elephant trunk., Comment: 13 pages, 7 figures
Published: 2024

23. An Ordinal Regression Framework for a Deep Learning Based Severity Assessment for Chest Radiographs

Author: Wienholt, Patrick, Hermans, Alexander, Khader, Firas, Puladi, Behrus, Leibe, Bastian, Kuhl, Christiane, Nebelung, Sven, and Truhn, Daniel
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: This study investigates the application of ordinal regression methods for categorizing disease severity in chest radiographs. We propose a framework that divides the ordinal regression problem into three parts: a model, a target function, and a classification function. Different encoding methods, including one-hot, Gaussian, progress-bar, and our soft-progress-bar, are applied using ResNet50 and ViT-B-16 deep learning models. We show that the choice of encoding has a strong impact on performance and that the best encoding depends on the chosen weighting of Cohen's kappa and also on the model architecture used. We make our code publicly available on GitHub., Comment: 17 pages, 3 figures, the code is available at: https://github.com/paddyOnGithub/ordinal_regression
Published: 2024

24. Automated Data-Driven Discovery of Material Models Based on Symbolic Regression: A Case Study on Human Brain Cortex

Author: Hou, Jixin, Chen, Xianyan, Wu, Taotao, Kuhl, Ellen, and Wang, Xianqiao
Subjects: Computer Science - Symbolic Computation, Quantitative Biology - Quantitative Methods, Quantitative Biology - Tissues and Organs
Abstract: We introduce a data-driven framework to automatically identify interpretable and physically meaningful hyperelastic constitutive models from sparse data. Leveraging symbolic regression, an algorithm based on genetic programming, our approach generates elegant hyperelastic models that achieve accurate data fitting through parsimonious mathematic formulae, while strictly adhering to hyperelasticity constraints such as polyconvexity. Our investigation spans three distinct hyperelastic models -- invariant-based, principal stretch-based, and normal strain-based -- and highlights the versatility of symbolic regression. We validate our new approach using synthetic data from five classic hyperelastic models and experimental data from the human brain to demonstrate algorithmic efficacy. Our results suggest that our symbolic regression robustly discovers accurate models with succinct mathematic expressions in invariant-based, stretch-based, and strain-based scenarios. Strikingly, the strain-based model exhibits superior accuracy, while both stretch- and strain-based models effectively capture the nonlinearity and tension-compression asymmetry inherent to human brain tissue. Polyconvexity examinations affirm the rigor of convexity within the training regime and demonstrate excellent extrapolation capabilities beyond this regime for all three models. However, the stretch-based models raise concerns regarding potential convexity loss under large deformations. Finally, robustness tests on noise-embedded data underscore the reliability of our symbolic regression algorithms. Our study confirms the applicability and accuracy of symbolic regression in the automated discovery of hyperelastic models for the human brain and gives rise to a wide variety of applications in other soft matter systems., Comment: 53 pages, 17 figures, and 6 tables
Published: 2024

25. PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition

Author: Brookes, Otto, Mirmehdi, Majid, Stephens, Colleen, Angedakin, Samuel, Corogenes, Katherine, Dowd, Dervla, Dieguez, Paula, Hicks, Thurston C., Jones, Sorrel, Lee, Kevin, Leinert, Vera, Lapuente, Juan, McCarthy, Maureen S., Meier, Amelia, Murai, Mizuki, Normand, Emmanuelle, Vergnes, Virginie, Wessling, Erin G., Wittig, Roman M., Langergraber, Kevin, Maldonado, Nuria, Yang, Xinyu, Zuberbuhler, Klaus, Boesch, Christophe, Arandjelovic, Mimi, Kuhl, Hjalmar, and Burghardt, Tilo
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We present the PanAf20K dataset, the largest and most diverse open-access annotated video dataset of great apes in their natural environment. It comprises more than 7 million frames across ~20,000 camera trap videos of chimpanzees and gorillas collected at 14 field sites in tropical Africa as part of the Pan African Programme: The Cultured Chimpanzee. The footage is accompanied by a rich set of annotations and benchmarks making it suitable for training and testing a variety of challenging and ecologically important computer vision tasks including ape detection and behaviour recognition. Furthering AI analysis of camera trap information is critical given the International Union for Conservation of Nature now lists all species in the great ape family as either Endangered or Critically Endangered. We hope the dataset can form a solid basis for engagement of the AI community to improve performance, efficiency, and result interpretation in order to support assessments of great ape presence, abundance, distribution, and behaviour and thereby aid conservation efforts., Comment: Accepted at IJCV
Published: 2024

26. Zirconium Oxynitride Thin Films for Photoelectrochemical Water Splitting.

Author: Streibel, Verena, Schönecker, Johanna, Wagner, Laura, Sirotti, Elise, Munnik, Frans, Kuhl, Matthias, Jiang, Chang-Ming, Eichhorn, Johanna, Santra, Saswati, and Sharp, Ian
Abstract: Transition metal oxynitrides are a promising class of functional materials for photoelectrochemical (PEC) applications. Although these compounds are most commonly synthesized via ammonolysis of oxide precursors, such synthetic routes often lead to poorly controlled oxygen-to-nitrogen anion ratios, and the harsh nitridation conditions are incompatible with many substrates, including transparent conductive oxides. Here, we report direct reactive sputter deposition of a family of zirconium oxynitride thin films and the comprehensive characterization of their tunable structural, optical, and functional PEC properties. Systematic increases of the oxygen content in the reactive sputter gas mixture enable access to different crystalline structures within the zirconium oxynitride family. Increasing oxygen contents lead to a transition from metallic to semiconducting to insulating phases. In particular, crystalline Zr2ON2-like films have band gaps in the UV-visible range and are n-type semiconductors. These properties, together with a valence band maximum position located favorably relative to the water oxidation potential, make them viable photoanode candidates. Using chopped linear sweep voltammetry, we indeed confirm that our Zr2ON2 films are PEC-active for the oxygen evolution reaction in alkaline electrolytes. We further show that high-vacuum annealing boosts their PEC performance characteristics. Although the observed photocurrents are low compared to state-of-the-art photoanodes, these dense and planar thin films can offer a valuable platform for studying oxynitride photoelectrodes, as well as for future nanostructuring, band gap engineering, and defect engineering efforts.
Published: 2024

27. Toward enriched Cognitive Learning with XAI

Author: Suffian, Muhammad, Kuhl, Ulrike, Alonso-Moral, Jose M., and Bogliolo, Alessandro
Subjects: Computer Science - Artificial Intelligence, I.2.8
Abstract: As computational systems supported by artificial intelligence (AI) techniques continue to play an increasingly pivotal role in making high-stakes recommendations and decisions across various domains, the demand for explainable AI (XAI) has grown significantly, extending its impact into cognitive learning research. Providing explanations for novel concepts is recognised as a fundamental aid in the learning process, particularly when addressing challenges stemming from knowledge deficiencies and skill application. Addressing these difficulties involves timely explanations and guidance throughout the learning process, prompting the interest of AI experts in developing explainer models. In this paper, we introduce an intelligent system (CL-XAI) for Cognitive Learning which is supported by XAI, focusing on two key research objectives: exploring how human learners comprehend the internal mechanisms of AI models using XAI tools and evaluating the effectiveness of such tools through human feedback. The use of CL-XAI is illustrated with a game-inspired virtual use case where learners tackle combinatorial problems to enhance problem-solving skills and deepen their understanding of complex concepts, highlighting the potential for transformative advances in cognitive learning and co-learning., Comment: 10 pages, 2 figures
Published: 2023

28. Democratizing biomedical simulation through automated model discovery and a universal material subroutine

Author: Peirlinck, Mathias, Linka, Kevin, Hurtado, Juan A., Holzapfel, Gerhard A., and Kuhl, Ellen
Published: 2024
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29. Datenbasierte Entscheidungsfindung trifft Digitalisierung – Einblicke in die prozesshafte unterrichtliche Nutzung digitaler Lerndaten durch Grundschullehrkräfte

Author: Hase, Alina and Kuhl, Poldi
Published: 2024
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30. Continuity equation for the flow of Fisher information in wave scattering

Author: Hüpfl, Jakob, Russo, Felix, Rachbauer, Lukas M., Bouchet, Dorian, Lu, Junjie, Kuhl, Ulrich, and Rotter, Stefan
Published: 2024
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31. Accelerating breast MRI acquisition with generative AI models

Author: Okolie, Augustine, Dirrichs, Timm, Huck, Luisa Charlotte, Nebelung, Sven, Arasteh, Soroosh Tayebi, Nolte, Teresa, Han, Tianyu, Kuhl, Christiane Katharina, and Truhn, Daniel
Published: 2024
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32. Teachers’ use of data from digital learning platforms for instructional design: a systematic review

Author: Hase, Alina and Kuhl, Poldi
Published: 2024
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33. Revising Alpine summer temperatures since 881 CE

Author: Kuhl, Eileen, Esper, Jan, Schneider, Lea, Trouet, Valerie, Kunz, Marcel, Klippel, Lara, Büntgen, Ulf, and Hartl, Claudia
Published: 2024
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34. An Observational Study to Determine the Real-Life Effectiveness of MP-AzeFlu® in Austrian Patients with Persistent Allergic Rhinitis

Author: Marth, Katharina, Renner, Andreas, Langmayr, Georg, Pohl, Wolfgang, Nguyen, Duc Tung, and Kuhl, Hans Christian
Published: 2024
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35. Time-efficient combined morphologic and quantitative joint MRI based on clinical image contrasts -- An exploratory in-situ study of standardized cartilage defects

Author: Lemainque, Teresa, Pridöhl, Nicola, Zhang, Shuo, Huppertz, Marc, Post, Manuel, Yüksel, Can, Yoneyama, Masami, Prescher, Andreas, Kuhl, Christiane, Truhn, Daniel, and Nebelung, Sven
Subjects: Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: OBJECTIVES: Quantitative MRI techniques such as T2 and T1$\rho$ mapping are beneficial in evaluating cartilage and meniscus. We aimed to evaluate the MIXTURE (Multi-Interleaved X-prepared Turbo-Spin Echo with IntUitive RElaxometry) sequences that provide morphologic images with clinical turbo spin-echo (TSE) contrasts and additional parameter maps versus reference TSE sequences in an in-situ model of human cartilage defects. MATERIALS AND METHODS: Prospectively, standardized cartilage defects of 8mm, 5mm, and 3mm diameter were created in the lateral femora of 10 human cadaveric knee specimens (81$\pm$10 years, nine male/one female). Using a clinical 3T MRI scanner and knee coil, MIXTURE sequences combining (i) proton-density weighted fat-saturated (PD-w FS) images and T2 maps and (ii) T1-weighted images and T1$\rho$ maps were acquired before and after defect creation, alongside the corresponding 2D TSE and 3D TSE reference sequences. Defect delineability, bone texture, and cartilage relaxation times were quantified. Inter-sequence comparisons were made using appropriate parametric and non-parametric tests. RESULTS: Overall, defect delineability and texture features were not significantly different between the MIXTURE and reference sequences. After defect creation, relaxation times increased significantly in the central femur (for T2) and all regions combined (for T1$\rho$). CONCLUSION: MIXTURE sequences permit time-efficient simultaneous morphologic and quantitative joint assessment based on clinical image contrasts. While providing T2 or T1$\rho$ maps in clinically feasible scan time, morphologic image features, i.e., cartilage defect delineability and bone texture, were comparable between MIXTURE and corresponding reference sequences., Comment: 12 pages (main body), 3 tables, 6 figures
Published: 2023

36. Theory and implementation of inelastic Constitutive Artificial Neural Networks

Author: Holthusen, Hagen, Lamm, Lukas, Brepols, Tim, Reese, Stefanie, and Kuhl, Ellen
Subjects: Computer Science - Machine Learning, Condensed Matter - Materials Science, 65, 74, I.6, J.2
Abstract: Nature has always been our inspiration in the research, design and development of materials and has driven us to gain a deep understanding of the mechanisms that characterize anisotropy and inelastic behavior. All this knowledge has been accumulated in the principles of thermodynamics. Deduced from these principles, the multiplicative decomposition combined with pseudo potentials are powerful and universal concepts. Simultaneously, the tremendous increase in computational performance enabled us to investigate and rethink our history-dependent material models to make the most of our predictions. Today, we have reached a point where materials and their models are becoming increasingly sophisticated. This raises the question: How do we find the best model that includes all inelastic effects to explain our complex data? Constitutive Artificial Neural Networks (CANN) may answer this question. Here, we extend the CANNs to inelastic materials (iCANN). Rigorous considerations of objectivity, rigid motion of the reference configuration, multiplicative decomposition and its inherent non-uniqueness, restrictions of energy and pseudo potential, and consistent evolution guide us towards the architecture of the iCANN satisfying thermodynamics per design. We combine feed-forward networks of the free energy and pseudo potential with a recurrent neural network approach to take time dependencies into account. We demonstrate that the iCANN is capable of autonomously discovering models for artificially generated data, the response of polymers for cyclic loading and the relaxation behavior of muscle data. As the design of the network is not limited to visco-elasticity, our vision is that the iCANN will reveal to us new ways to find the various inelastic phenomena hidden in the data and to understand their interaction. Our source code, data, and examples are available at doi.org/10.5281/zenodo.10066805, Comment: 54 pages, 14 figures, 14 tables
Published: 2023
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37. Two for One -- Combined Morphologic and Quantitative Knee Joint MRI Using a Versatile Turbo Spin-Echo Platform

Author: Lemainque, Teresa, Pridoehl, Nicola, Huppertz, Marc, Post, Manuel, Yüksel, Can, Radke, Karl Ludger, Zhang, Shuo, Yoneyama, Masami, Prescher, Andreas, Kuhl, Christiane, Truhn, Daniel, and Nebelung, Sven
Subjects: Physics - Medical Physics
Abstract: Introduction: Quantitative MRI techniques such as T2 and T1\r{ho} mapping are beneficial in evaluating knee joint pathologies; however, long acquisition times limit their clinical adoption. MIXTURE (Multi-Interleaved X-prepared Turbo-Spin Echo with IntUitive RElaxometry) provides a versatile turbo spin-echo (TSE) sequence platform for simultaneous morphologic and quantitative joint imaging yet lacks comparative evaluation in basic and translational research contexts. Methods: Two MIXTURE sequences were designed along clinical requirements: (i) MIX1, combining proton density (PD)-weighted fat-saturated (FS) images and quantitative T2 mapping (acquisition time: 4:59 min), and (ii) MIX2, combining T1-weighted images with quantitative T1\r{ho} mapping (6:38 min). MIXTURE sequences and their reference 2D and 3D TSE counterparts were acquired from ten human cadaveric knee joints using a clinical 3T MRI scanner and knee coil. Contrast, contrast-to-noise ratios, and coefficients of variation were comparatively evaluated using parametric tests. Clinical radiologists (n=3) assessed diagnostic quality as a function of sequence and anatomic structure using 5-point Likert scales and ordinal regression. The significance level was set to {\alpha}=0.01. Results: MIX1 and MIX2 had at least equal diagnostic quality compared to the 2D and 3D TSE sequences of the same image weighting. Contrast, contrast-to-noise ratios, and coefficients of variation were largely similar for the PD-weighted FS and T1-weighted images. Discussion: In clinically feasible scan times, the MIXTURE sequence platform yields (i) morphologic images of diagnostic quality and adjustable TSE-based contrasts and (ii) quantitative parameter mapping with additional insights on soft tissue composition and ultrastructure., Comment: 13 pages (main text), 7 figures, 3 tables
Published: 2023

38. A Finely Segmented Semi-Monolithic Detector tailored for High Resolution PET

Author: Kuhl, Yannick, Mueller, Florian, Naunheim, Stephan, Bovelett, Matthias, Lambertus, Janko, Schug, David, Weissler, Bjoern, Gegenmantel, Eike, Gebhardt, Pierre, and Schulz, Volkmar
Subjects: Physics - Medical Physics
Abstract: Preclinical research and organ-dedicated applications require high-resolution positron emission tomography (PET) detectors to visualize small structures and understand biological processes at a finer level of detail. Current commercial systems often employ finely pixelated or monolithic scintillators, each with its limitations. We present a semi-monolithic detector, tailored for high-resolution PET applications, and merging concepts of monolithic and pixelated crystals. The detector features slabs measuring (24 x 10 x 1) sq. mm, coupled to a 12 x 12 readout channel photosensor with 4 mm pitch. The slabs are grouped in two arrays of 44 slabs each to achieve a higher optical photon density. We employ a fan beam collimator for fast calibration to train machine-learning-based positioning models for all three dimensions, including slab identification and depth-of-interaction (DOI), utilizing gradient tree boosting (GTB). Energy calculation was based on a position-dependent energy calibration. Using an analytical timing calibration, time skews were corrected for coincidence timing resolution (CTR) estimation. Leveraging machine-learning-based calibration in all three dimensions, we achieved high detector spatial resolution: down to 1.18 mm full width at half maximum (FWHM) detector spatial resolution and 0.75 mm mean absolute error (MAE) in the planar-monolithic direction along the slabs, and 2.14 mm FWHM and 1.03 mm MAE for depth-of-interaction (DOI) at an energy window of (435-585) keV. Correct slab interaction identification exceeded 80%, alongside an energy resolution of 13.8% and a CTR of 450 ps FWHM. Therewith, the introduced finely segmented, high-resolution slab detector demonstrates an appealing performance suitable for high-resolution PET applications. The current benchtop-based detector calibration routine allows these detectors to be used in PET systems., Comment: 14 pages, 11 figures, IEEE NSS MIC RTSD 2023
Published: 2023

39. Bound states in the continuum induced via local symmetries in complex structures

Author: Wang, Cheng-Zhen, Kuhl, Ulrich, Dowling, Adin, Schanz, Holger, and Kottos, Tsampikos
Subjects: Physics - Optics, Nonlinear Sciences - Chaotic Dynamics, Quantum Physics
Abstract: Bound states in the continuum (BICs) defy conventional wisdom that assumes a spectral separation between propagating waves, that carry energy away, and spatially localized waves corresponding to discrete frequencies. They can be described as resonance states with infinite lifetime, i.e., leaky modes with zero leakage. The advent of metamaterials and nanophotonics allowed the creation of BICs in a variety of systems. Mainly, BICs have been realized by destructive interference between outgoing resonant modes or exploiting engineered global symmetries that enforce the decoupling of a symmetry-incompatible bound mode from the surrounding radiation modes. Here, we introduce theoretically BICs relying on a different mechanism, namely local symmetries that enforce a field concentration on a part of a complex system without implying any global symmetry. We experimentally implement such BICs using microwaves in a compact one-dimensional photonic network and show that they emerge from the annihilation of two topological singularities, a zero and a pole, of the measured scattering matrix. Our alternative for achieving BICs in complex wave systems may be useful for applications like sensing, lasing, and enhancement of nonlinear interactions that require high-$Q$ modes., Comment: 11 pages, 6 figures
Published: 2023
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40. On sparse regression, Lp-regularization, and automated model discovery

Author: McCulloch, Jeremy A., Pierre, Skyler R. St., Linka, Kevin, and Kuhl, Ellen
Subjects: Computer Science - Machine Learning, 65, 74, I.6, J.2
Abstract: Sparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide simple relationships among scientific variables. While the statistical tools for model discovery are well established in the context of linear regression, their generalization to nonlinear regression in material modeling is highly problem-specific and insufficiently understood. Here we explore the potential of neural networks for automatic model discovery and induce sparsity by a hybrid approach that combines two strategies: regularization and physical constraints. We integrate the concept of Lp regularization for subset selection with constitutive neural networks that leverage our domain knowledge in kinematics and thermodynamics. We train our networks with both, synthetic and real data, and perform several thousand discovery runs to infer common guidelines and trends: L2 regularization or ridge regression is unsuitable for model discovery; L1 regularization or lasso promotes sparsity, but induces strong bias; only L0 regularization allows us to transparently fine-tune the trade-off between interpretability and predictability, simplicity and accuracy, and bias and variance. With these insights, we demonstrate that Lp regularized constitutive neural networks can simultaneously discover both, interpretable models and physically meaningful parameters. We anticipate that our findings will generalize to alternative discovery techniques such as sparse and symbolic regression, and to other domains such as biology, chemistry, or medicine. Our ability to automatically discover material models from data could have tremendous applications in generative material design and open new opportunities to manipulate matter, alter properties of existing materials, and discover new materials with user-defined properties., Comment: 35 pages, 15 figures, 2 tables, 62 references
Published: 2023

41. Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models

Author: Arasteh, Soroosh Tayebi, Kuhl, Christiane, Saehn, Marwin-Jonathan, Isfort, Peter, Truhn, Daniel, and Nebelung, Sven
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: Developing robust artificial intelligence (AI) models that generalize well to unseen datasets is challenging and usually requires large and variable datasets, preferably from multiple institutions. In federated learning (FL), a model is trained collaboratively at numerous sites that hold local datasets without exchanging them. So far, the impact of training strategy, i.e., local versus collaborative, on the diagnostic on-domain and off-domain performance of AI models interpreting chest radiographs has not been assessed. Consequently, using 610,000 chest radiographs from five institutions across the globe, we assessed diagnostic performance as a function of training strategy (i.e., local vs. collaborative), network architecture (i.e., convolutional vs. transformer-based), generalization performance (i.e., on-domain vs. off-domain), imaging finding (i.e., cardiomegaly, pleural effusion, pneumonia, atelectasis, consolidation, pneumothorax, and no abnormality), dataset size (i.e., from n=18,000 to 213,921 radiographs), and dataset diversity. Large datasets not only showed minimal performance gains with FL but, in some instances, even exhibited decreases. In contrast, smaller datasets revealed marked improvements. Thus, on-domain performance was mainly driven by training data size. However, off-domain performance leaned more on training diversity. When trained collaboratively across diverse external institutions, AI models consistently surpassed models trained locally for off-domain tasks, emphasizing FL's potential in leveraging data diversity. In conclusion, FL can bolster diagnostic privacy, reproducibility, and off-domain reliability of AI models and, potentially, optimize healthcare outcomes., Comment: Published in Nature Scientific Reports
Published: 2023
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42. Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative Pretraining

Author: Han, Tianyu, Žigutytė, Laura, Huck, Luisa, Huppertz, Marc, Siepmann, Robert, Gandelsman, Yossi, Blüthgen, Christian, Khader, Firas, Kuhl, Christiane, Nebelung, Sven, Kather, Jakob, and Truhn, Daniel
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Detecting misleading patterns in automated diagnostic assistance systems, such as those powered by Artificial Intelligence, is critical to ensuring their reliability, particularly in healthcare. Current techniques for evaluating deep learning models cannot visualize confounding factors at a diagnostic level. Here, we propose a self-conditioned diffusion model termed DiffChest and train it on a dataset of 515,704 chest radiographs from 194,956 patients from multiple healthcare centers in the United States and Europe. DiffChest explains classifications on a patient-specific level and visualizes the confounding factors that may mislead the model. We found high inter-reader agreement when evaluating DiffChest's capability to identify treatment-related confounders, with Fleiss' Kappa values of 0.8 or higher across most imaging findings. Confounders were accurately captured with 11.1% to 100% prevalence rates. Furthermore, our pretraining process optimized the model to capture the most relevant information from the input radiographs. DiffChest achieved excellent diagnostic accuracy when diagnosing 11 chest conditions, such as pleural effusion and cardiac insufficiency, and at least sufficient diagnostic accuracy for the remaining conditions. Our findings highlight the potential of pretraining based on diffusion models in medical image classification, specifically in providing insights into confounding factors and model robustness.
Published: 2023

43. Medical Foundation Models are Susceptible to Targeted Misinformation Attacks

Author: Han, Tianyu, Nebelung, Sven, Khader, Firas, Wang, Tianci, Mueller-Franzes, Gustav, Kuhl, Christiane, Försch, Sebastian, Kleesiek, Jens, Haarburger, Christoph, Bressem, Keno K., Kather, Jakob Nikolas, and Truhn, Daniel
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract: Large language models (LLMs) have broad medical knowledge and can reason about medical information across many domains, holding promising potential for diverse medical applications in the near future. In this study, we demonstrate a concerning vulnerability of LLMs in medicine. Through targeted manipulation of just 1.1% of the model's weights, we can deliberately inject an incorrect biomedical fact. The erroneous information is then propagated in the model's output, whilst its performance on other biomedical tasks remains intact. We validate our findings in a set of 1,038 incorrect biomedical facts. This peculiar susceptibility raises serious security and trustworthiness concerns for the application of LLMs in healthcare settings. It accentuates the need for robust protective measures, thorough verification mechanisms, and stringent management of access to these models, ensuring their reliable and safe use in medical practice.
Published: 2023

44. Long-distance chronometric leveling with a transportable optical clock

Author: Grotti, J., Nosske, I., Koller, S. B., Herbers, S., Denker, H., Timmen, L., Vishnyakova, G., Grosche, G., Waterholter, T., Kuhl, A., Koke, S., Benkler, E., Giunta, M., Maisenbacher, L., Matveev, A., Dörscher, S., Schwarz, R., Al-Masoudi, A., Hänsch, T. W., Udem, T. H., Holzwarth, R., and Lisdat, C.
Subjects: Physics - Atomic Physics, Quantum Physics
Abstract: We have measured the geopotential difference between two locations separated by 457 km by comparison of two optical lattice clocks via an interferometric fiber link, utilizing the gravitational redshift of the clock transition frequency. The $^{87}$Sr clocks have been compared side-by-side before and after one of the clocks was moved to the remote location. The chronometrically measured geopotential difference of $3918.1(2.4)\,\mathrm{m^2 \, s^{-2}}$ agrees with an independent geodetic determination of $3915.88(0.30)\,\mathrm{m^2 \, s^{-2}}$. The uncertainty of the chronometric geopotential difference is equivalent to an uncertainty of $24~\mathrm{cm}$ in height.
Published: 2023

45. Precise measurements of W- and Z-boson transverse momentum spectra with the ATLAS detector using pp collisions at s=5.02 TeV and 13 TeV

Author: Aad, G., Abbott, B., Abeling, K., Abicht, N. J., Abidi, S. H., Aboulhorma, A., Abramowicz, H., Abreu, H., Abulaiti, Y., Acharya, B. S., Bourdarios, C. Adam, Adamczyk, L., Adamek, L., Addepalli, S. V., Addison, M. J., Adelman, J., Adiguzel, A., Adye, T., Affolder, A. A., Afik, Y., Agaras, M. N., Agarwala, J., Aggarwal, A., Agheorghiesei, C., Ahmad, A., Ahmadov, F., Ahmed, W. S., Ahuja, S., Ai, X., Aielli, G., Aikot, A., Tamlihat, M. Ait, Aitbenchikh, B., Aizenberg, I., Akbiyik, M., Åkesson, T. P. A., Akimov, A. V., Akiyama, D., Akolkar, N. N., Khoury, K. Al, Alberghi, G. L., Albert, J., Albicocco, P., Albouy, G. L., Alderweireldt, S., Aleksa, M., Aleksandrov, I. N., Alexa, C., Alexopoulos, T., Alfonsi, F., Algren, M., Alhroob, M., Ali, B., Ali, H. M. J., Ali, S., Alibocus, S. W., Aliev, M., Alimonti, G., Alkakhi, W., Allaire, C., Allbrooke, B. M. M., Allen, J. F., Flores, C. A. Allendes, Allport, P. P., Aloisio, A., Alonso, F., Alpigiani, C., Estevez, M. Alvarez, Fernandez, A. Alvarez, Cardoso, M. Alves, Alviggi, M. G., Aly, M., Coutinho, Y. Amaral, Ambler, A., Amelung, C., Amerl, M., Ames, C. G., Amidei, D., Santos, S. P. Amor Dos, Amos, K. R., Ananiev, V., Anastopoulos, C., Andeen, T., Anders, J. K., Andrean, S. Y., Andreazza, A., Angelidakis, S., Angerami, A., Anisenkov, A. V., Annovi, A., Antel, C., Anthony, M. T., Antipov, E., Antonelli, M., Anulli, F., Aoki, M., Aoki, T., Pozo, J. A. Aparisi, Aparo, M. A., Bella, L. Aperio, Appelt, C., Apyan, A., Aranzabal, N., Arcangeletti, C., Arce, A. T. H., Arena, E., Arguin, J.-F., Argyropoulos, S., Arling, J.-H., Arnaez, O., Arnold, H., Artoni, G., Asada, H., Asai, K., Asai, S., Asbah, N. A., Assahsah, J., Assamagan, K., Astalos, R., Atashi, S., Atkin, R. J., Atkinson, M., Atmani, H., Atmasiddha, P. A., Augsten, K., Auricchio, S., Auriol, A. D., Austrup, V. A., Avolio, G., Axiotis, K., Azuelos, G., Babal, D., Bachacou, H., Bachas, K., Bachiu, A., Backman, F., Badea, A., Bagnaia, P., Bahmani, M., Bailey, A. J., Bailey, V. R., Baines, J. T., Baines, L., Bakalis, C., Baker, O. K., Bakos, E., Gupta, D. Bakshi, Balakrishnan, V., Balasubramanian, R., Baldin, E. M., Balek, P., Ballabene, E., Balli, F., Baltes, L. M., Balunas, W. K., Balz, J., Banas, E., Bandieramonte, M., Bandyopadhyay, A., Bansal, S., Barak, L., Barakat, M., Barberio, E. L., Barberis, D., Barbero, M., Barel, M. Z., Barends, K. N., Barillari, T., Barisits, M.-S., Barklow, T., Baron, P., Moreno, D. A. Baron, Baroncelli, A., Barone, G., Barr, A. J., Barr, J. D., Navarro, L. Barranco, Barreiro, F., da Costa, J. Barreiro Guimarães, Barron, U., Teixeira, M. G. Barros, Barsov, S., Bartels, F., Bartoldus, R., Barton, A. E., Bartos, P., Basan, A., Baselga, M., Bassalat, A., Basso, M. J., Basson, C. R., Bates, R. L., Batlamous, S., Batley, J. R., Batool, B., Battaglia, M., Battulga, D., Bauce, M., Bauer, M., Bauer, P., Hurrell, L. T. Bazzano, Beacham, J. B., Beau, T., Beauchemin, P. H., Becherer, F., Bechtle, P., Beck, H. P., Becker, K., Beddall, A. J., Bednyakov, V. A., Bee, C. P., Beemster, L. J., Beermann, T. A., Begalli, M., Begel, M., Behera, A., Behr, J. K., Beirer, J. F., Beisiegel, F., Belfkir, M., Bella, G., Bellagamba, L., Bellerive, A., Bellos, P., Beloborodov, K., Benchekroun, D., Bendebba, F., Benhammou, Y., Benoit, M., Bensinger, J. R., Bentvelsen, S., Beresford, L., Beretta, M., Kuutmann, E. Bergeaas, Berger, N., Bergmann, B., Beringer, J., Bernardi, G., Bernius, C., Bernlochner, F. U., Bernon, F., Berry, T., Berta, P., Berthold, A., Bertram, I. A., Bethke, S., Betti, A., Bevan, A. J., Bhamjee, M., Bhatta, S., Bhattacharya, D. S., Bhattarai, P., Bhopatkar, V. S., Bi, R., Bianchi, R. M., Bianco, G., Biebel, O., Bielski, R., Biglietti, M., Bindi, M., Bingul, A., Bini, C., Biondini, A., Birch-sykes, C. J., Bird, G. A., Birman, M., Biros, M., Biryukov, S., Bisanz, T., Bisceglie, E., Biswal, J. P., Biswas, D., Bitadze, A., Bjørke, K., Bloch, I., Blocker, C., Blue, A., Blumenschein, U., Blumenthal, J., Bobbink, G. J., Bobrovnikov, V. S., Boehler, M., Boehm, B., Bogavac, D., Bogdanchikov, A. G., Bohm, C., Boisvert, V., Bokan, P., Bold, T., Bomben, M., Bona, M., Boonekamp, M., Booth, C. D., Borbély, A. G., Bordulev, I. S., Borecka-Bielska, H. M., Borissov, G., Bortoletto, D., Boscherini, D., Bosman, M., Sola, J. D. Bossio, Bouaouda, K., Bouchhar, N., Boudreau, J., Bouhova-Thacker, E. V., Boumediene, D., Bouquet, R., Boveia, A., Boyd, J., Boye, D., Boyko, I. R., Bracinik, J., Brahimi, N., Brandt, G., Brandt, O., Braren, F., Brau, B., Brau, J. E., Brener, R., Brenner, L., Brenner, R., Bressler, S., Britton, D., Britzger, D., Brock, I., Brock, R., Brooijmans, G., Brooks, W. K., Brost, E., Brown, L. M., Bruce, L. E., Bruckler, T. L., de Renstrom, P. A. Bruckman, Brüers, B., Bruni, A., Bruni, G., Bruschi, M., Bruscino, N., Buanes, T., Buat, Q., Buchin, D., Buckley, A. 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Published: 2024
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46. Constraints on simplified dark matter models involving an s-channel mediator with the ATLAS detector in pp collisions at s=13 TeV

Author: Aad, G., Aakvaag, E., Abbott, B., Abdelhameed, S., Abeling, K., Abicht, N. J., Abidi, S. H., Aboelela, M., Aboulhorma, A., Abramowicz, H., Abreu, H., Abulaiti, Y., Acharya, B. S., Ackermann, A., Bourdarios, C. Adam, Adamczyk, L., Addepalli, S. V., Addison, M. J., Adelman, J., Adiguzel, A., Adye, T., Affolder, A. A., Afik, Y., Agaras, M. N., Agarwala, J., Aggarwal, A., Agheorghiesei, C., Ahmadov, F., Ahmed, W. S., Ahuja, S., Ai, X., Aielli, G., Aikot, A., Tamlihat, M. Ait, Aitbenchikh, B., Akbiyik, M., Åkesson, T. P. A., Akimov, A. V., Akiyama, D., Akolkar, N. N., Aktas, S., Khoury, K. Al, Alberghi, G. L., Albert, J., Albicocco, P., Albouy, G. L., Alderweireldt, S., Alegria, Z. L., Aleksa, M., Aleksandrov, I. N., Alexa, C., Alexopoulos, T., Alfonsi, F., Algren, M., Alhroob, M., Ali, B., Ali, H. M. J., Ali, S., Alibocus, S. W., Aliev, M., Alimonti, G., Alkakhi, W., Allaire, C., Allbrooke, B. M. M., Allen, J. F., Flores, C. A. Allendes, Allport, P. P., Aloisio, A., Alonso, F., Alpigiani, C., Alsolami, Z. M. K., Estevez, M. Alvarez, Fernandez, A. Alvarez, Cardoso, M. Alves, Alviggi, M. G., Aly, M., Coutinho, Y. Amaral, Ambler, A., Amelung, C., Amerl, M., Ames, C. G., Amidei, D., Amirie, K. J., Santos, S. P. Amor Dos, Amos, K. R., An, S., Ananiev, V., Anastopoulos, C., Andeen, T., Anders, J. K., Anderson, A. C., Andrean, S. Y., Andreazza, A., Angelidakis, S., Angerami, A., Anisenkov, A. V., Annovi, A., Antel, C., Antipov, E., Antonelli, M., Anulli, F., Aoki, M., Aoki, T., Aparo, M. A., Bella, L. Aperio, Appelt, C., Apyan, A., Val, S. J. Arbiol, Arcangeletti, C., Arce, A. T. H., Arena, E., Arguin, J-F., Argyropoulos, S., Arling, J.-H., Arnaez, O., Arnold, H., Artoni, G., Asada, H., Asai, K., Asai, S., Asbah, N. A., Pickering, R. A. Ashby, Assamagan, K., Astalos, R., Astrand, K. S. V., Atashi, S., Atkin, R. J., Atkinson, M., Atmani, H., Atmasiddha, P. A., Augsten, K., Auricchio, S., Auriol, A. D., Austrup, V. 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Published: 2024
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47. Measurements of inclusive and differential cross-sections of tt¯γs production in pp collisions at tt¯γs = 13 TeV with the ATLAS detector

Author: Aad, G., Aakvaag, E., Abbott, B., Abeling, K., Abicht, N. J., Abidi, S. H., Aboelela, M., Aboulhorma, A., Abramowicz, H., Abreu, H., Abulaiti, Y., Acharya, B. S., Ackermann, A., Adam Bourdarios, C., Adamczyk, L., Addepalli, S. V., Addison, M. J., Adelman, J., Adiguzel, A., Adye, T., Affolder, A. A., Afik, Y., Agaras, M. N., Agarwala, J., Aggarwal, A., Agheorghiesei, C., Ahmad, A., Ahmadov, F., Ahmed, W. S., Ahuja, S., Ai, X., Aielli, G., Aikot, A., Ait Tamlihat, M., Aitbenchikh, B., Aizenberg, I., Akbiyik, M., Åkesson, T. P. A., Akimov, A. V., Akiyama, D., Akolkar, N. N., Aktas, S., Al Khoury, K., Alberghi, G. L., Albert, J., Albicocco, P., Albouy, G. L., Alderweireldt, S., Alegria, Z. L., Aleksa, M., Aleksandrov, I. N., Alexa, C., Alexopoulos, T., Alfonsi, F., Algren, M., Alhroob, M., Ali, B., Ali, H. M. J., Ali, S., Alibocus, S. W., Aliev, M., Alimonti, G., Alkakhi, W., Allaire, C., Allbrooke, B. M. M., Allen, J. F., Allendes Flores, C. A., Allport, P. P., Aloisio, A., Alonso, F., Alpigiani, C., Alvarez Estevez, M., Alvarez Fernandez, A., Alves Cardoso, M., Alviggi, M. G., Aly, M., Amaral Coutinho, Y., Ambler, A., Amelung, C., Amerl, M., Ames, C. G., Amidei, D., Amirie, K. J., Amor Dos Santos, S. P., Amos, K. R., An, S., Ananiev, V., Anastopoulos, C., Andeen, T., Anders, J. K., Andrean, S. Y., Andreazza, A., Angelidakis, S., Angerami, A., Anisenkov, A. V., Annovi, A., Antel, C., Anthony, M. T., Antipov, E., Antonelli, M., Anulli, F., Aoki, M., Aoki, T., Aparisi Pozo, J. A., Aparo, M. A., Aperio Bella, L., Appelt, C., Apyan, A., Arbiol Val, S. J., Arcangeletti, C., Arce, A. T. H., Arena, E., Arguin, J-F., Argyropoulos, S., Arling, J.-H., Arnaez, O., Arnold, H., Artoni, G., Asada, H., Asai, K., Asai, S., Asbah, N. A., Assamagan, K., Astalos, R., Astrand, K. S. V., Atashi, S., Atkin, R. J., Atkinson, M., Atmani, H., Atmasiddha, P. A., Augsten, K., Auricchio, S., Auriol, A. D., Austrup, V. A., Avolio, G., Axiotis, K., Azuelos, G., Babal, D., Bachacou, H., Bachas, K., Bachiu, A., Backman, F., Badea, A., Baer, T. M., Bagnaia, P., Bahmani, M., Bahner, D., Bai, K., Bailey, A. J., Baines, J. T., Baines, L., Baker, O. K., Bakos, E., Bakshi Gupta, D., Balakrishnan, V., Balasubramanian, R., Baldin, E. M., Balek, P., Ballabene, E., Balli, F., Baltes, L. M., Balunas, W. K., Balz, J., Banas, E., Bandieramonte, M., Bandyopadhyay, A., Bansal, S., Barak, L., Barakat, M., Barberio, E. L., Barberis, D., Barbero, M., Barel, M. Z., Barends, K. N., Barillari, T., Barisits, M-S., Barklow, T., Baron, P., Baron Moreno, D. A., Baroncelli, A., Barone, G., Barr, A. J., Barr, J. D., Barreiro, F., Barreiro Guimarães da Costa, J., Barron, U., Barros Teixeira, M. G., Barsov, S., Bartels, F., Bartoldus, R., Barton, A. E., Bartos, P., Basan, A., Baselga, M., Bassalat, A., Basso, M. J., Bates, R. L., Batlamous, S., Batool, B., Battaglia, M., Battulga, D., Bauce, M., Bauer, M., Bauer, P., Bazzano Hurrell, L. T., Beacham, J. B., Beau, T., Beaucamp, J. Y., Beauchemin, P. H., Bechtle, P., Beck, H. P., Becker, K., Beddall, A. J., Bednyakov, V. A., Bee, C. P., Beemster, L. J., Beermann, T. A., Begalli, M., Begel, M., Behera, A., Behr, J. K., Beirer, J. F., Beisiegel, F., Belfkir, M., Bella, G., Bellagamba, L., Bellerive, A., Bellos, P., Beloborodov, K., Benchekroun, D., Bendebba, F., Benhammou, Y., Benkendorfer, K. C., Beresford, L., Beretta, M., Bergeaas Kuutmann, E., Berger, N., Bergmann, B., Beringer, J., Bernardi, G., Bernius, C., Bernlochner, F. U., Bernon, F., Berrocal Guardia, A., Berry, T., Berta, P., Berthold, A., Bethke, S., Betti, A., Bevan, A. J., Bhalla, N. K., Bhamjee, M., Bhatta, S., Bhattacharya, D. S., Bhattarai, P., Bhide, K. D., Bhopatkar, V. S., Bianchi, R. M., Bianco, G., Biebel, O., Bielski, R., Biglietti, M., Billingsley, C. 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Published: 2024
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48. Search for heavy resonances in final states with four leptons and missing transverse momentum or jets in pp collisions at s = 13 TeV with the ATLAS detector

Author: Aad, G., Aakvaag, E., Abbott, B., Abeling, K., Abicht, N. J., Abidi, S. H., Aboulhorma, A., Abramowicz, H., Abreu, H., Abulaiti, Y., Acharya, B. S., Adam Bourdarios, C., Adamczyk, L., Addepalli, S. V., Addison, M. J., Adelman, J., Adiguzel, A., Adye, T., Affolder, A. A., Afik, Y., Agaras, M. N., Agarwala, J., Aggarwal, A., Agheorghiesei, C., Ahmad, A., Ahmadov, F., Ahmed, W. S., Ahuja, S., Ai, X., Aielli, G., Aikot, A., Ait Tamlihat, M., Aitbenchikh, B., Aizenberg, I., Akbiyik, M., Åkesson, T. P. A., Akimov, A. V., Akiyama, D., Akolkar, N. N., Aktas, S., Al Khoury, K., Alberghi, G. L., Albert, J., Albicocco, P., Albouy, G. L., Alderweireldt, S., Alegria, Z. L., Aleksa, M., Aleksandrov, I. N., Alexa, C., Alexopoulos, T., Alfonsi, F., Algren, M., Alhroob, M., Ali, B., Ali, H. M. J., Ali, S., Alibocus, S. W., Aliev, M., Alimonti, G., Alkakhi, W., Allaire, C., Allbrooke, B. M. M., Allen, J. F., Allendes Flores, C. A., Allport, P. P., Aloisio, A., Alonso, F., Alpigiani, C., Alvarez Estevez, M., Alvarez Fernandez, A., Alves Cardoso, M., Alviggi, M. G., Aly, M., Amaral Coutinho, Y., Ambler, A., Amelung, C., Amerl, M., Ames, C. G., Amidei, D., Amor Dos Santos, S. P., Amos, K. R., Ananiev, V., Anastopoulos, C., Andeen, T., Anders, J. K., Andrean, S. Y., Andreazza, A., Angelidakis, S., Angerami, A., Anisenkov, A. V., Annovi, A., Antel, C., Anthony, M. T., Antipov, E., Antonelli, M., Anulli, F., Aoki, M., Aoki, T., Aparisi Pozo, J. A., Aparo, M. A., Aperio Bella, L., Appelt, C., Apyan, A., Arbiol Val, S. J., Arcangeletti, C., Arce, A. T. H., Arena, E., Arguin, J-F., Argyropoulos, S., Arling, J.-H., Arnaez, O., Arnold, H., Artoni, G., Asada, H., Asai, K., Asai, S., Asbah, N. A., Assamagan, K., Astalos, R., Atashi, S., Atkin, R. J., Atkinson, M., Atmani, H., Atmasiddha, P. A., Augsten, K., Auricchio, S., Auriol, A. D., Austrup, V. A., Avolio, G., Axiotis, K., Azuelos, G., Babal, D., Bachacou, H., Bachas, K., Bachiu, A., Backman, F., Badea, A., Baer, T. M., Bagnaia, P., Bahmani, M., Bahner, D., Bai, K., Bailey, A. J., Bailey, V. R., Baines, J. T., Baines, L., Baker, O. K., Bakos, E., Bakshi Gupta, D., Balakrishnan, V., Balasubramanian, R., Baldin, E. M., Balek, P., Ballabene, E., Balli, F., Baltes, L. M., Balunas, W. K., Balz, J., Banas, E., Bandieramonte, M., Bandyopadhyay, A., Bansal, S., Barak, L., Barakat, M., Barberio, E. L., Barberis, D., Barbero, M., Barel, M. Z., Barends, K. N., Barillari, T., Barisits, M-S., Barklow, T., Baron, P., Baron Moreno, D. A., Baroncelli, A., Barone, G., Barr, A. J., Barr, J. D., Barreiro, F., Barreiro Guimarães da Costa, J., Barron, U., Barros Teixeira, M. G., Barsov, S., Bartels, F., Bartoldus, R., Barton, A. E., Bartos, P., Basan, A., Baselga, M., Bassalat, A., Basso, M. J., Basson, C. R., Bates, R. 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C., Cheong, S., Cheplakov, A., Cheremushkina, E., Cherepanova, E., Cherkaoui El Moursli, R., Cheu, E., Cheung, K., Chevalier, L., Chiarella, V., Chiarelli, G., Chiedde, N., Chiodini, G., Chisholm, A. S., Chitan, A., Chitishvili, M., Chizhov, M. V., Choi, K., Chou, Y., Chow, E. Y. S., Chu, K. L., Chu, M. C., Chu, X., Chudoba, J., Chwastowski, J. J., Cieri, D., Ciesla, K. M., Cindro, V., Ciocio, A., Cirotto, F., Citron, Z. H., Citterio, M., Ciubotaru, D. A., Clark, A., Clark, P. J., Clarry, C., Clavijo Columbie, J. M., Clawson, S. E., Clement, C., Clercx, J., Coadou, Y., Cobal, M., Coccaro, A., Coelho Barrue, R. F., Coelho Lopes De Sa, R., Coelli, S., Cole, B., Collot, J., Conde Muiño, P., Connell, M. P., Connell, S. H., Conroy, E. I., Conventi, F., Cooke, H. G., Cooper-Sarkar, A. M., Cordeiro Oudot Choi, A., Corpe, L. D., Corradi, M., Corriveau, F., Cortes-Gonzalez, A., Costa, M. J., Costanza, F., Costanzo, D., Cote, B. 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M., Della Pietra, M., Della Volpe, D., Dell’Acqua, A., Dell’Asta, L., Delmastro, M., Delsart, P. A., Demers, S., Demichev, M., Denisov, S. P., D’Eramo, L., Derendarz, D., Derue, F., Dervan, P., Desch, K., Deutsch, C., Di Bello, F. A., Di Ciaccio, A., Di Ciaccio, L., Di Domenico, A., Di Donato, C., Di Girolamo, A., Di Gregorio, G., Di Luca, A., Di Micco, B., Di Nardo, R., Diamantopoulou, M., Dias, F. A., Dias Do Vale, T., Diaz, M. A., Diaz Capriles, F. G., Didenko, M., Diehl, E. B., Díez Cornell, S., Diez Pardos, C., Dimitriadi, C., Dimitrievska, A., Dingfelder, J., Dinu, I-M., Dittmeier, S. J., Dittus, F., Djama, F., Djobava, T., Doglioni, C., Dohnalova, A., Dolejsi, J., Dolezal, Z., Dona, K. M., Donadelli, M., Dong, B., Donini, J., D’Onofrio, A., D’Onofrio, M., Dopke, J., Doria, A., Dos Santos Fernandes, N., Dougan, P., Dova, M. T., Doyle, A. T., Draguet, M. A., Dreyer, E., Drivas-koulouris, I., Drnevich, M., Drozdova, M., Du, D., du Pree, T. 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Published: 2024
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49. Search for a resonance decaying into a scalar particle and a Higgs boson in final states with leptons and two photons in proton-proton collisions at s = 13 TeV with the ATLAS detector

Author: Aad, G., Aakvaag, E., Abbott, B., Abdelhameed, S., Abeling, K., Abicht, N. J., Abidi, S. H., Aboelela, M., Aboulhorma, A., Abramowicz, H., Abreu, H., Abulaiti, Y., Acharya, B. S., Ackermann, A., Adam Bourdarios, C., Adamczyk, L., Addepalli, S. V., Addison, M. J., Adelman, J., Adiguzel, A., Adye, T., Affolder, A. A., Afik, Y., Agaras, M. N., Agarwala, J., Aggarwal, A., Agheorghiesei, C., Ahmadov, F., Ahmed, W. S., Ahuja, S., Ai, X., Aielli, G., Aikot, A., Ait Tamlihat, M., Aitbenchikh, B., Akbiyik, M., Åkesson, T. P. A., Akimov, A. V., Akiyama, D., Akolkar, N. N., Aktas, S., Al Khoury, K., Alberghi, G. L., Albert, J., Albicocco, P., Albouy, G. L., Alderweireldt, S., Alegria, Z. L., Aleksa, M., Aleksandrov, I. N., Alexa, C., Alexopoulos, T., Alfonsi, F., Algren, M., Alhroob, M., Ali, B., Ali, H. M. J., Ali, S., Alibocus, S. W., Aliev, M., Alimonti, G., Alkakhi, W., Allaire, C., Allbrooke, B. M. M., Allen, J. F., Allendes Flores, C. A., Allport, P. P., Aloisio, A., Alonso, F., Alpigiani, C., Alsolami, Z. M. K., Alvarez Estevez, M., Alvarez Fernandez, A., Alves Cardoso, M., Alviggi, M. G., Aly, M., Amaral Coutinho, Y., Ambler, A., Amelung, C., Amerl, M., Ames, C. G., Amidei, D., Amini, B., Amirie, K. J., Amor Dos Santos, S. P., Amos, K. R., An, S., Ananiev, V., Anastopoulos, C., Andeen, T., Anders, J. K., Anderson, A. C., Andrean, S. Y., Andreazza, A., Angelidakis, S., Angerami, A., Anisenkov, A. V., Annovi, A., Antel, C., Antipov, E., Antonelli, M., Anulli, F., Aoki, M., Aoki, T., Aparo, M. A., Aperio Bella, L., Appelt, C., Apyan, A., Arbiol Val, S. J., Arcangeletti, C., Arce, A. T. H., Arguin, J-F., Argyropoulos, S., Arling, J.-H., Arnaez, O., Arnold, H., Artoni, G., Asada, H., Asai, K., Asai, S., Asbah, N. A., Ashby Pickering, R. A., Assamagan, K., Astalos, R., Astrand, K. S. V., Atashi, S., Atkin, R. J., Atkinson, M., Atmani, H., Atmasiddha, P. A., Augsten, K., Auricchio, S., Auriol, A. D., Austrup, V. A., Avolio, G., Axiotis, K., Azuelos, G., Babal, D., Bachacou, H., Bachas, K., Bachiu, A., Backman, F., Badea, A., Baer, T. M., Bagnaia, P., Bahmani, M., Bahner, D., Bai, K., Baines, J. T., Baines, L., Baker, O. K., Bakos, E., Bakshi Gupta, D., Balabram Filho, L. E., Balakrishnan, V., Balasubramanian, R., Baldin, E. M., Balek, P., Ballabene, E., Balli, F., Baltes, L. M., Balunas, W. K., Balz, J., Bamwidhi, I., Banas, E., Bandieramonte, M., Bandyopadhyay, A., Bansal, S., Barak, L., Barakat, M., Barberio, E. L., Barberis, D., Barbero, M., Barel, M. Z., Barillari, T., Barisits, M-S., Barklow, T., Baron, P., Baron Moreno, D. A., Baroncelli, A., Barr, A. J., Barr, J. D., Barreiro, F., Barreiro Guimarães da Costa, J., Barron, U., Barros Teixeira, M. G., Barsov, S., Bartels, F., Bartoldus, R., Barton, A. E., Bartos, P., Basan, A., Baselga, M., Bassalat, A., Basso, M. J., Bataju, S., Bate, R., Bates, R. 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Published: 2024
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50. Precise test of lepton flavour universality in Wpps=13TeV-boson decays into muons and electrons in Wpps=13TeV collisions at Wpps=13TeV with the ATLAS detector

Author: Aad, G., Aakvaag, E., Abbott, B., Abdelhameed, S., Abeling, K., Abicht, N. J., Abidi, S. H., Aboelela, M., Aboulhorma, A., Abramowicz, H., Abreu, H., Abulaiti, Y., Acharya, B. S., Ackermann, A., Bourdarios, C. Adam, Adamczyk, L., Addepalli, S. V., Addison, M. J., Adelman, J., Adiguzel, A., Adye, T., Affolder, A. A., Afik, Y., Agaras, M. N., Agarwala, J., Aggarwal, A., Agheorghiesei, C., Ahmad, A., Ahmadov, F., Ahmed, W. S., Ahuja, S., Ai, X., Aielli, G., Aikot, A., Tamlihat, M. Ait, Aitbenchikh, B., Akbiyik, M., Åkesson, T. P. A., Akimov, A. V., Akiyama, D., Akolkar, N. N., Aktas, S., Khoury, K. Al, Alberghi, G. L., Albert, J., Albicocco, P., Albouy, G. L., Alderweireldt, S., Alegria, Z. L., Aleksa, M., Aleksandrov, I. N., Alexa, C., Alexopoulos, T., Alfonsi, F., Algren, M., Alhroob, M., Ali, B., Ali, H. M. J., Ali, S., Alibocus, S. W., Aliev, M., Alimonti, G., Alkakhi, W., Allaire, C., Allbrooke, B. M. M., Allen, J. F., Flores, C. A. Allendes, Allport, P. P., Aloisio, A., Alonso, F., Alpigiani, C., Alsolami, Z. M. K., Estevez, M. Alvarez, Fernandez, A. Alvarez, Cardoso, M. Alves, Alviggi, M. G., Aly, M., Coutinho, Y. Amaral, Ambler, A., Amelung, C., Amerl, M., Ames, C. G., Amidei, D., Amirie, K. J., Santos, S. P. Amor Dos, Amos, K. R., An, S., Ananiev, V., Anastopoulos, C., Andeen, T., Anders, J. K., Andrean, S. Y., Andreazza, A., Angelidakis, S., Angerami, A., Anisenkov, A. V., Annovi, A., Antel, C., Antipov, E., Antonelli, M., Anulli, F., Aoki, M., Aoki, T., Aparo, M. A., Bella, L. Aperio, Appelt, C., Apyan, A., Val, S. J. Arbiol, Arcangeletti, C., Arce, A. T. H., Arena, E., Arguin, J.-F., Argyropoulos, S., Arling, J.-H., Arnaez, O., Arnold, H., Artoni, G., Asada, H., Asai, K., Asai, S., Asbah, N. A., Ashby Pickering, R. A., Assamagan, K., Astalos, R., Astrand, K. S. V., Atashi, S., Atkin, R. J., Atkinson, M., Atmani, H., Atmasiddha, P. A., Augsten, K., Auricchio, S., Auriol, A. D., Austrup, V. 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