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1,639 results on '"Riegler, P"'

1. Principal Components for Neural Network Initialization

Author: Phan, Nhan, Nguyen, Thu, Halvorsen, Pål, and Riegler, Michael A.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Principal Component Analysis (PCA) is a commonly used tool for dimension reduction and denoising. Therefore, it is also widely used on the data prior to training a neural network. However, this approach can complicate the explanation of explainable AI (XAI) methods for the decision of the model. In this work, we analyze the potential issues with this approach and propose Principal Components-based Initialization (PCsInit), a strategy to incorporate PCA into the first layer of a neural network via initialization of the first layer in the network with the principal components, and its two variants PCsInit-Act and PCsInit-Sub. Explanations using these strategies are as direct and straightforward as for neural networks and are simpler than using PCA prior to training a neural network on the principal components. Moreover, as will be illustrated in the experiments, such training strategies can also allow further improvement of training via backpropagation.
Published: 2025

2. Bounding Radial Variation of positive harmonic Functions on Lipschitz Domains

Author: Fromherz, Jakob, Müller, Paul F. X., and Riegler, Katharina
Subjects: Mathematics - Analysis of PDEs, 31B25, 31B10, 31B05, 31A20
Abstract: We provide radial variational estimates for positive harmonic functions on Lipschitz domains in higher dimensions. The intention of this paper is to document an updated and refined version of arXiv:2003.07176 which modifies the proof of Mozolyako and Havin for Lipschitz domains.
Published: 2025

3. DPERC: Direct Parameter Estimation for Mixed Data

Author: Vo, Tuan L., Do, Quan Huu, Dang, Uyen, Nguyen, Thu, Halvorsen, Pål, Riegler, Michael A., and Nguyen, Binh T.
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning
Abstract: The covariance matrix is a foundation in numerous statistical and machine-learning applications such as Principle Component Analysis, Correlation Heatmap, etc. However, missing values within datasets present a formidable obstacle to accurately estimating this matrix. While imputation methods offer one avenue for addressing this challenge, they often entail a trade-off between computational efficiency and estimation accuracy. Consequently, attention has shifted towards direct parameter estimation, given its precision and reduced computational burden. In this paper, we propose Direct Parameter Estimation for Randomly Missing Data with Categorical Features (DPERC), an efficient approach for direct parameter estimation tailored to mixed data that contains missing values within continuous features. Our method is motivated by leveraging information from categorical features, which can significantly enhance covariance matrix estimation for continuous features. Our approach effectively harnesses the information embedded within mixed data structures. Through comprehensive evaluations of diverse datasets, we demonstrate the competitive performance of DPERC compared to various contemporary techniques. In addition, we also show by experiments that DPERC is a valuable tool for visualizing the correlation heatmap.
Published: 2025

4. Missing data imputation for noisy time-series data and applications in healthcare

Author: Le, Lien P., Thi, Xuan-Hien Nguyen, Nguyen, Thu, Riegler, Michael A., Halvorsen, Pål, and Nguyen, Binh T.
Subjects: Computer Science - Machine Learning, Statistics - Applications
Abstract: Healthcare time series data is vital for monitoring patient activity but often contains noise and missing values due to various reasons such as sensor errors or data interruptions. Imputation, i.e., filling in the missing values, is a common way to deal with this issue. In this study, we compare imputation methods, including Multiple Imputation with Random Forest (MICE-RF) and advanced deep learning approaches (SAITS, BRITS, Transformer) for noisy, missing time series data in terms of MAE, F1-score, AUC, and MCC, across missing data rates (10 % - 80 %). Our results show that MICE-RF can effectively impute missing data compared to deep learning methods and the improvement in classification of data imputed indicates that imputation can have denoising effects. Therefore, using an imputation algorithm on time series with missing data can, at the same time, offer denoising effects.
Published: 2024

5. Generating Rectifiable Measures through Neural Networks

Author: Riegler, Erwin, Bühler, Alex, Pan, Yang, and Bölcskei, Helmut
Subjects: Computer Science - Machine Learning, Computer Science - Information Theory, Mathematics - Probability, Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract: We derive universal approximation results for the class of (countably) $m$-rectifiable measures. Specifically, we prove that $m$-rectifiable measures can be approximated as push-forwards of the one-dimensional Lebesgue measure on $[0,1]$ using ReLU neural networks with arbitrarily small approximation error in terms of Wasserstein distance. What is more, the weights in the networks under consideration are quantized and bounded and the number of ReLU neural networks required to achieve an approximation error of $\varepsilon$ is no larger than $2^{b(\varepsilon)}$ with $b(\varepsilon)=\mathcal{O}(\varepsilon^{-m}\log^2(\varepsilon))$. This result improves Lemma IX.4 in Perekrestenko et al. as it shows that the rate at which $b(\varepsilon)$ tends to infinity as $\varepsilon$ tends to zero equals the rectifiability parameter $m$, which can be much smaller than the ambient dimension. We extend this result to countably $m$-rectifiable measures and show that this rate still equals the rectifiability parameter $m$ provided that, among other technical assumptions, the measure decays exponentially on the individual components of the countably $m$-rectifiable support set.
Published: 2024

6. Embryo 2.0: Merging Synthetic and Real Data for Advanced AI Predictions

Author: Presacan, Oriana, Dorobantiu, Alexandru, Thambawita, Vajira, Riegler, Michael A., Stensen, Mette H., Iliceto, Mario, Aldea, Alexandru C., and Sharma, Akriti
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Accurate embryo morphology assessment is essential in assisted reproductive technology for selecting the most viable embryo. Artificial intelligence has the potential to enhance this process. However, the limited availability of embryo data presents challenges for training deep learning models. To address this, we trained two generative models using two datasets, one we created and made publicly available, and one existing public dataset, to generate synthetic embryo images at various cell stages, including 2-cell, 4-cell, 8-cell, morula, and blastocyst. These were combined with real images to train classification models for embryo cell stage prediction. Our results demonstrate that incorporating synthetic images alongside real data improved classification performance, with the model achieving 97% accuracy compared to 95% when trained solely on real data. Notably, even when trained exclusively on synthetic data and tested on real data, the model achieved a high accuracy of 94%. Furthermore, combining synthetic data from both generative models yielded better classification results than using data from a single generative model. Four embryologists evaluated the fidelity of the synthetic images through a Turing test, during which they annotated inaccuracies and offered feedback. The analysis showed the diffusion model outperformed the generative adversarial network model, deceiving embryologists 66.6% versus 25.3% and achieving lower Frechet inception distance scores.
Published: 2024

7. Soft gravitons in three dimensions

Author: Cotler, Jordan, Jensen, Kristan, Prohazka, Stefan, Riegler, Max, and Salzer, Jakob
Subjects: High Energy Physics - Theory, General Relativity and Quantum Cosmology
Abstract: We consider quantum gravity with zero cosmological constant in three dimensions. First, we show that pure quantum gravity can be written as a magnetic Carrollian theory living on null infinity, described by Schwarzian-like degrees of freedom. Next, we couple quantum gravity to massless matter. Transition amplitudes exhibit several features that resemble soft graviton physics in four dimensions, despite the absence of a propagating graviton. As in four dimensions, we find three equivalent results: a soft graviton theorem, an infinite-dimensional BMS asymptotic symmetry, and a gravitational memory effect. We also resolve some extant puzzles concerning the partition function and Hilbert space of pure 3d gravity with zero cosmological constant., Comment: 36+7 pages, 2 figures
Published: 2024

8. Comparative Analysis of Audio Feature Extraction for Real-Time Talking Portrait Synthesis

Author: Salehi, Pegah, Sheshkal, Sajad Amouei, Thambawita, Vajira, Gautam, Sushant, Sabet, Saeed S., Johansen, Dag, Riegler, Michael A., and Halvorsen, Pål
Subjects: Computer Science - Sound, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Electrical Engineering and Systems Science - Audio and Speech Processing, 68T45, 68T07, 68T01
Abstract: This paper examines the integration of real-time talking-head generation for interviewer training, focusing on overcoming challenges in Audio Feature Extraction (AFE), which often introduces latency and limits responsiveness in real-time applications. To address these issues, we propose and implement a fully integrated system that replaces conventional AFE models with Open AI's Whisper, leveraging its encoder to optimize processing and improve overall system efficiency. Our evaluation of two open-source real-time models across three different datasets shows that Whisper not only accelerates processing but also improves specific aspects of rendering quality, resulting in more realistic and responsive talking-head interactions. These advancements make the system a more effective tool for immersive, interactive training applications, expanding the potential of AI-driven avatars in interviewer training., Comment: 16 pages, 6 figures, 3 tables. submitted to MDPI journal in as Big Data and Cognitive Computing
Published: 2024

9. Enhancing Structured-Data Retrieval with GraphRAG: Soccer Data Case Study

Author: Sepasdar, Zahra, Gautam, Sushant, Midoglu, Cise, Riegler, Michael A., and Halvorsen, Pål
Subjects: Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Databases, H.2, H.3, E.1, E.2
Abstract: Extracting meaningful insights from large and complex datasets poses significant challenges, particularly in ensuring the accuracy and relevance of retrieved information. Traditional data retrieval methods such as sequential search and index-based retrieval often fail when handling intricate and interconnected data structures, resulting in incomplete or misleading outputs. To overcome these limitations, we introduce Structured-GraphRAG, a versatile framework designed to enhance information retrieval across structured datasets in natural language queries. Structured-GraphRAG utilizes multiple knowledge graphs, which represent data in a structured format and capture complex relationships between entities, enabling a more nuanced and comprehensive retrieval of information. This graph-based approach reduces the risk of errors in language model outputs by grounding responses in a structured format, thereby enhancing the reliability of results. We demonstrate the effectiveness of Structured-GraphRAG by comparing its performance with that of a recently published method using traditional retrieval-augmented generation. Our findings show that Structured-GraphRAG significantly improves query processing efficiency and reduces response times. While our case study focuses on soccer data, the framework's design is broadly applicable, offering a powerful tool for data analysis and enhancing language model applications across various structured domains.
Published: 2024

10. Kvasir-VQA: A Text-Image Pair GI Tract Dataset

Author: Gautam, Sushant, Storås, Andrea, Midoglu, Cise, Hicks, Steven A., Thambawita, Vajira, Halvorsen, Pål, and Riegler, Michael A.
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: We introduce Kvasir-VQA, an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations to facilitate advanced machine learning tasks in Gastrointestinal (GI) diagnostics. This dataset comprises 6,500 annotated images spanning various GI tract conditions and surgical instruments, and it supports multiple question types including yes/no, choice, location, and numerical count. The dataset is intended for applications such as image captioning, Visual Question Answering (VQA), text-based generation of synthetic medical images, object detection, and classification. Our experiments demonstrate the dataset's effectiveness in training models for three selected tasks, showcasing significant applications in medical image analysis and diagnostics. We also present evaluation metrics for each task, highlighting the usability and versatility of our dataset. The dataset and supporting artifacts are available at https://datasets.simula.no/kvasir-vqa., Comment: to be published in VLM4Bio 2024, part of the ACM Multimedia (ACM MM) conference 2024
Published: 2024
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11. PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy

Author: Jha, Debesh, Tomar, Nikhil Kumar, Sharma, Vanshali, Trinh, Quoc-Huy, Biswas, Koushik, Pan, Hongyi, Jha, Ritika K., Durak, Gorkem, Hann, Alexander, Varkey, Jonas, Dao, Hang Viet, Van Dao, Long, Nguyen, Binh Phuc, Papachrysos, Nikolaos, Rieders, Brandon, Schmidt, Peter Thelin, Geissler, Enrik, Berzin, Tyler, Halvorsen, Pål, Riegler, Michael A., de Lange, Thomas, and Bagci, Ulas
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Colonoscopy is the primary method for examination, detection, and removal of polyps. However, challenges such as variations among the endoscopists' skills, bowel quality preparation, and the complex nature of the large intestine contribute to high polyp miss-rate. These missed polyps can develop into cancer later, underscoring the importance of improving the detection methods. To address this gap of lack of publicly available, multi-center large and diverse datasets for developing automatic methods for polyp detection and segmentation, we introduce PolypDB, a large scale publicly available dataset that contains 3934 still polyp images and their corresponding ground truth from real colonoscopy videos. PolypDB comprises images from five modalities: Blue Light Imaging (BLI), Flexible Imaging Color Enhancement (FICE), Linked Color Imaging (LCI), Narrow Band Imaging (NBI), and White Light Imaging (WLI) from three medical centers in Norway, Sweden, and Vietnam. We provide a benchmark on each modality and center, including federated learning settings using popular segmentation and detection benchmarks. PolypDB is public and can be downloaded at \url{https://osf.io/pr7ms/}. More information about the dataset, segmentation, detection, federated learning benchmark and train-test split can be found at \url{https://github.com/DebeshJha/PolypDB}., Comment: 3 Figures, 6 tables
Published: 2024

12. Quantizing Carrollian field theories

Author: Cotler, Jordan, Jensen, Kristan, Prohazka, Stefan, Raz, Amir, Riegler, Max, and Salzer, Jakob
Subjects: High Energy Physics - Theory, General Relativity and Quantum Cosmology
Abstract: Carrollian field theories have recently emerged as a candidate dual to flat space quantum gravity. We carefully quantize simple two-derivative Carrollian theories, revealing a strong sensitivity to the ultraviolet. They can be regulated upon being placed on a spatial lattice and working at finite inverse temperature. Unlike in conventional field theories, the details of the lattice-regulated Carrollian theories remain important at long distances even in the limit that the lattice spacing is sent to zero. We use that limit to define interacting continuum models with a tractable perturbative expansion. The ensuing theories are those of generalized free fields, with non-Gaussian correlations suppressed by positive powers of the lattice spacing, and an unbroken supertranslation symmetry., Comment: 48 pages, 2 figures; v2: minor changes, matches published version
Published: 2024
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13. Explainability of Machine Learning Models under Missing Data

Author: Vo, Tuan L., Nguyen, Thu, Lopez-Ramos, Luis M., Hammer, Hugo L., Riegler, Michael A., and Halvorsen, Pal
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Missing data is a prevalent issue that can significantly impair model performance and explainability. This paper briefly summarizes the development of the field of missing data with respect to Explainable Artificial Intelligence and experimentally investigates the effects of various imputation methods on SHAP (SHapley Additive exPlanations), a popular technique for explaining the output of complex machine learning models. Next, we compare different imputation strategies and assess their impact on feature importance and interaction as determined by Shapley values. Moreover, we also theoretically analyze the effects of missing values on Shapley values. Importantly, our findings reveal that the choice of imputation method can introduce biases that could lead to changes in the Shapley values, thereby affecting the explainability of the model. Moreover, we also show that a lower test prediction MSE (Mean Square Error) does not necessarily imply a lower MSE in Shapley values and vice versa. Also, while XGBoost (eXtreme Gradient Boosting) is a method that could handle missing data directly, using XGBoost directly on missing data can seriously affect explainability compared to imputing the data before training XGBoost. This study provides a comprehensive evaluation of imputation methods in the context of model explanations, offering practical guidance for selecting appropriate techniques based on dataset characteristics and analysis objectives. The results underscore the importance of considering imputation effects to ensure robust and reliable insights from machine learning models.
Published: 2024

14. Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data

Author: Sheshkal, Sajad Amouei, Gundersen, Morten, Riegler, Michael Alexander, Utheim, Øygunn Aass, Gundersen, Kjell Gunnar, and Hammer, Hugo Lewi
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Dry eye disease is a common disorder of the ocular surface, leading patients to seek eye care. Clinical signs and symptoms are currently used to diagnose dry eye disease. Metabolomics, a method for analyzing biological systems, has been found helpful in identifying distinct metabolites in patients and in detecting metabolic profiles that may indicate dry eye disease at early stages. In this study, we explored using machine learning and metabolomics information to identify which cataract patients suffered from dry eye disease. As there is no one-size-fits-all machine learning model for metabolomics data, choosing the most suitable model can significantly affect the quality of predictions and subsequent metabolomics analyses. To address this challenge, we conducted a comparative analysis of nine machine learning models on three metabolomics data sets from cataract patients with and without dry eye disease. The models were evaluated and optimized using nested k-fold cross-validation. To assess the performance of these models, we selected a set of suitable evaluation metrics tailored to the data set's challenges. The logistic regression model overall performed the best, achieving the highest area under the curve score of 0.8378, balanced accuracy of 0.735, Matthew's correlation coefficient of 0.5147, an F1-score of 0.8513, and a specificity of 0.5667. Additionally, following the logistic regression, the XGBoost and Random Forest models also demonstrated good performance.
Published: 2024

15. Daedalus 2: Autorotation Entry, Descent and Landing Experiment on REXUS29

Author: Bergmann, Philip, Riegler, Clemens, Klaschka, Zuri, Herbst, Tobias, Wolf, Jan M., Reigl, Maximilian, Koch, Niels, Menninger, Sarah, von Pichowski, Jan, Bös, Cedric, Barthó, Bence, Dunschen, Frederik, Mehringer, Johanna, Richter, Ludwig, and Werner, Lennart
Subjects: Computer Science - Robotics
Abstract: In recent years, interplanetary exploration has gained significant momentum, leading to a focus on the development of launch vehicles. However, the critical technology of edl mechanisms has not received the same level of attention and remains less mature and capable. To address this gap, we took advantage of the REXUS program to develop a pioneering edl mechanism. We propose an alternative to conventional, parachute based landing vehicles by utilizing autorotation. Our approach enables future additions such as steerability, controllability, and the possibility of a soft landing. To validate the technique and our specific implementation, we conducted a sounding rocket experiment on REXUS29. The systems design is outlined with relevant design decisions and constraints, covering software, mechanics, electronics and control systems. Furthermore, an emphasis will also be the organization and setup of the team entirely made up and executed by students. The flight results on REXUS itself are presented, including the most important outcomes and possible reasons for mission failure. We have not archived an autorotation based landing, but provide a reliable way of building and operating such vehicles. Ultimately, future works and possibilities for improvements are outlined. The research presented in this paper highlights the need for continued exploration and development of edl mechanisms for future interplanetary missions. By discussing our results, we hope to inspire further research in this area and contribute to the advancement of space exploration technology., Comment: 12 pages, 9 figures
Published: 2024

16. Comparative fluorophotometric evaluation of the ocular surface retention time of cross-linked and linear hyaluronic acid ocular eye drops on healthy dogs

Author: Grego, Andressa Lopes, Fankhauser, Andy D., Behan, Emily K., Atzet, Sarah K., Haeussler, D. J., Mello, Franz Riegler, Bortolini, Mariza, and Ferreira, Fabiano Montiani
Published: 2024
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17. SoccerNet-Echoes: A Soccer Game Audio Commentary Dataset

Author: Gautam, Sushant, Sarkhoosh, Mehdi Houshmand, Held, Jan, Midoglu, Cise, Cioppa, Anthony, Giancola, Silvio, Thambawita, Vajira, Riegler, Michael A., Halvorsen, Pål, and Shah, Mubarak
Subjects: Computer Science - Sound, Computer Science - Information Retrieval, Computer Science - Machine Learning, Computer Science - Multimedia, Electrical Engineering and Systems Science - Audio and Speech Processing, I.2.7, I.7
Abstract: The application of Automatic Speech Recognition (ASR) technology in soccer offers numerous opportunities for sports analytics. Specifically, extracting audio commentaries with ASR provides valuable insights into the events of the game, and opens the door to several downstream applications such as automatic highlight generation. This paper presents SoccerNet-Echoes, an augmentation of the SoccerNet dataset with automatically generated transcriptions of audio commentaries from soccer game broadcasts, enhancing video content with rich layers of textual information derived from the game audio using ASR. These textual commentaries, generated using the Whisper model and translated with Google Translate, extend the usefulness of the SoccerNet dataset in diverse applications such as enhanced action spotting, automatic caption generation, and game summarization. By incorporating textual data alongside visual and auditory content, SoccerNet-Echoes aims to serve as a comprehensive resource for the development of algorithms specialized in capturing the dynamics of soccer games. We detail the methods involved in the curation of this dataset and the integration of ASR. We also highlight the implications of a multimodal approach in sports analytics, and how the enriched dataset can support diverse applications, thus broadening the scope of research and development in the field of sports analytics.
Published: 2024

18. On the interplay of liquid-like and stress-driven dynamics in a metallic glass former observed by temperature scanning XPCS

Author: Frey, Maximilian, Neuber, Nico, Riegler, Sascha Sebastian, Cornet, Antoine, Chushkin, Yuriy, Zontone, Federico, Ruschel, Lucas, Adam, Bastian, Nabahat, Mehran, Yang, Fan, Shen, Jie, Westermeier, Fabian, Sprung, Michael, Cangialosi, Daniele, Di Lisio, Valerio, Gallino, Isabella, Busch, Ralf, Ruta, Beatrice, and Pineda, Eloi
Subjects: Condensed Matter - Materials Science, Condensed Matter - Soft Condensed Matter
Abstract: Modern detector technology and highly brilliant fourth-generation synchrotrons allow to improve the temporal resolution in time-resolved diffraction studies. Profiting from this, we applied temperature scanning X-ray photon correlation spectroscopy (XPCS) to probe the dynamics of a Pt-based metallic glass former in the glass, glass transition region, and supercooled liquid, covering up to six orders of magnitude in time scales. Our data demonstrates that the structural alpha-relaxation process is still observable in the glass, although it is partially masked by a faster source of decorrelation observed at atomic scale. We present an approach that interprets these findings as the superposition of heterogeneous liquid-like and stress-driven ballistic-like atomic motions. This work not only extends the dynamical range probed by standard isothermal XPCS, but also clarifies the fate of the alpha-relaxation across the glass transition and provides a new perception on the anomalous, compressed temporal decay of the density-density correlation functions observed in metallic glasses and many out-of-equilibrium soft materials.
Published: 2024

19. Development and evaluation of a kidney health questionnaire and estimates of chronic kidney disease prevalence in the Cooperative Health Research in South Tyrol (CHRIS) study

Author: Barbieri, Giulia, Cazzoletti, Lucia, Melotti, Roberto, Hantikainen, Essi, Lundin, Rebecca, Barin, Laura, Gögele, Martin, Riegler, Peter, Ferraro, Pietro Manuel, Pramstaller, Peter Paul, Gambaro, Giovanni, Zanolin, Maria Elisabetta, and Pattaro, Cristian
Published: 2024
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20. Disease carries art and wisdom: time for the people

Author: Riegler, Martin
Published: 2024
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21. A Machine Learning Approach to Concussion Risk Estimation Among Players Exhibiting Visible Signs in Professional Hockey

Author: Bruce, Jared M., Riegler, Kaitlin E., Meeuwisse, Willem, Comper, Paul, Hutchison, Michael G., Delaney, J. Scott, and Echemendia, Ruben J.
Published: 2024
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22. Einflussfaktoren auf die Rehospitalisierungsrate bei Alkoholabhängigkeit

Author: Koopmann, Anne, Hoffmann, Sabine, Riegler, Alisa, Cordes, Jaspar, and Kiefer, Falk
Published: 2024
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23. Advancing sleep detection by modelling weak label sets: A novel weakly supervised learning approach

Author: Boeker, Matthias, Thambawita, Vajira, Riegler, Michael, Halvorsen, Pål, and Hammer, Hugo L.
Subjects: Computer Science - Machine Learning
Abstract: Understanding sleep and activity patterns plays a crucial role in physical and mental health. This study introduces a novel approach for sleep detection using weakly supervised learning for scenarios where reliable ground truth labels are unavailable. The proposed method relies on a set of weak labels, derived from the predictions generated by conventional sleep detection algorithms. Introducing a novel approach, we suggest a novel generalised non-linear statistical model in which the number of weak sleep labels is modelled as outcome of a binomial distribution. The probability of sleep in the binomial distribution is linked to the outcomes of neural networks trained to detect sleep based on actigraphy. We show that maximizing the likelihood function of the model, is equivalent to minimizing the soft cross-entropy loss. Additionally, we explored the use of the Brier score as a loss function for weak labels. The efficacy of the suggested modelling framework was demonstrated using the Multi-Ethnic Study of Atherosclerosis dataset. A \gls{lstm} trained on the soft cross-entropy outperformed conventional sleep detection algorithms, other neural network architectures and loss functions in accuracy and model calibration. This research not only advances sleep detection techniques in scenarios where ground truth data is scarce but also contributes to the broader field of weakly supervised learning by introducing innovative approach in modelling sets of weak labels.
Published: 2024

24. Comparison of Two Detector Magnetic Systems for the Future Circular Hadron-Hadron Collider

Author: Klyukhin, Vyacheslav, Ball, Austin, Berriaud, Christophe Paul, Curé, Benoit, Dudarev, Alexey, Gaddi, Andrea, Gerwig, Hubert, Hervé, Alain, Mentink, Matthias, Riegler, Werner, Wagner, Udo, and Kate, Herman Ten
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: The conceptual design study of a Future Circular hadron-hadron Collider (FCC-hh) to be con-structed at CERN with a center-of-mass energy of the order of 100 TeV requires superconducting magnetic systems with a central magnetic flux density of an order of 4 T for the experimental detectors. The developed concept of the FCC-hh detector involves the use of an iron-free magnetic system consisting of three superconducting solenoids. A superconducting magnet with a minimal steel yoke is proposed as an alternative to the baseline iron-free design. In this study, both magnetic system options for the FCC-hh detector are modeled with the same electrical parameters using Cobham$'$s program TOSCA. All the main characteristics of both designs are compared and discussed., Comment: 1 pages, 10 figures, 2 equations, 14 references
Published: 2024
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25. Incommensurate magnetic order: A fingerprint for electronic correlations in hole-doped cuprates

Author: Klett, Michael, Beyer, Jacob, Riegler, David, Seufert, Jannis, Wölfle, Peter, Rachel, Stephan, and Thomale, Ronny
Subjects: Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Superconductivity
Abstract: Intertwined charge and magnetic fluctuations in high-$T_\text{c}$ copper oxide superconductors (cuprates) are hypothesized to be a consequence of their correlated electronic nature. Among other observables, this is apparent in the doping dependence of incommensurate magnetic order, known as the Yamada relation (YR). We analyze the Hubbard model to challenge the universality of YR as a function of interaction strength $U$ through Kotliar-Ruckenstein slave-boson (SB) mean-field theory and truncated unity functional renormalization group (TUFRG). While TUFRG tends to lock in to a doping dependence of the incommensurate magnetic ordering vector obtained for the perturbative weak-coupling limit, SB not only exhibits an enhanced sensitivity upon a variation of $U$ from weak to strong coupling, but also shows good agreement with experimental data. It supports the placement of weakly hole-doped cuprates in the intermediate-to-strong coupling regime.
Published: 2023
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26. Implementation of an advanced practice role for oxygen prescription by physiotherapists in pulmonary rehabilitation: an explanatory sequential mixed-method quality evaluation

Author: Riegler, Thomas F., Marcin, Thimo, and Brun, Patrick
Published: 2024
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27. Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontology

Author: Chatterjee, Ayan, Riegler, Michael A., Johnson, Miriam Sinkerud, Das, Jishnu, Pahari, Nibedita, Ramachandra, Raghavendra, Ghosh, Bikramaditya, Saha, Arpan, and Bajpai, Ram
Published: 2024
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28. A large-scale multivariate soccer athlete health, performance, and position monitoring dataset

Author: Midoglu, Cise, Kjæreng Winther, Andreas, Boeker, Matthias, Dahl Pettersen, Susann, Pedersen, Sigurd, Ragab, Nourhan, Kupka, Tomas, Hicks, Steven A., Bredsgaard Randers, Morten, Jain, Ramesh, Dagenborg, Håvard J., Pettersen, Svein Arne, Johansen, Dag, Riegler, Michael A., and Halvorsen, Pål
Published: 2024
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29. Characteristics associated with occurrence of stroke in patients with infective endocarditis – a retrospective cohort study

Author: Schuermann, H., von Rennenberg, R., Riegler, C., Rangus, I., Litmeier, S., Scheitz, J. F., Doehner, W., Audebert, H., Braemswig, T. B., and Nolte, C. H.
Published: 2024
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30. A dataset for predicting cloud cover over Europe

Author: Svennevik, Hanna, Hicks, Steven A., Riegler, Michael A., Storelvmo, Trude, and Hammer, Hugo L.
Published: 2024
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31. Semantic representation and comparative analysis of physical activity sensor observations using MOX2-5 sensor in real and synthetic datasets: a proof-of-concept-study

Author: Chatterjee, Ayan, Gerdes, Martin W., Prinz, Andreas, Riegler, Michael A., and Martinez, Santiago G.
Published: 2024
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32. Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Author: Ali, Sharib, Ghatwary, Noha, Jha, Debesh, Isik-Polat, Ece, Polat, Gorkem, Yang, Chen, Li, Wuyang, Galdran, Adrian, Ballester, Miguel-Ángel González, Thambawita, Vajira, Hicks, Steven, Poudel, Sahadev, Lee, Sang-Woong, Jin, Ziyi, Gan, Tianyuan, Yu, ChengHui, Yan, JiangPeng, Yeo, Doyeob, Lee, Hyunseok, Tomar, Nikhil Kumar, Haithami, Mahmood, Ahmed, Amr, Riegler, Michael A., Daul, Christian, Halvorsen, Pål, Rittscher, Jens, Salem, Osama E., Lamarque, Dominique, Cannizzaro, Renato, Realdon, Stefano, de Lange, Thomas, and East, James E.
Published: 2024
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33. Imputation using training labels and classification via label imputation

Author: Nguyen, Thu, Vo, Tuan L., Halvorsen, Pål, and Riegler, Michael A.
Subjects: Computer Science - Machine Learning, Statistics - Machine Learning
Abstract: Missing data is a common problem in practical data science settings. Various imputation methods have been developed to deal with missing data. However, even though the labels are available in the training data in many situations, the common practice of imputation usually only relies on the input and ignores the label. We propose Classification Based on MissForest Imputation (CBMI), a classification strategy that initializes the predicted test label with missing values and stacks the label with the input for imputation, allowing the label and the input to be imputed simultaneously. In addition, we propose the imputation using labels (IUL) algorithm, an imputation strategy that stacks the label into the input and illustrates how it can significantly improve the imputation quality. Experiments show that CBMI has classification accuracy when the test set contains missing data, especially for imbalanced data and categorical data. Moreover, for both the regression and classification, IUL consistently shows significantly better results than imputation based on only the input data.
Published: 2023

34. Carrollian c-functions and flat space holographic RG flows in BMS3/CCFT2

Author: Grumiller, Daniel and Riegler, Max
Subjects: High Energy Physics - Theory, General Relativity and Quantum Cosmology
Abstract: We discuss c-functions and their holographic counterpart for two-dimensional field theories with Carrollian conformal fixed points in the UV and the IR. Specifically, we construct asymptotically flat domain wall solutions of three-dimensional Einstein-dilaton gravity that model holographic RG flows between BMS3 invariant UV and IR fixed points. We prove three theorems for such flows: 1. for every holographic RG flow in AdS3, there is a corresponding one in flat space, 2. the BMS central charge in the UV cannot be smaller than in the IR, and 3. the UV/IR ratio of Virasoro central charges is identical to the UV/IR ratio of corresponding BMS central charges. Finally, we tentatively propose a Casini-Huerta-like c-functions for BMS3-invariant quantum field theories, inspired by the AdS3/CFT2 relation between monotonicity of the c-function and the quantum null energy condition., Comment: 10pp, 3figs; v2: included important ref. and added note
Published: 2023

35. Microbial competition for phosphorus limits the CO2 response of a mature forest

Author: Jiang, Mingkai, Crous, Kristine Y., Carrillo, Yolima, Macdonald, Catriona A., Anderson, Ian C., Boer, Matthias M., Farrell, Mark, Gherlenda, Andrew N., Castañeda-Gómez, Laura, Hasegawa, Shun, Jarosch, Klaus, Milham, Paul J., Ochoa-Hueso, Rául, Pathare, Varsha, Pihlblad, Johanna, Piñeiro, Juan, Powell, Jeff R., Power, Sally A., Reich, Peter B., Riegler, Markus, Zaehle, Sönke, Smith, Benjamin, Medlyn, Belinda E., and Ellsworth, David S.
Published: 2024
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36. Supplemental vibrissal extensions as an alternative to improve the tactile sensitivity of blind dogs – a preliminary approach investigation

Author: de Sampaio, Manuella Oliveira Borges, Montiani-Ferreira, Fabiano, Mello, Franz Riegler, Martins, Camila Bolmann, de Souza, Ana Leticia Groszewicz, Bortolini, Mariza, Klaumann, Paulo Roberto, and Moore, Bret A.
Published: 2024
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37. Search for source of invasive-range populations of a haplodiploid pest species reveals Wolbachia-driven mitochondrial selective sweep in native range

Author: Katlav, Alihan, Nguyen, Duong T., Chhagan, Asha, Jamieson, Lisa, Robertson, Alexander R., Cheesman, Jodie, Learmonth, Stewart, Trębicki, Piotr, Cook, James M., and Riegler, Markus
Published: 2024
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38. Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges

Author: Jha, Debesh, Sharma, Vanshali, Banik, Debapriya, Bhattacharya, Debayan, Roy, Kaushiki, Hicks, Steven A., Tomar, Nikhil Kumar, Thambawita, Vajira, Krenzer, Adrian, Ji, Ge-Peng, Poudel, Sahadev, Batchkala, George, Alam, Saruar, Ahmed, Awadelrahman M. A., Trinh, Quoc-Huy, Khan, Zeshan, Nguyen, Tien-Phat, Shrestha, Shruti, Nathan, Sabari, Gwak, Jeonghwan, Jha, Ritika K., Zhang, Zheyuan, Schlaefer, Alexander, Bhattacharjee, Debotosh, Bhuyan, M. K., Das, Pradip K., Fan, Deng-Ping, Parsa, Sravanthi, Ali, Sharib, Riegler, Michael A., Halvorsen, Pål, De Lange, Thomas, and Bagci, Ulas
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models' credibility for clinical deployment. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage qualitative evaluation for building more transparent and understandable AI-based colonoscopy systems.
Published: 2023

39. GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection

Author: Jha, Debesh, Sharma, Vanshali, Dasu, Neethi, Tomar, Nikhil Kumar, Hicks, Steven, Bhuyan, M. K., Das, Pradip K., Riegler, Michael A., Halvorsen, Pål, Bagci, Ulas, and de Lange, Thomas
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance. These challenges include data availability, biased outcomes, data quality, lack of transparency, and underperformance on unseen datasets from different distributions. The scarcity of large-scale, precisely labeled, and diverse datasets are the major challenge for clinical integration. This scarcity is also due to the legal restrictions and extensive manual efforts required for accurate annotations from clinicians. To address these challenges, we present \textit{GastroVision}, a multi-center open-access gastrointestinal (GI) endoscopy dataset that includes different anatomical landmarks, pathological abnormalities, polyp removal cases and normal findings (a total of 27 classes) from the GI tract. The dataset comprises 8,000 images acquired from B{\ae}rum Hospital in Norway and Karolinska University Hospital in Sweden and was annotated and verified by experienced GI endoscopists. Furthermore, we validate the significance of our dataset with extensive benchmarking based on the popular deep learning based baseline models. We believe our dataset can facilitate the development of AI-based algorithms for GI disease detection and classification. Our dataset is available at \url{https://osf.io/84e7f/}.
Published: 2023

40. BMS modular covariance and structure constants

Author: Bagchi, Arjun, Mondal, Saikat, Pal, Sanchari, and Riegler, Max
Subjects: High Energy Physics - Theory
Abstract: Two-dimensional (2d) field theories invariant under the Bondi-Metzner-Sachs algebra, or 2d BMSFTs in short, are putative holographic duals of Einstein gravity in 3d asymptotically flat spacetimes. When defined on a torus, these field theories come equipped with a modified modular structure. We use the modular covariance of the BMS torus two-point function to develop formulae for different three-point structure constants of the field theory. These structure constants indicate that BMSFTs follow the eigenstate thermalization hypothesis, albeit with some interesting changes to usual 2d CFTs. The singularity structures of the structure constants contain information on perturbations of cosmological horizons in 3d asymptotically flat spacetimes, which we show can also be obtained as a limit of BTZ quasinormal modes., Comment: 20 pages
Published: 2023

41. Using Student Personas When Developing Digital Mathematics Learning Resources to Improve Teacher Training

Author: Robert Weinhandl, Lena Kleinferchner, Viktoria Riegler, Carina Schobersberger, Tony Houghton, Zsolt Lavicza, and Vasiliki Laina
Abstract: In this study, we discuss how the use of student personas can support pre-service teachers developing digital mathematics learning resources and improve mathematics teacher training programs. The use of modern technologies in schools requires that pre-service teachers prepare for new challenges, including the development of digital learning resources. To be able to develop such resources during teacher training, pre-service mathematics teachers without direct contact with students need insights into particular characteristics of mathematics students. We argue that personas, a tool and approach adopted from user experience research, can provide such insights. For this purpose, we interviewed four pre-service mathematics teachers who were developing digital learning resources in the span of a semester. We analyzed the data using grounded qualitative content analysis approaches and identified the following key aspects of personas usage for mathematics teacher training: (A) personas can act as an information tool, (B) personas can act as a planning and feedback tool, (C) the use of personas can trigger reflection processes for pre-service teachers, and (D) sustained work with personas may increase its impact. Our results indicate that personas can offer pre-service teachers support when developing digital learning resources and contribute to their professional development.
Published: 2024
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42. Measurement of beauty production via non-prompt charm hadrons in p–Pb collisions at sNN = 5.02 TeV

Author: Acharya, S., Adamová, D., Agarwal, A., Aglieri Rinella, G., Aglietta, L., Agnello, M., Agrawal, N., Ahammed, Z., Ahmad, S., Ahn, S. U., Ahuja, I., Akindinov, A., Akishina, V., Al-Turany, M., Aleksandrov, D., Alessandro, B., Alfanda, H. M., Alfaro Molina, R., Ali, B., Alici, A., Alizadehvandchali, N., Alkin, A., Alme, J., Alocco, G., Alt, T., Altamura, A. R., Altsybeev, I., Alvarado, J. R., Alvarez, C. O. R., Anaam, M. N., Andrei, C., Andreou, N., Andronic, A., Andronov, E., Anguelov, V., Antinori, F., Antonioli, P., Apadula, N., Aphecetche, L., Appelshäuser, H., Arata, C., Arcelli, S., Arnaldi, R., Arneiro, J. G. M. C. A., Arsene, I. C., Arslandok, M., Augustinus, A., Averbeck, R., Averyanov, D., Azmi, M. D., Baba, H., Badalà, A., Bae, J., Baek, Y. W., Bai, X., Bailhache, R., Bailung, Y., Bala, R., Balbino, A., Baldisseri, A., Balis, B., Banerjee, D., Banoo, Z., Barbasova, V., Barile, F., Barioglio, L., Barlou, M., Barman, B., Barnaföldi, G. G., Barnby, L. S., Barreau, E., Barret, V., Barreto, L., Bartels, C., Barth, K., Bartsch, E., Bastid, N., Basu, S., Batigne, G., Battistini, D., Batyunya, B., Bauri, D., Bazo Alba, J. L., Bearden, I. G., Beattie, C., Becht, P., Behera, D., Belikov, I., Bell Hechavarria, A. D. C., Bellini, F., Bellwied, R., Belokurova, S., Beltran, L. G. E., Beltran, Y. A. V., Bencedi, G., Bensaoula, A., Beole, S., Berdnikov, Y., Berdnikova, A., Bergmann, L., Besoiu, M. G., Betev, L., Bhaduri, P. P., Bhasin, A., Bhattacharjee, B., Bianchi, L., Bielčík, J., Bielčíková, J., Bigot, A. P., Bilandzic, A., Biro, G., Biswas, S., Bize, N., Blair, J. T., Blau, D., Blidaru, M. B., Bluhme, N., Blume, C., Boca, G., Bock, F., Bodova, T., Bok, J., Boldizsár, L., Bombara, M., Bond, P. M., Bonomi, G., Borel, H., Borissov, A., Borquez Carcamo, A. G., Botta, E., Bouziani, Y. E. M., Bratrud, L., Braun-Munzinger, P., Bregant, M., Broz, M., Bruno, G. E., Buchakchiev, V. D., Buckland, M. D., Budnikov, D., Buesching, H., Bufalino, S., Buhler, P., Burmasov, N., Buthelezi, Z., Bylinkin, A., Bysiak, S. A., Cabanillas Noris, J. C., Cabrera, M. F. T., Cai, M., Caines, H., Caliva, A., Calvo Villar, E., Camacho, J. M. M., Camerini, P., Canedo, F. D. M., Cantway, S. L., Carabas, M., Carballo, A. A., Carnesecchi, F., Caron, R., Carvalho, L. A. D., Castillo Castellanos, J., Castoldi, M., Catalano, F., Cattaruzzi, S., Ceballos Sanchez, C., Cerri, R., Chakaberia, I., Chakraborty, P., Chandra, S., Chapeland, S., Chartier, M., Chattopadhay, S., Chattopadhyay, S., Chattopadhyay, S., Chen, M., Cheng, T., Cheshkov, C., Chibante Barroso, V., Chinellato, D. D., Chizzali, E. S., Cho, J., Cho, S., Chochula, P., Chochulska, Z. A., Choudhury, D., Christakoglou, P., Christensen, C. H., Christiansen, P., Chujo, T., Ciacco, M., Cicalo, C., Ciupek, M. R., Clai, G., Colamaria, F., Colburn, J. S., Colella, D., Colocci, M., Concas, M., Conesa Balbastre, G., Conesa del Valle, Z., Contin, G., Contreras, J. G., Coquet, M. L., Cortese, P., Cosentino, M. R., Costa, F., Costanza, S., Cot, C., Crochet, P., Cruz-Torres, R., Cui, P., Czarnynoga, M. M., Dainese, A., Dange, G., Danisch, M. C., Danu, A., Das, P., Das, P., Das, S., Dash, A. R., Dash, S., De Caro, A., de Cataldo, G., de Cuveland, J., De Falco, A., De Gruttola, D., De Marco, N., De Martin, C., De Pasquale, S., Deb, R., Del Grande, R., Dello Stritto, L., Deng, W., Devereaux, K. C., Dhankher, P., Di Bari, D., Di Mauro, A., Diab, B., Diaz, R. A., Dietel, T., Ding, Y., Ditzel, J., Divià, R., Djuvsland, Ø., Dmitrieva, U., Dobrin, A., Dönigus, B., Dubinski, J. M., Dubla, A., Dupieux, P., Dzalaiova, N., Eder, T. M., Ehlers, R. J., Eisenhut, F., Ejima, R., Elia, D., Erazmus, B., Ercolessi, F., Espagnon, B., Eulisse, G., Evans, D., Evdokimov, S., Fabbietti, L., Faggin, M., Faivre, J., Fan, F., Fan, W., Fantoni, A., Fasel, M., Feliciello, A., Feofilov, G., Fernández Téllez, A., Ferrandi, L., Ferrer, M. B., Ferrero, A., Ferrero, C., Ferretti, A., Feuillard, V. J. G., Filova, V., Finogeev, D., Fionda, F. M., Flatland, E., Flor, F., Flores, A. N., Foertsch, S., Fokin, I., Fokin, S., Follo, U., Fragiacomo, E., Frajna, E., Fuchs, U., Funicello, N., Furget, C., Furs, A., Fusayasu, T., Gaardhøje, J. J., Gagliardi, M., Gago, A. M., Gahlaut, T., Galvan, C. D., Gangadharan, D. R., Ganoti, P., Garabatos, C., Garcia, J. M., García Chávez, T., Garcia-Solis, E., Gargiulo, C., Gasik, P., Gaur, H. M., Gautam, A., Gay Ducati, M. B., Germain, M., Gernhaeuser, R. A., Ghosh, C., Giacalone, M., Gioachin, G., Giri, S. K., Giubellino, P., Giubilato, P., Glaenzer, A. M. C., Glässel, P., Glimos, E., Goh, D. J. Q., Gonzalez, V., Gordeev, P., Gorgon, M., Goswami, K., Gotovac, S., Grabski, V., Graczykowski, L. K., Grecka, E., Grelli, A., Grigoras, C., Grigoriev, V., Grigoryan, S., Grosa, F., Grosse-Oetringhaus, J. F., Grosso, R., Grund, D., Grunwald, N. A., Guardiano, G. G., Guernane, R., Guilbaud, M., Gulbrandsen, K., Gumprecht, J. J. W. K., Gündem, T., Gunji, T., Guo, W., Gupta, A., Gupta, R., Gupta, R., Gwizdziel, K., Gyulai, L., Hadjidakis, C., Haider, F. U., Haidlova, S., Haldar, M., Hamagaki, H., Han, Y., Hanley, B. G., Hannigan, R., Hansen, J., Haque, M. R., Harris, J. W., Harton, A., Hartung, M. V., Hassan, H., Hatzifotiadou, D., Hauer, P., Havener, L. B., Hellbär, E., Helstrup, H., Hemmer, M., Herman, T., Hernandez, S. G., Herrera Corral, G., Herrmann, S., Hetland, K. F., Heybeck, B., Hillemanns, H., Hippolyte, B., Hobus, I. P. M., Hoffmann, F. W., Hofman, B., Hong, G. H., Horst, M., Horzyk, A., Hou, Y., Hristov, P., Huhn, P., Huhta, L. M., Humanic, T. J., Hutson, A., Hutter, D., Hwang, M. C., Ilkaev, R., Inaba, M., Innocenti, G. M., Ippolitov, M., Isakov, A., Isidori, T., Islam, M. S., Iurchenko, S., Ivanov, M., Ivanov, M., Ivanov, V., Iversen, K. E., Jablonski, M., Jacak, B., Jacazio, N., Jacobs, P. M., Jadlovska, S., Jadlovsky, J., Jaelani, S., Jahnke, C., Jakubowska, M. J., Janik, M. A., Janson, T., Ji, S., Jia, S., Jiang, T., Jimenez, A. A. P., Jonas, F., Jones, D. M., Jowett, J. M., Jung, J., Jung, M., Junique, A., Jusko, A., Kaewjai, J., Kalinak, P., Kalweit, A., Karasu Uysal, A., Karatovic, D., Karatzenis, N., Karavichev, O., Karavicheva, T., Karpechev, E., Karwowska, M. J., Kebschull, U., Keidel, R., Keil, M., Ketzer, B., Khade, S. S., Khan, A. M., Khan, S., Khanzadeev, A., Kharlov, Y., Khatun, A., Khuntia, A., Khuranova, Z., Kileng, B., Kim, B., Kim, C., Kim, D. J., Kim, E. J., Kim, J., Kim, J., Kim, J., Kim, M., Kim, S., Kim, T., Kimura, K., Kirkova, A., Kirsch, S., Kisel, I., Kiselev, S., Kisiel, A., Kitowski, J. P., Klay, J. L., Klein, J., Klein, S., Klein-Bösing, C., Kleiner, M., Klemenz, T., Kluge, A., Kobdaj, C., Kohara, R., Kollegger, T., Kondratyev, A., Kondratyeva, N., Konig, J., Konigstorfer, S. A., Konopka, P. J., Kornakov, G., Korwieser, M., Koryciak, S. D., Koster, C., Kotliarov, A., Kovacic, N., Kovalenko, V., Kowalski, M., Kozhuharov, V., Kozlov, G., Králik, I., Kravčáková, A., Krcal, L., Krivda, M., Krizek, F., Krizkova Gajdosova, K., Krug, C., Krüger, M., Krupova, D. M., Kryshen, E., Kučera, V., Kuhn, C., Kuijer, P. G., Kumaoka, T., Kumar, D., Kumar, L., Kumar, N., Kumar, S., Kundu, S., Kurashvili, P., Kurepin, A., Kurepin, A. B., Kuryakin, A., Kushpil, S., Kuskov, V., Kutyla, M., Kuznetsov, A., Kweon, M. J., Kwon, Y., La Pointe, S. L., La Rocca, P., Lakrathok, A., Lamanna, M., Landou, A. R., Langoy, R., Larionov, P., Laudi, E., Lautner, L., Laveaga, R. A. N., Lavicka, R., Lea, R., Lee, H., Legrand, I., Legras, G., Lehrbach, J., Lejeune, A. M., Lelek, T. M., Lemmon, R. C., León Monzón, I., Lesch, M. M., Lesser, E. D., Lévai, P., Li, M., Li, X., Liang-gilman, B. E., Lien, J., Lietava, R., Likmeta, I., Lim, B., Lim, S. H., Lindenstruth, V., Lindner, A., Lippmann, C., Liu, D. H., Liu, J., Liveraro, G. S. S., Lofnes, I. M., Loizides, C., Lokos, S., Lömker, J., Lopez, X., López Torres, E., Lotteau, C., Lu, P., Lu, Z., Lugo, F. V., Luhder, J. R., Lunardon, M., Luparello, G., Ma, Y. G., Mager, M., Maire, A., Majerz, E. M., Makariev, M. V., Malaev, M., Malfattore, G., Malik, N. M., Malik, Q. W., Malik, S. K., Malinina, L., Mallick, D., Mallick, N., Mandaglio, G., Mandal, S. K., Manea, A., Manko, V., Manso, F., Manzari, V., Mao, Y., Marcjan, R. W., Margagliotti, G. V., Margotti, A., Marín, A., Markert, C., Martinengo, P., Martínez, M. I., Martínez García, G., Martins, M. P. P., Masciocchi, S., Masera, M., Masoni, A., Massacrier, L., Massen, O., Mastroserio, A., Matonoha, O., Mattiazzo, S., Matyja, A., Mazuecos, A. L., Mazzaschi, F., Mazzilli, M., Mdhluli, J. E., Melikyan, Y., Melo, M., Menchaca-Rocha, A., Mendez, J. E. M., Meninno, E., Menon, A. S., Menzel, M. W., Meres, M., Miake, Y., Micheletti, L., Mihaylov, D. L., Mikhaylov, K., Minafra, N., Miśkowiec, D., Modak, A., Mohanty, B., Mohisin Khan, M., Molander, M. A., Monira, S., Mordasini, C., Moreira De Godoy, D. A., Morozov, I., Morsch, A., Mrnjavac, T., Muccifora, V., Muhuri, S., Mulligan, J. D., Mulliri, A., Munhoz, M. G., Munzer, R. H., Murakami, H., Murray, S., Musa, L., Musinsky, J., Myrcha, J. W., Naik, B., Nambrath, A. I., Nandi, B. K., Nania, R., Nappi, E., Nassirpour, A. F., Nath, A., Nath, S., Nattrass, C., Naydenov, M. N., Neagu, A., Negru, A., Nekrasova, E., Nellen, L., Nepeivoda, R., Nese, S., Nicassio, N., Nielsen, B. S., Nielsen, E. G., Nikolaev, S., Nikulin, S., Nikulin, V., Noferini, F., Noh, S., Nomokonov, P., Norman, J., Novitzky, N., Nowakowski, P., Nyanin, A., Nystrand, J., Oh, S., Ohlson, A., Okorokov, V. A., Oleniacz, J., Onnerstad, A., Oppedisano, C., Ortiz Velasquez, A., Otwinowski, J., Oya, M., Oyama, K., Pachmayer, Y., Padhan, S., Pagano, D., Paić, G., Paisano-Guzmán, S., Palasciano, A., Panebianco, S., Pantouvakis, C., Park, H., Park, H., Park, J., Parkkila, J. E., Patley, Y., Patra, R. N., Paul, B., Pei, H., Peitzmann, T., Peng, X., Pennisi, M., Perciballi, S., Peresunko, D., Perez, G. M., Pestov, Y., Petersen, M. T., Petrov, V., Petrovici, M., Piano, S., Pikna, M., Pillot, P., Pinazza, O., Pinsky, L., Pinto, C., Pisano, S., Płoskoń, M., Planinic, M., Pliquett, F., Plociennik, D. K., Poghosyan, M. G., Polichtchouk, B., Politano, S., Poljak, N., Pop, A., Porteboeuf-Houssais, S., Pozdniakov, V., Pozos, I. Y., Pradhan, K. K., Prasad, S. K., Prasad, S., Preghenella, R., Prino, F., Pruneau, C. A., Pshenichnov, I., Puccio, M., Pucillo, S., Qiu, S., Quaglia, L., Ragoni, S., Rai, A., Rakotozafindrabe, A., Ramello, L., Rami, F., Rasa, M., Räsänen, S. S., Rath, R., Rauch, M. P., Ravasenga, I., Read, K. F., Reckziegel, C., Redelbach, A. R., Redlich, K., Reetz, C. A., Regules-Medel, H. D., Rehman, A., Reidt, F., Reme-Ness, H. A., Rescakova, Z., Reygers, K., Riabov, A., Riabov, V., Ricci, R., Richter, M., Riedel, A. A., Riegler, W., Riffero, A. G., Rignanese, M., Ripoli, C., Ristea, C., Rodriguez, M. V., Rodríguez Cahuantzi, M., Rodríguez Ramírez, S. A., Røed, K., Rogalev, R., Rogochaya, E., Rogoschinski, T. S., Rohr, D., Röhrich, D., Rojas Torres, S., Rokita, P. S., Romanenko, G., Ronchetti, F., Rosas, E. D., Roslon, K., Rossi, A., Roy, A., Roy, S., Rubini, N., Rudolph, J. A., Ruggiano, D., Rui, R., Russek, P. G., Russo, R., Rustamov, A., Ryabinkin, E., Ryabov, Y., Rybicki, A., Ryu, J., Rzesa, W., Sabiu, B., Sadovsky, S., Saetre, J., Šafařík, K., Saha, S. K., Saha, S., Sahoo, B., Sahoo, R., Sahoo, S., Sahu, D., Sahu, P. K., Saini, J., Sajdakova, K., Sakai, S., Salvan, M. P., Sambyal, S., Samitz, D., Sanna, I., Saramela, T. 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Published: 2024
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43. Multiplicity dependence of charged-particle intra-jet properties in pp collisions at s = 13 TeV

Author: Acharya, S., Adamová, D., Rinella, G. Aglieri, Aglietta, L., Agnello, M., Agrawal, N., Ahammed, Z., Ahmad, S., Ahn, S. U., Ahuja, I., Akindinov, A., Al-Turany, M., Aleksandrov, D., Alessandro, B., Alfanda, H. M., Molina, R. Alfaro, Ali, B., Alici, A., Alizadehvandchali, N., Alkin, A., Alme, J., Alocco, G., Alt, T., Altamura, A. R., Altsybeev, I., Alvarado, J. R., Anaam, M. N., Andrei, C., Andreou, N., Andronic, A., Andronov, E., Anguelov, V., Antinori, F., Antonioli, P., Apadula, N., Aphecetche, L., Appelshäuser, H., Arata, C., Arcelli, S., Aresti, M., Arnaldi, R., Arneiro, J. G. M. C. A., Arsene, I. C., Arslandok, M., Augustinus, A., Averbeck, R., Azmi, M. D., Baba, H., Badalà, A., Bae, J., Baek, Y. W., Bai, X., Bailhache, R., Bailung, Y., Bala, R., Balbino, A., Baldisseri, A., Balis, B., Banerjee, D., Banoo, Z., Barile, F., Barioglio, L., Barlou, M., Barman, B., Barnaföldi, G. G., Barnby, L. S., Barreau, E., Barret, V., Barreto, L., Bartels, C., Barth, K., Bartsch, E., Bastid, N., Basu, S., Batigne, G., Battistini, D., Batyunya, B., Bauri, D., Alba, J. L. Bazo, Bearden, I. G., Beattie, C., Becht, P., Behera, D., Belikov, I., Bell Hechavarria, A. D. C., Bellini, F., Bellwied, R., Belokurova, S., Beltran, L. G. E., Beltran, Y. A. V., Bencedi, G., Beole, S., Berdnikov, Y., Berdnikova, A., Bergmann, L., Besoiu, M. G., Betev, L., Bhaduri, P. P., Bhasin, A., Bhat, M. A., Bhattacharjee, B., Bianchi, L., Bianchi, N., Bielčík, J., Bielčíková, J., Bigot, A. P., Bilandzic, A., Biro, G., Biswas, S., Bize, N., Blair, J. T., Blau, D., Blidaru, M. B., Bluhme, N., Blume, C., Boca, G., Bock, F., Bodova, T., Boi, S., Bok, J., Boldizsár, L., Bombara, M., Bond, P. M., Bonomi, G., Borel, H., Borissov, A., Borquez Carcamo, A. G., Bossi, H., Botta, E., Bouziani, Y. E. M., Bratrud, L., Braun-Munzinger, P., Bregant, M., Broz, M., Bruno, G. E., Buckland, M. 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V., Rodríguez Cahuantzi, M., Rodríguez Ramírez, S. A., Røed, K., Rogalev, R., Rogochaya, E., Rogoschinski, T. S., Rohr, D., Röhrich, D., Rojas, P. F., Rojas Torres, S., Rokita, P. S., Romanenko, G., Ronchetti, F., Rosano, A., Rosas, E. D., Roslon, K., Rossi, A., Roy, A., Roy, S., Rubini, N., Ruggiano, D., Rui, R., Russek, P. G., Russo, R., Rustamov, A., Ryabinkin, E., Ryabov, Y., Rybicki, A., Rytkonen, H., Ryu, J., Rzesa, W., Saarimaki, O. A. M., Sadhu, S., Sadovsky, S., Saetre, J., Šafařík, K., Saha, P., Saha, S. K., Saha, S., Sahoo, B., Sahoo, R., Sahoo, S., Sahu, D., Sahu, P. K., Saini, J., Sajdakova, K., Sakai, S., Salvan, M. P., Sambyal, S., Samitz, D., Sanna, I., Saramela, T. B., Sarkar, D., Sarma, P., Sarritzu, V., Sarti, V. M., Sas, M. H. P., Sawan, S., Scapparone, E., Schambach, J., Scheid, H. S., Schiaua, C., Schicker, R., Schlepper, F., Schmah, A., Schmidt, C., Schmidt, H. R., Schmidt, M. O., Schmidt, M., Schmidt, N. V., Schmier, A. R., Schotter, R., Schröter, A., Schukraft, J., Schweda, K., Scioli, G., Scomparin, E., Seger, J. E., Sekiguchi, Y., Sekihata, D., Selina, M., Selyuzhenkov, I., Senyukov, S., Seo, J. J., Serebryakov, D., Serkin, L., Šerkšnytė, L., Sevcenco, A., Shaba, T. J., Shabetai, A., Shahoyan, R., Shangaraev, A., Sharma, B., Sharma, D., Sharma, H., Sharma, M., Sharma, S., Sharma, S., Sharma, U., Shatat, A., Sheibani, O., Shigaki, K., Shimomura, M., Shin, J., Shirinkin, S., Shou, Q., Sibiriak, Y., Siddhanta, S., Siemiarczuk, T., Silva, T. F., Silvermyr, D., Simantathammakul, T., Simeonov, R., Singh, B., Singh, B., Singh, K., Singh, R., Singh, R., Singh, R., Singh, S., Singh, V. K., Singhal, V., Sinha, T., Sitar, B., Sitta, M., Skaali, T. B., Skorodumovs, G., Slupecki, M., Smirnov, N., Snellings, R. J. M., Solheim, E. H., Song, J., Sonnabend, C., Sonneveld, J. M., Soramel, F., Soto-hernandez, A. B., Spijkers, R., Sputowska, I., Staa, J., Stachel, J., Stan, I., Steffanic, P. J., Stiefelmaier, S. F., Stocco, D., Storehaug, I., Stratmann, P., Strazzi, S., Sturniolo, A., Stylianidis, C. P., Suaide, A. A. P., Suire, C., Sukhanov, M., Suljic, M., Sultanov, R., Sumberia, V., Sumowidagdo, S., Szarka, I., Szymkowski, M., Taghavi, S. F., Taillepied, G., Takahashi, J., Tambave, G. J., Tang, S., Tang, Z., Tapia Takaki, J. D., Tapus, N., Tarasovicova, L. A., Tarzila, M. G., Tassielli, G. F., Tauro, A., Tavira García, A., Tejeda Muñoz, G., Telesca, A., Terlizzi, L., Terrevoli, C., Thakur, S., Thomas, D., Tikhonov, A., Tiltmann, N., Timmins, A. R., Tkacik, M., Tkacik, T., Toia, A., Tokumoto, R., Tomohiro, K., Topilskaya, N., Toppi, M., Tork, T., Torres, V. V., Ramos, A. G. Torres, Trifiró, A., Triolo, A. S., Tripathy, S., Tripathy, T., Trogolo, S., Trubnikov, V., Trzaska, W. H., Trzcinski, T. P., Tumkin, A., Turrisi, R., Tveter, T. S., Ullaland, K., Ulukutlu, B., Uras, A., Urioni, M., Usai, G. L., Vala, M., Valle, N., van Doremalen, L. V. R., van Leeuwen, M., van Veen, C. A., van Weelden, R. J. G., Vande Vyvre, P., Varga, D., Varga, Z., Torres, P. Vargas, Vasileiou, M., Vasiliev, A., Vázquez Doce, O., Vazquez Rueda, O., Vechernin, V., Vercellin, E., Vergara Limón, S., Verma, R., Vermunt, L., Vértesi, R., Verweij, M., Vickovic, L., Vilakazi, Z., Villalobos Baillie, O., Villani, A., Vinogradov, A., Virgili, T., Virta, M. M. O., Vislavicius, V., Vodopyanov, A., Volkel, B., Völkl, M. A., Voloshin, S. A., Volpe, G., von Haller, B., Vorobyev, I., Vozniuk, N., Vrláková, J., Wan, J., Wang, C., Wang, D., Wang, Y., Wang, Y., Wegrzynek, A., Weiglhofer, F. T., Wenzel, S. C., Wessels, J. P., Wiechula, J., Wikne, J., Wilk, G., Wilkinson, J., Willems, G. A., Windelband, B., Winn, M., Wright, J. R., Wu, W., Wu, Y., Xiong, Z., Xu, R., Yadav, A., Yadav, A. K., Yalcin, S., Yamaguchi, Y., Yang, S., Yano, S., Yeats, E. R., Yin, Z., Yoo, I.-K., Yoon, J. H., Yu, H., Yuan, S., Yuncu, A., Zaccolo, V., Zampolli, C., Zanone, F., Zardoshti, N., Zarochentsev, A., Závada, P., Zaviyalov, N., Zhalov, M., Zhang, B., Zhang, C., Zhang, L., Zhang, M., Zhang, S., Zhang, X., Zhang, Y., Zhang, Z., Zhao, M., Zherebchevskii, V., Zhi, Y., Zhong, C., Zhou, D., Zhou, Y., Zhu, J., Zhu, Y., Zugravel, S. C., and Zurlo, N.
Published: 2024
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44. Measurement of beauty-quark production in pp collisions at s = 13 TeV via non-prompt D mesons

Author: Acharya, S., Adamová, D., Agarwal, A., Aglieri Rinella, G., Aglietta, L., Agnello, M., Agrawal, N., Ahammed, Z., Ahmad, S., Ahn, S. U., Ahuja, I., Akindinov, A., Akishina, V., Al-Turany, M., Aleksandrov, D., Alessandro, B., Alfanda, H. M., Alfaro Molina, R., Ali, B., Alici, A., Alizadehvandchali, N., Alkin, A., Alme, J., Alocco, G., Alt, T., Altamura, A. R., Altsybeev, I., Alvarado, J. R., Anaam, M. N., Andrei, C., Andreou, N., Andronic, A., Andronov, E., Anguelov, V., Antinori, F., Antonioli, P., Apadula, N., Aphecetche, L., Appelshäuser, H., Arata, C., Arcelli, S., Aresti, M., Arnaldi, R., Arneiro, J. G. M. C. A., Arsene, I. C., Arslandok, M., Augustinus, A., Averbeck, R., Azmi, M. D., Baba, H., Badalà, A., Bae, J., Baek, Y. W., Bai, X., Bailhache, R., Bailung, Y., Bala, R., Balbino, A., Baldisseri, A., Balis, B., Banerjee, D., Banoo, Z., Barile, F., Barioglio, L., Barlou, M., Barman, B., Barnaföldi, G. G., Barnby, L. S., Barreau, E., Barret, V., Barreto, L., Bartels, C., Barth, K., Bartsch, E., Bastid, N., Basu, S., Batigne, G., Battistini, D., Batyunya, B., Bauri, D., Bazo Alba, J. L., Bearden, I. G., Beattie, C., Becht, P., Behera, D., Belikov, I., Bell Hechavarria, A. D. C., Bellini, F., Bellwied, R., Belokurova, S., Beltran, L. G. E., Beltran, Y. A. V., Bencedi, G., Bensaoula, A., Beole, S., Berdnikov, Y., Berdnikova, A., Bergmann, L., Besoiu, M. G., Betev, L., Bhaduri, P. P., Bhasin, A., Bhat, M. A., Bhattacharjee, B., Bianchi, L., Bianchi, N., Bielčík, J., Bielčíková, J., Bigot, A. P., Bilandzic, A., Biro, G., Biswas, S., Bize, N., Blair, J. T., Blau, D., Blidaru, M. B., Bluhme, N., Blume, C., Boca, G., Bock, F., Bodova, T., Bok, J., Boldizsár, L., Bombara, M., Bond, P. M., Bonomi, G., Borel, H., Borissov, A., Borquez Carcamo, A. G., Bossi, H., Botta, E., Bouziani, Y. E. M., Bratrud, L., Braun-Munzinger, P., Bregant, M., Broz, M., Bruno, G. E., Buckland, M. D., Budnikov, D., Buesching, H., Bufalino, S., Buhler, P., Burmasov, N., Buthelezi, Z., Bylinkin, A., Bysiak, S. A., Cabanillas Noris, J. C., Cabrera, M. F. T., Cai, M., Caines, H., Caliva, A., Calvo Villar, E., Camacho, J. M. M., Camerini, P., Canedo, F. D. M., Cantway, S. L., Carabas, M., Carballo, A. A., Carnesecchi, F., Caron, R., Carvalho, L. A. D., Castillo Castellanos, J., Castoldi, M., Catalano, F., Cattaruzzi, S., Ceballos Sanchez, C., Cerri, R., Chakaberia, I., Chakraborty, P., Chandra, S., Chapeland, S., Chartier, M., Chattopadhay, S., Chattopadhyay, S., Chattopadhyay, S., Cheng, T., Cheshkov, C., Chibante Barroso, V., Chinellato, D. D., Chizzali, E. S., Cho, J., Cho, S., Chochula, P., Choudhury, D., Christakoglou, P., Christensen, C. H., Christiansen, P., Chujo, T., Ciacco, M., Cicalo, C., Ciupek, M. R., Clai, G., Colamaria, F., Colburn, J. S., Colella, D., Colocci, M., Concas, M., Conesa Balbastre, G., Conesa del Valle, Z., Contin, G., Contreras, J. G., Coquet, M. L., Cortese, P., Cosentino, M. R., Costa, F., Costanza, S., Cot, C., Crkovská, J., Crochet, P., Cruz-Torres, R., Cui, P., Dainese, A., Dange, G., Danisch, M. C., Danu, A., Das, P., Das, P., Das, S., Dash, A. R., Dash, S., De Caro, A., de Cataldo, G., de Cuveland, J., De Falco, A., De Gruttola, D., De Marco, N., De Martin, C., De Pasquale, S., Deb, R., Del Grande, R., Dello Stritto, L., Deng, W., Devereaux, K. C., Dhankher, P., Di Bari, D., Di Mauro, A., Diab, B., Diaz, R. A., Dietel, T., Ding, Y., Ditzel, J., Divià, R., Dixit, D. U., Djuvsland, Ø., Dmitrieva, U., Dobrin, A., Dönigus, B., Dubinski, J. M., Dubla, A., Dudi, S., Dupieux, P., Dzalaiova, N., Eder, T. M., Ehlers, R. J., Eisenhut, F., Ejima, R., Elia, D., Erazmus, B., Ercolessi, F., Espagnon, B., Eulisse, G., Evans, D., Evdokimov, S., Fabbietti, L., Faggin, M., Faivre, J., Fan, F., Fan, W., Fantoni, A., Fasel, M., Feliciello, A., Feofilov, G., Fernández Téllez, A., Ferrandi, L., Ferrer, M. B., Ferrero, A., Ferrero, C., Ferretti, A., Feuillard, V. J. G., Filova, V., Finogeev, D., Fionda, F. M., Flatland, E., Flor, F., Flores, A. N., Foertsch, S., Fokin, I., Fokin, S., Follo, U., Fragiacomo, E., Frajna, E., Fuchs, U., Funicello, N., Furget, C., Furs, A., Fusayasu, T., Gaardhøje, J. J., Gagliardi, M., Gago, A. M., Gahlaut, T., Galvan, C. D., Gangadharan, D. R., Ganoti, P., Garabatos, C., García Chávez, T., Garcia-Solis, E., Gargiulo, C., Gasik, P., Gaur, H. M., Gautam, A., Gay Ducati, M. B., Germain, M., Ghimouz, A., Ghosh, C., Giacalone, M., Gioachin, G., Giubellino, P., Giubilato, P., Glaenzer, A. M. C., Glässel, P., Glimos, E., Goh, D. J. Q., Gonzalez, V., Gordeev, P., Gorgon, M., Goswami, K., Gotovac, S., Grabski, V., Graczykowski, L. K., Grecka, E., Grelli, A., Grigoras, C., Grigoriev, V., Grigoryan, S., Grosa, F., Grosse-Oetringhaus, J. F., Grosso, R., Grund, D., Grunwald, N. A., Guardiano, G. G., Guernane, R., Guilbaud, M., Gulbrandsen, K., Gündem, T., Gunji, T., Guo, W., Gupta, A., Gupta, R., Gupta, R., Gwizdziel, K., Gyulai, L., Hadjidakis, C., Haider, F. U., Haidlova, S., Haldar, M., Hamagaki, H., Hamdi, A., Han, Y., Hanley, B. G., Hannigan, R., Hansen, J., Haque, M. R., Harris, J. W., Harton, A., Hartung, M. V., Hassan, H., Hatzifotiadou, D., Hauer, P., Havener, L. B., Hellbär, E., Helstrup, H., Hemmer, M., Herman, T., Hernandez, S. G., Herrera Corral, G., Herrmann, F., Herrmann, S., Hetland, K. F., Heybeck, B., Hillemanns, H., Hippolyte, B., Hoffmann, F. W., Hofman, B., Hong, G. H., Horst, M., Horzyk, A., Hou, Y., Hristov, P., Huhn, P., Huhta, L. M., Humanic, T. J., Hutson, A., Hutter, D., Hwang, M. C., Ilkaev, R., Ilyas, H., Inaba, M., Innocenti, G. M., Ippolitov, M., Isakov, A., Isidori, T., Islam, M. S., Ivanov, M., Ivanov, M., Ivanov, V., Iversen, K. E., Jablonski, M., Jacak, B., Jacazio, N., Jacobs, P. M., Jadlovska, S., Jadlovsky, J., Jaelani, S., Jahnke, C., Jakubowska, M. J., Janik, M. A., Janson, T., Ji, S., Jia, S., Jiang, T., Jimenez, A. A. P., Jonas, F., Jones, D. M., Jowett, J. M., Jung, J., Jung, M., Junique, A., Jusko, A., Kaewjai, J., Kalinak, P., Kalweit, A., Karasu Uysal, A., Karatovic, D., Karavichev, O., Karavicheva, T., Karpechev, E., Karwowska, M. J., Kebschull, U., Keidel, R., Keijdener, D. L. D., Keil, M., Ketzer, B., Khade, S. S., Khan, A. M., Khan, S., Khanzadeev, A., Kharlov, Y., Khatun, A., Khuntia, A., Khuranova, Z., Kileng, B., Kim, B., Kim, C., Kim, D. J., Kim, E. J., Kim, J., Kim, J., Kim, J., Kim, M., Kim, S., Kim, T., Kimura, K., Kirkova, A., Kirsch, S., Kisel, I., Kiselev, S., Kisiel, A., Kitowski, J. P., Klay, J. L., Klein, J., Klein, S., Klein-Bösing, C., Kleiner, M., Klemenz, T., Kluge, A., Kobdaj, C., Kohara, R., Kollegger, T., Kondratyev, A., Kondratyeva, N., Konig, J., Konigstorfer, S. A., Konopka, P. J., Kornakov, G., Korwieser, M., Koryciak, S. D., Kotliarov, A., Kovacic, N., Kovalenko, V., Kowalski, M., Kozhuharov, V., Králik, I., Kravčáková, A., Krcal, L., Krivda, M., Krizek, F., Krizkova Gajdosova, K., Krug, C., Krüger, M., Krupova, D. M., Kryshen, E., Kučera, V., Kuhn, C., Kuijer, P. G., Kumaoka, T., Kumar, D., Kumar, L., Kumar, N., Kumar, S., Kundu, S., Kurashvili, P., Kurepin, A., Kurepin, A. B., Kuryakin, A., Kushpil, S., Kuskov, V., Kutyla, M., Kweon, M. J., Kwon, Y., La Pointe, S. L., La Rocca, P., Lakrathok, A., Lamanna, M., Landou, A. R., Langoy, R., Larionov, P., Laudi, E., Lautner, L., Laveaga, R. A. N., Lavicka, R., Lea, R., Lee, H., Legrand, I., Legras, G., Lehrbach, J., Lelek, T. M., Lemmon, R. C., León Monzón, I., Lesch, M. M., Lesser, E. D., Lévai, P., Li, X., Liang-gilman, B. E., Lien, J., Lietava, R., Likmeta, I., Lim, B., Lim, S. H., Lindenstruth, V., Lindner, A., Lippmann, C., Liu, D. H., Liu, J., Liveraro, G. S. S., Lofnes, I. M., Loizides, C., Lokos, S., Lömker, J., Loncar, P., Lopez, X., López Torres, E., Lu, P., Lugo, F. V., Luhder, J. R., Lunardon, M., Luparello, G., Ma, Y. G., Mager, M., Maire, A., Majerz, E. M., Makariev, M. V., Malaev, M., Malfattore, G., Malik, N. M., Malik, Q. W., Malik, S. K., Malinina, L., Mallick, D., Mallick, N., Mandaglio, G., Mandal, S. K., Manea, A., Manko, V., Manso, F., Manzari, V., Mao, Y., Marcjan, R. W., Margagliotti, G. V., Margotti, A., Marín, A., Markert, C., Martinengo, P., Martínez, M. I., Martínez García, G., Martins, M. P. P., Masciocchi, S., Masera, M., Masoni, A., Massacrier, L., Massen, O., Mastroserio, A., Matonoha, O., Mattiazzo, S., Matyja, A., Mazuecos, A. L., Mazzaschi, F., Mazzilli, M., Mdhluli, J. E., Melikyan, Y., Menchaca-Rocha, A., Mendez, J. E. M., Meninno, E., Menon, A. S., Menzel, M. W., Meres, M., Miake, Y., Micheletti, L., Mihaylov, D. L., Mikhaylov, K., Minafra, N., Miśkowiec, D., Modak, A., Mohanty, B., Mohisin Khan, M., Molander, M. A., Monira, S., Mordasini, C., Moreira De Godoy, D. A., Morozov, I., Morsch, A., Mrnjavac, T., Muccifora, V., Muhuri, S., Mulligan, J. D., Mulliri, A., Munhoz, M. G., Munzer, R. H., Murakami, H., Murray, S., Musa, L., Musinsky, J., Myrcha, J. W., Naik, B., Nambrath, A. I., Nandi, B. K., Nania, R., Nappi, E., Nassirpour, A. F., Nath, A., Nattrass, C., Naydenov, M. N., Neagu, A., Negru, A., Nekrasova, E., Nellen, L., Nepeivoda, R., Nese, S., Neskovic, G., Nicassio, N., Nielsen, B. S., Nielsen, E. G., Nikolaev, S., Nikulin, S., Nikulin, V., Noferini, F., Noh, S., Nomokonov, P., Norman, J., Novitzky, N., Nowakowski, P., Nyanin, A., Nystrand, J., Oh, S., Ohlson, A., Okorokov, V. A., Oleniacz, J., Onnerstad, A., Oppedisano, C., Ortiz Velasquez, A., Otwinowski, J., Oya, M., Oyama, K., Pachmayer, Y., Padhan, S., Pagano, D., Paić, G., Paisano-Guzmán, S., Palasciano, A., Panebianco, S., Park, H., Park, H., Parkkila, J. E., Patley, Y., Paul, B., Paulino, M. M. D. 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V., Rodríguez Cahuantzi, M., Rodríguez Ramírez, S. A., Røed, K., Rogalev, R., Rogochaya, E., Rogoschinski, T. S., Rohr, D., Röhrich, D., Rojas Torres, S., Rokita, P. S., Romanenko, G., Ronchetti, F., Rosas, E. D., Roslon, K., Rossi, A., Roy, A., Roy, S., Rubini, N., Ruggiano, D., Rui, R., Russek, P. G., Russo, R., Rustamov, A., Ryabinkin, E., Ryabov, Y., Rybicki, A., Ryu, J., Rzesa, W., Saarimaki, O. A. M., Sadhu, S., Sadovsky, S., Saetre, J., Šafařík, K., Saha, S. K., Saha, S., Sahoo, B., Sahoo, R., Sahoo, S., Sahu, D., Sahu, P. K., Saini, J., Sajdakova, K., Sakai, S., Salvan, M. P., Sambyal, S., Samitz, D., Sanna, I., Saramela, T. B., Sarkar, D., Sarma, P., Sarritzu, V., Sarti, V. M., Sas, M. H. P., Sawan, S., Scapparone, E., Schambach, J., Scheid, H. S., Schiaua, C., Schicker, R., Schlepper, F., Schmah, A., Schmidt, C., Schmidt, H. R., Schmidt, M. O., Schmidt, M., Schmidt, N. V., Schmier, A. R., Schotter, R., Schröter, A., Schukraft, J., Schweda, K., Scioli, G., Scomparin, E., Seger, J. E., Sekiguchi, Y., Sekihata, D., Selina, M., Selyuzhenkov, I., Senyukov, S., Seo, J. J., Serebryakov, D., Serkin, L., Šerkšnytė, L., Sevcenco, A., Shaba, T. J., Shabetai, A., Shahoyan, R., Shangaraev, A., Sharma, B., Sharma, D., Sharma, H., Sharma, M., Sharma, S., Sharma, S., Sharma, U., Shatat, A., Sheibani, O., Shigaki, K., Shimomura, M., Shin, J., Shirinkin, S., Shou, Q., Sibiriak, Y., Siddhanta, S., Siemiarczuk, T., Silva, T. F., Silvermyr, D., Simantathammakul, T., Simeonov, R., Singh, B., Singh, B., Singh, K., Singh, R., Singh, R., Singh, R., Singh, S., Singh, V. K., Singhal, V., Sinha, T., Sitar, B., Sitta, M., Skaali, T. B., Skorodumovs, G., Smirnov, N., Snellings, R. J. M., Solheim, E. H., Song, J., Sonnabend, C., Sonneveld, J. M., Soramel, F., Soto-hernandez, A. B., Spijkers, R., Sputowska, I., Staa, J., Stachel, J., Stan, I., Steffanic, P. J., Stiefelmaier, S. F., Stocco, D., Storehaug, I., Strangmann, N. J., Stratmann, P., Strazzi, S., Sturniolo, A., Stylianidis, C. P., Suaide, A. A. P., Suire, C., Sukhanov, M., Suljic, M., Sultanov, R., Sumberia, V., Sumowidagdo, S., Szarka, I., Szymkowski, M., Taghavi, S. F., Taillepied, G., Takahashi, J., Tambave, G. J., Tang, S., Tang, Z., Tapia Takaki, J. D., Tapus, N., Tarasovicova, L. A., Tarzila, M. G., Tassielli, G. F., Tauro, A., Tavira García, A., Tejeda Muñoz, G., Telesca, A., Terlizzi, L., Terrevoli, C., Thakur, S., Thomas, D., Tikhonov, A., Tiltmann, N., Timmins, A. R., Tkacik, M., Tkacik, T., Toia, A., Tokumoto, R., Tomassini, S., Tomohiro, K., Topilskaya, N., Toppi, M., Tork, T., Torres, V. V., Torres Ramos, A. G., Trifiró, A., Triolo, A. S., Tripathy, S., Tripathy, T., Trubnikov, V., Trzaska, W. H., Trzcinski, T. P., Tumkin, A., Turrisi, R., Tveter, T. S., Ullaland, K., Ulukutlu, B., Uras, A., Urioni, M., Usai, G. L., Vala, M., Valle, N., van Doremalen, L. V. 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H., Yu, H., Yuan, S., Yuncu, A., Zaccolo, V., Zampolli, C., Zang, M., Zanone, F., Zardoshti, N., Zarochentsev, A., Závada, P., Zaviyalov, N., Zhalov, M., Zhang, B., Zhang, C., Zhang, L., Zhang, M., Zhang, S., Zhang, X., Zhang, Y., Zhang, Z., Zhao, M., Zherebchevskii, V., Zhi, Y., Zhong, C., Zhou, D., Zhou, Y., Zhu, J., Zhu, Y., Zugravel, S. C., and Zurlo, N.
Published: 2024
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45. Investigating strangeness enhancement with multiplicity in pp collisions using angular correlations

Author: Acharya, S., Adamová, D., Agarwal, A., Aglieri Rinella, G., Aglietta, L., Agnello, M., Agrawal, N., Ahammed, Z., Ahmad, S., Ahn, S. U., Ahuja, I., Akindinov, A., Akishina, V., Al-Turany, M., Aleksandrov, D., Alessandro, B., Alfanda, H. M., Alfaro Molina, R., Ali, B., Alici, A., Alizadehvandchali, N., Alkin, A., Alme, J., Alocco, G., Alt, T., Altamura, A. R., Altsybeev, I., Alvarado, J. R., Alvarez, C. O. R., Anaam, M. N., Andrei, C., Andreou, N., Andronic, A., Andronov, E., Anguelov, V., Antinori, F., Antonioli, P., Apadula, N., Aphecetche, L., Appelshäuser, H., Arata, C., Arcelli, S., Aresti, M., Arnaldi, R., Arneiro, J. G. M. C. A., Arsene, I. C., Arslandok, M., Augustinus, A., Averbeck, R., Azmi, M. D., Baba, H., Badalà, A., Bae, J., Baek, Y. W., Bai, X., Bailhache, R., Bailung, Y., Bala, R., Balbino, A., Baldisseri, A., Balis, B., Banerjee, D., Banoo, Z., Barbasova, V., Barile, F., Barioglio, L., Barlou, M., Barman, B., Barnaföldi, G. G., Barnby, L. S., Barreau, E., Barret, V., Barreto, L., Bartels, C., Barth, K., Bartsch, E., Bastid, N., Basu, S., Batigne, G., Battistini, D., Batyunya, B., Bauri, D., Bazo Alba, J. L., Bearden, I. G., Beattie, C., Becht, P., Behera, D., Belikov, I., Bell Hechavarria, A. D. C., Bellini, F., Bellwied, R., Belokurova, S., Beltran, L. G. E., Beltran, Y. A. V., Bencedi, G., Bensaoula, A., Beole, S., Berdnikov, Y., Berdnikova, A., Bergmann, L., Besoiu, M. G., Betev, L., Bhaduri, P. P., Bhasin, A., Bhattacharjee, B., Bianchi, L., Bianchi, N., Bielčík, J., Bielčíková, J., Bigot, A. P., Bilandzic, A., Biro, G., Biswas, S., Bize, N., Blair, J. T., Blau, D., Blidaru, M. B., Bluhme, N., Blume, C., Boca, G., Bock, F., Bodova, T., Bok, J., Boldizsár, L., Bombara, M., Bond, P. M., Bonomi, G., Borel, H., Borissov, A., Borquez Carcamo, A. G., Bossi, H., Botta, E., Bouziani, Y. E. M., Bratrud, L., Braun-Munzinger, P., Bregant, M., Broz, M., Bruno, G. E., Buchakchiev, V. D., Buckland, M. D., Budnikov, D., Buesching, H., Bufalino, S., Buhler, P., Burmasov, N., Buthelezi, Z., Bylinkin, A., Bysiak, S. A., Cabanillas Noris, J. C., Cabrera, M. F. T., Cai, M., Caines, H., Caliva, A., Calvo Villar, E., Camacho, J. M. M., Camerini, P., Canedo, F. D. M., Cantway, S. L., Carabas, M., Carballo, A. A., Carnesecchi, F., Caron, R., Carvalho, L. A. D., Castillo Castellanos, J., Castoldi, M., Catalano, F., Cattaruzzi, S., Ceballos Sanchez, C., Cerri, R., Chakaberia, I., Chakraborty, P., Chandra, S., Chapeland, S., Chartier, M., Chattopadhay, S., Chattopadhyay, S., Chattopadhyay, S., Chen, M., Cheng, T., Cheshkov, C., Chibante Barroso, V., Chinellato, D. D., Chizzali, E. S., Cho, J., Cho, S., Chochula, P., Chochulska, Z. A., Choudhury, D., Christakoglou, P., Christensen, C. H., Christiansen, P., Chujo, T., Ciacco, M., Cicalo, C., Ciupek, M. R., Clai, G., Colamaria, F., Colburn, J. 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L., Klein, J., Klein, S., Klein-Bösing, C., Kleiner, M., Klemenz, T., Kluge, A., Kobdaj, C., Kohara, R., Kollegger, T., Kondratyev, A., Kondratyeva, N., Konig, J., Konigstorfer, S. A., Konopka, P. J., Kornakov, G., Korwieser, M., Koryciak, S. D., Koster, C., Kotliarov, A., Kovacic, N., Kovalenko, V., Kowalski, M., Kozhuharov, V., Králik, I., Kravčáková, A., Krcal, L., Krivda, M., Krizek, F., Krizkova Gajdosova, K., Krug, C., Krüger, M., Krupova, D. M., Kryshen, E., Kučera, V., Kuhn, C., Kuijer, P. G., Kumaoka, T., Kumar, D., Kumar, L., Kumar, N., Kumar, S., Kundu, S., Kurashvili, P., Kurepin, A., Kurepin, A. B., Kuryakin, A., Kushpil, S., Kuskov, V., Kutyla, M., Kuznetsov, A., Kweon, M. J., Kwon, Y., La Pointe, S. L., La Rocca, P., Lakrathok, A., Lamanna, M., Landou, A. R., Langoy, R., Larionov, P., Laudi, E., Lautner, L., Laveaga, R. A. N., Lavicka, R., Lea, R., Lee, H., Legrand, I., Legras, G., Lehrbach, J., Lejeune, A. M., Lelek, T. M., Lemmon, R. C., León Monzón, I., Lesch, M. 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M., Solheim, E. H., Song, J., Sonnabend, C., Sonneveld, J. M., Soramel, F., Soto-hernandez, A. B., Spijkers, R., Sputowska, I., Staa, J., Stachel, J., Stan, I., Steffanic, P. J., Stiefelmaier, S. F., Stocco, D., Storehaug, I., Strangmann, N. J., Stratmann, P., Strazzi, S., Sturniolo, A., Stylianidis, C. P., Suaide, A. A. P., Suire, C., Sukhanov, M., Suljic, M., Sultanov, R., Sumberia, V., Sumowidagdo, S., Szarka, I., Szymkowski, M., Taghavi, S. F., Taillepied, G., Takahashi, J., Tambave, G. J., Tang, S., Tang, Z., Tapia Takaki, J. D., Tapus, N., Tarasovicova, L. A., Tarzila, M. G., Tassielli, G. F., Tauro, A., Tavira García, A., Tejeda Muñoz, G., Telesca, A., Terlizzi, L., Terrevoli, C., Thakur, S., Thomas, D., Tikhonov, A., Tiltmann, N., Timmins, A. R., Tkacik, M., Tkacik, T., Toia, A., Tokumoto, R., Tomassini, S., Tomohiro, K., Topilskaya, N., Toppi, M., Tork, T., Torres, V. V., Torres Ramos, A. G., Trifiró, A., Triloki, T., Triolo, A. S., Tripathy, S., Tripathy, T., Trubnikov, V., Trzaska, W. H., Trzcinski, T. P., Tsolanta, C., Tu, R., Tumkin, A., Turrisi, R., Tveter, T. S., Ullaland, K., Ulukutlu, B., Uras, A., Urioni, M., Usai, G. L., Vala, M., Valle, N., van Doremalen, L. V. R., van Leeuwen, M., van Veen, C. A., van Weelden, R. J. G., Vande Vyvre, P., Varga, D., Varga, Z., Vargas Torres, P., Vasileiou, M., Vasiliev, A., Vázquez Doce, O., Vazquez Rueda, O., Vechernin, V., Vercellin, E., Vergara Limón, S., Verma, R., Vermunt, L., Vértesi, R., Verweij, M., Vickovic, L., Vilakazi, Z., Villalobos Baillie, O., Villani, A., Vinogradov, A., Virgili, T., Virta, M. M. O., Vislavicius, V., Vodopyanov, A., Volkel, B., Völkl, M. A., Voloshin, S. A., Volpe, G., von Haller, B., Vorobyev, I., Vozniuk, N., Vrláková, J., Wan, J., Wang, C., Wang, D., Wang, Y., Wang, Y., Wegrzynek, A., Weiglhofer, F. T., Wenzel, S. C., Wessels, J. P., Wiechula, J., Wikne, J., Wilk, G., Wilkinson, J., Willems, G. A., Windelband, B., Winn, M., Wright, J. R., Wu, W., Wu, Y., Xiong, Z., Xu, R., Yadav, A., Yadav, A. K., Yamaguchi, Y., Yang, S., Yano, S., Yeats, E. R., Yin, Z., Yoo, I.-K., Yoon, J. H., Yu, H., Yuan, S., Yuncu, A., Zaccolo, V., Zampolli, C., Zanone, F., Zardoshti, N., Zarochentsev, A., Závada, P., Zaviyalov, N., Zhalov, M., Zhang, B., Zhang, C., Zhang, L., Zhang, M., Zhang, M., Zhang, S., Zhang, X., Zhang, Y., Zhang, Z., Zhao, M., Zherebchevskii, V., Zhi, Y., Zhou, D., Zhou, Y., Zhu, J., Zhu, S., Zhu, Y., Zugravel, S. C., and Zurlo, N.
Published: 2024
Full Text: View/download PDF

46. Studying strangeness and baryon production mechanisms through angular correlations between charged Ξ baryons and identified hadrons in pp collisions at s = 13 TeV

Author: Acharya, S., Adamová, D., Adolfsson, J., Aglieri Rinella, G., Agnello, M., Agrawal, N., Ahammed, Z., Ahmad, S., Ahn, S. U., Ahuja, I., Akindinov, A., Al-Turany, M., Aleksandrov, D., Alessandro, B., Alfanda, H. M., Alfaro Molina, R., Ali, B., Alici, A., Alizadehvandchali, N., Alkin, A., Alme, J., Alocco, G., Alt, T., Altamura, A. R., Altsybeev, I., Alvarado, J. R., Anaam, M. N., Andrei, C., Andreou, N., Andronic, A., Anguelov, V., Antinori, F., Antonioli, P., Apadula, N., Aphecetche, L., Appelshäuser, H., Arata, C., Arcelli, S., Aresti, M., Arnaldi, R., Arneiro, J. G. M. C. A., Arsene, I. C., Arslandok, M., Augustinus, A., Averbeck, R., Azmi, M. D., Baba, H., Badalà, A., Bae, J., Baek, Y. W., Bai, X., Bailhache, R., Bailung, Y., Balbino, A., Baldisseri, A., Balis, B., Banerjee, D., Banoo, Z., Barbera, R., Barile, F., Barioglio, L., Barlou, M., Barman, B., Barnaföldi, G. G., Barnby, L. S., Barret, V., Barreto, L., Bartels, C., Barth, K., Bartsch, E., Bastid, N., Basu, S., Batigne, G., Battistini, D., Batyunya, B., Bauri, D., Bazo Alba, J. L., Bearden, I. G., Beattie, C., Becht, P., Behera, D., Belikov, I., Bell Hechavarria, A. D. C., Bellini, F., Bellwied, R., Belokurova, S., Beltran, Y. A. V., Bencedi, G., Beole, S., Berdnikov, Y., Berdnikova, A., Bergmann, L., Besoiu, M. G., Betev, L., Bhaduri, P. P., Bhasin, A., Bhat, M. A., Bhattacharjee, B., Bianchi, L., Bianchi, N., Bielčík, J., Bielčíková, J., Biernat, J., Bigot, A. P., Bilandzic, A., Biro, G., Biswas, S., Bize, N., Blair, J. T., Blau, D., Blidaru, M. B., Bluhme, N., Blume, C., Boca, G., Bock, F., Bodova, T., Bogdanov, A., Boi, S., Bok, J., Boldizsár, L., Bombara, M., Bond, P. M., Bonomi, G., Borel, H., Borissov, A., Borquez Carcamo, A. G., Bossi, H., Botta, E., Bouziani, Y. E. M., Bratrud, L., Braun-Munzinger, P., Bregant, M., Broz, M., Bruno, G. E., Buckland, M. D., Budnikov, D., Buesching, H., Bufalino, S., Buhler, P., Burmasov, N., Buthelezi, Z., Bylinkin, A., Bysiak, S. A., Cai, M., Caines, H., Caliva, A., Calvo Villar, E., Camacho, J. M. M., Camerini, P., Canedo, F. D. M., Cantway, S. L., Carabas, M., Carballo, A. A., Carnesecchi, F., Caron, R., Carvalho, L. A. D., Castillo Castellanos, J., Catalano, F., Ceballos Sanchez, C., Chakaberia, I., Chakraborty, P., Chandra, S., Chapeland, S., Chartier, M., Chattopadhyay, S., Chattopadhyay, S., Cheng, T., Cheshkov, C., Cheynis, B., Chibante Barroso, V., Chinellato, D. D., Chizzali, E. S., Cho, J., Cho, S., Chochula, P., Choudhury, D., Christakoglou, P., Christensen, C. H., Christiansen, P., Chujo, T., Ciacco, M., Cicalo, C., Cindolo, F., Ciupek, M. R., Clai, G., Colamaria, F., Colburn, J. S., Colella, D., Colocci, M., Concas, M., Conesa Balbastre, G., Conesa del Valle, Z., Contin, G., Contreras, J. G., Coquet, M. L., Cortese, P., Cosentino, M. R., Costa, F., Costanza, S., Cot, C., Crkovská, J., Crochet, P., Cruz-Torres, R., Cui, P., Dainese, A., Danisch, M. C., Danu, A., Das, P., Das, P., Das, S., Dash, A. R., Dash, S., De Caro, A., de Cataldo, G., de Cuveland, J., De Falco, A., De Gruttola, D., De Marco, N., De Martin, C., De Pasquale, S., Deb, R., Del Grande, R., Dello Stritto, L., Deng, W., Dhankher, P., Di Bari, D., Di Mauro, A., Diab, B., Diaz, R. A., Dietel, T., Ding, Y., Ditzel, J., Divià, R., Dixit, D. U., Djuvsland, Ø., Dmitrieva, U., Dobrin, A., Dönigus, B., Dubinski, J. M., Dubla, A., Dudi, S., Dupieux, P., Durkac, M., Dzalaiova, N., Eder, T. M., Ehlers, R. J., Eisenhut, F., Ejima, R., Elia, D., Erazmus, B., Ercolessi, F., Espagnon, B., Eulisse, G., Evans, D., Evdokimov, S., Fabbietti, L., Faggin, M., Faivre, J., Fan, F., Fan, W., Fantoni, A., Fasel, M., Feliciello, A., Feofilov, G., Fernández Téllez, A., Ferrandi, L., Ferrer, M. B., Ferrero, A., Ferrero, C., Ferretti, A., Feuillard, V. J. G., Filova, V., Finogeev, D., Fionda, F. M., Flatland, E., Flor, F., Flores, A. N., Foertsch, S., Fokin, I., Fokin, S., Fragiacomo, E., Frajna, E., Fuchs, U., Funicello, N., Furget, C., Furs, A., Fusayasu, T., Gaardhøje, J. J., Gagliardi, M., Gago, A. M., Gahlaut, T., Galvan, C. D., Gangadharan, D. R., Ganoti, P., Garabatos, C., García Chávez, T., Garcia-Solis, E., Gargiulo, C., Gasik, P., Gautam, A., Gay Ducati, M. B., Germain, M., Ghimouz, A., Ghosh, C., Giacalone, M., Gioachin, G., Giubellino, P., Giubilato, P., Glaenzer, A. M. C., Glässel, P., Glimos, E., Goh, D. J. Q., Gonzalez, V., Gordeev, P., Gorgon, M., Goswami, K., Gotovac, S., Grabski, V., Graczykowski, L. K., Grecka, E., Grelli, A., Grigoras, C., Grigoriev, V., Grigoryan, S., Grosa, F., Grosse-Oetringhaus, J. F., Grosso, R., Grund, D., Grunwald, N. A., Guardiano, G. G., Guernane, R., Guilbaud, M., Gulbrandsen, K., Gündem, T., Gunji, T., Guo, W., Gupta, A., Gupta, R., Gupta, R., Gwizdziel, K., Gyulai, L., Hadjidakis, C., Haider, F. U., Haidlova, S., Hamagaki, H., Hamdi, A., Han, Y., Hanley, B. G., Hannigan, R., Hansen, J., Haque, M. R., Harris, J. W., Harton, A., Hassan, H., Hatzifotiadou, D., Hauer, P., Havener, L. B., Heckel, S. T., Hellbär, E., Helstrup, H., Hemmer, M., Herman, T., Herrera Corral, G., Herrmann, F., Herrmann, S., Hetland, K. F., Heybeck, B., Hillemanns, H., Hippolyte, B., Hoffmann, F. W., Hofman, B., Hong, G. H., Horst, M., Horzyk, A., Hou, Y., Hristov, P., Hughes, C., Huhn, P., Huhta, L. M., Humanic, T. J., Hutson, A., Hutter, D., Ilkaev, R., Ilyas, H., Inaba, M., Innocenti, G. M., Ippolitov, M., Isakov, A., Isidori, T., Islam, M. S., Ivanov, M., Ivanov, M., Ivanov, V., Iversen, K. E., Jablonski, M., Jacak, B., Jacazio, N., Jacobs, P. M., Jadlovska, S., Jadlovsky, J., Jaelani, S., Jahnke, C., Jakubowska, M. J., Janik, M. A., Janson, T., Ji, S., Jia, S., Jimenez, A. A. P., Jonas, F., Jones, D. M., Jowett, J. M., Jung, J., Jung, M., Junique, A., Jusko, A., Kaewjai, J., Kalinak, P., Kalteyer, A. S., Kalweit, A., Kaplin, V., Karasu Uysal, A., Karatovic, D., Karavichev, O., Karavicheva, T., Karczmarczyk, P., Karpechev, E., Karwowska, M. J., Kebschull, U., Keidel, R., Keijdener, D. L. D., Keil, M., Ketzer, B., Khade, S. S., Khan, A. M., Khan, S., Khanzadeev, A., Kharlov, Y., Khatun, A., Khuntia, A., Kileng, B., Kim, B., Kim, C., Kim, D. J., Kim, E. J., Kim, J., Kim, J. S., Kim, J., Kim, J., Kim, M., Kim, S., Kim, T., Kimura, K., Kirsch, S., Kisel, I., Kiselev, S., Kisiel, A., Kitowski, J. P., Klay, J. L., Klein, J., Klein, S., Klein-Bösing, C., Kleiner, M., Klemenz, T., Kluge, A., Knospe, A. G., Kobdaj, C., Kollegger, T., Kondratyev, A., Kondratyeva, N., Kondratyuk, E., Konig, J., Konigstorfer, S. A., Konopka, P. J., Kornakov, G., Korwieser, M., Koryciak, S. D., Kotliarov, A., Kovalenko, V., Kowalski, M., Kozhuharov, V., Králik, I., Kravčáková, A., Krcal, L., Krivda, M., Krizek, F., Krizkova Gajdosova, K., Kroesen, M., Krüger, M., Krupova, D. M., Kryshen, E., Kučera, V., Kuhn, C., Kuijer, P. G., Kumaoka, T., Kumar, D., Kumar, L., Kumar, N., Kumar, S., Kundu, S., Kurashvili, P., Kurepin, A., Kurepin, A. B., Kuryakin, A., Kushpil, S., Kuskov, V., Kweon, M. J., Kwon, Y., La Pointe, S. L., La Rocca, P., Lakrathok, A., Lamanna, M., Landou, A. R., Langoy, R., Larionov, P., Laudi, E., Lautner, L., Lavicka, R., Lea, R., Lee, H., Legrand, I., Legras, G., Lehrbach, J., Lelek, T. M., Lemmon, R. C., León Monzón, I., Lesch, M. M., Lesser, E. D., Lévai, P., Li, X., Lien, J., Lietava, R., Likmeta, I., Lim, B., Lim, S. H., Lindenstruth, V., Lindner, A., Lippmann, C., Liu, D. H., Liu, J., Liveraro, G. S. S., Lofnes, I. M., Loizides, C., Lokos, S., Lömker, J., Loncar, P., Lopez, X., López Torres, E., Lu, P., Lugo, F. V., Luhder, J. R., Lunardon, M., Luparello, G., Ma, Y. G., Mager, M., Maire, A., Majerz, E. M., Makariev, M. V., Malaev, M., Malfattore, G., Malik, N. M., Malik, Q. W., Malik, S. K., Malinina, L., Mallick, D., Mallick, N., Mandaglio, G., Mandal, S. K., Manko, V., Manso, F., Manzari, V., Mao, Y., Marcjan, R. W., Margagliotti, G. V., Margotti, A., Marín, A., Markert, C., Martinengo, P., Martínez, M. I., Martínez García, G., Martins, M. P. P., Masciocchi, S., Masera, M., Masoni, A., Massacrier, L., Massen, O., Mastroserio, A., Matonoha, O., Mattiazzo, S., Matyja, A., Mayer, C., Mazuecos, A. L., Mazzaschi, F., Mazzilli, M., Mdhluli, J. E., Melikyan, Y., Menchaca-Rocha, A., Mendez, J. E. M., Meninno, E., Menon, A. S., Meres, M., Mhlanga, S., Miake, Y., Micheletti, L., Mihaylov, D. L., Mikhaylov, K., Mishra, A. N., Miśkowiec, D., Modak, A., Mohanty, B., Mohisin Khan, M., Molander, M. A., Monira, S., Mordasini, C., Moreira De Godoy, D. A., Morozov, I., Morsch, A., Mrnjavac, T., Muccifora, V., Muhuri, S., Mulligan, J. D., Mulliri, A., Munhoz, M. G., Munzer, R. H., Murakami, H., Murray, S., Musa, L., Musinsky, J., Myrcha, J. W., Naik, B., Nambrath, A. I., Nandi, B. K., Nania, R., Nappi, E., Nassirpour, A. F., Nath, A., Nattrass, C., Naydenov, M. N., Neagu, A., Negru, A., Nekrasova, E., Nellen, L., Nepeivoda, R., Nese, S., Neskovic, G., Nicassio, N., Nielsen, B. S., Nielsen, E. G., Nikolaev, S., Nikulin, S., Nikulin, V., Noferini, F., Noh, S., Nomokonov, P., Norman, J., Novitzky, N., Nowakowski, P., Nyanin, A., Nystrand, J., Ogino, M., Oh, S., Ohlson, A., Okorokov, V. A., Oleniacz, J., Oliveira Da Silva, A. C., Onnerstad, A., Oppedisano, C., Ortiz Velasquez, A., Otwinowski, J., Oya, M., Oyama, K., Pachmayer, Y., Padhan, S., Pagano, D., Paić, G., Paisano-Guzmán, S., Palasciano, A., Panebianco, S., Park, H., Park, H., Park, J., Parkkila, J. E., Patley, Y., Patra, R. N., Paul, B., Pei, H., Peitzmann, T., Peng, X., Pennisi, M., Perciballi, S., Peresunko, D., Perez, G. M., Pestov, Y., Petrov, V., Petrovici, M., Pezzi, R. P., Piano, S., Pikna, M., Pillot, P., Pinazza, O., Pinsky, L., Pinto, C., Pisano, S., Płoskoń, M., Planinic, M., Pliquett, F., Poghosyan, M. G., Polichtchouk, B., Politano, S., Poljak, N., Pop, A., Porteboeuf-Houssais, S., Pozdniakov, V., Pozos, I. Y., Pradhan, K. K., Prasad, S. K., Prasad, S., Preghenella, R., Prino, F., Pruneau, C. A., Pshenichnov, I., Puccio, M., Pucillo, S., Pugelova, Z., Qiu, S., Quaglia, L., Ragoni, S., Rai, A., Rakotozafindrabe, A., Ramello, L., Rami, F., Rancien, T. A., Rasa, M., Räsänen, S. S., Rath, R., Rauch, M. P., Ravasenga, I., Read, K. F., Reckziegel, C., Redelbach, A. R., Redlich, K., Reetz, C. A., Regules-Medel, H. D., Rehman, A., Reidt, F., Reme-Ness, H. A., Rescakova, Z., Reygers, K., Riabov, A., Riabov, V., Ricci, R., Richter, M., Riedel, A. A., Riegler, W., Riffero, A. G., Ristea, C., Rodriguez, M. V., Rodríguez Cahuantzi, M., Rodríguez Ramírez, S. A., Røed, K., Rogalev, R., Rogochaya, E., Rogoschinski, T. S., Rohr, D., Röhrich, D., Rojas, P. F., Rojas Torres, S., Rokita, P. S., Romanenko, G., Ronchetti, F., Rosano, A., Rosas, E. D., Roslon, K., Rossi, A., Roy, A., Roy, S., Rubini, N., Ruggiano, D., Rui, R., Russek, P. G., Russo, R., Rustamov, A., Ryabinkin, E., Ryabov, Y., Rybicki, A., Rytkonen, H., Ryu, J., Rzesa, W., Saarimaki, O. A. M., Sadhu, S., Sadovsky, S., Saetre, J., Šafařík, K., Saha, P., Saha, S. K., Saha, S., Sahoo, B., Sahoo, B., Sahoo, R., Sahoo, S., Sahu, D., Sahu, P. K., Saini, J., Sajdakova, K., Sakai, S., Salvan, M. P., Sambyal, S., Samitz, D., Sanna, I., Saramela, T. B., Sarma, P., Sarritzu, V., Sarti, V. M., Sas, M. H. P., Sawan, S., Schambach, J., Scheid, H. S., Schiaua, C., Schicker, R., Schlepper, F., Schmah, A., Schmidt, C., Schmidt, H. R., Schmidt, M. O., Schmidt, M., Schmidt, N. V., Schmier, A. R., Schotter, R., Schröter, A., Schukraft, J., Schweda, K., Scioli, G., Scomparin, E., Seger, J. E., Sekiguchi, Y., Sekihata, D., Selina, M., Selyuzhenkov, I., Senyukov, S., Seo, J. J., Serebryakov, D., Šerkšnytė, L., Sevcenco, A., Shaba, T. J., Shabetai, A., Shahoyan, R., Shangaraev, A., Sharma, A., Sharma, B., Sharma, D., Sharma, H., Sharma, M., Sharma, S., Sharma, S., Sharma, U., Shatat, A., Sheibani, O., Shigaki, K., Shimomura, M., Shin, J., Shirinkin, S., Shou, Q., Sibiriak, Y., Siddhanta, S., Siemiarczuk, T., Silva, T. F., Silvermyr, D., Simantathammakul, T., Simeonov, R., Singh, B., Singh, B., Singh, K., Singh, R., Singh, R., Singh, R., Singh, S., Singh, V. K., Singhal, V., Sinha, T., Sitar, B., Sitta, M., Skaali, T. B., Skorodumovs, G., Slupecki, M., Smirnov, N., Snellings, R. J. M., Solheim, E. H., Song, J., Sonnabend, C., Soramel, F., Soto-hernandez, A. B., Spijkers, R., Sputowska, I., Staa, J., Stachel, J., Stan, I., Steffanic, P. J., Stiefelmaier, S. F., Stocco, D., Storehaug, I., Stratmann, P., Strazzi, S., Sturniolo, A., Stylianidis, C. P., Suaide, A. A. P., Suire, C., Sukhanov, M., Suljic, M., Sultanov, R., Sumberia, V., Sumowidagdo, S., Swain, S., Szarka, I., Szymkowski, M., Taghavi, S. F., Taillepied, G., Takahashi, J., Tambave, G. J., Tang, S., Tang, Z., Tapia Takaki, J. D., Tapus, N., Tarasovicova, L. A., Tarzila, M. G., Tassielli, G. F., Tauro, A., Tavira García, A., Tejeda Muñoz, G., Telesca, A., Terlizzi, L., Terrevoli, C., Thakur, S., Thomas, D., Tikhonov, A., Tiltmann, N., Timmins, A. R., Tkacik, M., Tkacik, T., Toia, A., Tokumoto, R., Tomohiro, K., Topilskaya, N., Toppi, M., Tork, T., Torres, V. V., Torres Ramos, A. G., Trifiró, A., Triolo, A. S., Tripathy, S., Tripathy, T., Trogolo, S., Trubnikov, V., Trzaska, W. H., Trzcinski, T. P., Tumkin, A., Turrisi, R., Tveter, T. S., Ullaland, K., Ulukutlu, B., Uras, A., Usai, G. L., Vala, M., Valle, N., van Doremalen, L. V. R., van Leeuwen, M., van Veen, C. A., van Weelden, R. J. G., Vande Vyvre, P., Varga, D., Varga, Z., Vargas Torres, P., Vasileiou, M., Vasiliev, A., Vázquez Doce, O., Vazquez Rueda, O., Vechernin, V., Vercellin, E., Vergara Limón, S., Verma, R., Vermunt, L., Vértesi, R., Verweij, M., Vickovic, L., Vilakazi, Z., Villalobos Baillie, O., Villani, A., Vinogradov, A., Virgili, T., Virta, M. M. O., Vislavicius, V., Vodopyanov, A., Volkel, B., Völkl, M. A., Voloshin, K., Voloshin, S. A., Volpe, G., von Haller, B., Vorobyev, I., Vozniuk, N., Vrláková, J., Wan, J., Wang, C., Wang, D., Wang, Y., Wang, Y., Wegrzynek, A., Weiglhofer, F. T., Wenzel, S. C., Wessels, J. P., Wiechula, J., Wikne, J., Wilk, G., Wilkinson, J., Willems, G. A., Windelband, B., Winn, M., Wright, J. R., Wu, W., Wu, Y., Xu, R., Yadav, A., Yadav, A. K., Yalcin, S., Yamaguchi, Y., Yang, S., Yano, S., Yin, Z., Yoo, I.-K., Yoon, J. H., Yu, H., Yuan, S., Yuncu, A., Zaccolo, V., Zampolli, C., Zanone, F., Zardoshti, N., Zarochentsev, A., Závada, P., Zaviyalov, N., Zhalov, M., Zhang, B., Zhang, C., Zhang, L., Zhang, M., Zhang, S., Zhang, X., Zhang, Y., Zhang, Z., Zhao, M., Zherebchevskii, V., Zhi, Y., Zhou, D., Zhou, Y., Zhu, J., Zhu, Y., Zugravel, S. C., and Zurlo, N.
Published: 2024
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47. Interplay of spin and charge order in the electron-doped cuprates

Author: Riegler, David, Seufert, Jannis, Neto, Eduardo H. da Silva, Wölfle, Peter, Thomale, Ronny, and Klett, Michael
Subjects: Condensed Matter - Strongly Correlated Electrons
Abstract: We study magnetic and charge order in the electron-doped high-$T_c$ cuprates based on the one-band Hubbard model with onsite ($U$) and nearest-neighbor $(V)$ interactions. To investigate the interplay between the orders, we employ the Kotliar-Ruckenstein slave-boson method and analyze fluctuations descending from an antiferromagnetic parent state. Our analysis reveals incommensurate charge order whose ordering vector matches the doping-dependence of resonant inelastic x-ray scattering (RIXS) measurements in Nd$_{2-x}$Ce$_x$CuO$_4$ (NCCO). From our calculations of paramagnon dispersion as well as dynamical charge and spin structure factors, we reproduce all qualitative features of the RIXS signal., Comment: 17 pages, 11 figures, more condensed version
Published: 2023
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48. Correlation visualization under missing values: a comparison between imputation and direct parameter estimation methods

Author: Pham, Nhat-Hao, Vo, Khanh-Linh, Vu, Mai Anh, Nguyen, Thu, Riegler, Michael A., Halvorsen, Pål, and Nguyen, Binh T.
Subjects: Computer Science - Machine Learning, Statistics - Machine Learning
Abstract: Correlation matrix visualization is essential for understanding the relationships between variables in a dataset, but missing data can pose a significant challenge in estimating correlation coefficients. In this paper, we compare the effects of various missing data methods on the correlation plot, focusing on two common missing patterns: random and monotone. We aim to provide practical strategies and recommendations for researchers and practitioners in creating and analyzing the correlation plot. Our experimental results suggest that while imputation is commonly used for missing data, using imputed data for plotting the correlation matrix may lead to a significantly misleading inference of the relation between the features. We recommend using DPER, a direct parameter estimation approach, for plotting the correlation matrix based on its performance in the experiments.
Published: 2023

49. Blockwise Principal Component Analysis for monotone missing data imputation and dimensionality reduction

Author: Do, Tu T., Vu, Mai Anh, Vo, Tuan L., Ly, Hoang Thien, Nguyen, Thu, Hicks, Steven A., Riegler, Michael A., Halvorsen, Pål, and Nguyen, Binh T.
Subjects: Computer Science - Machine Learning
Abstract: Monotone missing data is a common problem in data analysis. However, imputation combined with dimensionality reduction can be computationally expensive, especially with the increasing size of datasets. To address this issue, we propose a Blockwise principal component analysis Imputation (BPI) framework for dimensionality reduction and imputation of monotone missing data. The framework conducts Principal Component Analysis (PCA) on the observed part of each monotone block of the data and then imputes on merging the obtained principal components using a chosen imputation technique. BPI can work with various imputation techniques and can significantly reduce imputation time compared to conducting dimensionality reduction after imputation. This makes it a practical and efficient approach for large datasets with monotone missing data. Our experiments validate the improvement in speed. In addition, our experiments also show that while applying MICE imputation directly on missing data may not yield convergence, applying BPI with MICE for the data may lead to convergence.
Published: 2023

50. Mask-conditioned latent diffusion for generating gastrointestinal polyp images

Author: Macháček, Roman, Mozaffari, Leila, Sepasdar, Zahra, Parasa, Sravanthi, Halvorsen, Pål, Riegler, Michael A., and Thambawita, Vajira
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: In order to take advantage of AI solutions in endoscopy diagnostics, we must overcome the issue of limited annotations. These limitations are caused by the high privacy concerns in the medical field and the requirement of getting aid from experts for the time-consuming and costly medical data annotation process. In computer vision, image synthesis has made a significant contribution in recent years as a result of the progress of generative adversarial networks (GANs) and diffusion probabilistic models (DPM). Novel DPMs have outperformed GANs in text, image, and video generation tasks. Therefore, this study proposes a conditional DPM framework to generate synthetic GI polyp images conditioned on given generated segmentation masks. Our experimental results show that our system can generate an unlimited number of high-fidelity synthetic polyp images with the corresponding ground truth masks of polyps. To test the usefulness of the generated data, we trained binary image segmentation models to study the effect of using synthetic data. Results show that the best micro-imagewise IOU of 0.7751 was achieved from DeepLabv3+ when the training data consists of both real data and synthetic data. However, the results reflect that achieving good segmentation performance with synthetic data heavily depends on model architectures.
Published: 2023

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