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174,115 results on '"FREY A"'

102. Investigating the Shortcomings of the Flow Convergence Method for Quantification of Mitral Regurgitation in a Pulsatile In-Vitro Environment and with Computational Fluid Dynamics: Investigating the Shortcomings of the Flow Convergence Method for Quantification of Mitral Regurgitation...

Author

Leister, Robin, Karl, Roger, Stroh, Lubov, Mereles, Derliz, Eden, Matthias, Neff, Luis, de Simone, Raffaele, Romano, Gabriele, Kriegseis, Jochen, Karck, Matthias, Lichtenstern, Christoph, Frey, Norbert, Frohnapfel, Bettina, Stroh, Alexander, and Engelhardt, Sandy

Published
2025
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103. Magnetoelectric nanodiscs enable wireless transgene-free neuromodulation

Author

Kim, Ye Ji, Kent, Noah, Vargas Paniagua, Emmanuel, Driscoll, Nicolette, Tabet, Anthony, Koehler, Florian, Malkin, Elian, Frey, Ethan, Manthey, Marie, Sahasrabudhe, Atharva, Cannon, Taylor M., Nagao, Keisuke, Mankus, David, Bisher, Margaret, de Nola, Giovanni, Lytton-Jean, Abigail, Signorelli, Lorenzo, Gregurec, Danijela, and Anikeeva, Polina

Published
2025
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105. Safety of psychotropic medications in pregnancy: an umbrella review

Author

Fabiano, Nicholas, Wong, Stanley, Gupta, Arnav, Tran, Jason, Bhambra, Nishaant, Min, Kevin K., Dragioti, Elena, Barbui, Corrado, Fiedorowicz, Jess G., Gosling, Corentin J., Cortese, Samuele, Gandhi, Jasmine, Saraf, Gayatri, Shorr, Risa, Vigod, Simone N., Frey, Benicio N., Delorme, Richard, and Solmi, Marco

Published
2025
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109. Is the University of California Drifting toward Conformism? The Challenges of Representation and the Climate for Academic Freedom. Research & Occasional Paper Series: CSHE.5.2023

Author

University of California, Berkeley. Center for Studies in Higher Education (CSHE), Steven Brint, and Komi Frey

Abstract

In this essay, we explore the consequences of the University of California's policies to address racial disparities and its support for social justice activism as influences on its commitment to academic freedom and other intellectual values. This is a story of the interaction between two essential public university missions -- one civic, the other intellectual -- and the slow effacement of one by the other. The University's expressed commitments to academic freedom and the culture of rationalism have not been abandoned, but they are too often considered secondary or when confronted by new administrative initiatives and social movement activism related to diversity, equity, and inclusion (DEI). The experimental use of mandatory DEI statements on a number of the ten UC campuses, within willing academic departments, as initial screening mechanisms in faculty hiring is the most dramatic of the new administrative policies that have been put into place to advance faculty diversity. This policy can be considered the most problematic of a series of efforts that the UC campuses and the UC Office of the President have taken for more than a decade to prioritize representation in academic appointments. Our intent is to encourage a discussion of these policies within UC in light of the University's fundamental commitments to open intellectual inquiry, the discovery and dissemination of a wide range of new knowledge, and a culture of rationalism.

Published
2023

110. Assessing the Evolution of LLM Capabilities for Knowledge Graph Engineering in 2023

Author

Frey, Johannes, Meyer, Lars-Peter, Brei, Felix, Gründer-Fahrer, Sabine, Martin, Michael, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Meroño Peñuela, Albert, editor, Corcho, Oscar, editor, Groth, Paul, editor, Simperl, Elena, editor, Tamma, Valentina, editor, Nuzzolese, Andrea Giovanni, editor, Poveda-Villalón, Maria, editor, Sabou, Marta, editor, Presutti, Valentina, editor, Celino, Irene, editor, Revenko, Artem, editor, Raad, Joe, editor, Sartini, Bruno, editor, and Lisena, Pasquale, editor

Published
2025
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111. SwiftFaceFormer: An Efficient and Lightweight Hybrid Architecture for Accurate Face Recognition Applications

Author

Luevano, Luis S., Martínez-Díaz, Yoanna, Méndez-Vázquez, Heydi, González-Mendoza, Miguel, Frey, Davide, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Antonacopoulos, Apostolos, editor, Chaudhuri, Subhasis, editor, Chellappa, Rama, editor, Liu, Cheng-Lin, editor, Bhattacharya, Saumik, editor, and Pal, Umapada, editor

Published
2025
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113. Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms

Author

Stanton, Samuel, Alberstein, Robert, Frey, Nathan, Watkins, Andrew, and Cho, Kyunghyun

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

There is a growing body of work seeking to replicate the success of machine learning (ML) on domains like computer vision (CV) and natural language processing (NLP) to applications involving biophysical data. One of the key ingredients of prior successes in CV and NLP was the broad acceptance of difficult benchmarks that distilled key subproblems into approachable tasks that any junior researcher could investigate, but good benchmarks for biophysical domains are rare. This scarcity is partially due to a narrow focus on benchmarks which simulate biophysical data; we propose instead to carefully abstract biophysical problems into simpler ones with key geometric similarities. In particular we propose a new class of closed-form test functions for biophysical sequence optimization, which we call Ehrlich functions. We provide empirical results demonstrating these functions are interesting objects of study and can be non-trivial to solve with a standard genetic optimization baseline.

Published
2024

114. The Belle II Detector Upgrades Framework Conceptual Design Report

Author

Aihara, H., Aloisio, A., Auguste, D. P., Aversano, M., Babeluk, M., Bahinipati, S., Banerjee, Sw., Barbero, M., Baudot, J., Beaubien, A., Becherer, F., Bergauer, T., Bernlochner., F. U., Bertacchi, V., Bertolone, G., Bespin, C., Bessner, M., Bettarini, S., Bevan, A. J., Bhuyan, B., Bona, M., Bonis, J. F., Borah, J., Bosi, F., Boudagga, R., Bozek, A., Bračko, M., Branchini, P., Breugnon, P., Browder, T. E., Buch, Y., Budano, A., Campajola, M., Casarosa, G., Cecchi, C., Chen, C., Choudhury, S., Corona, L., de Marino, G., De Nardo, G., De Pietro, G., de Sangro, R., Dey, S., Dingfelder, J. C., Dong, T. V., Dorokhov, A., Dujany, G., Epifanov, D., Federici, L., Ferber, T., Fillinger, T., Finck, Ch., Finocchiaro, G., Forti, F., Frey, A., Friedl, M., Gabrielli, A., Gaioni, L., Gao, Y., Gaudino, G., Gaur, V., Gaz, A., Giordano, R., Giroletti, S., Gobbo, B., Godang, R., Haide, I., Han, Y., Hara, K., Hayasaka, K., Hearty, C., Heidelbach, A., Higuchi, T., Himmi, A., Hoferichter, M., Howgill, D. A., Hu-Guo, C., Iijima, T., Inami, K., Irmler, C., Ishikawa, A., Itoh, R., Iyer, D., Jacobs, W. W., Jaffe, D. E., Jin, Y., Junginger, T., Kandra, J., Kojima, K., Koga, T., Korobov, A. A., Korpar, S., Križan, P., Krüger, H., Kuhr, T., Kumar, A., Kumar, R ., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lacasta, C., Lai, Y. -T., Lalwani, K., Lam, T., Lanceri, L., Lee, M. J., Leonidopoulos, C., Levit, D., Lewis, P. M., Libby, J. F., Liu, Q. Y., Liu, Z. Y., Liventsev, D., Longo, S., Mancinelli, G., Manghisoni, M., Manoni, E., Marinas, C., Martellini, C., Martens, A., Massa, M., Massaccesi, L., Mawas, F., Mazorra, J., Merola, M., Miller, C., Minuti, M., Mizuk, R., Modak, A., Moggi, A., Mohanty, G. B., Moneta, S., Muller, Th., Na, I., Nakamura, K. R., Nakao, M., Natochii, A., Niebuhr, C., Nishida, S., Novosel, A., Pangaud, P., Parker, B., Passeri, A., Pedlar, T. K., Peinaud, Y., Peng, Y., Peschke, R., Pestotnik, R., Pham, T. H., Piccolo, M., Piilonen, L. E., Prell, S., Purohit, M. V., Ratti, L., Re, V., Reuter, L., Riceputi, E., Ripp-Baudot, I., Rizzo, G., Roney, J. M., Russo, A., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schall, L., Schnell, G., Schwanda, C., Schwartz, A. J., Schwenker, B., Schwickardi, M., Seljak, A., Serrano, J., Shiu, J. -G., Shwartz, B., Simon, F., Soffer, A., Song, W. M., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Tanaka, S., Taniguchi, N., Teotia, V., Tessema, N., Thalmeier, R., Torassa, E., Trabelsi, K., Trantou, F. F., Traversi, G., Urquijo, P., Vahsen, S. E., Valin, I., Varner, G. S., Varvell, K. E., Vitale, L., Vobbilisetti, V., Wang, X. L., Wessel, C., Wienands, H. U., Won, E., Xu, D., Yamada, S., Yin, J. H., Yoshihara, K., Yuan, C. Z., Zani, L., Zong, Z., and Zou, S.

Subjects
High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract

We describe the planned near-term and potential longer-term upgrades of the Belle II detector at the SuperKEKB electron-positron collider operating at the KEK laboratory in Tsukuba, Japan. These upgrades will allow increasingly sensitive searches for possible new physics beyond the Standard Model in flavor, tau, electroweak and dark sector physics that are both complementary to and competitive with the LHC and other experiments., Comment: Editor: F. Forti 170 pages

Published
2024

115. Playing with Fire? A Mean Field Game Analysis of Fire Sales and Systemic Risk under Regulatory Capital Constraints

Author

Frey, Rüdiger and Traxler, Theresa

Subjects
Quantitative Finance - Mathematical Finance
Abstract

We study the impact of regulatory capital constraints on fire sales and financial stability in a large banking system using a mean field game model. In our model banks adjust their holdings of a risky asset via trading strategies with finite trading rate in order to maximize expected profits. Moreover, a bank is liquidated if it violates a stylized regulatory capital constraint. We assume that the drift of the asset value is affected by the average change in the position of the banks in the system. This creates strategic interaction between the trading behavior of banks and thus leads to a game. The equilibria of this game are characterized by a system of coupled PDEs. We solve this system explicitly for a test case without regulatory constraints and numerically for the regulated case. We find that capital constraints can lead to a systemic crisis where a substantial proportion of the banking system defaults simultaneously. Moreover, we discuss proposals from the literature on macroprudential regulation. In particular, we show that in our setup a systemic crisis does not arise if the banking system is sufficiently well capitalized or if improved mechanisms for the resolution of banks violating the risk capital constraints are in place.

Published
2024

116. The unpaved road towards efficient selective breeding in insects for food and feed

Author

Hansen, Laura Skrubbeltrang, Laursen, Stine Frey, Bahrndorff, Simon, Sørensen, Jesper Givskov, Sahana, Goutam, Kristensen, Torsten Nygaard, and Nielsen, Hanne Marie

Subjects
Quantitative Biology - Other Quantitative Biology
Abstract

Insect production for food and feed presents a promising supplement to ensure food safety and address the adverse impacts of agriculture on climate and environment in the future. However, optimisation is required for insect production to realise its full potential. This can be by targeted improvement of traits of interest through selective breeding, an approach which has so far been underexplored and underutilised in insect farming. Here we present a comprehensive review of the selective breeding framework in the context of insect production. We systematically evaluate adjustments of selective breeding techniques to the realm of insects and highlight the essential components integral to the breeding process. The discussion covers every step of a conventional breeding scheme, such as formulation of breeding objectives, phenotyping, estimation of genetic parameters and breeding values, selection of appropriate breeding strategies, and mitigation of issues associated with genetic diversity depletion and inbreeding. This review combines knowledge from diverse disciplines, bridging the gap between animal breeding, quantitative genetics, evolutionary biology, and entomology, offering an integrated view of the insect breeding research area and uniting knowledge which has previously remained scattered across diverse fields of expertise.

Published
2024
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117. WeatherQA: Can Multimodal Language Models Reason about Severe Weather?

Author

Ma, Chengqian, Hua, Zhanxiang, Anderson-Frey, Alexandra, Iyer, Vikram, Liu, Xin, and Qin, Lianhui

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Physics - Atmospheric and Oceanic Physics
Abstract

Severe convective weather events, such as hail, tornadoes, and thunderstorms, often occur quickly yet cause significant damage, costing billions of dollars every year. This highlights the importance of forecasting severe weather threats hours in advance to better prepare meteorologists and residents in at-risk areas. Can modern large foundation models perform such forecasting? Existing weather benchmarks typically focus only on predicting time-series changes in certain weather parameters (e.g., temperature, moisture) with text-only features. In this work, we introduce WeatherQA, the first multimodal dataset designed for machines to reason about complex combinations of weather parameters (a.k.a., ingredients) and predict severe weather in real-world scenarios. The dataset includes over 8,000 (multi-images, text) pairs for diverse severe weather events. Each pair contains rich information crucial for forecasting -- the images describe the ingredients capturing environmental instability, surface observations, and radar reflectivity, and the text contains forecast analyses written by human experts. With WeatherQA, we evaluate state-of-the-art vision language models, including GPT4, Claude3.5, Gemini-1.5, and a fine-tuned Llama3-based VLM, by designing two challenging tasks: (1) multi-choice QA for predicting affected area and (2) classification of the development potential of severe convection. These tasks require deep understanding of domain knowledge (e.g., atmospheric dynamics) and complex reasoning over multimodal data (e.g., interactions between weather parameters). We show a substantial gap between the strongest VLM, GPT4o, and human reasoning. Our comprehensive case study with meteorologists further reveals the weaknesses of the models, suggesting that better training and data integration are necessary to bridge this gap. WeatherQA link: https://github.com/chengqianma/WeatherQA., Comment: 26 pages, 9 figures

Published
2024

118. GAPses: Versatile smart glasses for comfortable and fully-dry acquisition and parallel ultra-low-power processing of EEG and EOG

Author

Frey, Sebastian, Lucchini, Mattia Alberto, Kartsch, Victor, Ingolfsson, Thorir Mar, Bernardi, Andrea Helga, Segessenmann, Michael, Osieleniec, Jakub, Benatti, Simone, Benini, Luca, and Cossettini, Andrea

Subjects
Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

Recent advancements in head-mounted wearable technology are revolutionizing the field of biopotential measurement, but the integration of these technologies into practical, user-friendly devices remains challenging due to issues with design intrusiveness, comfort, and data privacy. To address these challenges, this paper presents GAPSES, a novel smart glasses platform designed for unobtrusive, comfortable, and secure acquisition and processing of electroencephalography (EEG) and electrooculography (EOG) signals. We introduce a direct electrode-electronics interface with custom fully dry soft electrodes to enhance comfort for long wear. An integrated parallel ultra-low-power RISC-V processor (GAP9, Greenwaves Technologies) processes data at the edge, thereby eliminating the need for continuous data streaming through a wireless link, enhancing privacy, and increasing system reliability in adverse channel conditions. We demonstrate the broad applicability of the designed prototype through validation in a number of EEG-based interaction tasks, including alpha waves, steady-state visual evoked potential analysis, and motor movement classification. Furthermore, we demonstrate an EEG-based biometric subject recognition task, where we reach a sensitivity and specificity of 98.87% and 99.86% respectively, with only 8 EEG channels and an energy consumption per inference on the edge as low as 121 uJ. Moreover, in an EOG-based eye movement classification task, we reach an accuracy of 96.68% on 11 classes, resulting in an information transfer rate of 94.78 bit/min, which can be further increased to 161.43 bit/min by reducing the accuracy to 81.43%. The deployed implementation has an energy consumption of 24 uJ per inference and a total system power of only 16.28 mW, allowing for continuous operation of more than 12 h with a small 75 mAh battery., Comment: 10 pages, 5 figures, 5 tables. This paper has been submitted to IEEE Transactions on Biomedical Circuits and Systems

Published
2024

119. Measurement of the branching fractions of $\bar{B}\to D^{(*)} K^- K^{(*)0}_{(S)}$ and $\bar{B}\to D^{(*)}D_s^{-}$ decays at Belle II

Author

Belle II Collaboration, Adachi, I., Aggarwal, L., Aihara, H., Akopov, N., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Corona, L., Cui, J. X., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., de Marino, G., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dorner, D., Dort, K., Dossett, D., Dreyer, S., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Eliachevitch, M., Epifanov, D., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Gudkova, K., Haide, I., Halder, S., Han, Y., Hara, T., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Konno, T., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, J., Kumar, M., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lalwani, K., Lam, T., Lange, J. S., Laurenza, M., Lautenbach, K., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Levit, D., Lewis, P. M., Li, L. K., Li, S. X., Li, Y., Li, Y. B., Libby, J., Liptak, Z., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., Matvienko, D., Maurya, S. K., McKenna, J. A., Meier, F., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Mizuk, R., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Neu, M., Niiyama, M., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Pakhlova, G., Pardi, S., Parham, K., Park, H., Park, J., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Reuter, L., Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schneider, S., Schnepf, M., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sumihama, M., Svidras, H., Takizawa, M., Tamponi, U., Tanaka, S., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Vossen, A., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Wiechczynski, J., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yang, S. B., Yelton, J., Yin, J. H., Yook, Y. M., Yoshihara, K., Yuan, C. Z., Zani, L., Zeng, F., Zhang, B., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We present measurements of the branching fractions of eight $\overline B{}^0\to D^{(*)+} K^- K^{(*)0}_{(S)}$, $B^{-}\to D^{(*)0} K^- K^{(*)0}_{(S)}$ decay channels. The results are based on data from SuperKEKB electron-positron collisions at the $\Upsilon(4S)$ resonance collected with the Belle II detector, corresponding to an integrated luminosity of $362~\text{fb}^{-1}$. The event yields are extracted from fits to the distributions of the difference between expected and observed $B$ meson energy, and are efficiency-corrected as a function of $m(K^-K^{(*)0}_{(S)})$ and $m(D^{(*)}K^{(*)0}_{(S)})$ in order to avoid dependence on the decay model. These results include the first observation of $\overline B{}^0\to D^+K^-K_S^0$, $B^-\to D^{*0}K^-K_S^0$, and $\overline B{}^0\to D^{*+}K^-K_S^0$ decays and a significant improvement in the precision of the other channels compared to previous measurements. The helicity-angle distributions and the invariant mass distributions of the $K^- K^{(*)0}_{(S)}$ systems are compatible with quasi-two-body decays via a resonant transition with spin-parity $J^P=1^-$ for the $K^-K_S^0$ systems and $J^P= 1^+$ for the $K^-K^{*0}$ systems. We also present measurements of the branching fractions of four $\overline B{}^0\to D^{(*)+} D_s^-$, $B^{-}\to D^{(*)0} D_s^- $ decay channels with a precision compatible to the current world averages., Comment: 34 pages, 14 figures. arXiv admin note: text overlap with arXiv:2305.01321

Published
2024
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120. Toward Reliable Ad-hoc Scientific Information Extraction: A Case Study on Two Materials Datasets

Author

Ghosh, Satanu, Brodnik, Neal R., Frey, Carolina, Holgate, Collin, Pollock, Tresa M., Daly, Samantha, and Carton, Samuel

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract

We explore the ability of GPT-4 to perform ad-hoc schema based information extraction from scientific literature. We assess specifically whether it can, with a basic prompting approach, replicate two existing material science datasets, given the manuscripts from which they were originally manually extracted. We employ materials scientists to perform a detailed manual error analysis to assess where the model struggles to faithfully extract the desired information, and draw on their insights to suggest research directions to address this broadly important task., Comment: Update on 12/11/2024: Added some relevant literature that we missed in previous version of the paper

Published
2024
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121. Revealing faint compact radio jets at redshifts above 5 with very long baseline interferometry

Author

Krezinger, Máté, Baldini, Giovanni, Giroletti, Marcello, Sbarrato, Tullia, Ghisellini, Gabriele, Giovannini, Gabriele, An, Tao, Gabányi, Krisztina É., and Frey, Sándor

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Over the past two decades, our knowledge of the high-redshift (z > 5) radio quasars has expanded, thanks to dedicated high-resolution very long baseline interferometry (VLBI) observations. Distant quasars provide unique information about the formation and evolution of the first galaxies and supermassive black holes in the Universe. Powerful relativistic jets are likely to have played an essential role in these processes. However, the sample of VLBI-observed radio quasars is still too small to allow meaningful statistical conclusions. We extend the list of the VLBI observed radio quasars to investigate how the source structure and physical parameters are related to radio loudness. We assembled a sample of 10 faint radio quasars located at 5 < z < 6 with their radio-loudness indices spanning between 0.9-76. We observed the selected targets with the European VLBI Network (EVN) at 1.7 GHz. The milliarcsecond-scale resolution of VLBI at this frequency allows us to probe the compact innermost parts of radio-emitting relativistic jets. In addition to the single-band VLBI observations, we collected single-dish and low-resolution radio interferometric data to investigate the spectral properties and variability of our sources. The detection rate of this high-redshift, low-flux-density sample is 90%, with only one target (J0306+1853) remaining undetected. The other 9 sources appear core-dominated and show a single, faint and compact radio core on this angular scale. The derived radio powers are typical of FRII radio galaxies and quasars. By extending our sample with other VLBI-detected z > 5 sources from the literature, we found that the core brightness temperatures and monochromatic radio powers tend to increase with radio loudness.

Published
2024
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122. Measurements of the branching fractions of $\Xi_{c}^{0}\to\Xi^{0}\pi^{0}$, $\Xi_{c}^{0}\to\Xi^{0}\eta$, and $\Xi_{c}^{0}\to\Xi^{0}\eta^{\prime}$ and asymmetry parameter of $\Xi_{c}^{0}\to\Xi^{0}\pi^{0}$

Author

Belle, Collaborations, Belle II, Adachi, I., Aggarwal, L., Aihara, H., Akopov, N., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheema, P., Chen, C., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Corona, L., Cui, J. X., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dort, K., Dossett, D., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Eliachevitch, M., Epifanov, D., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Gudkova, K., Haide, I., Halder, S., Han, Y., Hara, T., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Higuchi, T., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobs, W. W., Jang, E. -J., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kaleta, M., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lalwani, K., Lam, T., Lange, J. S., Laurenza, M., Leboucher, R., Lee, M. J., Lemettais, C., Leo, P., Levit, D., Lewis, P. M., Li, L. K., Li, S. X., Li, Y., Li, Y. B., Libby, J., Liptak, Z., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Mohanty, G. B., Moneta, S., Moser, H. -G., Mrvar, M., Nakamura, I., Nakao, M., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Niiyama, M., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Pakhlova, G., Pardi, S., Parham, K., Park, H., Park, J., Park, S. -H., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Reuter, L., Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schneider, S., Schnepf, M., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Solovieva, E., Song, W., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sumihama, M., Sumisawa, K., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tanaka, S., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Vossen, A., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, Z., Warburton, A., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yook, Y. M., Yoshihara, K., Yuan, C. Z., Zani, L., Zeng, F., Zhang, B., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

We present a study of $\Xi_{c}^{0}\to\Xi^{0}\pi^{0}$, $\Xi_{c}^{0}\to\Xi^{0}\eta$, and $\Xi_{c}^{0}\to\Xi^{0}\eta^{\prime}$ decays using the Belle and Belle~II data samples, which have integrated luminosities of 980~$\mathrm{fb}^{-1}$ and 426~$\mathrm{fb}^{-1}$, respectively. We measure the following relative branching fractions $${\cal B}(\Xi_{c}^{0}\to\Xi^{0}\pi^{0})/{\cal B}(\Xi_{c}^{0}\to\Xi^{-}\pi^{+}) = 0.48 \pm 0.02 ({\rm stat}) \pm 0.03 ({\rm syst}) ,$$ $${\cal B}(\Xi_{c}^{0}\to\Xi^{0}\eta)/{\cal B}(\Xi_{c}^{0}\to\Xi^{-}\pi^{+}) = 0.11 \pm 0.01 ({\rm stat}) \pm 0.01 ({\rm syst}) ,$$ $${\cal B}(\Xi_{c}^{0}\to\Xi^{0}\eta^{\prime})/{\cal B}(\Xi_{c}^{0}\to\Xi^{-}\pi^{+}) = 0.08 \pm 0.02 ({\rm stat}) \pm 0.01 ({\rm syst}) $$ for the first time, where the uncertainties are statistical ($\rm stat$) and systematic ($\rm syst$). By multiplying by the branching fraction of the normalization mode, ${\mathcal B}(\Xi_{c}^{0}\to\Xi^{-}\pi^{+})$, we obtain the following absolute branching fraction results $(6.9 \pm 0.3 ({\rm stat}) \pm 0.5 ({\rm syst}) \pm 1.3 ({\rm norm})) \times 10^{-3}$, $(1.6 \pm 0.2 ({\rm stat}) \pm 0.2 ({\rm syst}) \pm 0.3 ({\rm norm})) \times 10^{-3}$, and $(1.2 \pm 0.3 ({\rm stat}) \pm 0.1 ({\rm syst}) \pm 0.2 ({\rm norm})) \times 10^{-3}$, for $\Xi_{c}^{0}$ decays to $\Xi^{0}\pi^{0}$, $\Xi^{0}\eta$, and $\Xi^{0}\eta^{\prime}$ final states, respectively. The third errors are from the uncertainty on ${\mathcal B}(\Xi_{c}^{0}\to\Xi^{-}\pi^{+})$. The asymmetry parameter for $\Xi_{c}^{0}\to\Xi^{0}\pi^{0}$ is measured to be $\alpha(\Xi_{c}^{0}\to\Xi^{0}\pi^{0}) = -0.90\pm0.15({\rm stat})\pm0.23({\rm syst})$., Comment: 23 pages, 5 figures, accepted for publication by JHEP

Published
2024
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123. Random carbon tax policy and investment into emission abatement technologies

Author

Colaneri, Katia, Frey, Rüdiger, and Köck, Verena

Subjects
Mathematics - Optimization and Control
Abstract

We study the problem of a profit maximizing electricity producer who has to pay carbon taxes and who decides on investments into technologies for the abatement of carbon emissions in an environment where carbon tax policy is random and where the investment in the abatement technology is divisible, irreversible and subject to transaction costs. We consider two approaches for modelling the randomness in taxes. First we assume a precise probabilistic model for the tax process, namely a pure jump Markov process (so-called tax risk); this leads to a stochastic control problem for the investment strategy. Second, we analyze the case of an {uncertainty-averse} producer who uses a differential game to decide on optimal production and investment. We carry out a rigorous mathematical analysis of the producer's optimization problem and of the associated nonlinear PDEs in both cases. Numerical methods are used to study quantitative properties of the optimal investment strategy. We find that in the tax risk case the investment in abatement technologies is typically lower than in a benchmark scenario with deterministic taxes. However, there are a couple of interesting new twists related to production technology, divisibility of the investment, tax rebates and investor expectations. In the stochastic differential game on the other hand an increase in uncertainty might stipulate more investment.

Published
2024

124. Search for the decay $B^{0}\to\gamma\gamma$ using Belle and Belle II data

Author

Belle, Collaborations, Belle II, Adachi, I., Aggarwal, L., Aihara, H., Akopov, N., Aloisio, A., Said, S. Al, Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Belous, K., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasxoli, J., Chang, M. -C., Chang, P., Cheema, P., Chen, C., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Corona, L., Cui, J. X., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dorner, D., Dort, K., Dossett, D., Dreyer, S., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Eliachevitch, M., Epifanov, D., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Ganiev, E., Garcia-Hernandez, M., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Gudkova, K., Haide, I., Han, Y., Hara, T., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, J., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lalwani, K., Lam, T., Lange, J. S., Laurenza, M., Leboucher, R., Lee, M. J., Lemettais, C., Leo, P., Levit, D., Li, L. K., Li, S. X., Li, Y., Li, Y. B., Libby, J., Liptak, Z., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., Matvienko, D., Maurya, S. K., McKenna, J. A., Meier, F., Merola, M., Metzner, F., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Nakamura, I., Nakao, M., Nakazawa, Y., Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Niiyama, M., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Pakhlova, G., Pardi, S., Parham, K., Park, H., Park, J., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Prudiiev, I., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Reuter, L., Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schneider, S., Schnell, G., Schnepf, M., Schoenning, K., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sue, Y., Sumihama, M., Suwonjandee, N., Svidras, H., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Vossen, A., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanuki, S., Wessel, C., Won, E., Xie, Y., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yook, Y. M., Yoshihara, K., Yuan, C. Z., Yusa, Y., Zani, L., Zeng, F., Zhang, B., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We report the result of a search for the rare decay $B^{0} \to \gamma \gamma$ using a combined dataset of $753\times10^{6}$ $B\bar{B}$ pairs collected by the Belle experiment and $387\times10^{6}$ $B\bar{B}$ pairs collected by the Belle II experiment from decays of the $\rm \Upsilon(4S)$ resonance produced in $e^{+}e^{-}$ collisions. A simultaneous fit to the Belle and Belle II data sets yields $11.0^{+6.5}_{-5.5}$ signal events, corresponding to a 2.5$\sigma$ significance. We determine the branching fraction $\mathcal{B}(B^{0} \to \gamma\gamma) = (3.7^{+2.2}_{-1.8}(\rm stat)\pm0.5(\rm syst))\times10^{-8}$ and set a 90% credibility level upper limit of $\mathcal{B}(B^{0} \to \gamma\gamma) < 6.4\times10^{-8}$., Comment: Published in PRD(L)

Published
2024
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125. Measurement of the energy dependence of the $e^+e^- \to B\bar{B}$, $B\bar{B}{}^*$, and $B^*\bar{B}{}^*$ cross sections at Belle~II

Author

Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Bauer, M., Baur, A., Beaubien, A., Becherer, F., Becker, J., Behera, P. K., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., Das, S., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., de Marino, G., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dorner, D., Dort, K., Dossett, D., Dreyer, S., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Eliachevitch, M., Epifanov, D., Feichtinger, P., Ferber, T., Ferlewicz, D., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Garmash, A., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Grammatico, T., Granderath, S., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Guan, Y., Gudkova, K., Halder, S., Han, Y., Hara, K., Hara, T., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Hershenhorn, A., Higuchi, T., Hill, E. C., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Inguglia, G., Ipsita, N., Ishikawa, A., Ito, S., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kakuno, H., Kaleta, M., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Ketter, C., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Konno, T., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Kraetzschmar, T. M. G., Križan, P., Krokovny, P., Kulii, Y., Kuhr, T., Kumar, J., Kumar, M., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Leitl, P., Leo, P., Levit, D., Lewis, P. M., Li, C., Li, L. K., Li, S. X., Li, Y., Li, Y. B., Libby, J., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lozar, A., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martel, L., Martellini, C., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuda, T., Matsuoka, K., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Milesi, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Molina-Gonzalez, N., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Nisar, N. K., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Onuki, Y., Oskin, P., Otani, F., Pakhlov, P., Pakhlova, G., Paladino, A., Panta, A., Paoloni, E., Pardi, S., Parham, K., Park, H., Park, J., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Pestotnik, R., Pham, F., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Privalov, S., Purwar, H., Rad, N., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Ravindran, K., Reif, M., Reiter, S., Remnev, M., Reuter, L., Ripp-Baudot, I., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Russo, G., Sahoo, D., Sanders, D. A., Sandilya, S., Sangal, A., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schneider, S., Schnepf, M., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Smith, K., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stottler, Z. S., Stroili, R., Strube, J., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanaka, S., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Toutounji, N., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Uematsu, Y., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varner, G. S., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Zani, L., Zeng, F., Zhang, B., Zhang, Y., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhou, X. Y., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We report measurements of the $e^+e^- \to B\bar{B}$, $B\bar{B}{}^*$, and $B^*\bar{B}{}^*$ cross sections at four energies, 10653, 10701, 10746 and 10805 MeV, using data collected by the Belle~II experiment. We reconstruct one $B$ meson in a large number of hadronic final states and use its momentum to identify the production process. In the first $2-5$ MeV above $B^*\bar{B}{}^*$ threshold, the $e^+e^- \to B^*\bar{B}{}^*$ cross section increases rapidly. This may indicate the presence of a pole close to the threshold., Comment: 30 pages, 15 figures, version accepted by JHEP

Published
2024

126. Asynchronous BFT Asset Transfer: Quasi-Anonymous, Light, and Consensus-Free

Author

Albouy, Timothé, Anceaume, Emmanuelle, Frey, Davide, Gestin, Mathieu, Rauch, Arthur, Raynal, Michel, and Taïani, François

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

This paper introduces a new asynchronous Byzantine-tolerant asset transfer system (cryptocurrency) with three noteworthy properties: quasi-anonymity, lightness, and consensus-freedom. Quasi-anonymity means no information is leaked regarding the receivers and amounts of the asset transfers. Lightness means that the underlying cryptographic schemes are \textit{succinct} (\textit{i.e.}, they produce short-sized and quickly verifiable proofs) and each process only stores its own transfers while keeping communication cost as low as possible. Consensus-freedom means the system does not rely on a total order of asset transfers. The proposed algorithm is the first asset transfer system that simultaneously fulfills all these properties in the presence of asynchrony and Byzantine processes. To obtain them, the paper adopts a modular approach combining a new distributed object called ``agreement proof'' and well-known techniques such as commitments, universal accumulators, and zero-knowledge proofs.

Published
2024

127. Leveraging small language models for Text2SPARQL tasks to improve the resilience of AI assistance

Author

Brei, Felix, Frey, Johannes, and Meyer, Lars-Peter

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Information Retrieval
Abstract

In this work we will show that language models with less than one billion parameters can be used to translate natural language to SPARQL queries after fine-tuning. Using three different datasets ranging from academic to real world, we identify prerequisites that the training data must fulfill in order for the training to be successful. The goal is to empower users of semantic web technology to use AI assistance with affordable commodity hardware, making them more resilient against external factors., Comment: To appear in Proceedings of the Workshop on Linked Data-driven Resilience Research 2024 (D2R2) co-located with Extended Semantic Web Conference 2024 (ESWC 2024)

Published
2024

128. Test of light-lepton universality in $\tau$ decays with the Belle II experiment

Author

Belle II Collaboration, Adachi, I., Adamczyk, K., Aggarwal, L., Aihara, H., Akopov, N., Aloisio, A., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bianchi, F., Bierwirth, L., Bilka, T., Bilokin, S., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Browder, T. E., Budano, A., Bussino, S., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Corona, L., Cui, J. X., Das, S., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., de Marino, G., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dorner, D., Dort, K., Dossett, D., Dreyer, S., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Eliachevitch, M., Epifanov, D., Feichtinger, P., Ferber, T., Ferlewicz, D., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Grammatico, T., Granderath, S., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Guan, Y., Gudkova, K., Han, Y., Hara, T., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Inguglia, G., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Junkerkalefeld, H., Kaleta, M., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Konno, T., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Kraetzschmar, T. M. G., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, J., Kumar, M., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lalwani, K., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Lautenbach, K., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Leo, P., Lemettais, C., Levit, D., Lewis, P. M., Li, C., Li, L. K., Li, S. X., Li, Y., Li, Y. B., Libby, J., Liptak, Z., Liu, M. H., Liu, Q. Y., Liu, Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakamura, K. R., Nakao, M., Nakazawa, H., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Ninkovic, J., Nishida, S., Novosel, A., Ogawa, S., Onishchuk, Y., Ono, H., Otani, F., Pakhlov, P., Pakhlova, G., Panta, A., Pardi, S., Parham, K., Park, H., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Prudiiev, I., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Russo, G., Sanders, D. A., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schwanda, C., Schwickardi, M., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shi, X. D., Shillington, T., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanaka, S., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yusa, Y., Zani, L., Zeng, F., Zhang, B., Zhang, Y., Zhilich, V., Zhou, Q. D., Zhou, X. Y., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We present a measurement of the ratio $R_\mu = \mathcal{B}(\tau^-\to \mu^-\bar\nu_\mu\nu_\tau) / \mathcal{B}(\tau^-\to e^-\bar\nu_e\nu_\tau)$ of branching fractions $\mathcal{B}$ of the $\tau$ lepton decaying to muons or electrons using data collected with the Belle II detector at the SuperKEKB $e^+e^-$ collider. The sample has an integrated luminosity of $362\!\pm\!2\,\text{fb}^{-1}$ at a centre-of-mass energy of $10.58\,\text{GeV}$. Using an optimised event selection, a binned maximum likelihood fit is performed using the momentum spectra of the electron and muon candidates. The result, $R_\mu = 0.9675 \pm 0.0007 \pm 0.0036$, where the first uncertainty is statistical and the second is systematic, is the most precise to date. It provides a stringent test of the light-lepton universality, translating to a ratio of the couplings of the muon and electron to the $W$ boson in $\tau$ decays of $0.9974 \pm 0.0019$, in agreement with the standard model expectation of unity., Comment: 22 pages, 7 figures

Published
2024
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129. Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma

Author

Gomaa, Ahmed, Huang, Yixing, Hagag, Amr, Schmitter, Charlotte, Höfler, Daniel, Weissmann, Thomas, Breininger, Katharina, Schmidt, Manuel, Stritzelberger, Jenny, Delev, Daniel, Coras, Roland, Dörfler, Arnd, Schnell, Oliver, Frey, Benjamin, Gaipl, Udo S., Semrau, Sabine, Bert, Christoph, Fietkau, Rainer, and Putz, Florian

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Background: This research aims to improve glioblastoma survival prediction by integrating MR images, clinical and molecular-pathologic data in a transformer-based deep learning model, addressing data heterogeneity and performance generalizability. Method: We propose and evaluate a transformer-based non-linear and non-proportional survival prediction model. The model employs self-supervised learning techniques to effectively encode the high-dimensional MRI input for integration with non-imaging data using cross-attention. To demonstrate model generalizability, the model is assessed with the time-dependent concordance index (Cdt) in two training setups using three independent public test sets: UPenn-GBM, UCSF-PDGM, and RHUH-GBM, each comprising 378, 366, and 36 cases, respectively. Results: The proposed transformer model achieved promising performance for imaging as well as non-imaging data, effectively integrating both modalities for enhanced performance (UPenn-GBM test-set, imaging Cdt 0.645, multimodal Cdt 0.707) while outperforming state-of-the-art late-fusion 3D-CNN-based models. Consistent performance was observed across the three independent multicenter test sets with Cdt values of 0.707 (UPenn-GBM, internal test set), 0.672 (UCSF-PDGM, first external test set) and 0.618 (RHUH-GBM, second external test set). The model achieved significant discrimination between patients with favorable and unfavorable survival for all three datasets (logrank p 1.9\times{10}^{-8}, 9.7\times{10}^{-3}, and 1.2\times{10}^{-2}). Conclusions: The proposed transformer-based survival prediction model integrates complementary information from diverse input modalities, contributing to improved glioblastoma survival prediction compared to state-of-the-art methods. Consistent performance was observed across institutions supporting model generalizability.

Published
2024

130. Dimensionality reduction in bulk-boundary reaction-diffusion systems

Author

Burkart, Tom, Müller, Benedikt J., and Frey, Erwin

Subjects
Physics - Biological Physics
Abstract

Intracellular protein patterns regulate many vital cellular functions, such as the processing of spatiotemporal information or the control of shape deformations. To do so, pattern-forming systems can be sensitive to the cell geometry by means of coupling the protein dynamics on the cell membrane to dynamics in the cytosol. Recent studies demonstrated that modeling the cytosolic dynamics in terms of an averaged protein pool disregards possibly crucial aspects of the pattern formation, most importantly concentration gradients normal to the membrane. At the same time, the coupling of two domains (surface and volume) with different dimensions renders many standard tools for the numerical analysis of self-organizing systems inefficient. Here, we present a generic framework for projecting the cytosolic dynamics onto the lower-dimensional surface that respects the influence of cytosolic concentration gradients in static and evolving geometries. This method uses a priori physical information about the system to approximate the cytosolic dynamics by a small number of dominant characteristic concentration profiles (basis), akin to basis transformations of finite element methods. As a proof of concept, we apply our framework to a toy model for volume-dependent interrupted coarsening, evaluate the accuracy of the results for various basis choices, and discuss the optimal basis choice for biologically relevant systems. Our analysis presents an efficient yet accurate method for analysing pattern formation with surface-volume coupling in evolving geometries., Comment: 16 pages, 11 figures

Published
2024

131. Search for lepton-flavor-violating $\tau^- \to \mu^-\mu^+\mu^-$ decays at Belle II

Author

Belle II Collaboration, Adachi, I., Aggarwal, L., Aihara, H., Akopov, N., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bianchi, F., Bierwirth, L., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Chen, C., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., Das, S., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dorner, D., Dort, K., Dossett, D., Dreyer, S., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Eliachevitch, M., Feichtinger, P., Ferber, T., Ferlewicz, D., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Grammatico, T., Granderath, S., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Guan, Y., Gudkova, K., Halder, S., Han, Y., Hara, T., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Inguglia, G., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Junkerkalefeld, H., Kaleta, M., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Konno, T., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Kraetzschmar, T. M. G., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, J., Kumar, M., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lalwani, K., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Lautenbach, K., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Leo, P., Lemettais, C., Levit, D., Lewis, P. M., Li, L. K., Li, S. X., Li, Y., Li, Y. B., Libby, J., Liu, M. H., Liu, Q. Y., Liu, Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakamura, K. R., Nakao, M., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Ninkovic, J., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Otani, F., Pakhlov, P., Pakhlova, G., Pardi, S., Parham, K., Park, H., Park, J., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Prudiev, I., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Reuter, L., Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Russo, G., Sanders, D. A., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schneider, S., Schwanda, C., Schwickardi, M., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Smith, K., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanaka, S., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Tsaklidis, I., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Vossen, A., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yook, Y. M., Yoshihara, K., Yuan, C. Z., Zani, L., Zeng, F., Zhang, B., Zhang, Y., Zhilich, V., Zhou, Q. D., Zhou, X. Y., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We present the result of a search for the charged-lepton-flavor violating decay $\tau^- \to \mu^-\mu^+\mu^-$ using a $424fb^{-1}$ sample of data recorded by the Belle II experiment at the SuperKEKB $e^{-}e^{+}$ collider. The selection of $e^{-}e^{+}\to\tau^+\tau^-$ events is based on an inclusive reconstruction of the non-signal tau decay, and on a boosted decision tree to suppress background. We observe one signal candidate, which is compatible with the expectation from background processes. We set a $90\%$ confidence level upper limit of $1.9 \times 10^{-8}$ on the branching fraction of the \taumu decay, which is the most stringent bound to date.

Published
2024
Full Text
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132. Coat stiffening can explain invagination of clathrin-coated membranes

Author

Frey, Felix and Schwarz, Ulrich S.

Subjects
Quantitative Biology - Subcellular Processes, Condensed Matter - Soft Condensed Matter
Abstract

Clathrin-mediated endocytosis is the main pathway used by eukaryotic cells to take up extracellular material, but the dominant physical mechanisms driving this process are still elusive. Recently several high-resolution imaging techniques have been used on different cell lines to measure the geometrical properties of clathrin-coated pits over their whole lifetime. Here we first show that the combination of all datasets with the recently introduced cooperative curvature model defines a consensus pathway, which is characterized by a flat to-curved transition at finite area, followed by linear growth and subsequent saturation of curvature. We then apply an energetic model for the composite of plasma membrane and clathrin coat to this consensus pathway to show that the dominant mechanism for invagination could be coat stiffening, which might originate from cooperative interactions between the different clathrin molecules and progressively drives the system towards its intrinsic curvature. Our theory predicts that two length scales determine the invagination pathway, namely the patch size at which the flat-to-curved transition occurs and the final pit radius., Comment: revtex, 13 pages, 5 figures in PDF-format

Published
2024

133. A Massively Parallel Performance Portable Free-space Spectral Poisson Solver

Author

Mayani, Sonali, Montanaro, Veronica, Cerfon, Antoine, Frey, Matthias, Muralikrishnan, Sriramkrishnan, and Adelmann, Andreas

Subjects
Physics - Computational Physics, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Vico et al. (2016) suggest a fast algorithm for computing volume potentials, beneficial to fields with problems requiring the solution of Poisson's equation with free-space boundary conditions, such as the beam and plasma physics communities. Currently, the standard method for solving the free-space Poisson equation is the algorithm of Hockney and Eastwood (1988), which is second order in convergence at best. The algorithm proposed by Vico et al. converges spectrally for sufficiently smooth functions i.e. faster than any fixed order in the number of grid points. In this paper, we implement a performance portable version of the traditional Hockney-Eastwood and the novel Vico-Greengard Poisson solver as part of the IPPL (Independent Parallel Particle Layer) library. For sufficiently smooth source functions, the Vico-Greengard algorithm achieves higher accuracy than the Hockney-Eastwood method with the same grid size, reducing the computational demands of high resolution simulations since one could use coarser grids to achieve them. More concretely, to get a relative error of $10^{-4}$ between the numerical and analytical solution, one requires only $16^3$ grid points in the former, but $128^3$ in the latter, more than a 99% memory footprint reduction. Additionally, we propose an algorithmic improvement to the Vico-Greengard method which further reduces its memory footprint. This is particularly important for GPUs which have limited memory resources, and should be taken into account when selecting numerical algorithms for performance portable codes. Finally, we showcase performance through GPU and CPU scaling studies on the Perlmutter (NERSC) supercomputer, with efficiencies staying above 50% in the strong scaling case., Comment: 18 pages, 11 figures

Published
2024

134. Dataset of Quotation Attribution in German News Articles

Author

Petersen-Frey, Fynn and Biemann, Chris

Subjects
Computer Science - Computation and Language
Abstract

Extracting who says what to whom is a crucial part in analyzing human communication in today's abundance of data such as online news articles. Yet, the lack of annotated data for this task in German news articles severely limits the quality and usability of possible systems. To remedy this, we present a new, freely available, creative-commons-licensed dataset for quotation attribution in German news articles based on WIKINEWS. The dataset provides curated, high-quality annotations across 1000 documents (250,000 tokens) in a fine-grained annotation schema enabling various downstream uses for the dataset. The annotations not only specify who said what but also how, in which context, to whom and define the type of quotation. We specify our annotation schema, describe the creation of the dataset and provide a quantitative analysis. Further, we describe suitable evaluation metrics, apply two existing systems for quotation attribution, discuss their results to evaluate the utility of our dataset and outline use cases of our dataset in downstream tasks., Comment: To be published at LREC-COLING 2024

Published
2024

135. Autonomous Forest Inventory with Legged Robots: System Design and Field Deployment

Author

Mattamala, Matías, Chebrolu, Nived, Casseau, Benoit, Freißmuth, Leonard, Frey, Jonas, Tuna, Turcan, Hutter, Marco, and Fallon, Maurice

Subjects
Computer Science - Robotics
Abstract

We present a solution for autonomous forest inventory with a legged robotic platform. Compared to their wheeled and aerial counterparts, legged platforms offer an attractive balance of endurance and low soil impact for forest applications. In this paper, we present the complete system architecture of our forest inventory solution which includes state estimation, navigation, mission planning, and real-time tree segmentation and trait estimation. We present preliminary results for three campaigns in forests in Finland and the UK and summarize the main outcomes, lessons, and challenges. Our UK experiment at the Forest of Dean with the ANYmal D legged platform, achieved an autonomous survey of a 0.96 hectare plot in 20 min, identifying over 100 trees with typical DBH accuracy of 2 cm., Comment: Accepted to the IEEE ICRA Workshop on Field Robotics 2024

Published
2024

136. Squeezing the quantum noise of a gravitational-wave detector below the standard quantum limit

Author

Jia, Wenxuan, Xu, Victoria, Kuns, Kevin, Nakano, Masayuki, Barsotti, Lisa, Evans, Matthew, Mavalvala, Nergis, Abbott, Rich, Abouelfettouh, Ibrahim, Adhikari, Rana, Ananyeva, Alena, Appert, Stephen, Arai, Koji, Aritomi, Naoki, Aston, Stuart, Ball, Matthew, Ballmer, Stefan, Barker, David, Berger, Beverly, Betzwieser, Joseph, Bhattacharjee, Dripta, Billingsley, Garilynn, Bode, Nina, Bonilla, Edgard, Bossilkov, Vladimir, Branch, Adam, Brooks, Aidan, Brown, Daniel, Bryant, John, Cahillane, Craig, Cao, Huy-tuong, Capote, Elenna, Chen, Yanbei, Clara, Filiberto, Collins, Josh, Compton, Camilla, Cottingham, Robert, Coyne, Dennis, Crouch, Ryan, Csizmazia, Janos, Cullen, Torrey, Dartez, Louis, Demos, Nicholas, Dohmen, Ezekiel, Driggers, Jenne, Dwyer, Sheila, Effler, Anamaria, Ejlli, Aldo, Etzel, Todd, Feicht, Jon, Frey, Raymond, Frischhertz, William, Fritschel, Peter, Frolov, Valery, Fulda, Paul, Fyffe, Michael, Ganapathy, Dhruva, Gateley, Bubba, Giaime, Joe, Giardina, Dwayne, Glanzer, Jane, Goetz, Evan, Jones, Aaron, Gras, Slawomir, Gray, Corey, Griffith, Don, Grote, Hartmut, Guidry, Tyler, Hall, Evan, Hanks, Jonathan, Hanson, Joe, Heintze, Matthew, Helmling-cornell, Adrian, Huang, Hsiang-yu, Inoue, Yuki, James, Alasdair, Jennings, Austin, Karat, Srinath, Kasprzack, Marie, Kawabe, Keita, Kijbunchoo, Nutsinee, Kissel, Jeffrey, Kontos, Antonios, Kumar, Rahul, Landry, Michael, Lantz, Brian, Laxen, Michael, Lee, Kyung-ha, Lesovsky, Madeline, Llamas, Francisco, Lormand, Marc, Loughlin, Hudsonalexander, Macas, Ronaldas, Macinnis, Myron, Makarem, Camille, Mannix, Benjaminrobert, Mansell, Georgia, Martin, Rodica, Maxwell, Nyath, Mccarrol, Garrett, Mccarthy, Richard, Mcclelland, David, Mccormick, Scott, Mcculler, Lee, Mcrae, Terry, Mera, Fernando, Merilh, Edmond, Meylahn, Fabian, Mittleman, Richard, Moraru, Dan, Moreno, Gerardo, Mould, Matthew, Mullavey, Adam, Nelson, Timothy, Neunzert, Ansel, Oberling, Jason, Ohanlon, Timothy, Osthelder, Charles, Ottaway, David, Overmier, Harry, Parker, William, Pele, Arnaud, Pham, Huyen, Pirello, Marc, Quetschke, Volker, Ramirez, Karla, Reyes, Jonathan, Richardson, Jonathan, Robinson, Mitchell, Rollins, Jameson, Romie, Janeen, Ross, Michael, Sadecki, Travis, Sanchez, Anthony, Sanchez, Eduardo, Sanchez, Luis, Savage, Richard, Schaetzl, Dean, Schiworski, Mitchell, Schnabel, Roman, Schofield, Robert, Schwartz, Eyal, Sellers, Danny, Shaffer, Thomas, Short, Ryan, Sigg, Daniel, Slagmolen, Bram, Soni, Siddharth, Sun, Ling, Tanner, David, Thomas, Michael, Thomas, Patrick, Thorne, Keith, Torrie, Calum, Traylor, Gary, Vajente, Gabriele, Vanosky, Jordan, Vecchio, Alberto, Veitch, Peter, Vibhute, Ajay, Vonreis, Erik, Warner, Jim, Weaver, Betsy, Weiss, Rainer, Whittle, Chris, Willke, Benno, Wipf, Christopher, Yamamoto, Hiro, Yu, Haocun, Zhang, Liyuan, and Zucker, Michael

Subjects
General Relativity and Quantum Cosmology, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Instrumentation and Detectors, Quantum Physics
Abstract

Precision measurements of space and time, like those made by the detectors of the Laser Interferometer Gravitational-wave Observatory (LIGO), are often confronted with fundamental limitations imposed by quantum mechanics. The Heisenberg uncertainty principle dictates that the position and momentum of an object cannot both be precisely measured, giving rise to an apparent limitation called the Standard Quantum Limit (SQL). Reducing quantum noise below the SQL in gravitational-wave detectors, where photons are used to continuously measure the positions of freely falling mirrors, has been an active area of research for decades. Here we show how the LIGO A+ upgrade reduced the detectors' quantum noise below the SQL by up to 3 dB while achieving a broadband sensitivity improvement, more than two decades after this possibility was first presented.

Published
2024
Full Text
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137. Determination of the CKM angle $\phi_{3}$ from a combination of Belle and Belle II results

Author

Belle, Collaborations, Belle II, Adachi, I., Aggarwal, L., Aihara, H., Akopov, N., Aloisio, A., Said, S. Al, Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Belous, K., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Bilokin, S., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Choi, S. -K., Choi, Y., Choudhury, S., Corona, L., Das, S., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., de Marino, G., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Dong, T. V., Dorigo, M., Dort, K., Dossett, D., Dreyer, S., Dubey, S., Dujany, G., Ecker, P., Eliachevitch, M., Epifanov, D., Feichtinger, P., Ferber, T., Ferlewicz, D., Fillinger, T., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Grammatico, T., Granderath, S., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Guan, Y., Gudkova, K., Halder, S., Han, Y., Hara, T., Hayashii, H., Hazra, S., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Junkerkalefeld, H., Kalita, D., Kaliyar, A. B., Kandra, J., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Kraetzschmar, T. M. G., Križan, P., Krokovny, P., Kuhr, T., Kumar, J., Kumar, M., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Lee, M. J., Levit, D., Lewis, P. M., Li, C., Li, L. K., Li, Y., Li, Y. B., Libby, J., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martel, L., Martellini, C., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Miller, C., Mirra, M., Miyabayashi, K., Miyake, H., Mohanty, G. B., Molina-Gonzalez, N., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakamura, K. R., Nakao, M., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Oskin, P., Otani, F., Pakhlov, P., Pakhlova, G., Panta, A., Pardi, S., Parham, K., Park, H., Park, S. -H., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Russo, G., Sanders, D. A., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schnell, G., Schwanda, C., Schwickardi, M., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Uematsu, Y., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Zhang, B., Zhang, Y., Zhilich, V., Zhou, Q. D., and Zhukova, V. I.

Subjects
High Energy Physics - Experiment
Abstract

We report a determination of the CKM angle $\phi_{3}$, also known as $\gamma$, from a combination of measurements using samples of up to 711~fb$^{-1}$ from the Belle experiment and up to 362~fb$^{-1}$ from the Belle II experiment. We combine results from analyses of $B^+\to DK^+, B^+\to D\pi^+$, and $B^+ \to D^{*}K^+$ decays, where $D$ is an admixture of $D^0$ and $\overline{D}{}^{0}$ mesons, in a likelihood fit to obtain $\phi_{3} = (78.6^{+7.2}_{-7.3})^{\circ}$. We also briefly discuss the interpretation of this result., Comment: 31 pages, 4 figures

Published
2024

138. Learning with 3D rotations, a hitchhiker's guide to SO(3)

Author

Geist, A. René, Frey, Jonas, Zobro, Mikel, Levina, Anna, and Martius, Georg

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract

Many settings in machine learning require the selection of a rotation representation. However, choosing a suitable representation from the many available options is challenging. This paper acts as a survey and guide through rotation representations. We walk through their properties that harm or benefit deep learning with gradient-based optimization. By consolidating insights from rotation-based learning, we provide a comprehensive overview of learning functions with rotation representations. We provide guidance on selecting representations based on whether rotations are in the model's input or output and whether the data primarily comprises small angles., Comment: Published at ICML 2024

Published
2024

139. Measurement of the branching fraction of the decay $B^- \to D^0 \rho(770)^-$ at Belle II

Author

Belle II Collaboration, Adachi, I., Aggarwal, L., Aihara, H., Akopov, N., Aloisio, A., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bianchi, F., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Corona, L., Cui, J. X., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., de Marino, G., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dorner, D., Dort, K., Dossett, D., Dreyer, S., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Eliachevitch, M., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Granderath, S., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Gudkova, K., Halder, S., Han, Y., Hara, T., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Jia, S., Jin, Y., Junkerkalefeld, H., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Konno, T., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Kraetzschmar, T. M. G., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, J., Kumar, M., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lalwani, K., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Leo, P., Lemettais, C., Levit, D., Lewis, P. M., Li, L. K., Li, S. X., Li, Y., Li, Y. B., Libby, J., Liptak, Z., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., Matvienko, D., Maurya, S. K., Mawas, F., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Miller, C., Mirra, M., Mitra, S., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Neu, M., Niiyama, M., Nishida, S., Novosel, A., Ogawa, S., Onishchuk, Y., Ono, H., Pakhlova, G., Pardi, S., Parham, K., Park, H., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schnepf, M., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shen, C. P., Shi, X. D., Shillington, T., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Tsaklidis, I., Ueda, I., Unger, K., Unno, Y., Uno, K., Uno, S., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Werbycka, O., Wessel, C., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yusa, Y., Zani, L., Zeng, F., Zhang, B., Zhang, Y., Zhilich, V., Zhou, Q. D., Zhou, X. Y., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We measure the branching fraction of the decay $B^- \to D^0 \rho(770)^-$ using data collected with the Belle II detector. The data contain 387 million $B\overline{B}$ pairs produced in $e^+e^-$ collisions at the $\Upsilon(4S)$ resonance. We reconstruct $8360\pm 180$ decays from an analysis of the distributions of the $B^-$ energy and the $\rho(770)^-$ helicity angle. We determine the branching fraction to be $(0.939 \pm 0.021\mathrm{(stat)} \pm 0.050\mathrm{(syst)})\%$, in agreement with previous results. Our measurement improves the relative precision of the world average by more than a factor of two.

Published
2024
Full Text
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140. Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision

Author

Mattamala, Matías, Frey, Jonas, Libera, Piotr, Chebrolu, Nived, Martius, Georg, Cadena, Cesar, Hutter, Marco, and Fallon, Maurice

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we present Wild Visual Navigation (WVN), an online self-supervised learning system for visual traversability estimation. The system is able to continuously adapt from a short human demonstration in the field, only using onboard sensing and computing. One of the key ideas to achieve this is the use of high-dimensional features from pre-trained self-supervised models, which implicitly encode semantic information that massively simplifies the learning task. Further, the development of an online scheme for supervision generator enables concurrent training and inference of the learned model in the wild. We demonstrate our approach through diverse real-world deployments in forests, parks, and grasslands. Our system is able to bootstrap the traversable terrain segmentation in less than 5 min of in-field training time, enabling the robot to navigate in complex, previously unseen outdoor terrains. Code: https://bit.ly/498b0CV - Project page:https://bit.ly/3M6nMHH, Comment: Extended version of arXiv:2305.08510

Published
2024

141. Observation of Gravitational Waves from the Coalescence of a $2.5\text{-}4.5~M_\odot$ Compact Object and a Neutron Star

Author

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akçay, S., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Azrad, D., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentara, I., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Char, P., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chattopadhyay, D., Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Dominguez, D., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Eleveld, R. M., Emma, M., Endo, K., Engl, A. J., Enloe, E., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Felicetti, R., Fenyvesi, E., Ferguson, D. L., Ferraiuolo, S., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fujimori, T., Fulda, P., Fyffe, M., Gadre, B., Gair, J. R., Galaudage, S., Galdi, V., Gallagher, H., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Garaventa, B., García-Bellido, J., Núñez, C. García, García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., Gennari, V., George, J., George, R., Gerberding, O., Gergely, L., Ghonge, S., Ghosh, Archisman, Ghosh, Sayantan, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., Gong, Y., González, G., Goodarzi, P., Goode, S., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Govorkova, K., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, A., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurs, J., Gutierrez, N., Guzman, F., H, H. -Y., Haba, D., Haberland, M., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Hardison, A. R., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Hart, J., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Hayes, R., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Henderson-Sapir, O., Hendry, M., Heng, I. S., Hennes, E., Henshaw, C., Hertog, T., Heurs, M., Hewitt, A. L., Heyns, J., Higginbotham, S., Hild, S., Hill, S., Himemoto, Y., Hirata, N., Hirose, C., Hoang, S., Hochheim, S., Hofman, D., Holland, N. A., Holley-Bockelmann, K., Holmes, Z. J., Holz, D. E., Honet, L., Hong, C., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hrishikesh, C. A., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huxford, R., Huynh-Dinh, T., Iampieri, L., Iandolo, G. A., Ianni, M., Iess, A., Imafuku, H., Inayoshi, K., Inoue, Y., Iorio, G., Iqbal, M. H., Irwin, J., Ishikawa, R., Isi, M., Ismail, M. A., Itoh, Y., Iwanaga, H., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacquet, C., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., James, P. A., Jamshidi, R., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jaume, R., Javed, W., Jennings, A., Jia, W., Jiang, J., Kubisz, J., Johanson, C., Johns, G. R., Johnson, N. A., Johnson-McDaniel, N. K., Johnston, M. C., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Jose, S., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kaku, I., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, T., Katsavounidis, E., Katzman, W., Kaushik, R., Kawabe, K., Kawamoto, R., Kazemi, A., Kedia, A., Keitel, D., Kelley-Derzon, J., Kennington, J., Kesharwani, R., Key, J. S., Khadela, R., Khadka, S., Khalili, F. Y., Khan, F., Khan, I., Khanam, T., Khursheed, M., Khusid, N. M., Kiendrebeogo, W., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, M. H., Kim, S., Kim, Y. -M., Kimball, C., Kinley-Hanlon, M., Kinnear, M., Kissel, J. S., Klimenko, S., Knee, A. 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Subjects
Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract

We report the observation of a coalescing compact binary with component masses $2.5\text{-}4.5~M_\odot$ and $1.2\text{-}2.0~M_\odot$ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO-Virgo-KAGRA detector network on 2023 May 29 by the LIGO Livingston Observatory. The primary component of the source has a mass less than $5~M_\odot$ at 99% credibility. We cannot definitively determine from gravitational-wave data alone whether either component of the source is a neutron star or a black hole. However, given existing estimates of the maximum neutron star mass, we find the most probable interpretation of the source to be the coalescence of a neutron star with a black hole that has a mass between the most massive neutron stars and the least massive black holes observed in the Galaxy. We provisionally estimate a merger rate density of $55^{+127}_{-47}~\text{Gpc}^{-3}\,\text{yr}^{-1}$ for compact binary coalescences with properties similar to the source of GW230529_181500; assuming that the source is a neutron star-black hole merger, GW230529_181500-like sources constitute about 60% of the total merger rate inferred for neutron star-black hole coalescences. The discovery of this system implies an increase in the expected rate of neutron star-black hole mergers with electromagnetic counterparts and provides further evidence for compact objects existing within the purported lower mass gap., Comment: 45 pages (10 pages author list, 13 pages main text, 1 page acknowledgements, 13 pages appendices, 8 pages bibliography), 17 figures, 16 tables. Update to match version published in The Astrophysical Journal Letters. Data products available from https://zenodo.org/records/10845779

Published
2024
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142. NLP4Gov: A Comprehensive Library for Computational Policy Analysis

Author

Chakraborti, Mahasweta, Bonagiri, Sailendra Akash, Virgüez-Ruiz, Santiago, and Frey, Seth

Subjects
Computer Science - Human-Computer Interaction
Abstract

Formal rules and policies are fundamental in formally specifying a social system: its operation, boundaries, processes, and even ontology. Recent scholarship has highlighted the role of formal policy in collective knowledge creation, game communities, the production of digital public goods, and national social media governance. Researchers have shown interest in how online communities convene tenable self-governance mechanisms to regulate member activities and distribute rights and privileges by designating responsibilities, roles, and hierarchies. We present NLP4Gov, an interactive kit to train and aid scholars and practitioners alike in computational policy analysis. The library explores and integrates methods and capabilities from computational linguistics and NLP to generate semantic and symbolic representations of community policies from text records. Versatile, documented, and accessible, NLP4Gov provides granular and comparative views into institutional structures and interactions, along with other information extraction capabilities for downstream analysis.

Published
2024
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143. Buprenorphine-Naloxone for Opioid Use Disorder: Reduction in Mortality and Increased Remission

Author

Paul, Krishna K., Frey, Christian G., Troung, Stanley, Paglicawan, Laura vita Q., Cunningham, Kathryn A., Hill, T. Preston, Bothwell, Lauren G., Golovko, Georgiy, Pillay, Yeoshina, and Jehle, Dietrich

Subjects
opioid use disorder, Suboxone, Opioid agonist, buprenorphine
Abstract

Introduction: As fentanyl has become more readily available, opioid-related morbidity and mortality in the United States has increased dramatically. Preliminary studies suggest that high-affinity, partial mu-opioid receptor agonists such as the combination product buprenorphine-naloxone may reduce mortality from overdose and promote remission. With the escalating prevalence of opioid use disorder (OUD), it is essential to evaluate the effectiveness of opioid agonists like buprenorphine-naloxone. This study examines mortality and remission rates for OUD patients prescribed buprenorphine-naloxone to determine the efficacy of this treatment toward these outcomes.Methods: We carried out a retrospective analysis using the US Collaborative Network database in TriNetX, examining de-identified medical records from nearly 92 million patients across 56 healthcare organizations. The study spanned the years from January 1, 2017–May 13, 2022. Cohort 1 included OUD patients who began buprenorphine-naloxone treatment within one-year post-diagnosis, while Cohort 2, the control group, consisted of OUD patients who were not administered buprenorphine. The study measured mortality and remission rates within a year of the index event, incorporating propensity score matching for age, gender, and race/ethnicity.Results: Prior to propensity matching, we identified a total of 221,967 patients with OUD. Following exclusions, 61,656 patients treated with buprenorphine-naloxone showed 34% fewer deaths within one year of diagnosis compared to 159,061 patients who did not receive buprenorphine (2.6% vs 4.0%; relative risk [RR] 0.661; 95% confidence interval [CI] 0.627–0.698; P < 0.001). The remission rate was approximately 1.9 times higher in the buprenorphine-naloxone group compared to the control group (18.8% vs 10.1%; RR 1.862; 95% CI 1.812–1.914; P < 0.001). After propensity matching, the effect on mortality decreased but remained statistically significant (2.6% vs 3.0%; RR 0.868; 95% CI 0.813–0.927; P < 0.001) and the remission rate remained consistent (18.8% vs 10.4%; RR 1.812; 95% CI 1.750–1.876; P < 0.001). Number needed to treat for benefit was 249 for death and 12 for remission.Conclusion: Buprenorphine-naloxone was associated with significantly reduced mortality and increased remission rates for patients with opioid use disorder and should be used as a primary treatment. The recognition and implementation of treatment options like buprenorphine-naloxone is vital in alleviating the impact of OUD.

Published
2024

144. Blinatumomab for MRD-Negative Acute Lymphoblastic Leukemia in Adults.

Author

Litzow, Mark, Sun, Zhuoxin, Mattison, Ryan, Paietta, Elisabeth, Roberts, Kathryn, Zhang, Yanming, Racevskis, Janis, Lazarus, Hillard, Rowe, Jacob, Arber, Daniel, Wieduwilt, Matthew, Liedtke, Michaela, Bergeron, Julie, Wood, Brent, Zhao, Yaqi, Wu, Gang, Chang, Ti-Cheng, Zhang, Wenchao, Pratz, Keith, Dinner, Shira, Frey, Noelle, Gore, Steven, Bhatnagar, Bhavana, Atallah, Ehab, Uy, Geoffrey, Jeyakumar, Deepa, Lin, Tara, Willman, Cheryl, DeAngelo, Daniel, Patel, Shejal, Elliott, Michelle, Advani, Anjali, Tzachanis, Dimitrios, Vachhani, Pankit, Bhave, Rupali, Sharon, Elad, Little, Richard, Erba, Harry, Stone, Richard, Luger, Selina, Mullighan, Charles, and Tallman, Martin

Subjects
Adult, Aged, Female, Humans, Male, Middle Aged, Antibodies, Bispecific, Antineoplastic Agents, Antineoplastic Combined Chemotherapy Protocols, Consolidation Chemotherapy, Disease-Free Survival, Induction Chemotherapy, Kaplan-Meier Estimate, Neoplasm, Residual, Precursor B-Cell Lymphoblastic Leukemia-Lymphoma, Recurrence, Remission Induction, Survival Analysis
Abstract

BACKGROUND: Many older adults with B-cell precursor acute lymphoblastic leukemia (BCP-ALL) have a relapse despite having a measurable residual disease (MRD)-negative complete remission with combination chemotherapy. The addition of blinatumomab, a bispecific T-cell engager molecule that is approved for the treatment of relapsed, refractory, and MRD-positive BCP-ALL, may have efficacy in patients with MRD-negative remission. METHODS: In a phase 3 trial, we randomly assigned patients 30 to 70 years of age with BCR::ABL1-negative BCP-ALL (with :: indicating fusion) who had MRD-negative remission (defined as

Published
2024

145. An Exploratory Mixed-Methods Analysis of Factors Contributing to Students' Perceptions of Inclusion in Introductory STEM Courses

Author

Alessandra M. York, Kathryn G. Miller, Michael J. Cahill, Mindy A. Bernstein, Ashton M. Barber, Hannah E. Blomgren, and Regina F. Frey

Abstract

In this exploratory mixed-methods analysis of students' perceptions of inclusion in introductory STEM courses for STEM majors, we asked students to rate inclusion in their class and to provide an open-text explanation of their rating. Analyzing 1930 qualitative responses resulted in a codebook containing academic, identity, and nonspecific categories. The majority of responses (>80%) cited academic factors such as interactions between students and instructors or course elements and policies. Most academic responses aligned with evidence-based teaching practices fostering inclusion, describing a range of strategies and policies instructors can implement to increase students' perceptions of inclusion. A small number of student responses indicated that their perception of the required knowledge background for the course impacted course inclusivity. Few differences in frequency distributions were found between subgroups examined (gender, race and ethnicity, self-reported inclusion score, and discipline). Additionally, tracking a subset of students (135) across three courses revealed that most (80%) cited different factors influencing their perception of inclusion in each course. This suggests students' perceptions of inclusive practices are complex, and most students recognize multiple factors that influence their inclusion. Overall, our findings suggest instructors can significantly influence students' perceptions of inclusion by using multiple inclusive teaching strategies and course policies.

Published
2024
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146. Video-Based Modeling Examples and Comparative Self-Explanation Prompts for Teaching a Complex Problem-Solving Strategy

Author

Julius Moritz Meier, Peter Hesse, Stephan Abele, Alexander Renkl, and Inga Glogger-Frey

Abstract

Background: In example-based learning, examples are often combined with generative activities, such as comparative self-explanations of example cases. Comparisons induce heavy demands on working memory, especially in complex domains. Hence, only stronger learners may benefit from comparative self-explanations. While static text-based examples can be compared easily, this is challenging for transient video-based modelling examples used in complex domains because simultaneous processing of two videos is not feasible. Objectives: To allow for such comparisons, we combined video-based modelling examples with static representations (i.e., summarizing tables) of the observed optimal and a suboptimal solution of the problem-solving process. A comparative self-explanation prompt asked learners to compare the different solution approaches. Our study investigated the impact of video-based modelling examples versus independent problem-solving on cognitive load and problem-solving skill development. Moreover, we investigated the effects of comparative versus sequential self-explanation prompts, depending on learners' prior knowledge. Methods: In an experiment, 118 automotive apprentices learned a car malfunction diagnosis strategy. Apprentices were divided into three groups: (1) modelling examples with comparative self-explanation prompts, (2) modelling examples with sequential prompts, and (3) no examples or prompts. Diagnostic knowledge and skills were assessed before and after the intervention. Cognitive load was measured retrospectively. Results and conclusions: Despite no observed effects on cognitive load, modelling examples enhanced diagnostic knowledge and diagnostic skills with scaffolds, though not independent diagnostic skills without scaffolds. The need for more practice opportunities to foster independent diagnostic skills is assumed. Additionally, comparative prompts seem promising for learners with higher prior knowledge. Takeaways: Video-based modelling examples were more beneficial for learning than practising to apply the diagnostic strategy. Static representations allow for comparisons of video examples and comparative prompts are promising for learners with higher prior knowledge (cf. expertise-reversal effect). Further research, especially on the effects on cognitive load, is necessary.

Published
2024
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147. Better Self-Explaining Backwards or Forwards? Prompting Self-Explanation in Video-Based Modelling Examples for Learning a Diagnostic Strategy

Author

Julius Meier, Peter Hesse, Stephan Abele, Alexander Renkl, and Inga Glogger-Frey

Abstract

Self-explanation prompts in example-based learning are usually directed backwards: Learners are required to self-explain problem-solving steps just presented ("retrospective" prompts). However, it might also help to self-explain upcoming steps ("anticipatory" prompts). The effects of the prompt type may differ for learners with various expertise levels, with anticipatory prompts being better for learners with more expertise. In an experiment, we employed extensive modelling examples and different types of self-explanations prompts to teach 78 automotive apprentices a complex and job-relevant problem-solving strategy, namely the diagnosis of car malfunctions. We tested the effects of these modelling examples and self-explanation prompts on problem-solving strategy knowledge and skill, self-efficacy, and cognitive load while learning. In two conditions, the apprentices learned with modelling examples and received either retrospective or anticipatory prompts. The third condition was a control condition receiving no modelling examples, but the respective open problems. In comparison with the control condition, modelling examples did not promote learning. However, we observed differential effects of the self-explanation prompts depending on the learner's prior knowledge level. Apprentices with higher prior knowledge learned more when learning with anticipatory prompts. Apprentices with less prior knowledge experienced a greater increase in self-efficacy and a higher germane cognitive load when learning with retrospective prompts. These findings suggest using different self-explanation prompts for learners possessing varying levels of expertise.

Published
2024
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148. Identifying the environmental drivers of corridors and predicting connectivity between seasonal ranges in multiple populations of Alpine ibex ( Capra ibex ) as tools for conserving migration

Author

Chauveau, Victor, Garel, Mathieu, Toïgo, Carole, Anderwald, Pia, Beurier, Mathieu, Bouche, Michel, Bunz, Yoann, Cagnacci, Francesca, Canut, Marie, Cavailhes, Jérôme, Champly, Ilka, Filli, Flurin, Frey-Roos, Alfred, Gressmann, Gunther, Herfindal, Ivar, Jurgeit, Florian, Martinelli, Laura, Papet, Rodolphe, Petit, Elodie, Ramanzin, Maurizio, Semenzato, Paola, Vannard, Eric, Loison, Anne, Coulon, Aurélie, and Marchand, Pascal

Published
2024

149. Development of three biofuel CRMs for the quality parameters in biodiesel and wood pellet via a joint research project: Development of three biofuel CRMs for the quality parameters in biodiesel and wood pellet via a joint research project

Author

Isleyen, Alper, Özcan, Kemal, Tunc, Murat, Boztepe, Aylin, Coşkun, Fatma Gonca, Moshammer, Kai, Shehab, Moaaz, Stratulat, Camelia, Bratu, Adriana, Hafner-Vuk, Katarina, Vogl, Jochen, Strzelec, Michał, Calvo, Mariana Villegas, Frey, Anne Mette, and Strauss, Helena

Published
2024
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