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2,771,542 results on '"Chang A."'

1. The Impact of Preschool Teachers' Dual Learning on Creativity

Author

Yuan-Cheng Chang, Yu-Fang Chen, Ya-Ting Cheng, and Kuo-Pin Chao

Abstract

This study aims to explore the impact of dual learning on preschool teachers' thriving at work and creative performance, and whether preschool teachers' thriving at work acts as a mediating factor in the relationship between dual learning and teachers' creative work. The participants were preschool teachers from Taiwan. Convenience sampling was used, resulting in a sample size of 388 people. The study results found that preschool teachers' dual learning, which includes both exploitative and exploratory learning, can improve the novelty and usefulness of their work and creativity. Furthermore, the study found that preschool teachers' thriving at work mediates the relationship between dual learning and creativity. This study also discovered that dual learning should combine and complement both exploitative and exploratory learning to achieve balance and the most significant effect. Therefore, it is essential to first help preschool teachers engage in both exploitative and exploratory learning. Schools and kindergartens can also provide relevant courses to help teachers combine these two learning methods. By integrating and complementing their advantages and disadvantages, a balance in learning can be achieved, thereby promoting teachers' creativity in their work.

Published
2024

2. Toward Robust Early Detection of Alzheimer's Disease via an Integrated Multimodal Learning Approach

Author

Chen, Yifei, Zhu, Shenghao, Fang, Zhaojie, Liu, Chang, Zou, Binfeng, Wang, Yuhe, Chang, Shuo, Jia, Fan, Qin, Feiwei, Fan, Jin, Peng, Yong, and Wang, Changmiao

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Alzheimer's Disease (AD) is a complex neurodegenerative disorder marked by memory loss, executive dysfunction, and personality changes. Early diagnosis is challenging due to subtle symptoms and varied presentations, often leading to misdiagnosis with traditional unimodal diagnostic methods due to their limited scope. This study introduces an advanced multimodal classification model that integrates clinical, cognitive, neuroimaging, and EEG data to enhance diagnostic accuracy. The model incorporates a feature tagger with a tabular data coding architecture and utilizes the TimesBlock module to capture intricate temporal patterns in Electroencephalograms (EEG) data. By employing Cross-modal Attention Aggregation module, the model effectively fuses Magnetic Resonance Imaging (MRI) spatial information with EEG temporal data, significantly improving the distinction between AD, Mild Cognitive Impairment, and Normal Cognition. Simultaneously, we have constructed the first AD classification dataset that includes three modalities: EEG, MRI, and tabular data. Our innovative approach aims to facilitate early diagnosis and intervention, potentially slowing the progression of AD. The source code and our private ADMC dataset are available at https://github.com/JustlfC03/MSTNet., Comment: 5 pages, 2 figures

Published
2024

3. Imagen 3

Author

Imagen-Team-Google, Baldridge, Jason, Bauer, Jakob, Bhutani, Mukul, Brichtova, Nicole, Bunner, Andrew, Chan, Kelvin, Chen, Yichang, Dieleman, Sander, Du, Yuqing, Eaton-Rosen, Zach, Fei, Hongliang, de Freitas, Nando, Gao, Yilin, Gladchenko, Evgeny, Colmenarejo, Sergio Gómez, Guo, Mandy, Haig, Alex, Hawkins, Will, Hu, Hexiang, Huang, Huilian, Igwe, Tobenna Peter, Kaplanis, Christos, Khodadadeh, Siavash, Kim, Yelin, Konyushkova, Ksenia, Langner, Karol, Lau, Eric, Luo, Shixin, Mokrá, Soňa, Nandwani, Henna, Onoe, Yasumasa, Oord, Aäron van den, Parekh, Zarana, Pont-Tuset, Jordi, Qi, Hang, Qian, Rui, Ramachandran, Deepak, Rane, Poorva, Rashwan, Abdullah, Razavi, Ali, Riachi, Robert, Srinivasan, Hansa, Srinivasan, Srivatsan, Strudel, Robin, Uria, Benigno, Wang, Oliver, Wang, Su, Waters, Austin, Wolff, Chris, Wright, Auriel, Xiao, Zhisheng, Xiong, Hao, Xu, Keyang, van Zee, Marc, Zhang, Junlin, Zhang, Katie, Zhou, Wenlei, Zolna, Konrad, Aboubakar, Ola, Akbulut, Canfer, Akerlund, Oscar, Albuquerque, Isabela, Anderson, Nina, Andreetto, Marco, Aroyo, Lora, Bariach, Ben, Barker, David, Ben, Sherry, Berman, Dana, Biles, Courtney, Blok, Irina, Botadra, Pankil, Brennan, Jenny, Brown, Karla, Buckley, John, Bunel, Rudy, Bursztein, Elie, Butterfield, Christina, Caine, Ben, Carpenter, Viral, Casagrande, Norman, Chang, Ming-Wei, Chang, Solomon, Chaudhuri, Shamik, Chen, Tony, Choi, John, Churbanau, Dmitry, Clement, Nathan, Cohen, Matan, Cole, Forrester, Dektiarev, Mikhail, Du, Vincent, Dutta, Praneet, Eccles, Tom, Elue, Ndidi, Feden, Ashley, Fruchter, Shlomi, Garcia, Frankie, Garg, Roopal, Ge, Weina, Ghazy, Ahmed, Gipson, Bryant, Goodman, Andrew, Górny, Dawid, Gowal, Sven, Gupta, Khyatti, Halpern, Yoni, Han, Yena, Hao, Susan, Hayes, Jamie, Hertz, Amir, Hirst, Ed, Hou, Tingbo, Howard, Heidi, Ibrahim, Mohamed, Ike-Njoku, Dirichi, Iljazi, Joana, Ionescu, Vlad, Isaac, William, Jana, Reena, Jennings, Gemma, Jenson, Donovon, Jia, Xuhui, Jones, Kerry, Ju, Xiaoen, Kajic, Ivana, Ayan, Burcu Karagol, Kelly, Jacob, Kothawade, Suraj, Kouridi, Christina, Ktena, Ira, Kumakaw, Jolanda, Kurniawan, Dana, Lagun, Dmitry, Lavitas, Lily, Lee, Jason, Li, Tao, Liang, Marco, Li-Calis, Maggie, Liu, Yuchi, Alberca, Javier Lopez, Lu, Peggy, Lum, Kristian, Ma, Yukun, Malik, Chase, Mellor, John, Mosseri, Inbar, Murray, Tom, Nematzadeh, Aida, Nicholas, Paul, Oliveira, João Gabriel, Ortiz-Jimenez, Guillermo, Paganini, Michela, Paine, Tom Le, Paiss, Roni, Parrish, Alicia, Peckham, Anne, Peswani, Vikas, Petrovski, Igor, Pfaff, Tobias, Pirozhenko, Alex, Poplin, Ryan, Prabhu, Utsav, Qi, Yuan, Rahtz, Matthew, Rashtchian, Cyrus, Rastogi, Charvi, Raul, Amit, Rebuffi, Sylvestre-Alvise, Ricco, Susanna, Riedel, Felix, Robinson, Dirk, Rohatgi, Pankaj, Rosgen, Bill, Rumbley, Sarah, Ryu, Moonkyung, Salgado, Anthony, Singla, Sahil, Schroff, Florian, Schumann, Candice, Shah, Tanmay, Shillingford, Brendan, Shivakumar, Kaushik, Shtatnov, Dennis, Singer, Zach, Sluzhaev, Evgeny, Sokolov, Valerii, Sottiaux, Thibault, Stimberg, Florian, Stone, Brad, Stutz, David, Su, Yu-Chuan, Tabellion, Eric, Tang, Shuai, Tao, David, Thomas, Kurt, Thornton, Gregory, Toor, Andeep, Udrescu, Cristian, Upadhyay, Aayush, Vasconcelos, Cristina, Vasiloff, Alex, Voynov, Andrey, Walker, Amanda, Wang, Luyu, Wang, Miaosen, Wang, Simon, Wang, Stanley, Wang, Qifei, Wang, Yuxiao, Weisz, Ágoston, Wiles, Olivia, Wu, Chenxia, Xu, Xingyu Federico, Xue, Andrew, Yang, Jianbo, Yu, Luo, Yurtoglu, Mete, Zand, Ali, Zhang, Han, Zhang, Jiageng, Zhao, Catherine, Zhaxybay, Adilet, Zhou, Miao, Zhu, Shengqi, Zhu, Zhenkai, Bloxwich, Dawn, Bordbar, Mahyar, Cobo, Luis C., Collins, Eli, Dai, Shengyang, Doshi, Tulsee, Dragan, Anca, Eck, Douglas, Hassabis, Demis, Hsiao, Sissie, Hume, Tom, Kavukcuoglu, Koray, King, Helen, Krawczyk, Jack, Li, Yeqing, Meier-Hellstern, Kathy, Orban, Andras, Pinsky, Yury, Subramanya, Amar, Vinyals, Oriol, Yu, Ting, and Zwols, Yori

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.

Published
2024

4. Electrically-Driven Two-Dimensional Semiconductor Microcavity Laser

Author

Chen, Zheng-Zhe, Lin, Hsiang-Ting, Chang, Chiao-Yun, Muhammad, Adil, Tsai, Po-Cheng, Kao, Tsung Sheng, Chen, Chi, Chang, Shu-Wei, Lin, Shih-Yen, and Shih, Min-Hsiung

Subjects
Physics - Optics
Abstract

Two-dimensional (2-D) monolayer transition-metal dichalcogenides (TMDCs) are promising materials for realizing ultracompact, low-threshold semiconductor lasers. And the development of the electrical-driven TMDC devices is crucial for enhancing the integration potential of practical optoelectronic systems. However, at current stage, the electrically-driven 2-D TMDC laser has never been realized. Herein, we have developed the first electrically-driven 2-D TMDC microcavity laser.

Published
2024

5. BI-MDRG: Bridging Image History in Multimodal Dialogue Response Generation

Author

Yoon, Hee Suk, Yoon, Eunseop, Tee, Joshua Tian Jin, Zhang, Kang, Heo, Yu-Jung, Chang, Du-Seong, and Yoo, Chang D.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Multimedia
Abstract

Multimodal Dialogue Response Generation (MDRG) is a recently proposed task where the model needs to generate responses in texts, images, or a blend of both based on the dialogue context. Due to the lack of a large-scale dataset specifically for this task and the benefits of leveraging powerful pre-trained models, previous work relies on the text modality as an intermediary step for both the image input and output of the model rather than adopting an end-to-end approach. However, this approach can overlook crucial information about the image, hindering 1) image-grounded text response and 2) consistency of objects in the image response. In this paper, we propose BI-MDRG that bridges the response generation path such that the image history information is utilized for enhanced relevance of text responses to the image content and the consistency of objects in sequential image responses. Through extensive experiments on the multimodal dialogue benchmark dataset, we show that BI-MDRG can effectively increase the quality of multimodal dialogue. Additionally, recognizing the gap in benchmark datasets for evaluating the image consistency in multimodal dialogue, we have created a curated set of 300 dialogues annotated to track object consistency across conversations., Comment: ECCV 2024

Published
2024

6. RT-Surv: Improving Mortality Prediction After Radiotherapy with Large Language Model Structuring of Large-Scale Unstructured Electronic Health Records

Author

Park, Sangjoon, Wee, Chan Woo, Choi, Seo Hee, Kim, Kyung Hwan, Chang, Jee Suk, Yoon, Hong In, Lee, Ik Jae, Kim, Yong Bae, Cho, Jaeho, Keum, Ki Chang, Lee, Chang Geol, Byun, Hwa Kyung, and Koom, Woong Sub

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

Accurate patient selection is critical in radiotherapy (RT) to prevent ineffective treatments. Traditional survival prediction models, relying on structured data, often lack precision. This study explores the potential of large language models (LLMs) to structure unstructured electronic health record (EHR) data, thereby improving survival prediction accuracy through comprehensive clinical information integration. Data from 34,276 patients treated with RT at Yonsei Cancer Center between 2013 and 2023 were analyzed, encompassing both structured and unstructured data. An open-source LLM was used to structure the unstructured EHR data via single-shot learning, with its performance compared against a domain-specific medical LLM and a smaller variant. Survival prediction models were developed using statistical, machine learning, and deep learning approaches, incorporating both structured and LLM-structured data. Clinical experts evaluated the accuracy of the LLM-structured data. The open-source LLM achieved 87.5% accuracy in structuring unstructured EHR data without additional training, significantly outperforming the domain-specific medical LLM, which reached only 35.8% accuracy. Larger LLMs were more effective, particularly in extracting clinically relevant features like general condition and disease extent, which closely correlated with patient survival. Incorporating LLM-structured clinical features into survival prediction models significantly improved accuracy, with the C-index of deep learning models increasing from 0.737 to 0.820. These models also became more interpretable by emphasizing clinically significant factors. This study shows that general-domain LLMs, even without specific medical training, can effectively structure large-scale unstructured EHR data, substantially enhancing the accuracy and interpretability of clinical predictive models., Comment: 23 pages, 2 tables, 4 figures

Published
2024

7. Bias-Aware Low-Rank Adaptation: Mitigating Catastrophic Inheritance of Large Language Models

Author

Chang, Yupeng, Chang, Yi, and Wu, Yuan

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Large language models (LLMs) have exhibited remarkable proficiency across a diverse array of natural language processing (NLP) tasks. However, adapting LLMs to downstream applications typically necessitates computationally intensive and memory-demanding fine-tuning procedures. To mitigate these burdens, parameter-efficient fine-tuning (PEFT) techniques have emerged as a promising approach to tailor LLMs with minimal computational overhead. While PEFT methods offer substantial advantages, they do not fully address the pervasive issue of bias propagation from pre-training data. In this work, we introduce Bias-Aware Low-Rank Adaptation (BA-LoRA), a novel PEFT method designed to counteract bias inheritance. BA-LoRA incorporates three distinct regularization terms: (1) consistency regularizer, (2) diversity regularizer, and (3) singular vector decomposition regularizer. These regularizers collectively aim to improve the generative models' consistency, diversity, and generalization capabilities during the fine-tuning process. Through extensive experiments on a variety of natural language understanding (NLU) and natural language generation (NLG) tasks, employing prominent LLMs such as LLaMA, Mistral, and Gemma, we demonstrate that BA-LoRA surpasses the performance of LoRA and its state-of-the-art variants. Moreover, our method effectively mitigates the deleterious effects of pre-training bias, leading to more reliable and robust model outputs. The code is available at https://github.com/cyp-jlu-ai/BA-LoRA., Comment: Work in progress

Published
2024

8. Two-dimensional Keldysh theory for non-resonant strong-field ionization of monolayer 2D materials

Author

Her, Tsing-Hua, Chang, Che-Hao, Darden, Kenan, Chang, Tsun-Hsu, and Yao, Hsin-Yu

Subjects
Physics - Optics, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

The Keldysh theory of photoionization for solids is generalized to atomically thin two-dimensional semiconductors. We derive a closed-form formula and its asymptotic forms for a two-band model with a Kane dispersion. These formulas exhibit characteristically different behaviors from their bulk counterparts which are attributed to the scaling of the 2D density of states. We validate our formulas by comparing them to recent strong-field ionization experiments in monolayer MoS2 with good agreement. Our work is expected to find a wide range of applications in intense light - 2D material interaction., Comment: 16 pages, 7 figures

Published
2024

9. Correlation between alpha-decay half-lives and symmetry energy

Author

Choi, Yong-Beom, Gil, Hana, Hyun, Chang Ho, and Lee, Chang-Hwan

Subjects
Nuclear Theory
Abstract

We study the alpha-decay half-lives of 84 <= Z <= 92 even-even nuclei in the semiclassical WKB approximation framework using the density-dependent cluster model and the density distribution described by various Korea-IBS-Daegu-SKKU (KIDS) models. Main goal of this work is to find a correlation between the alpha-decay half-lives and the stiffness of the symmtery energy. Parameters of KIDS model are determined to reproduce the nuclear data (energy and charge radii of 40Ca, 48Ca and 208Pb) and the neutron star observations including constraints by NICER, tidal deformability from GW170817, and the maximum mass limit of neutron star observations. We use the KIDS models (A-D) which have distinctive values for the parameters controlling the stiffness of the nuclear matter equation of state. We find that the alpha-decay half-lives increase systematically from KIDS-A model to KIDS-D model. This implies a correlation between the alpha-decay half-lives and the symmetry energy. We find that the correlation can be understood from the dependence of the particle distribution in the core and the surface region on the symmetry energy.

Published
2024

10. Determination of $|V_{ub}|$ from simultaneous measurements of untagged $B^0\to\pi^- \ell^+ \nu_{\ell}$ and $B^+\to\rho^0 \ell^+\nu_{\ell}$ decays

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., Bauer, M., 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., 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., 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., 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., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., 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., Granderath, S., 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., Jackson, P., Jacobs, W. W., 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., 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., 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., 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., Metzner, F., 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., Charan, A. Narimani, 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., 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., Sanders, D. A., 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., 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., Uchida, M., 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., 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., Zhou, X. Y., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We present a measurement of $|V_{ub}|$ from a simultaneous study of the charmless semileptonic decays $B^0\to\pi^- \ell^+ \nu_{\ell}$ and $B^+\to\rho^0 \ell^+\nu_{\ell}$, where $\ell = e, \mu$. This measurement uses a data sample of 387 million $B\overline{B}$ meson pairs recorded by the Belle~II detector at the SuperKEKB electron-positron collider between 2019 and 2022. The two decays are reconstructed without identifying the partner $B$ mesons. We simultaneously measure the differential branching fractions of $B^0\to\pi^- \ell^+ \nu_{\ell}$ and $B^+\to\rho^0 \ell^+\nu_{\ell}$ decays as functions of $q^2$ (momentum transfer squared). From these, we obtain total branching fractions $B(B^0\to\pi^- \ell^+ \nu_{\ell}) = (1.516 \pm 0.042 (\mathrm{stat}) \pm 0.059 (\mathrm{syst})) \times 10^{-4}$ and $B(B^+\to\rho^0 \ell^+\nu_{\ell}) = (1.625 \pm 0.079 (\mathrm{stat}) \pm 0.180 (\mathrm{syst})) \times 10^{-4}$. By fitting the measured $B^0\to\pi^- \ell^+ \nu_{\ell}$ partial branching fractions as functions of $q^2$, together with constraints on the non-perturbative hadronic contribution from lattice QCD calculations, we obtain $|V_{ub}|$ = $(3.93 \pm 0.09 \pm 0.13 \pm 0.19) \times 10^{-3}$. Here, the first uncertainty is statistical, the second is systematic, and the third is theoretical.

Published
2024

11. Measurement of $CP$ asymmetries in $B^0 \to K^0_S \pi^0 \gamma$ decays at Belle II

Author

Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., 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., Bilokin, S., Biswas, D., 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., 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., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Guan, Y., Gudkova, K., Halder, S., Han, Y., Hara, K., 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., Jackson, P., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Joo, K. K., 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., 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., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Leo, P., Levit, D., Li, C., Li, L. K., Li, S. X., Li, Y., Li, Y. B., Libby, J., Lin, Y. -R., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Luo, 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., 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., Molina-Gonzalez, N., 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., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Onuki, Y., Oskin, P., Otani, F., Pakhlov, P., Pakhlova, G., Panta, A., Pardi, S., Parham, K., Park, H., 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., Ravindran, K., 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., Sangal, A., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schwanda, C., Schwartz, A. J., 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., 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., 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., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xie, Y., Xu, X. P., Yabsley, B. D., Yamada, S., 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 report measurements of time-dependent $CP$ asymmetries in $B^0 \to K^0_S \pi^0 \gamma$ decays based on a data sample of $(388\pm6)\times10^6$ $B\bar{B}$ events collected at the $\Upsilon(4S)$ resonance with the Belle II detector. The Belle II experiment operates at the SuperKEKB asymmetric-energy $e^+e^-$ collider. We measure decay-time distributions to determine $CP$-violating parameters $S$ and $C$. We determine these parameters for two ranges of $K^0_S \pi^0$ invariant mass: $m(K^0_S \pi^0)\in (0.8, 1.0)$ $GeV/c^2$, which is dominated by $B^0 \to K^{*0} (\to K^0_S \pi^0) \gamma$ decays, and a complementary region $m(K^0_S \pi^0)\in (0.6, 0.8)\cup(1.0, 1.8)$ $GeV/c^2$. Our results have improved precision as compared to previous measurements and are consistent with theory predictions., Comment: 10 pages, 4 figures

Published
2024

12. Measurement of branching fractions, CP asymmetry, and isospin asymmetry for $\boldsymbol{B\rightarrow\rho\gamma}$ decays using Belle and Belle II data

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, 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., 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., Choudhury, S., Corona, L., 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., 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., 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., 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., Joo, K. K., Junkerkalefeld, H., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., 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., 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., Lautenbach, K., Leboucher, R., Diberder, F. R. Le, 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., Mizuk, R., 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., Schwanda, C., Schwartz, A. J., Schwickardi, M., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., 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., Sumisawa, K., Sutcliffe, W., Svidras, H., Takizawa, M., Tamponi, U., Tanaka, S., 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., Wiechczynski, J., 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., Zhang, B., Zhang, Y., Zhilich, V., Zhou, Q. D., Zhou, X. Y., and Zhukova, V. I.

Subjects
High Energy Physics - Experiment
Abstract

We present measurements of $B^{+}\rightarrow\rho^{+}\gamma$ and $B^{0}\rightarrow\rho^{0}\gamma$ decays using a combined data sample of $772 \times 10^6$ $B\overline{B}$ pairs collected by the Belle experiment and $387\times 10^6$ $B\overline{B}$ pairs collected by the Belle II experiment in $e^{+}e^{-}$ collisions at the $\Upsilon (4S)$ resonance. After an optimized selection, a simultaneous fit to the Belle and Belle II data sets yields $114\pm 12$ $B^{+}\rightarrow\rho^{+}\gamma$ and $99\pm 12$ $B^{0}\rightarrow\rho^{0}\gamma$ decays. The measured branching fractions are $(13.1^{+2.0 +1.3}_{-1.9 -1.2})\times 10^{-7}$ and $(7.5\pm 1.3^{+1.0}_{-0.8})\times 10^{-7}$ for $B^{+}\rightarrow\rho^{+}\gamma$ and $B^{0}\rightarrow\rho^{0}\gamma$ decays, respectively, where the first uncertainty is statistical and the second is systematic. We also measure the isospin asymmetry $A_{\rm I}(B\rightarrow\rho\gamma)=(10.9^{+11.2 +7.8}_{-11.7 -7.3})\%$ and the direct CP asymmetry $A_{CP}(B^{+}\rightarrow\rho^{+}\gamma)=(-8.2\pm 15.2^{+1.6}_{-1.2})\%$., Comment: 12 pages, 4 figures

Published
2024

13. Reverse Engineering the Fly Brain Using FlyCircuit Database

Author

Ching, Yu-Tai, Cho, Chin-Ping, Tang, Fu-Kai, Chang, Yi-Chiun, Cheng, Chang-Chieh, He, Guan-Wei, Chang, Ann-Shyn, and Chuang, Chaochun

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

A method to reverse engineering of a fly brain using the {\it FlyCircuit} database is presented. This method was designed based on the assumption that similar neurons could serve identical functions. We thus cluster the neurons based on the similarity between neurons. The procedures are to partition the neurons in the database into groups, and then assemble the groups into potential modules. Some of the modules correspond to known neuropils, including Medulla were obtained. The same clustering algorithm was applied to analyze Medulla's structure. Another possible application of the clustering result is to study the brain-wide neuron connectome by looking at the connectivity between groups of neurons.

Published
2024

14. A Survey of Data Synthesis Approaches

Author

Chang, Hsin-Yu, Chen, Pei-Yu, Chou, Tun-Hsiang, Kao, Chang-Sheng, Yu, Hsuan-Yun, Lin, Yen-Ting, and Chen, Yun-Nung

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

This paper provides a detailed survey of synthetic data techniques. We first discuss the expected goals of using synthetic data in data augmentation, which can be divided into four parts: 1) Improving Diversity, 2) Data Balancing, 3) Addressing Domain Shift, and 4) Resolving Edge Cases. Synthesizing data are closely related to the prevailing machine learning techniques at the time, therefore, we summarize the domain of synthetic data techniques into four categories: 1) Expert-knowledge, 2) Direct Training, 3) Pre-train then Fine-tune, and 4) Foundation Models without Fine-tuning. Next, we categorize the goals of synthetic data filtering into four types for discussion: 1) Basic Quality, 2) Label Consistency, and 3) Data Distribution. In section 5 of this paper, we also discuss the future directions of synthetic data and state three direction that we believe is important: 1) focus more on quality, 2) the evaluation of synthetic data, and 3) multi-model data augmentation.

Published
2024

15. Towards a Holistic Framework for Multimodal Large Language Models in Three-dimensional Brain CT Report Generation

Author

Li, Cheng-Yi, Chang, Kao-Jung, Yang, Cheng-Fu, Wu, Hsin-Yu, Chen, Wenting, Bansal, Hritik, Chen, Ling, Yang, Yi-Ping, Chen, Yu-Chun, Chen, Shih-Pin, Lirng, Jiing-Feng, Chang, Kai-Wei, and Chiou, Shih-Hwa

Subjects
Computer Science - Computation and Language
Abstract

Multi-modal large language models (MLLMs) have been given free rein to explore exciting medical applications with a primary focus on radiology report generation. Nevertheless, the preliminary success in 2D radiology captioning is incompetent to reflect the real-world diagnostic challenge in the volumetric 3D anatomy. To mitigate three crucial limitation aspects in the existing literature, including (1) data complexity, (2) model capacity, and (3) evaluation metric fidelity, we collected an 18,885 text-scan pairs 3D-BrainCT dataset and applied clinical visual instruction tuning (CVIT) to train BrainGPT models to generate radiology-adherent 3D brain CT reports. Statistically, our BrainGPT scored BLEU-1 = 44.35, BLEU-4 = 20.38, METEOR = 30.13, ROUGE-L = 47.6, and CIDEr-R = 211.77 during internal testing and demonstrated an accuracy of 0.91 in captioning midline shifts on the external validation CQ500 dataset. By further inspecting the captioned report, we reported that the traditional metrics appeared to measure only the surface text similarity and failed to gauge the information density of the diagnostic purpose. To close this gap, we proposed a novel Feature-Oriented Radiology Task Evaluation (FORTE) to estimate the report's clinical relevance (lesion feature and landmarks). Notably, the BrainGPT model scored an average FORTE F1-score of 0.71 (degree=0.661; landmark=0.706; feature=0.693; impression=0.779). To demonstrate that BrainGPT models possess objective readiness to generate human-like radiology reports, we conducted a Turing test that enrolled 11 physician evaluators, and around 74% of the BrainGPT-generated captions were indistinguishable from those written by humans. Our work embodies a holistic framework that showcased the first-hand experience of curating a 3D brain CT dataset, fine-tuning anatomy-sensible language models, and proposing robust radiology evaluation metrics., Comment: 6 figures, 5 supplementary figures, 8 supplementary tables

Published
2024

16. Measurement of the integrated luminosity of data samples collected during 2019-2022 by the Belle II experiment

Author

The Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Ahn, J. K., 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., 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., 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., De La Cruz-Burelo, E., De La Motte, S. A., de Marino, G., 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., Dort, K., Dossett, D., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Epifanov, D., Eppelt, J., 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., Gironella, P., 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, 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, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Humair, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kaleta, M., Kalita, D., Kandra, J., Kang, K. H., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kim, Y. J., 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., Kumar, R., Kumara, K., 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., Lee, M. J., Lemettais, C., Leo, P., Levit, D., Lewis, P. M., Li, C., Li, L. K., Li, S. X., Li, W. Z., Li, Y., Li, Y. B., Liao, Y. P., Libby, J., Lin, 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., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., 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., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Pakhlov, P., Pakhlova, G., Paoloni, E., Pardi, S., Parham, K., Park, H., Park, J., Park, K., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Pedlar, T. K., Peschke, R., Pestotnik, R., 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., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schnepf, M., Schwanda, C., Schwartz, A. J., Seino, Y., Selce, A., Senyo, K., Serrano, J., 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., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Song, W., 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., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Ueda, I., 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., 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., Yoshihara, K., Yuan, C. Z., Zani, L., Zhang, B., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

A series of data samples was collected with the Belle II detector at the SuperKEKB collider from March 2019 to June 2022. We determine the integrated luminosities of these data samples using three distinct methodologies involving Bhabha ($e^+e^- \to e^+e^-(n\gamma)$), digamma ($e^+e^- \to \gamma\gamma(n\gamma)$), and dimuon ($e^+e^- \to \mu^+ \mu^- (n\gamma)$) events. The total integrated luminosity obtained with Bhabha, digamma, and dimuon events is (426.52 $\pm$ 0.03 $\pm$ 2.48)~fb$^{-1}$, (427.32 $\pm$ 0.03 $\pm$ 2.56)~fb$^{-1}$, and (424.84 $\pm$ 0.04 $\pm$ 3.88)~fb$^{-1}$, where the first uncertainties are statistical and the second are systematic. The resulting total integrated luminosity obtained from the combination of the three methods is (426.88 $\pm$ 1.93)~fb$^{-1}$., Comment: 12 pages, 3 figures

Published
2024

17. Field Experiences in Early Intervention/Early Childhood Special Education (EI/ECSE): Preparing Teachers for Success in Diverse Early Education Settings

Author

Ragan H. Mcleod, Zhen Chai, Debra Berry Malmberg, Ya-Chih Chang, Nancy Hunt, Courtney O'Grady, Kimberly Tomeny, Jisun R. Oh, and Ankita Bhattashali

Abstract

Field experiences provide opportunities for early childhood and early childhood special education (EC/ECSE) educators to implement effective practices in learning settings, and are, therefore, a vital part of EC/ECSE teacher preparation. In this article, we describe field placement models from four universities in the United States: The Bridge Project, Getting Started Early, Peer Coaching to Increase Naturalistic Developmental Behavioral Interventions, and University Supervisors Coaching Teacher Candidates: Supporting Young Bi/Multilingual Children with Disabilities. Although there is variety in the settings and effective practices supported through these field placements, performance feedback and collaboration are clear themes across models.

Published
2024

18. The Role of Lusheng Chinese Musical Instruments in Promoting Literacy through Educational Functions and Transmission

Author

Chang Xu and Khomkrich Karin

Abstract

Lusheng is a traditional wind musical instrument cherished by various ethnic minorities in China. The objective of this study is to investigate the role of Lusheng Chinese musical instruments in promoting literacy through educational functions and transmission. Conducted in Guizhou Province, a region rich in ethnic diversity, this study employs qualitative methods and engages two key informants who are local artisans with interview and observation forms. The data analysis reveals multifaceted insights into Lusheng's transmission and promotion. Findings highlight the challenges posed by modernization to traditional craftsmanship, emphasizing the urgency of preserving Lusheng production skills. The inclusion of Lusheng in the National Intangible Cultural Heritage list and the establishment of educational programs in primary, and secondary schools, and universities underscore the government's commitment to safeguarding Lusheng culture. Moreover, the research underscores the transformative impact of modern media on Lusheng's dissemination, and cross-cultural engagement is recognized as vital for expanding its influence. Balancing tradition and innovation remains a challenge. The study suggests collaborative efforts among government, academia, and society to develop a comprehensive conservation strategy, ensuring the continued transmission and promotion of Lusheng culture.

Published
2024

19. The Impact of Creativity on Creative Performance among University Students in Higher Vocational Education in China: The Mediating Role of Autonomous Motivation

Author

Lianlian Ji and Yuan-Cheng Chang

Abstract

This study aims to explore whether the creativity of university students in Higher Vocational Education in China can enhance their creative performance through autonomous motivation. The theoretical foundation of this study is interactionist model of creativity, the study employs structural equation modeling for analysis and validation. A survey assessing creativity, creative performance, and autonomous motivation was administered to students from higher vocational schools in China, yielding a valid sample of 392. The overall model results indicate that the creativity of vocational college students has a significant positive impact on creative performance, creativity positively predicted autonomous motivation, which enhanced creative performance. The autonomous motivation partially mediates the relationship between creativity and creative performance. The findings recommend that higher vocational institutions initiate creative activities to foster students' creativity and continuously bolster their autonomous motivation, thus enhancing the creative performance of future technical and skilled professionals -- vocational college students.

Published
2024

20. The Preservation and Transmission of Gannan Tea Picking Opera Knowledge after the Cultural Revolution in 1949

Author

Junyong Chang and Weerayut Seekhunlio

Abstract

The study focuses on the preservation and transmission of Gannan Tea Picking Opera knowledge after the Cultural Revolution in 1949 using qualitative research methods such as interviews and observations with seven key informants. The following findings were obtained, which can be divided into three distinct periods: The tortuous development phase from 1949 to 1966 marked significant growth and development, with the establishment of specialized schools and the implementation of the "three reforms" contributing to knowledge transmission and preservation. The survival dilemma period from 1966 to 1976 during the Cultural Revolution imposed constraints on artistic expression, but the return of artists from rural areas and their role in recruiting apprentices supported the continuity of Gannan Tea Picking Opera. Finally, the Ganzhou Gannan Tea Picking Opera Troupe and educational institutions contributed to the revival of this traditional art form during the period of rebirth from 1976 to 2023. Retired artists continue to play a vital role in knowledge preservation and dissemination, reflecting the enduring resilience of Gannan Tea Picking Opera.

Published
2024

21. Summer Cancer Research Experience for High School Students from Historically Marginalized Populations in Kansas City

Author

Lisa M. Harlan-Williams, Marcia Pomeroy, W. Todd Moore, Karin Chang, Devin C. Koestler, Emily Nissen, John Fife, Megha Ramaswamy, Danny R. Welch, and Roy A. Jensen

Abstract

The Accelerate Cancer Education (ACE) summer research program at The University of Kansas Cancer Center (KUCC) is a six-week, cancer-focused, summer research experience for high school students from historically marginalized populations in the Kansas City metropolitan area. Cancer affects all populations and continues to be the second leading cause of death in the United States, and a large number of disparities impact racial and ethnic minorities, including increased cancer incidence and mortality. Critically, strategies to bolster diversity, equity, inclusion, and accessibility are needed to address persistent cancer disparities. The ACE program offers an educational opportunity for a population of students who otherwise would not have easy access onto a medical center campus to make connections with cancer physicians and researchers and provides a vital response to the need for a more diverse and expansive oncology workforce. Students grow their technical, social, and professional skills and develop self-efficacy and long-lasting connections that help them matriculate and persist through post-secondary education. Developed in 2018, the ACE program has trained 37 high school junior and senior students. This article describes the need for and how we successfully developed and implemented the ACE program.

Published
2024

22. Missing More than School: Reducing Chronic Absence for American Indian and Alaska Native Students

Author

Western Educational Equity Assistance Center (WEEAC) at WestEd, Attendance Works, Hedy N. Chang, and Cecelia Leong

Abstract

This brief highlights the urgent need to address chronic absenteeism among American Indian and Alaska Native (AI/AN) students in the United States. It explores the challenges that AI/AN students and their families face in terms of regular school attendance. Systemic barriers, unwelcoming?school environments, biased disciplinary practices, and a lack of culturally relevant curricula are all referenced as?major contributors to chronic absenteeism. The brief emphasizes the importance of working with parents, tribal leaders, community members, educators, policymakers, and other allies to improve AI/AN student attendance rates. Recommendations include investing in positive learning environments tailored specifically to these students' needs, implementing tiered approaches that include prevention strategies, and enacting targeted interventions aimed at removing barriers to attendance. This brief provides practical solutions,?focusing?on successful initiatives such as attendance recognition programs, inclusive graduation events that foster a sense of belonging, and collaboration with tribal governments to provide transportation or medical services.

Published
2024

23. A stress-induced source of phonon bursts and quasiparticle poisoning.

Author

Anthony-Petersen, Robin, Biekert, Andreas, Bunker, Raymond, Chang, Clarence, Chang, Yen-Yung, Chaplinsky, Luke, Fascione, Eleanor, Fink, Caleb, Garcia-Sciveres, Maurice, Germond, Richard, Guo, Wei, Hertel, Scott, Hong, Ziqing, Kurinsky, Noah, Li, Xinran, Lin, Junsong, Lisovenko, Marharyta, Mahapatra, Rupak, Mayer, Adam, McKinsey, Daniel, Mehrotra, Siddhant, Mirabolfathi, Nader, Neblosky, Brian, Page, William, Patel, Pratyush, Penning, Bjoern, Pinckney, H, Platt, Mark, Pyle, Matt, Reed, Maggie, Romani, Roger, Santana Queiroz, Hadley, Sadoulet, Bernard, Serfass, Bruno, Smith, Ryan, Sorensen, Peter, Suerfu, Burkhant, Suzuki, Aritoki, Underwood, Ryan, Velan, Vetri, Wang, Gensheng, Wang, Yue, Watkins, Samuel, Williams, Michael, Yefremenko, Volodymyr, and Zhang, Jianjie

Abstract

The performance of superconducting qubits is degraded by a poorly characterized set of energy sources breaking the Cooper pairs responsible for superconductivity, creating a condition often called quasiparticle poisoning. Both superconducting qubits and low threshold dark matter calorimeters have observed excess bursts of quasiparticles or phonons that decrease in rate with time. Here, we show that a silicon crystal glued to its holder exhibits a rate of low-energy phonon events that is more than two orders of magnitude larger than in a functionally identical crystal suspended from its holder in a low-stress state. The excess phonon event rate in the glued crystal decreases with time since cooldown, consistent with a source of phonon bursts which contributes to quasiparticle poisoning in quantum circuits and the low-energy events observed in cryogenic calorimeters. We argue that relaxation of thermally induced stress between the glue and crystal is the source of these events.

Published
2024

24. Exosomal miRNA 16-5p/29a-3p from pancreatic cancer induce adipose atrophy by inhibiting adipogenesis and promoting lipolysis.

Author

Tien, Sui-Chih, Chang, Chin-Chun, Huang, Ching-Hsuan, Peng, Hsuan-Yu, Chang, Yu-Ting, Chang, Ming-Chu, Lee, Wen-Hwa, and Hu, Chun-Mei

Subjects
Cell biology, Functional aspects of cell biology, Molecular physiology, Pathophysiology, Physiology
Abstract

Over 80% of the patients with pancreatic ductal adenocarcinoma (PDAC) have cachexia/wasting syndrome. Cachexia is associated with reduced survival, decreased quality of life, and higher metastasis rates. Here, we demonstrate that fat loss is the earliest feature of PDAC-exosome-induced cachexia. MicroRNA sequencing of exosomal components from normal and cancer-derived exosomes revealed enrichment of miR-16-5p, miR-21-5p, miR-29a-3p, and miR-125b-5p in serum exosomes of mice harboring PDAC and patients with PDAC. Further, miR-16-5p and miR-29a-3p inhibited adipogenesis through decreasing Erlin2 and Cmpk1 expression which downregulates C/EBPβ and PPARγ. Synergistically, miR-29a-3p promotes lipolysis through increasing ATGL expression by suppressing MCT1 expression. Furthermore, PDAC-exosomes deprived of miR-16-5p and miR-29a-3p fail to induce fat loss. Hence, miR-16-5p and miR-29a-3p exosomal miRs are essential for PDAC-induced fat loss. Thus, we unravel that PDAC induces adipose atrophy via exosomal miRs. This knowledge may provide new diagnostic and therapeutic strategies for PDAC-induced cachexia.

Published
2024

25. Study of the decay $D^0\rightarrow \rho(770)^-e^+\nu_e$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

We present a study of the semileptonic decay $D^0\rightarrow \pi^-\pi^0e^{+}\nu_{e}$ using an $e^+e^-$ annihilation data sample of $7.93~\mathrm{fb}^{-1}$ collected at the center-of-mass energy of 3.773 GeV with the BESIII detector. The branching fraction of $D^0\to \rho(770)^-e^+\nu_e$ is measured to be $(1.439 \pm 0.033(\rm stat.) \pm 0.027(\rm syst.)) \times10^{-3}$, which is a factor 1.6 more precise than previous measurements. By performing an amplitude analysis, we measure the hadronic form-factor ratios of $D^0\to \rho(770)^-e^+\nu_e$ at $q^2=0$ assuming the single-pole-dominance parametrization: $r_{V}=V(0)/A_1(0)=1.548\pm0.079(\rm stat.)\pm0.041(\rm syst.)$ and $r_{2}=A_2(0)/A_1(0)=0.823\pm0.056(\rm stat.)\pm0.026(\rm syst.)$., Comment: 12 pages, 3 figures

Published
2024

26. Weyl Fermion with various chiralities in a f-electron ferromagnetic system: PrB4

Author

Ryu, Dong-Choon, Kim, Junwon, Kim, Kyoo, Kim, Bongjae, Kang, Chang-Jong, and Min, B. I.

Subjects
Condensed Matter - Strongly Correlated Electrons
Abstract

Rare-earth tetraborides ($R$B$_{4}$) have attracted a lot of recent attention due to their intriguing electronic, magnetic, and topological properties. We have theoretically investigated topological properties of PrB$_{4}$, which is unique among $R$B$_{4}$ family due to its ferromagnetic ground state. We have discovered that PrB$_{4}$ is an intrinsic magnetic Weyl system possessing multiple topological band crossings with various chiral charges. Density-functional-theory band calculations combined with tight-binding band analysis reveal large Fermi-arc surface states, which are characteristic fingerprints of Weyl fermions. Anomalous Hall conductivity is estimated to be very large, ranging from 500 to 1000 ($\Omega \cdot$cm)$^{-1}$ near the Fermi level, which also demonstrates the topological Weyl character of ferromagnetic PrB$_{4}$. These findings suggest that PrB$_{4}$, being a potential candidate of magnetic Weyl system, would be a promising rare-earth topological system for applications to next-generation spintronic and photonic devices., Comment: 7 pages, 6 figures

Published
2024
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27. Map-level baryonification: Efficient modelling of higher-order correlations in the weak lensing and thermal Sunyaev-Zeldovich fields

Author

Anbajagane, Dhayaa, Pandey, Shivam, and Chang, Chihway

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

Semi-analytic methods can generate baryon-corrected fields from N-body simulations (``baryonification'') and are rapidly becoming a ubiquitous tool in modeling structure formation on non-linear scales. We extend this formalism to consistently model the weak lensing and thermal Sunyaev-Zeldovich (tSZ) fields directly on the full-sky, with an emphasis on higher-order correlations. We use the auto- and cross- $N$th-order moments, with $N \in \{2, 3, 4\}$, as a summary statistic of the lensing and tSZ fields, and show that our model can jointly fit these statistics measured in IllustrisTNG to within measurement uncertainties, for scales above $\gtrsim 1 {\rm Mpc}$ and across multiple redshifts. The model predictions change only minimally when including additional information from secondary halo properties, such as halo concentration and ellipticity. Each individual moment is dependent on halos of different mass ranges and has different sensitivities to the model parameters. A simulation-based forecast on the ULAGAM simulation suite shows that the combination of all moments, measured from current and upcoming lensing and tSZ surveys, can jointly constrain cosmology and baryons to high precision. The lensing and tSZ field are sensitive to different combinations of the baryonification parameters, with degeneracy directions that are often orthogonal, and the combination of the two fields leads to significantly better constraints on both cosmology and astrophysics. Our pipeline for map-level baryonification is publicly available at https://github.com/DhayaaAnbajagane/Baryonification., Comment: 23 pages, 12 figures. Pipeline available at https://github.com/DhayaaAnbajagane/Baryonification

Published
2024

28. F3T: A soft tactile unit with 3D force and temperature mathematical decoupling ability for robots

Author

Yang, Xiong, Ren, Hao, Guo, Dong, Ling, Zhengrong, Zhang, Tieshan, Li, Gen, Tang, Yifeng, Zhao, Haoxiang, Wang, Jiale, Chang, Hongyuan, Dong, Jia, and Shen, Yajing

Subjects
Computer Science - Robotics
Abstract

The human skin exhibits remarkable capability to perceive contact forces and environmental temperatures, providing intricate information essential for nuanced manipulation. Despite recent advancements in soft tactile sensors, a significant challenge remains in accurately decoupling signals - specifically, separating force from directional orientation and temperature - resulting in fail to meet the advanced application requirements of robots. This research proposes a multi-layered soft sensor unit (F3T) designed to achieve isolated measurements and mathematical decoupling of normal pressure, omnidirectional tangential forces, and temperature. We developed a circular coaxial magnetic film featuring a floating-mountain multi-layer capacitor, facilitating the physical decoupling of normal and tangential forces in all directions. Additionally, we incorporated an ion gel-based temperature sensing film atop the tactile sensor. This sensor is resilient to external pressure and deformation, enabling it to measure temperature and, crucially, eliminate capacitor errors induced by environmental temperature changes. This innovative design allows for the decoupled measurement of multiple signals, paving the way for advancements in higher-level robot motion control, autonomous decision-making, and task planning.

Published
2024

29. Con-ReCall: Detecting Pre-training Data in LLMs via Contrastive Decoding

Author

Wang, Cheng, Wang, Yiwei, Hooi, Bryan, Cai, Yujun, Peng, Nanyun, and Chang, Kai-Wei

Subjects
Computer Science - Computation and Language
Abstract

The training data in large language models is key to their success, but it also presents privacy and security risks, as it may contain sensitive information. Detecting pre-training data is crucial for mitigating these concerns. Existing methods typically analyze target text in isolation or solely with non-member contexts, overlooking potential insights from simultaneously considering both member and non-member contexts. While previous work suggested that member contexts provide little information due to the minor distributional shift they induce, our analysis reveals that these subtle shifts can be effectively leveraged when contrasted with non-member contexts. In this paper, we propose Con-ReCall, a novel approach that leverages the asymmetric distributional shifts induced by member and non-member contexts through contrastive decoding, amplifying subtle differences to enhance membership inference. Extensive empirical evaluations demonstrate that Con-ReCall achieves state-of-the-art performance on the WikiMIA benchmark and is robust against various text manipulation techniques.

Published
2024

30. Boosting Certificate Robustness for Time Series Classification with Efficient Self-Ensemble

Author

Dong, Chang, Li, Zhengyang, Zheng, Liangwei, Chen, Weitong, and Zhang, Wei Emma

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security, Statistics - Machine Learning, H.3.3
Abstract

Recently, the issue of adversarial robustness in the time series domain has garnered significant attention. However, the available defense mechanisms remain limited, with adversarial training being the predominant approach, though it does not provide theoretical guarantees. Randomized Smoothing has emerged as a standout method due to its ability to certify a provable lower bound on robustness radius under $\ell_p$-ball attacks. Recognizing its success, research in the time series domain has started focusing on these aspects. However, existing research predominantly focuses on time series forecasting, or under the non-$\ell_p$ robustness in statistic feature augmentation for time series classification~(TSC). Our review found that Randomized Smoothing performs modestly in TSC, struggling to provide effective assurances on datasets with poor robustness. Therefore, we propose a self-ensemble method to enhance the lower bound of the probability confidence of predicted labels by reducing the variance of classification margins, thereby certifying a larger radius. This approach also addresses the computational overhead issue of Deep Ensemble~(DE) while remaining competitive and, in some cases, outperforming it in terms of robustness. Both theoretical analysis and experimental results validate the effectiveness of our method, demonstrating superior performance in robustness testing compared to baseline approaches., Comment: 6 figures, 4 tables, 10 pages

Published
2024
Full Text
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31. Towards a Unified View of Preference Learning for Large Language Models: A Survey

Author

Gao, Bofei, Song, Feifan, Miao, Yibo, Cai, Zefan, Yang, Zhe, Chen, Liang, Hu, Helan, Xu, Runxin, Dong, Qingxiu, Zheng, Ce, Xiao, Wen, Zhang, Ge, Zan, Daoguang, Lu, Keming, Yu, Bowen, Liu, Dayiheng, Cui, Zeyu, Yang, Jian, Sha, Lei, Wang, Houfeng, Sui, Zhifang, Wang, Peiyi, Liu, Tianyu, and Chang, Baobao

Subjects
Computer Science - Computation and Language
Abstract

Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial factors to achieve success is aligning the LLM's output with human preferences. This alignment process often requires only a small amount of data to efficiently enhance the LLM's performance. While effective, research in this area spans multiple domains, and the methods involved are relatively complex to understand. The relationships between different methods have been under-explored, limiting the development of the preference alignment. In light of this, we break down the existing popular alignment strategies into different components and provide a unified framework to study the current alignment strategies, thereby establishing connections among them. In this survey, we decompose all the strategies in preference learning into four components: model, data, feedback, and algorithm. This unified view offers an in-depth understanding of existing alignment algorithms and also opens up possibilities to synergize the strengths of different strategies. Furthermore, we present detailed working examples of prevalent existing algorithms to facilitate a comprehensive understanding for the readers. Finally, based on our unified perspective, we explore the challenges and future research directions for aligning large language models with human preferences., Comment: 23 pages, 6 figures

Published
2024

32. CoolerSpace: A Language for Physically Correct and Computationally Efficient Color Programming

Author

Chen, Ethan, Chang, Jiwon, and Zhu, Yuhao

Subjects
Computer Science - Programming Languages, Computer Science - Graphics
Abstract

Color programmers manipulate lights, materials, and the resulting colors from light-material interactions. Existing libraries for color programming provide only a thin layer of abstraction around matrix operations. Color programs are, thus, vulnerable to bugs arising from mathematically permissible but physically meaningless matrix computations. Correct implementations are difficult to write and optimize. We introduce CoolerSpace to facilitate physically correct and computationally efficient color programming. CoolerSpace raises the level of abstraction of color programming by allowing programmers to focus on describing the logic of color physics. Correctness and efficiency are handled by CoolerSpace. The type system in CoolerSpace assigns physical meaning and dimensions to user-defined objects. The typing rules permit only legal computations informed by color physics and perception. Along with type checking, CoolerSpace also generates performance-optimized programs using equality saturation. CoolerSpace is implemented as a Python library and compiles to ONNX, a common intermediate representation for tensor computations. CoolerSpace not only prevents common errors in color programming, but also does so without run-time overhead: even unoptimized CoolerSpace programs out-perform existing Python-based color programming systems by up to 5.7 times; our optimizations provide up to an additional 1.4 times speed-up.

Published
2024
Full Text
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33. Searching for the massless dark photon in $c\to u\gamma'$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

In the effective field theory, the massless dark photon $\gamma'$ can only couple with the Standard Model particle through operators of dimension higher than four, thereby offering a high sensitivity to the new physics energy scale. Using $7.9~\rm{fb^{-1}}$ of $e^+e^-$ collision data collected at $\sqrt{s}=3.773$ GeV with the BESIII detector at the BEPCII collider, we measure the effective flavor-changing neutral current coupling of $cu\gamma'$ in $D^0\to\omega\gamma'$ and $D^0\to\gamma\gamma'$ processes to search for the massless dark photon. No significant signals are observed, and the upper limits at the 90% confidence level on the massless dark photon branching fraction are set to be $1.1\times10^{-5}$ and $2.0\times10^{-6}$ for $D^0\to\omega\gamma'$ and $D^0\to\gamma\gamma'$, respectively. These results provide the most stringent constraint on the new physics energy scale associated with $cu\gamma'$ coupling in the world, with the new physics energy scale related parameter $|\mathbb{C}|^2+|\mathbb{C}_5|^2<8.2\times10^{-17}~\rm{GeV}^{-2}$ at the 90% confidence level, playing a unique role in the dark sector search with the charm sector., Comment: 9 pages, 4 figures

Published
2024

34. Continual Diffuser (CoD): Mastering Continual Offline Reinforcement Learning with Experience Rehearsal

Author

Hu, Jifeng, Shen, Li, Huang, Sili, Yang, Zhejian, Chen, Hechang, Sun, Lichao, Chang, Yi, and Tao, Dacheng

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Artificial neural networks, especially recent diffusion-based models, have shown remarkable superiority in gaming, control, and QA systems, where the training tasks' datasets are usually static. However, in real-world applications, such as robotic control of reinforcement learning (RL), the tasks are changing, and new tasks arise in a sequential order. This situation poses the new challenge of plasticity-stability trade-off for training an agent who can adapt to task changes and retain acquired knowledge. In view of this, we propose a rehearsal-based continual diffusion model, called Continual Diffuser (CoD), to endow the diffuser with the capabilities of quick adaptation (plasticity) and lasting retention (stability). Specifically, we first construct an offline benchmark that contains 90 tasks from multiple domains. Then, we train the CoD on each task with sequential modeling and conditional generation for making decisions. Next, we preserve a small portion of previous datasets as the rehearsal buffer and replay it to retain the acquired knowledge. Extensive experiments on a series of tasks show CoD can achieve a promising plasticity-stability trade-off and outperform existing diffusion-based methods and other representative baselines on most tasks.

Published
2024

35. A brown dwarf orbiting around the planetary-nebula central binary KV Vel

Author

Qian, S. -B., Zhu, L. -Y., Li, F. -X., Li, L. -J., Han, Z. -T., He, J. -J., Zang, L., Chang, L. -F., Sun, Q. -B., Li, M. -Y., Zhang, H. -T., and Yan, F. -Z.

Subjects
Astrophysics - Solar and Stellar Astrophysics
Abstract

KV Vel is a non-eclipsing short-period (P = 0.3571 days) close binary containing a very hot subdwarf primary (77000 K) and a cool low-mass secondary star (3400 K) that is located at the center of the planetary nebula DS 1. The changes in the orbital period of the close binary were analyzed based on 262 new times of light maximum together with those compiled from the literature. It is discovered that the O-C curve shows a small-amplitude (0.0034 days) cyclic period variation with a period of 29.55 years. The explanation by the solar-type magnetic activity cycles of the cool component is ruled out because the required energies are much larger than the total radiant energy of this component in a whole cycle. Therefore, the cyclic variation was plausibly explained as the light-travel time effect via the presence of a tertiary component, which is supported by the periodic changes of the O-C curve and the rather symmetric and stable light curves obtained by TESS. The mass of the tertiary companion is determined to be M_3sini' = 0.060(7) M_sun. If the third body is coplanar with the central binary (i.e., i' = 62.5{\deg}), the mass of the tertiary component is computed as M_3 ~ 0.068 M\sun, and thus it would be below the stable hydrogen-burning limit and is a brown dwarf. The orbital separation is shorter than 9.35 astronomical units (AU). KV Vel together with its surrounding planetary nebula and the brown-dwarf companion may be formed through the common-envelope evolution after the primary filled its Roche lobe during the early asymptotic giant branch stage.

Published
2024
Full Text
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36. Heisenberg-limit spin squeezing with spin Bogoliubov Hamiltonian

Author

Zhang, Jun, Chang, Sheng, and Zhang, Wenxian

Subjects
Condensed Matter - Quantum Gases, Physics - Optics, Quantum Physics
Abstract

It is well established that the optimal spin squeezing under a one-axis-twisting Hamiltonian follows a scaling law of $J^{-2/3}$ for $J$ interacting atoms after a quench dynamics. Here we prove analytically and numerically that the spin squeezing of the ground state of the one-axis-twisting Hamiltonian actually reaches the Heisenberg limit $J^{-1}$. By constructing a bilinear Bogoliubov Hamiltonian with the raising and lowering spin operators, we exactly diagonalize the spin Bogoliubov Hamiltonian, which includes the one-axis twisting Hamiltonian as a limiting case. The ground state of the spin Bogoliubov Hamiltonian exhibits wonderful spin squeezing, which approaches to the Heisenberg limit in the case of the one-axis twisting Hamiltonian. It is possible to realize experimentally the spin squeezed ground state of the one-axis-twisting Hamiltonian in dipolar spinor condensates, ultracold atoms in optical lattices, spins in a cavity, or alkali atoms in a vapor cell.

Published
2024

37. Integrated photonic nonreciprocal devices based on susceptibility-programmable medium

Author

Zhang, Yan-Lei, Li, Ming, Xu, Xin-Biao, Wang, Zhu-Bo, Dong, Chun-Hua, Guo, Guang-Can, Zou, Chang-Ling, and Zou, Xu-Bo

Subjects
Physics - Optics
Abstract

The switching and control of optical fields based on nonlinear optical effects are often limited to relatively weak nonlinear susceptibility and strong optical pump fields. Here, an optical medium with programmable susceptibility tensor based on polarizable atoms is proposed. Under a structured optical pump, the ground state population of atoms could be efficiently controlled by tuning the chirality and intensity of optical fields, and thus the optical response of the medium is programmable in both space and time. We demonstrate the potential of this approach by engineering the spatial distribution of the complex susceptibility tensor of the medium in photonic structures to realize nonreciprocal optical effects. Specifically, we investigate the advantages of chiral interaction between atoms and photons in an atom-cladded waveguide, theoretically showing that reconfigurable, strong, and fastly switchable isolation of optical signals in a selected optical mode is possible. The susceptibility-programmable medium provides a promising way to efficiently control the optical field, opening up a wide range of applications for integrated photonic devices and structured optics., Comment: 7 pages, 4 figures

Published
2024

38. NYK-MS: A Well-annotated Multi-modal Metaphor and Sarcasm Understanding Benchmark on Cartoon-Caption Dataset

Author

Chang, Ke, Li, Hao, Zhang, Junzhao, and Wu, Yunfang

Subjects
Computer Science - Computation and Language
Abstract

Metaphor and sarcasm are common figurative expressions in people's communication, especially on the Internet or the memes popular among teenagers. We create a new benchmark named NYK-MS (NewYorKer for Metaphor and Sarcasm), which contains 1,583 samples for metaphor understanding tasks and 1,578 samples for sarcasm understanding tasks. These tasks include whether it contains metaphor/sarcasm, which word or object contains metaphor/sarcasm, what does it satirize and why does it contains metaphor/sarcasm, all of the 7 tasks are well-annotated by at least 3 annotators. We annotate the dataset for several rounds to improve the consistency and quality, and use GUI and GPT-4V to raise our efficiency. Based on the benchmark, we conduct plenty of experiments. In the zero-shot experiments, we show that Large Language Models (LLM) and Large Multi-modal Models (LMM) can't do classification task well, and as the scale increases, the performance on other 5 tasks improves. In the experiments on traditional pre-train models, we show the enhancement with augment and alignment methods, which prove our benchmark is consistent with previous dataset and requires the model to understand both of the two modalities., Comment: 13 pages, 6 figures

Published
2024

39. Unlocking the Wisdom of Large Language Models: An Introduction to The Path to Artificial General Intelligence

Author

Chang, Edward Y.

Subjects
Computer Science - Artificial Intelligence, I.2.7
Abstract

This booklet, "Unlocking the Wisdom of Large Language Models," serves as an introduction to the comprehensive work "The Path to Artificial General Intelligence." Through a series of nine aphorisms, we distill key insights and principles that underpin the larger exploration of AI's future through adversarial LLM dialogue. We propose this approach as a potential path to realizing artificial general intelligence (AGI). This booklet also includes the titles, abstracts, and introductions of the chapters in the main book, and presents the first two chapters in their entirety.

Published
2024

40. Benchmarking Cognitive Domains for LLMs: Insights from Taiwanese Hakka Culture

Author

Chang, Chen-Chi, Chen, Ching-Yuan, Lee, Hung-Shin, and Lee, Chih-Cheng

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

This study introduces a comprehensive benchmark designed to evaluate the performance of large language models (LLMs) in understanding and processing cultural knowledge, with a specific focus on Hakka culture as a case study. Leveraging Bloom's Taxonomy, the study develops a multi-dimensional framework that systematically assesses LLMs across six cognitive domains: Remembering, Understanding, Applying, Analyzing, Evaluating, and Creating. This benchmark extends beyond traditional single-dimensional evaluations by providing a deeper analysis of LLMs' abilities to handle culturally specific content, ranging from basic recall of facts to higher-order cognitive tasks such as creative synthesis. Additionally, the study integrates Retrieval-Augmented Generation (RAG) technology to address the challenges of minority cultural knowledge representation in LLMs, demonstrating how RAG enhances the models' performance by dynamically incorporating relevant external information. The results highlight the effectiveness of RAG in improving accuracy across all cognitive domains, particularly in tasks requiring precise retrieval and application of cultural knowledge. However, the findings also reveal the limitations of RAG in creative tasks, underscoring the need for further optimization. This benchmark provides a robust tool for evaluating and comparing LLMs in culturally diverse contexts, offering valuable insights for future research and development in AI-driven cultural knowledge preservation and dissemination., Comment: Submitted to O-COCOSDA 2024

Published
2024

41. VoxHakka: A Dialectally Diverse Multi-speaker Text-to-Speech System for Taiwanese Hakka

Author

Chen, Li-Wei, Lee, Hung-Shin, and Chang, Chen-Chi

Subjects
Computer Science - Sound, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

This paper introduces VoxHakka, a text-to-speech (TTS) system designed for Taiwanese Hakka, a critically under-resourced language spoken in Taiwan. Leveraging the YourTTS framework, VoxHakka achieves high naturalness and accuracy and low real-time factor in speech synthesis while supporting six distinct Hakka dialects. This is achieved by training the model with dialect-specific data, allowing for the generation of speaker-aware Hakka speech. To address the scarcity of publicly available Hakka speech corpora, we employed a cost-effective approach utilizing a web scraping pipeline coupled with automatic speech recognition (ASR)-based data cleaning techniques. This process ensured the acquisition of a high-quality, multi-speaker, multi-dialect dataset suitable for TTS training. Subjective listening tests conducted using comparative mean opinion scores (CMOS) demonstrate that VoxHakka significantly outperforms existing publicly available Hakka TTS systems in terms of pronunciation accuracy, tone correctness, and overall naturalness. This work represents a significant advancement in Hakka language technology and provides a valuable resource for language preservation and revitalization efforts., Comment: Submitted to O-COCOSDA 2024

Published
2024

42. Purification-Agnostic Proxy Learning for Agentic Copyright Watermarking against Adversarial Evidence Forgery

Author

Bao, Erjin, Chang, Ching-Chun, Wang, Hanrui, and Echizen, Isao

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Cryptography and Security
Abstract

With the proliferation of AI agents in various domains, protecting the ownership of AI models has become crucial due to the significant investment in their development. Unauthorized use and illegal distribution of these models pose serious threats to intellectual property, necessitating effective copyright protection measures. Model watermarking has emerged as a key technique to address this issue, embedding ownership information within models to assert rightful ownership during copyright disputes. This paper presents several contributions to model watermarking: a self-authenticating black-box watermarking protocol using hash techniques, a study on evidence forgery attacks using adversarial perturbations, a proposed defense involving a purification step to counter adversarial attacks, and a purification-agnostic proxy learning method to enhance watermark reliability and model performance. Experimental results demonstrate the effectiveness of these approaches in improving the security, reliability, and performance of watermarked models.

Published
2024

43. Study of $D^{+} \to K_{S}^{0}K^{*}(892)^{+}$ in $D^{+} \to K_{S}^{0} K_{S}^{0} \pi^{+}$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. 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Subjects
High Energy Physics - Experiment
Abstract

Using a data sample of $e^+e^-$ collisions corresponding to an integrated luminosity of 7.93 $\rm fb^{-1}$ collected with the BESIII detector at the center-of-mass energy 3.773~GeV, we perform the first amplitude analysis of the decay $D^{+} \to K_{S}^{0} K_{S}^{0} \pi^{+}$. The absolute branching fraction of $D^{+} \to K_{S}^{0}K_{S}^{0} \pi^{+}$ is measured to be $(2.97 \pm 0.09_{\rm stat.} \pm 0.05_{\rm syst.})\times10^{-3}$. The dominant intermediate process is $D^{+} \to K_{S}^{0}K^{*}(892)^{+}$, whose branching fraction is determined to be $(8.72 \pm 0.28_{\rm stat.} \pm 0.15_{\rm syst.}) \times 10^{-3}$, including all the $K^*(892)^+$ decays.

Published
2024

44. Computer-generated holography enables high-uniformity, high-efficiency depth-of-focus extension in endoscopic OCT

Author

Gu, Chengfu, Zhang, Haoran, Lan, Qi, Zhang, Weiyi, Liu, Chang, and Yang, Jianlong

Subjects
Physics - Optics
Abstract

Fiber-form optics extends the high-resolution tomographic imaging capabilities of Optical Coherence Tomography (OCT) to the inside of the human body, i.e., endoscopic OCT. However, it still faces challenges due to the trade-off between probe size, resolution, and Depth Of Focus (DOF). Here we introduce a method for extending the DOF in endoscopic OCT with high uniformity and efficiency. On the basis of multi-level diffractive optics, we leverage the multi-dimensional light field modulation capabilities of Computer-Generated Holography (CGH), to achieve precise control of the intensity distribution of the off-axis portion of the OCT probe light. Our method eliminates the need for an objective lens, allowing for direct fabrication at the distal facet of a single-mode fiber using femtosecond laser two-photon 3D printing. The superiority of our method has been verified through numerical simulation, beam measurement, and imaging results obtained with our home-built endoscopic OCT system., Comment: 5 pages, 5 figures

Published
2024

45. Driving noncollinear interlayer exchange coupling intrinsically in magnetic trilayers

Author

Peng, Guan-Wei, Wang, Hung-Chin, Zhong, Yu-Jie, Kaun, Chao-Cheng, and Chang, Ching-Hao

Subjects
Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics, Quantum Physics
Abstract

Ferromagnetic side layers sandwiching a nonmagnetic spacer as a metallic trilayer has become a pivotal platform for achieving spintronic devices. Recent experiments demonstrate that manipulating the width or the nature of conducting spacer induces noncollinear magnetic alignment between the side layers. Our theoretical analysis reveals that altering the width of spacer significantly affects the interlayer exchange coupling (IEC), resulting in noncollinear alignment. Through analytic and first-principles methods, our study on the Fe/Ag/Fe trilayer shows that at a specific width of the Ag spacer, the magnetic moments of side layers tend to be perpendicular. This alignment is mediated by Ag quantum well states, exhibiting spin spirals across the trilayer. Our results reveal that the noncollinear IEC offers a degree of freedom to control magnetic devices and boot spintronic technology with improved transport capabilities., Comment: 14 pages and 5 figures, accept in Materials Today Physics

Published
2024
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46. Totally Nonnegative Pfaffian for Solitons in BKP Equation

Author

Chang, Jen Hsu

Subjects
Nonlinear Sciences - Exactly Solvable and Integrable Systems
Abstract

The BKP equation is obtained from the reduction of B type in the KP hierarchy under the orthogonal type transformation group for the KP equation. The skew Schur Q functions can be used to construct the Tau functions of solitons in the BKP equation. Then the totally nonnegative Pfaffian can be defined via the skew Schur Q functions to obtain nonsingular line solitons solution in the BKP equation. The totally nonnegative Pfaffians are investigated. The line solitons interact to form web like structure in the near field region and their resonances appearing in soliton graph could be investigated by the totally nonnegative Pfaffians., Comment: 22 pages, 3 firures

Published
2024

47. Decoupled and Interactive Regression Modeling for High-performance One-stage 3D Object Detection

Author

Xiao, Weiping, Wu, Yiqiang, Liu, Chang, Qin, Yu, Li, Xiaomao, and Xin, Liming

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Inadequate bounding box modeling in regression tasks constrains the performance of one-stage 3D object detection. Our study reveals that the primary reason lies in two aspects: (1) The limited center-offset prediction seriously impairs the bounding box localization since many highest response positions significantly deviate from object centers. (2) The low-quality sample ignored in regression tasks significantly impacts the bounding box prediction since it produces unreliable quality (IoU) rectification. To tackle these problems, we propose Decoupled and Interactive Regression Modeling (DIRM) for one-stage detection. Specifically, Decoupled Attribute Regression (DAR) is implemented to facilitate long regression range modeling for the center attribute through an adaptive multi-sample assignment strategy that deeply decouples bounding box attributes. On the other hand, to enhance the reliability of IoU predictions for low-quality results, Interactive Quality Prediction (IQP) integrates the classification task, proficient in modeling negative samples, with quality prediction for joint optimization. Extensive experiments on Waymo and ONCE datasets demonstrate that DIRM significantly improves the performance of several state-of-the-art methods with minimal additional inference latency. Notably, DIRM achieves state-of-the-art detection performance on both the Waymo and ONCE datasets.

Published
2024

48. Ultrafast manipulations of nanoscale skyrmioniums

Author

Dong, Haiming, Fu, Panpan, Duan, Yifeng, and Chang, Kai

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Applied Physics
Abstract

The advancement of next-generation magnetic devices depends on fast manipulating magnetic microstructures on the nanoscale. A universal method is presented for rapidly and reliably generating, controlling, and driving nano-scale skyrmioniums, through high-throughput micromagnetic simulations. Ultrafast switches are realized between skyrmionium and skyrmion states and rapidly change their polarities in monolayer magnetic nanodiscs by perpendicular magnetic fields. The transition mechanism by alternating magnetic fields differs from that under steady magnetic fields. New skyrmionic textures, such as flower-like and windmill-like skyrmions, are discovered. Moreover, this nanoscale skyrmionium can move rapidly and stably in nanoribbons using weaker spin-polarized currents. Explicit discussions are held regarding the physical mechanisms involved in ultrafast manipulations of skyrmioniums. This work provides further physical insight into the manipulation and applications of topological skyrmionic structures for developing low-power consumption and nanostorage devices.

Published
2024

49. A Real-time Instanton Approach to Quantum Activation

Author

Lee, Chang-Woo, Brookes, Paul, Park, Kee-Su, Szymańska, Marzena H., and Ginossar, Eran

Subjects
Quantum Physics
Abstract

Driven-dissipative nonlinear systems exhibit rich critical behavior, related to bifurcation, bistability and switching, which underlie key phenomena in areas ranging from physics, chemistry and biology to social sciences and economics. The importance of rare fluctuations leading to a dramatic jump between two very distinct states, such as survival and extinction in population dynamics, success and bankruptcy in economics and the occurrence of earthquakes or of epileptic seizures, have been already established. In the quantum domain, switching is of importance in both chemical reactions and the devices used in quantum state detection and amplification. In particular, the simplest driven single oscillator model serves as an insightful starting point. Here we describe switching induced by quantum fluctuations and illustrate that an instanton approach within Keldysh field theory can provide a deep insight into such phenomena. We provide a practical recipe to compute the switching rates semi-analytically, which agrees remarkably well with exact solutions across a wide domain of drive amplitudes spanning many orders of magnitude. Being set up in the framework of Keldysh coherent states path integrals, our approach opens the possibility of studying quantum activation in many-body systems where other approaches are inapplicable.

Published
2024

50. Tail Bounds for Functions of Weighted Tensor Sums Derived from Random Walks on Riemannian Manifolds

Author

Chang, Shih-Yu

Subjects
Mathematics - Probability, Mathematics - Operator Algebras, Mathematics - Spectral Theory
Abstract

This paper presents significant advancements in tensor analysis and the study of random walks on manifolds. It introduces new tensor inequalities derived using the Mond-Pecaric method, which enriches the existing mathematical tools for tensor analysis. This method, developed by mathematicians Mond and Pecaric, is a powerful technique for establishing inequalities in linear operators and matrices, using functional analysis and operator theory principles. The paper also proposes novel lower and upper bounds for estimating column sums of transition matrices based on their spectral information, which is critical for understanding random walk behavior. Additionally, it derives bounds for the right tail of weighted tensor sums derived from random walks on manifolds, utilizing the spectrum of the Laplace-Beltrami operator over the underlying manifolds and new tensor inequalities to enhance the understanding of these complex mathematical structures.

Published
2024

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