Author: "ZHOU An" - Searchworks@Jio Institute Digital Library Search Results

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4,158,846 results on '"ZHOU An"'

1. Study on the Control Effect of Geological Structure on Water-bearing System and the Identification of Water Inrush Source

Author: WU Zhigao, MA Wenhao, ZHOU Anchao, WANG Xiaoqiang, CAO Kuanjian, and SHANG Peilin
Subjects: geological structure, water-bearing system, control effect, water source, identification, Chemical engineering, TP155-156, Materials of engineering and construction. Mechanics of materials, TA401-492, Technology
Abstract: [Purposes] By taking the water inrush event of Jiaguo Coal Mine as an example, the control effects of regional geological structure and mine field geological structure on the water-bearing system of Taiyuan Formation in coal mine are systematically studied. The water-bearing system of Taiyuan Formation in the mine field is regarded as an independent hydrogeological unit. Geological structure controls and affects of the supply, runoff and drain conditions of the karst fissure water flow system of Taiyuan Formation in the mine field are investigated. [Methods] This research has been carried out by using hydro-chemical characteristics and analysis of D, 18O stable isotope contents, combined with fine field geological survey and observation of underground water gushing point. [Findings] It is confirmed that the water inrush source of Jiaguo Coal Mine comes from the supply of Wangtao River. It is predicted and verified by drilling that the karst fissure of Taiyuan Formation K2 limestone covered by loose sediment of Wangtao River is the water conducting channel of water inrush source. Controlled by the karst fissure water flow system of Taiyuan Formation in the mine field, the Wangtao River flows through the K2 limestone aquifer of Taiyuan Formation under the riverbed, and flows along the layer to the low-lying structure of the mine field depression, resulting in water inrush at the K2 limestone karst fissure of the main inclined shaft. This work provides a reliable basis for the prevention and control of mine water disasters in Jiaguo Coal Mine, and also provides an effective way for the identification of water inrush sources in mines.
Published: 2024
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2. A bidirectional Mendelian randomization study integrating genome-wide association studies, expression quantitative trait locus, and methylation quantitative trait locus data revealed causal relationship between heavy cigarette dependence and Barrett’s esophagus

Author: Zhou An, Meichun Zeng, and Xianhua Wang
Subjects: Medicine (General), R5-920
Abstract: Background: The association between smoking dependence and the risk of developing Barrett’s esophagus remains unclear. This study aimed to investigate whether a causal relationship exists between smoking dependence and Barrett’s esophagus, using Mendelian randomization analysis. Methods: Two-sample Mendelian randomization analysis was conducted to evaluate the impact of smoking and Barrett’s esophagus. Additionally, we applied summary data-based Mendelian randomization techniques to combine information from genome-wide association studies (GWAS) with expression quantitative trait locus and methylation quantitative trait locus. Results: Multivariable Mendelian randomization showed an association between smoking per day (odds ratio = 1.2, 95% confidence interval: 1.038–1.38, p = 0.014) or current smoking (odds ratio = 2.41, 95% confidence interval: 1.06–5.5, p = 0.037) and Barrett’s esophagus. Inverse variance-weighted methods of bidirectional Mendelian randomization also revealed that smoking per day was significantly associated with elevated risks of Barrett’s esophagus (odds ratio = 1.34, 95% confidence interval: 1.092–1.649, p = 0.005), while Barrett’s esophagus was also a susceptibility factor for smoking per day (odds ratio = 1.05, 95% confidence interval: 1.017–1.087, p = 0.003). By incorporating consistent summary data-based Mendelian randomization associations between DNA methylation and smoke/Barrett’s esophagus, gene expression and smoke/Barrett’s esophagus, and DNA methylation and gene expression, we identified that the genetic variant-cg00935895-RBM43 (ENSG00000184898)-smoke/Barrett’s esophagus axis exerted an effect on smoke/Barrett’s esophagus by altering the DNA methylation level, which regulated the expression level of RBM43. Conclusions: Our study provides evidence of a bidirectional causal association between smoking and Barrett’s esophagus from a genetic perspective, which sheds new light on the potential role of RBM43 as a mediator in facilitating the impact of smoking and Barrett’s esophagus.
Published: 2025
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3. Changes in characteristics of gut microbiota and their correlation with clinical indicators in cirrhosis patients after transjugular intrahepatic portosystemic shunt

Author: LI Shengpeng, XIAO Xu, and ZHOU An
Subjects: transjugular intrahepatic portosystemic shunt, gut microbiota, 16s rrna, bacteroides, bifidobacterium, Medicine (General), R5-920
Abstract: Objective To compare the differences in gut microbiota between cirrhosis patients receiving transjugular intrahepatic portosystemic shunt (TIPS) and those not, and explore the relationship between TIPS surgery and gut microbiota in order to provide new ideas for improving the prognosis of cirrhosis patients after TIPS surgery based on gut microbiota. Methods Those who had received TIPS surgery previously were assigned into the operation group, and those without the surgery served as the control group. Their stool samples were collected for 16S rRNA sequencing, and the correlation between gut microbiota and clinical indicators was analyzed. Results There were no significant differences in gender, age, body mass index (BMI), etiology and other baseline data between the 2 groups (P>0.05), but obvious differences were observed in clinical serological indicators between them, including INR, APTT, RBC and PT counts, and HGB, ALP, ALB, total bilirubin (TBIL), and URE levels (P0.05), but beta diversity was obviously different (P
Published: 2024
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4. The effect and mechanism of new energy demonstration city policies on green innovation of new energy enterprises in China

Author: ZHOU Anhua, WANG Saige
Subjects: new energy demonstration city, green innovation, new energy enterprises, research and development investment, environmental regulation, difference-in-differences model, Environmental sciences, GE1-350, Biology (General), QH301-705.5
Abstract: [Objective] Green innovation is a key driving force for addressing environmental challenges and achieving high-quality development. Studying the impact and mechanism of policies for new energy demonstration cities on green innovation can provide theoretical support and empirical evidence at the enterprise level for the evaluation and optimization of policies in new energy demonstration cities. [Methods] Using the Gini coefficient, Theil index, and spatial Markov chain method, this study revealed the balanced development and spatial-temporal shift characteristics of green innovation in Chinese new energy enterprises from 2008 to 2020. A difference-in-differences model was employed to assess the effects and mechanisms of new energy demonstration city policies on green innovation. [Results] (1) In the research period, inequality in green innovation among Chinese new energy enterprises in the new energy industry gradually increased over time, exhibiting a “Matthew effect”. (2) New energy demonstration city policies significantly enhanced the green innovation of new energy enterprises. Specifically, compared to areas without the implementation of such policies, the green innovation of listed companies in cities with these policies increased by 14%, as supported by a series of robustness tests. (3) The policies achieved the enhancement in green innovation by boosting research and development investments and strengthening environmental regulations. (4) Heterogeneity analysis showed that the impact of new energy demonstration city policies on green innovation was more pronounced for companies with smaller scales and tighter financing constraints. [Conclusion] The implementation of new energy demonstration city policies has a positive influence on promoting green innovation in the new energy industry. The research results provide policy references for the government to promote new energy demonstration city policies and enhance the green innovation of new energy enterprises.
Published: 2023
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5. Progress of Different Programmed Cell Death Pathways in Kidney Cancer

Author: WU Mingzhe, WANG Fuchun, PAN Haojie, ZHOU An'an, XIAO Xi, and TIAN Junqiang
Subjects: kidney cancer, programmed cell death, apoptosis, autophagy, necroptosis, pyroptosis, ferroptosis, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Programmed cell death (PCD) is a genetically determined, active and orderly cell death in the organism, and it affects the evolution of the organism, maintenance of its homeostasis, and development of several tissues and organs. The abnormal regulation of this process is closely related to various human diseases, including cancer. The identified pathways of PCD include apoptosis, autophagy, necroptosis, pyroptosis, and ferroptosis, which can be activated when cells are stimulated by various internal and external environmental factors. These pathways can induce cell death or maintain cell survival in kidney cancer cells under the regulation of various signaling molecules, thus affecting tumor progression or therapeutic efficacy. In this paper, the role of these PCD pathways in the development of kidney cancer was reviewed in light of recent research advances to provide new directions for the in-depth study of the pathogenesis of kidney cancer and the development of targeted antitumor drugs.
Published: 2023
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6. Ultra-high-energy {\gamma}-ray emission associated with the tail of a bow-shock pulsar wind nebula

Author: Cao, Zhen, Aharonian, F., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Bian, W., Bukevich, A. V., Cai, C. M., Cao0, W. Y., Cao0, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, H. X., Chen, Liang, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen5, S., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, X. B., Chen, X. J., Chen, Y., Cheng, N., Cheng, Y. D., Chu, M. C., Cui, M. Y., Cui, S. W., Cui0, X. H., Dai5, Y. D. Cui B. Z., Dai, H. L., Dai0, Z. G., Danzengluobu, Diao, Y. X., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang5, J., Fang, J. H., Fang, K., Feng, C. F., Feng, H. Feng L., Feng, S. H., Feng, X. T., Feng, Y., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, Q., Gao, W., Gao, W. K., Ge5, M. M., Geng, T. T. Ge L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, J., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han5, Y. A., Hannuksela, O. A., Hasan, M., He, H. H., He, H. N., He, J. Y., He, X. Y., He, Y., Hernández-Cadena, S., Hou, Y. K. Hor B. W., Hou, C., Hou, X., Hu, H. B., Hu, S. C., Huang, C., Huang, D. H., Huang, J. J., Huang, T. Q., Huang, W. J. Huang X. T., Huang, X. Y., Huang, Y., Huang, Y. Y., Ji, X. L., Jia, H. Y., Jia, K., Jiang, H. B., Jiang0, K., Jiang, X. W., Jiang5, Z. J., Jin, M., Kaci, S., Kang, M. M., Karpikov, I., Khangulyan, D., Kuleshov, D., Kurinov, K., Li, B. B., Li0, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li0, Jian, Li, Jie, Li, K., Li, L., Li, R. L., Li, S. D., Li, T. Y., Li, W. L., Li, X. R., Li0, Xin, Li, Y. Z., Li, Zhe, Li0, Zhuo, Liu0, E. W. Liang Y. F. Liang S. J. Lin B., Liu, C., Liu, D., Liu, D. B., Liu, H., Liu5, H. D., Liu, J., Liu, J. L., Liu, J. R., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, X., Liu, Y., Liu, Y. N., Lou, Y. Q., Luo, Q. Luo Y., Lv, H. K., Ma0, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mou, G. B., Mu5, H. J., Nan, Y. C., Neronov, A., Ng, K. C. Y., Ni, M. Y., Nie, L., Ou, L. J., Pattarakijwanich, P., Pei, Z. Y., Qi, J. C., Qi, M. Y., Qin0, J. J., Raza, A., Ren, C. Y., Ruffolo, D., Sáiz, A., Saeed, M., Semikoz, D., Shao, L., Shchegolev, O., Shen, Y. Z., Sheng, X. D., Shi0, Z. D., Shu, F. W., Song0, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun0, D. X., Sun, Q. N., Sun5, X. N. Sun Z. B., Takata, J., Tan, P. H. T. Tam H. B., Tang, Q. W., Tang, R., Tang0, Z. B., Tian, W. W., Tong, C. N., Wang5, L. H. Wan C., Wang0, G. W., Wang, H. G., Wang, H. H. Wang J. C., Wang0, K., Wang, Kai, Wang, L. P., Wang, L. Y., Wang, R., Wang, W. Wang X. G. Wang X. J., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Z. H., Wang5, Z. X., Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen5, T., Weng, S. S., Wu, C. Y., Wu, H. R., Wu, Q. W., Wu, S., Wu, X. F., Wu0, Y. S., Xi, S. Q., Xia0, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, Y. L., Xing, Y., Xiong, D. R., Xiong, Z., Xu, D. L., Xu, R. F., Xu0, R. X., Xu, W. L., Xue, L., Yan5, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, C. Y., Yang, F. F., Yang, L. L. Yang M. J., Yang0, R. Z., Yang, W. X., Yao, Y. H., Yao, Z. G., Ye, X. A., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu0, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng5, W., Zha, M., Zhang, B. B., Zhang, B. T., Zhang, F., Zhang, H., Zhang5, H. M. Zhang H. Y., Zhang0, J. L., Zhang5, Li, Zhang5, P. F., Zhang0, P. P., Zhang, R., Zhang, S. R., Zhang, S. S., Zhang, W. Y., Zhang, X., Zhang, X. P., Zhang, Yi, Zhang, Yong, Zhang0, Z. P., Zhao, J., Zhao0, L., Zhao, L. Z., Zhao, S. P., Zhao, X. H., Zhao0, Z. H., Zheng5, F., Zhong, W. J., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu0, B. Y., Zhu, C. G., Zhu, F. R., Zhu0, H., Zhu, K. J., Zou, Y. C., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: In this study, we present a comprehensive analysis of an unidentified point-like ultra-high-energy (UHE) $\gamma$-ray source, designated as 1LHAASO J1740+0948u, situated in the vicinity of the middle-aged pulsar PSR J1740+1000. The detection significance reached 17.1$\sigma$ (9.4$\sigma$) above 25$\,$TeV (100$\,$TeV). The source energy spectrum extended up to 300$\,$TeV, which was well fitted by a log-parabola function with $N0 = (1.93\pm0.23) \times 10^{-16} \rm{TeV^{-1}\,cm^{-2}\,s^{-2}}$, $\alpha = 2.14\pm0.27$, and $\beta = 1.20\pm0.41$ at E0 = 30$\,$TeV. The associated pulsar, PSR J1740+1000, resides at a high galactic latitude and powers a bow-shock pulsar wind nebula (BSPWN) with an extended X-ray tail. The best-fit position of the gamma-ray source appeared to be shifted by $0.2^{\circ}$ with respect to the pulsar position. As the (i) currently identified pulsar halos do not demonstrate such offsets, and (ii) centroid of the gamma-ray emission is approximately located at the extension of the X-ray tail, we speculate that the UHE $\gamma$-ray emission may originate from re-accelerated electron/positron pairs that are advected away in the bow-shock tail.
Published: 2025
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7. SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Author: Team, M-A-P, Du, Xinrun, Yao, Yifan, Ma, Kaijing, Wang, Bingli, Zheng, Tianyu, Zhu, Kang, Liu, Minghao, Liang, Yiming, Jin, Xiaolong, Wei, Zhenlin, Zheng, Chujie, Deng, Kaixing, Guo, Shuyue, Jia, Shian, Jiang, Sichao, Liao, Yiyan, Li, Rui, Li, Qinrui, Li, Sirun, Li, Yizhi, Li, Yunwen, Ma, Dehua, Ni, Yuansheng, Que, Haoran, Wang, Qiyao, Wen, Zhoufutu, Wu, Siwei, Xing, Tianshun, Xu, Ming, Yang, Zhenzhu, Wang, Zekun Moore, Zhou, Junting, Bai, Yuelin, Bu, Xingyuan, Cai, Chenglin, Chen, Liang, Chen, Yifan, Cheng, Chengtuo, Cheng, Tianhao, Ding, Keyi, Huang, Siming, Huang, Yun, Li, Yaoru, Li, Yizhe, Li, Zhaoqun, Liang, Tianhao, Lin, Chengdong, Lin, Hongquan, Ma, Yinghao, Peng, Zhongyuan, Peng, Zifan, Qi, Qige, Qiu, Shi, Qu, Xingwei, Tan, Yizhou, Wang, Zili, Wang, Chenqing, Wang, Hao, Wang, Yiya, Wang, Yubo, Xu, Jiajun, Yang, Kexin, Yuan, Ruibin, Yue, Yuanhao, Zhan, Tianyang, Zhang, Chun, Zhang, Jingyang, Zhang, Xiyue, Zhang, Xingjian, Zhang, Yue, Zhao, Yongchi, Zheng, Xiangyu, Zhong, Chenghua, Gao, Yang, Li, Zhoujun, Liu, Dayiheng, Liu, Qian, Liu, Tianyu, Ni, Shiwen, Peng, Junran, Qin, Yujia, Su, Wenbo, Wang, Guoyin, Wang, Shi, Yang, Jian, Yang, Min, Cao, Meng, Yue, Xiang, Zhang, Zhaoxiang, Zhou, Wangchunshu, Liu, Jiaheng, Lin, Qunshu, Huang, Wenhao, and Zhang, Ge
Subjects: Computer Science - Computation and Language
Abstract: Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledge encompasses over 200 specialized disciplines, far exceeding the scope of existing benchmarks. The capabilities of LLMs in many of these specialized fields-particularly in light industry, agriculture, and service-oriented disciplines-remain inadequately evaluated. To address this gap, we present SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines. Our benchmark employs a novel Human-LLM collaborative filtering mechanism to eliminate trivial or ambiguous questions through iterative refinement based on both LLM responses and expert feedback. Our experimental results reveal significant room for improvement in the performance of current state-of-the-art LLMs across diverse knowledge domains (e.g., the reasoning-focused model DeepSeek-R1 achieved the highest accuracy of 61.82% on SuperGPQA), highlighting the considerable gap between current model capabilities and artificial general intelligence. Additionally, we present comprehensive insights from our management of a large-scale annotation process, involving over 80 expert annotators and an interactive Human-LLM collaborative system, offering valuable methodological guidance for future research initiatives of comparable scope.
Published: 2025

8. On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective

Author: Huang, Yue, Gao, Chujie, Wu, Siyuan, Wang, Haoran, Wang, Xiangqi, Zhou, Yujun, Wang, Yanbo, Ye, Jiayi, Shi, Jiawen, Zhang, Qihui, Li, Yuan, Bao, Han, Liu, Zhaoyi, Guan, Tianrui, Chen, Dongping, Chen, Ruoxi, Guo, Kehan, Zou, Andy, Kuen-Yew, Bryan Hooi, Xiong, Caiming, Stengel-Eskin, Elias, Zhang, Hongyang, Yin, Hongzhi, Zhang, Huan, Yao, Huaxiu, Yoon, Jaehong, Zhang, Jieyu, Shu, Kai, Zhu, Kaijie, Krishna, Ranjay, Swayamdipta, Swabha, Shi, Taiwei, Shi, Weijia, Li, Xiang, Li, Yiwei, Hao, Yuexing, Jia, Zhihao, Li, Zhize, Chen, Xiuying, Tu, Zhengzhong, Hu, Xiyang, Zhou, Tianyi, Zhao, Jieyu, Sun, Lichao, Huang, Furong, Sasson, Or Cohen, Sattigeri, Prasanna, Reuel, Anka, Lamparth, Max, Zhao, Yue, Dziri, Nouha, Su, Yu, Sun, Huan, Ji, Heng, Xiao, Chaowei, Bansal, Mohit, Chawla, Nitesh V., Pei, Jian, Gao, Jianfeng, Backes, Michael, Yu, Philip S., Gong, Neil Zhenqiang, Chen, Pin-Yu, Li, Bo, and Zhang, Xiangliang
Subjects: Computer Science - Computers and Society
Abstract: Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions. This paper presents a comprehensive framework to address these challenges through three key contributions. First, we systematically review global AI governance laws and policies from governments and regulatory bodies, as well as industry practices and standards. Based on this analysis, we propose a set of guiding principles for GenFMs, developed through extensive multidisciplinary collaboration that integrates technical, ethical, legal, and societal perspectives. Second, we introduce TrustGen, the first dynamic benchmarking platform designed to evaluate trustworthiness across multiple dimensions and model types, including text-to-image, large language, and vision-language models. TrustGen leverages modular components--metadata curation, test case generation, and contextual variation--to enable adaptive and iterative assessments, overcoming the limitations of static evaluation methods. Using TrustGen, we reveal significant progress in trustworthiness while identifying persistent challenges. Finally, we provide an in-depth discussion of the challenges and future directions for trustworthy GenFMs, which reveals the complex, evolving nature of trustworthiness, highlighting the nuanced trade-offs between utility and trustworthiness, and consideration for various downstream applications, identifying persistent challenges and providing a strategic roadmap for future research. This work establishes a holistic framework for advancing trustworthiness in GenAI, paving the way for safer and more responsible integration of GenFMs into critical applications. To facilitate advancement in the community, we release the toolkit for dynamic evaluation.
Published: 2025

9. Amplitude analysis of $\psi(3686)\to \gamma K_S^0 K_S^0 $

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., 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, M. 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. 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, X. Y., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. K., Choi, S. K., Chu, X., Cibinetto, G., Cossio, F., Cottee-Meldrum, J., 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., Ding, Y. X., 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, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, L., Feng, Q. X., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y., Gao, Y. N., Gao, Y. Y., Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, J. D., 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, K. D., 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, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Hu, Z. M., Huang, G. S., Huang, K. 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Z., Li, Y., Li, Y. G., Li, Y. P., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. B., Liao, M. H., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, L. Q., 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. J., 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, W. T., Liu, X., Liu, X. K., Liu, X. Y., 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. H., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, J. S., Luo, M. X., Luo, T., Luo, X. L., Lv, Z. Y., Lyu, X. R., Lyu, Y. F., Lyu, Y. H., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, Q. M., Ma, R. Q., Ma, R. Y., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. 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Z., Ye, M., Ye, M. H., Ye, Z. J., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, L. Q., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, H., Yuan, J., Yuan, L., Yuan, S. C., Yuan, X. Q., Yuan, Y., Yuan, Z. Y., Yue, C. X., Yue, Ying, Zafar, A. A., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., 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, N., Zhang, P., Zhang, Q., 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, Y. P., Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. L., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, X. R., Zheng, Y. H., Zhong, B., Zhong, C., Zhou, H., Zhou, J. Q., Zhou, J. Y., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. X., Zhou, Y. Z., 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, W. J., Zhu, W. Z., Zhu, Y. C., Zhu, Z. A., Zhuang, X. Y., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: Using $(2712\pm14)\times10^6$ $\psi(3686)$ events collected with the BESIII detector, we perform the first amplitude analysis of the radiative decay $\psi(3686)\to \gamma K_S^0 K_S^0$ within the mass region $M_{K_S^0 K_S^0 }<2.8$ GeV/$c^2$. Employing a one-channel K-matrix approach for the description of the dynamics of the $K^0_S K^0_S$ system, the data sample is well described with four poles for the $f_0$-wave and three poles for the $f_2$-wave. The determined pole positions are consistent with those of well-established resonance states. The observed $f_0$ and $f_{2}$ states are found to be qualitatively consistent with those produced in radiative $J/\psi$ decays, indicating the similarity between the two charmonium states in their radiative decays., Comment: 20 pages, 4 figures, submitted to JHEP
Published: 2025

10. Prediction for close approaches with terrestrial planets of asteroids from the main belt

Author: Zhou, Yufan Fane, Li, Zhiyuan, Li, Hailiang, and Zhou, Liyong
Subjects: Astrophysics - Earth and Planetary Astrophysics
Abstract: Potentially Hazardous Asteroids (PHAs), a special subset of Near-Earth Objects, are both dangerous and scientifically valuable. PHAs that truly undergo close approaches with the Earth (dubbed CAPHAs) are of particular interest and extensively studied. The concept and study of CAPHA can be extended to other Solar system planets, which have significant implications for future planet-based observations and explorations. In this work, we conduct numerical simulations that incorporate the Yarkovsky effect to study the transformation of main belt asteroids into CAPHAs of terrestrial planets, using precise nominal timesteps, especially to ensure the reliability of the results for Mercury and Venus. Our simulations predict a total of 1893 Mercury-CAPHAs, 3014 Venus-CAPHAs, 3791 Earth-CAPHAs and 18066 Mars-CAPHAs, with an occurrence frequency of about 1, 9, 15 and 66 per year, respectively. The values for Mars-CAPHAs are consistent with our previous work, which were based on simulations with a larger nominal timestep. The predicted occurrence frequency and velocity distribution of Earth-CAPHAs are in reasonable agreement with the observed population of Earth-CAPHAs. We also find that certain asteroids can be caught in close approach with different planets at different times, raising an interesting possibility of using them as transportation between terrestrial planets in the future., Comment: 8 pages, 4 figures, accepted for publication in MNRAS. arXiv admin note: text overlap with arXiv:2405.02614
Published: 2025

11. A Smooth Transition Between Induction and Deduction: Fast Abductive Learning Based on Probabilistic Symbol Perception

Author: Jia, Lin-Han, Han, Si-Yu, Guo, Lan-Zhe, Zhou, Zhi, Li, Zhao-Long, Li, Yu-Feng, and Zhou, Zhi-Hua
Subjects: Computer Science - Machine Learning
Abstract: Abductive learning (ABL) that integrates strengths of machine learning and logical reasoning to improve the learning generalization, has been recently shown effective. However, its efficiency is affected by the transition between numerical induction and symbolical deduction, leading to high computational costs in the worst-case scenario. Efforts on this issue remain to be limited. In this paper, we identified three reasons why previous optimization algorithms for ABL were not effective: insufficient utilization of prediction, symbol relationships, and accumulated experience in successful abductive processes, resulting in redundant calculations to the knowledge base. To address these challenges, we introduce an optimization algorithm named as Probabilistic Symbol Perception (PSP), which makes a smooth transition between induction and deduction and keeps the correctness of ABL unchanged. We leverage probability as a bridge and present an efficient data structure, achieving the transfer from a continuous probability sequence to discrete Boolean sequences with low computational complexity. Experiments demonstrate the promising results.
Published: 2025

12. Baichuan-M1: Pushing the Medical Capability of Large Language Models

Author: Wang, Bingning, Zhao, Haizhou, Zhou, Huozhi, Song, Liang, Xu, Mingyu, Cheng, Wei, Zeng, Xiangrong, Zhang, Yupeng, Huo, Yuqi, Wang, Zecheng, Zhao, Zhengyun, Pan, Da, Yang, Fan, Kou, Fei, Li, Fei, Chen, Fuzhong, Dong, Guosheng, Liu, Han, Zhang, Hongda, He, Jin, Yang, Jinjie, Wu, Kangxi, Wu, Kegeng, Su, Lei, Niu, Linlin, Sun, Linzhuang, Wang, Mang, Fan, Pengcheng, Shen, Qianli, Xin, Rihui, Dang, Shunya, Zhou, Songchi, Chen, Weipeng, Luo, Wenjing, Chen, Xin, Men, Xin, Lin, Xionghai, Dong, Xuezhen, Zhang, Yan, Duan, Yifei, Zhou, Yuyan, Ma, Zhi, and Wu, Zhiying
Subjects: Computer Science - Computation and Language
Abstract: The current generation of large language models (LLMs) is typically designed for broad, general-purpose applications, while domain-specific LLMs, especially in vertical fields like medicine, remain relatively scarce. In particular, the development of highly efficient and practical LLMs for the medical domain is challenging due to the complexity of medical knowledge and the limited availability of high-quality data. To bridge this gap, we introduce Baichuan-M1, a series of large language models specifically optimized for medical applications. Unlike traditional approaches that simply continue pretraining on existing models or apply post-training to a general base model, Baichuan-M1 is trained from scratch with a dedicated focus on enhancing medical capabilities. Our model is trained on 20 trillion tokens and incorporates a range of effective training methods that strike a balance between general capabilities and medical expertise. As a result, Baichuan-M1 not only performs strongly across general domains such as mathematics and coding but also excels in specialized medical fields. We have open-sourced Baichuan-M1-14B, a mini version of our model, which can be accessed through the following links., Comment: 33 pages, technical report
Published: 2025

13. Uncertainty-Aware Graph Structure Learning

Author: Han, Shen, Zhou, Zhiyao, Chen, Jiawei, Hao, Zhezheng, Zhou, Sheng, Wang, Gang, Feng, Yan, Chen, Chun, and Wang, Can
Subjects: Computer Science - Machine Learning
Abstract: Graph Neural Networks (GNNs) have become a prominent approach for learning from graph-structured data. However, their effectiveness can be significantly compromised when the graph structure is suboptimal. To address this issue, Graph Structure Learning (GSL) has emerged as a promising technique that refines node connections adaptively. Nevertheless, we identify two key limitations in existing GSL methods: 1) Most methods primarily focus on node similarity to construct relationships, while overlooking the quality of node information. Blindly connecting low-quality nodes and aggregating their ambiguous information can degrade the performance of other nodes. 2) The constructed graph structures are often constrained to be symmetric, which may limit the model's flexibility and effectiveness. To overcome these limitations, we propose an Uncertainty-aware Graph Structure Learning (UnGSL) strategy. UnGSL estimates the uncertainty of node information and utilizes it to adjust the strength of directional connections, where the influence of nodes with high uncertainty is adaptively reduced. Importantly, UnGSL serves as a plug-in module that can be seamlessly integrated into existing GSL methods with minimal additional computational cost. In our experiments, we implement UnGSL into six representative GSL methods, demonstrating consistent performance improvements., Comment: This paper has been accepted by TheWebConf 2025
Published: 2025

14. UniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery

Author: Li, Ruifeng, Li, Mingqian, Liu, Wei, Zhou, Yuhua, Zhou, Xiangxin, Yao, Yuan, Zhang, Qiang, and Chen, Hongyang
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Quantitative Biology - Biomolecules, 68U07
Abstract: Drug discovery is crucial for identifying candidate drugs for various diseases.However, its low success rate often results in a scarcity of annotations, posing a few-shot learning problem. Existing methods primarily focus on single-scale features, overlooking the hierarchical molecular structures that determine different molecular properties. To address these issues, we introduce Universal Matching Networks (UniMatch), a dual matching framework that integrates explicit hierarchical molecular matching with implicit task-level matching via meta-learning, bridging multi-level molecular representations and task-level generalization. Specifically, our approach explicitly captures structural features across multiple levels, such as atoms, substructures, and molecules, via hierarchical pooling and matching, facilitating precise molecular representation and comparison. Additionally, we employ a meta-learning strategy for implicit task-level matching, allowing the model to capture shared patterns across tasks and quickly adapt to new ones. This unified matching framework ensures effective molecular alignment while leveraging shared meta-knowledge for fast adaptation. Our experimental results demonstrate that UniMatch outperforms state-of-the-art methods on the MoleculeNet and FS-Mol benchmarks, achieving improvements of 2.87% in AUROC and 6.52% in delta AUPRC. UniMatch also shows excellent generalization ability on the Meta-MolNet benchmark., Comment: accepted as ICLR 2025 Spotlight
Published: 2025

15. APB: Accelerating Distributed Long-Context Inference by Passing Compressed Context Blocks across GPUs

Author: Huang, Yuxiang, Li, Mingye, Han, Xu, Xiao, Chaojun, Zhao, Weilin, Ao, Sun, Zhou, Hao, Zhou, Jie, Liu, Zhiyuan, and Sun, Maosong
Subjects: Computer Science - Machine Learning, Computer Science - Computation and Language
Abstract: While long-context inference is crucial for advancing large language model (LLM) applications, its prefill speed remains a significant bottleneck. Current approaches, including sequence parallelism strategies and compute reduction through approximate attention mechanisms, still fall short of delivering optimal inference efficiency. This hinders scaling the inputs to longer sequences and processing long-context queries in a timely manner. To address this, we introduce APB, an efficient long-context inference framework that leverages multi-host approximate attention to enhance prefill speed by reducing compute and enhancing parallelism simultaneously. APB introduces a communication mechanism for essential key-value pairs within a sequence parallelism framework, enabling a faster inference speed while maintaining task performance. We implement APB by incorporating a tailored FlashAttn kernel alongside optimized distribution strategies, supporting diverse models and parallelism configurations. APB achieves speedups of up to 9.2x, 4.2x, and 1.6x compared with FlashAttn, RingAttn, and StarAttn, respectively, without any observable task performance degradation. We provide the implementation and experiment code of APB in https://github.com/thunlp/APB., Comment: Preprint
Published: 2025

16. Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction

Author: Huang, Ailin, Wu, Boyong, Wang, Bruce, Yan, Chao, Hu, Chen, Feng, Chengli, Tian, Fei, Shen, Feiyu, Li, Jingbei, Chen, Mingrui, Liu, Peng, Miao, Ruihang, You, Wang, Chen, Xi, Yang, Xuerui, Huang, Yechang, Zhang, Yuxiang, Gong, Zheng, Zhang, Zixin, Zhou, Hongyu, Sun, Jianjian, Li, Brian, Feng, Chengting, Wan, Changyi, Hu, Hanpeng, Wu, Jianchang, Zhen, Jiangjie, Ming, Ranchen, Yuan, Song, Zhang, Xuelin, Zhou, Yu, Li, Bingxin, Ma, Buyun, Wang, Hongyuan, An, Kang, Ji, Wei, Li, Wen, Wen, Xuan, Kong, Xiangwen, Ma, Yuankai, Liang, Yuanwei, Mou, Yun, Ahmidi, Bahtiyar, Wang, Bin, Li, Bo, Miao, Changxin, Xu, Chen, Wang, Chenrun, Shi, Dapeng, Sun, Deshan, Hu, Dingyuan, Sai, Dula, Liu, Enle, Huang, Guanzhe, Yan, Gulin, Wang, Heng, Jia, Haonan, Zhang, Haoyang, Gong, Jiahao, Guo, Junjing, Liu, Jiashuai, Liu, Jiahong, Feng, Jie, Wu, Jie, Wu, Jiaoren, Yang, Jie, Wang, Jinguo, Zhang, Jingyang, Lin, Junzhe, Li, Kaixiang, Xia, Lei, Zhou, Li, Zhao, Liang, Gu, Longlong, Chen, Mei, Wu, Menglin, Li, Ming, Li, Mingxiao, Li, Mingliang, Liang, Mingyao, Wang, Na, Hao, Nie, Wu, Qiling, Tan, Qinyuan, Sun, Ran, Shuai, Shuai, Pang, Shaoliang, Yang, Shiliang, Gao, Shuli, Yuan, Shanshan, Liu, Siqi, Deng, Shihong, Jiang, Shilei, Liu, Sitong, Cao, Tiancheng, Wang, Tianyu, Deng, Wenjin, Xie, Wuxun, Ming, Weipeng, He, Wenqing, Sun, Wen, Han, Xin, Huang, Xin, Deng, Xiaomin, Liu, Xiaojia, Wu, Xin, Zhao, Xu, Wei, Yanan, Yu, Yanbo, Cao, Yang, Li, Yangguang, Ma, Yangzhen, Xu, Yanming, Wang, Yaoyu, Shi, Yaqiang, Wang, Yilei, Zhou, Yizhuang, Zhong, Yinmin, Zhang, Yang, Wei, Yaoben, Luo, Yu, Lu, Yuanwei, Yin, Yuhe, Luo, Yuchu, Ding, Yuanhao, Yan, Yuting, Dai, Yaqi, Yang, Yuxiang, Xie, Zhe, Ge, Zheng, Sun, Zheng, Huang, Zhewei, Chang, Zhichao, Guan, Zhisheng, Yang, Zidong, Zhang, Zili, Jiao, Binxing, Jiang, Daxin, Shum, Heung-Yeung, Chen, Jiansheng, Li, Jing, Zhou, Shuchang, Zhang, Xiangyu, Zhang, Xinhao, and Zhu, Yibo
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Real-time speech interaction, serving as a fundamental interface for human-machine collaboration, holds immense potential. However, current open-source models face limitations such as high costs in voice data collection, weakness in dynamic control, and limited intelligence. To address these challenges, this paper introduces Step-Audio, the first production-ready open-source solution. Key contributions include: 1) a 130B-parameter unified speech-text multi-modal model that achieves unified understanding and generation, with the Step-Audio-Chat version open-sourced; 2) a generative speech data engine that establishes an affordable voice cloning framework and produces the open-sourced lightweight Step-Audio-TTS-3B model through distillation; 3) an instruction-driven fine control system enabling dynamic adjustments across dialects, emotions, singing, and RAP; 4) an enhanced cognitive architecture augmented with tool calling and role-playing abilities to manage complex tasks effectively. Based on our new StepEval-Audio-360 evaluation benchmark, Step-Audio achieves state-of-the-art performance in human evaluations, especially in terms of instruction following. On open-source benchmarks like LLaMA Question, shows 9.3% average performance improvement, demonstrating our commitment to advancing the development of open-source multi-modal language technologies. Our code and models are available at https://github.com/stepfun-ai/Step-Audio.
Published: 2025

17. FitLight: Federated Imitation Learning for Plug-and-Play Autonomous Traffic Signal Control

Author: Ye, Yutong, Zhou, Yingbo, Liu, Zhusen, Du, Xiao, Zhou, Hao, Lian, Xiang, and Chen, Mingsong
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Although Reinforcement Learning (RL)-based Traffic Signal Control (TSC) methods have been extensively studied, their practical applications still raise some serious issues such as high learning cost and poor generalizability. This is because the ``trial-and-error'' training style makes RL agents extremely dependent on the specific traffic environment, which also requires a long convergence time. To address these issues, we propose a novel Federated Imitation Learning (FIL)-based framework for multi-intersection TSC, named FitLight, which allows RL agents to plug-and-play for any traffic environment without additional pre-training cost. Unlike existing imitation learning approaches that rely on pre-training RL agents with demonstrations, FitLight allows real-time imitation learning and seamless transition to reinforcement learning. Due to our proposed knowledge-sharing mechanism and novel hybrid pressure-based agent design, RL agents can quickly find a best control policy with only a few episodes. Moreover, for resource-constrained TSC scenarios, FitLight supports model pruning and heterogeneous model aggregation, such that RL agents can work on a micro-controller with merely 16{\it KB} RAM and 32{\it KB} ROM. Extensive experiments demonstrate that, compared to state-of-the-art methods, FitLight not only provides a superior starting point but also converges to a better final solution on both real-world and synthetic datasets, even under extreme resource limitations.
Published: 2025

18. CRB-Rate Tradeoff in RSMA-enabled Near-Field Integrated Multi-Target Sensing and Multi-User Communications

Author: Zhou, Jiasi, Zhou, Cong, Sun, Yanjing, and Tellambura, Chintha
Subjects: Computer Science - Information Theory
Abstract: Extremely large-scale antenna arrays enhance spectral efficiency and spatial resolution in integrated sensing and communication (ISAC) networks while expanding the Rayleigh distance, triggering a shift from conventional far-field plane waves to near-field (NF) spherical waves. However, full-digital beamforming is infeasible due to the need for dedicated radio frequency (RF) chains. To address this, NF-ISAC with a rate-splitting multiple access (RSMA) scheme is developed for advanced interference management, considering fully-connected and partially-connected hybrid analog and digital (HAD) beamforming architectures. Specifically, the Cram\'{e}r-Rao bound (CRB) for joint distance and angle sensing is derived, and the achievable performance region between the max-min communication rate and the multi-target CRB is defined. To fully characterize the Pareto boundary of the CRB-rate region, a sensing-centric minimization problem is formulated under communication rate constraints for two HAD beamforming architectures. A penalty dual decomposition (PDD)-based double-loop algorithm is developed to optimize fully-connected HAD beamformers. To reduce computational complexity, a two-stage design algorithm for fully connected HAD beamforming is also proposed. Additionally, the PDD-based double-loop algorithm is extended to the partially-connected HAD architecture. Simulations demonstrate the proposed schemes and algorithms: 1) achieve performance comparable to a fully digital beamformer with fewer RF chains, 2) outperform space division multiple access and far-field ISAC, and 3) yield enhanced CRB-rate trade-off performance., Comment: 13 pages, 9 figures
Published: 2025

19. CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language Models

Author: Zhou, Guanghao, Qiu, Panjia, Fan, Mingyuan, Chen, Cen, Chu, Mingyuan, Zhang, Xin, and Zhou, Jun
Subjects: Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: Despite explicit alignment efforts for large language models (LLMs), they can still be exploited to trigger unintended behaviors, a phenomenon known as "jailbreaking." Current jailbreak attack methods mainly focus on discrete prompt manipulations targeting closed-source LLMs, relying on manually crafted prompt templates and persuasion rules. However, as the capabilities of open-source LLMs improve, ensuring their safety becomes increasingly crucial. In such an environment, the accessibility of model parameters and gradient information by potential attackers exacerbates the severity of jailbreak threats. To address this research gap, we propose a novel \underline{C}ontext-\underline{C}oherent \underline{J}ailbreak \underline{A}ttack (CCJA). We define jailbreak attacks as an optimization problem within the embedding space of masked language models. Through combinatorial optimization, we effectively balance the jailbreak attack success rate with semantic coherence. Extensive evaluations show that our method not only maintains semantic consistency but also surpasses state-of-the-art baselines in attack effectiveness. Additionally, by integrating semantically coherent jailbreak prompts generated by our method into widely used black-box methodologies, we observe a notable enhancement in their success rates when targeting closed-source commercial LLMs. This highlights the security threat posed by open-source LLMs to commercial counterparts. We will open-source our code if the paper is accepted.
Published: 2025

20. Progress of the TianQin project

Author: Luo, Jun, Bai, Shaojun, Bai, Yan-Zheng, Cai, Lin, Dang, Hao, Dong, Qijia, Duan, Hui-Zong, Du, Yuanbo, Fan, Lei, Fu, Xinju, Gao, Yong, Gou, Xingyu, Guo, Changlei, Hong, Wei, Hu, Bin, Hu, Heran, Hu, Ming, Hu, Yi-Ming, Huang, Fa Peng, Gu, Defeng, Ji, Xin, Jiang, Yuan-Ze, Li, En-Kun, Li, Hongyin, Li, Ming, Li, Yong, Li, Zhu, Li, Zizheng, Lian, JunXiang, Liang, Yu-Rong, Lin, Xudong, Liu, Jianping, Liu, Lin-Xia, Liu, Kui, Liu, Li, Liu, Minghe, Liu, Qi, Liu, Yan-Chong, Liu, Yue, Luo, Peng-Shun, Luo, Yingxin, Ma, Yi-Qiu, Ma, Yun, Meng, Yunhe, Milyukov, Vadim, Peng, Jian-Guo, Postnov, Konstantin, Qu, Shao-Bo, Shan, Tilei, Shao, Cheng-Gang, Shi, Changfu, Song, Pei-Yi, Song, Yunfei, Su, Wei, Tan, Ding Yin, Tan, Shuping, Tan, Yu-Jie, Tan, Wenhai, Tu, Liangcheng, Wang, Cheng-Rui, Wang, Guoyong, Wang, Lijiao, Wang, Pan-Pan, Wang, Shun, Wang, Xiaoyong, Wang, Xudong, Wang, Yan, Wei, Ran, Wu, Shu-Chao, Xu, Jie, Xu, Zhi-Lin, Xue, Chao, Yan, Hao, Yan, Yong, Yang, Changpeng, Yang, Shanqing, Yeh, Hsien-Chi, Yin, Hang, Tong, Yelong, Yu, Jian-Bo, Yuan, Wen-Hao, Zhang, Bu-Tian, Zhang, Dexuan, Zhang, Jian-dong, Zhang, Jie, Zhang, Lihua, Zhang, Xuefeng, Zhao, Guoying, Zhao, Liqian, Zhao, Xin, Zhou, An-Nan, Zhou, Hao, Zhou, Peng, Zhou, Yupeng, Zhou, Ze-Bing, Zhu, Fan, Zhu, Liang-Gui, Zhu, Lin, Zou, Kui, and Mei, Jianwei
Subjects: General Relativity and Quantum Cosmology, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: TianQin is a future space-based gravitational wave observatory targeting the frequency window of $10^{-4}$ Hz $\sim 1$ Hz. A large variety of gravitational wave sources are expected in this frequency band, including the merger of massive black hole binaries, the inspiral of extreme/intermediate mass ratio systems, stellar-mass black hole binaries, Galactic compact binaries, and so on. TianQin will consist of three Earth orbiting satellites on nearly identical orbits with orbital radii of about $10^5$ km. The satellites will form a normal triangle constellation whose plane is nearly perpendicular to the ecliptic plane. The TianQin project has been progressing smoothly following the ``0123" technology roadmap. In step ``0", the TianQin laser ranging station has been constructed and it has successfully ranged to all the five retro-reflectors on the Moon. In step ``1", the drag-free control technology has been tested and demonstrated using the TianQin-1 satellite. In step ``2", the inter-satellite laser interferometry technology will be tested using the pair of TianQin-2 satellites. The TianQin-2 mission has been officially approved and the satellites will be launched around 2026. In step ``3", i.e., the TianQin-3 mission, three identical satellites will be launched around 2035 to form the space-based gravitational wave detector, TianQin, and to start gravitational wave detection in space., Comment: 45 pages, 3 figures
Published: 2025

21. Search for the Cabibbo-suppressed decays $\Lambda_c^{+}\to\Sigma^0K^{+}\pi^{0}$ and $\Lambda_c^{+}\to\Sigma^0K^{+}\pi^{+}\pi^{-}$

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., 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, M. 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, X. Y., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. K., Choi, S. K., Chu, X., 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., Ding, Y. X., 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, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., 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., Gao, Y. N., Gao, Y. Y., Garbolino, S., Garzia, I., 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, K. D., 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, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Hu, Z. M., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, P., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hüsken, N., der Wiesche, N. in, Jackson, J., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. J., 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., Lan, Q., Lan, W. N., Lei, T. T., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, C. K., 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, Lei, Li, M. H., Li, M. R., Li, P. L., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. B., Liao, M. H., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, L. Q., 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. J., 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, W. T., Liu, X., Liu, X. Y., 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. H., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, J. S., Luo, M. X., Luo, T., Luo, X. L., Lv, Z. Y., Lyu, X. R., Lyu, Y. F., Lyu, Y. H., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, Q. M., Ma, R. Q., Ma, R. Y., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Malik, Q. A., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Y. H., 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., 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., Qi, F. Z., Qi, H. R., Qi, M., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qin, J. J., Qin, J. L., Qin, L. Q., Qin, L. Y., Qin, P. B., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Rivetti, A., Rolo, M., Rong, G., Rong, S. S., 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, J. L., Shi, J. Y., Shi, S. Y., Shi, X., Song, H. L., 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, Y. C., Sun, Y. H., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, J. J., Tang, L. F., Tang, Y. A., Tao, L. Y., Tat, M., Teng, J. X., Tian, J. Y., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wang, B., Wang, Bo, Wang, C., Wang, Cong, Wang, D. Y., Wang, H. J., Wang, J. J., Wang, K., Wang, L. L., Wang, L. W., 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. J., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Yuan, Wang, Z., Wang, Z. L., Wang, Z. Q., Wang, Z. Y., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, Lianjie, Wu, S. G., Wu, S. M., Wu, X., Wu, X. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, K. J., 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, T. D., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, H. Y., Yan, L., Yan, W. B., Yan, W. C., Yan, W. H., Yan, W. P., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, R. J., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. H., Yang, Y. Q., Yang, Y. X., Yang, Y. Z., Ye, M., Ye, M. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, L. Q., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, H., Yuan, J., Yuan, L., Yuan, S. C., Yuan, X. Q., Yuan, Y., Yuan, Z. Y., Yue, C. X., Yue, Ying, Zafar, A. A., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., 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, N., Zhang, P., Zhang, Q., 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, Y. P., Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. L., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, X. R., Zheng, Y. H., Zhong, B., Zhong, C., Zhou, H., Zhou, J. Y., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., 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, W. J., Zhu, W. Z., Zhu, Y. C., Zhu, Z. A., Zhuang, X. Y., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: Utilizing 4.5 $fb^-$ of $e^+e^-$ annihilation data collected at center-of-mass energies ranging from 4599.53 MeV to 4698.82 MeV by the BESIII detector at the BEPCII collider, we search for the singly Cabibbo-suppressed hadronic decays $\Lambda_{c}^{+}\to\Sigma^{0} K^{+}\pi^{0}$ and $\Lambda_{c}^{+}\to\Sigma^{0}K^{+}\pi^+\pi^-$ with a single-tag method. No significant signals are observed for both decays. The upper limits on the branching fractions at the $90\%$ confidence level are determined to be $5.0\times 10^{-4}$ for $\Lambda_{c}^{+}\to\Sigma^{0} K^{+}\pi^{0}$ and $6.5\times 10^{-4}$ for $\Lambda_c^{+}\to\Sigma^0K^{+}\pi^{+}\pi^{-}$., Comment: 12 pages, 6 figures
Published: 2025

22. Open-Set Cross-Network Node Classification via Unknown-Excluded Adversarial Graph Domain Alignment

Author: Shen, Xiao, Chen, Zhihao, Pan, Shirui, Zhou, Shuang, Yang, Laurence T., and Zhou, Xi
Subjects: Computer Science - Social and Information Networks
Abstract: Existing cross-network node classification methods are mainly proposed for closed-set setting, where the source network and the target network share exactly the same label space. Such a setting is restricted in real-world applications, since the target network might contain additional classes that are not present in the source. In this work, we study a more realistic open-set cross-network node classification (O-CNNC) problem, where the target network contains all the known classes in the source and further contains several target-private classes unseen in the source. Borrowing the concept from open-set domain adaptation, all target-private classes are defined as an additional unknown class. To address the challenging O-CNNC problem, we propose an unknown-excluded adversarial graph domain alignment (UAGA) model with a separate-adapt training strategy. Firstly, UAGA roughly separates known classes from unknown class, by training a graph neural network encoder and a neighborhood-aggregation node classifier in an adversarial framework. Then, unknown-excluded adversarial domain alignment is customized to align only target nodes from known classes with the source, while pushing target nodes from unknown class far away from the source, by assigning positive and negative domain adaptation coefficient to known class nodes and unknown class nodes. Extensive experiments on real-world datasets demonstrate significant outperformance of the proposed UAGA over state-of-the-art methods on O-CNNC.
Published: 2025

23. Angular analysis of $B^0\rightarrow K^{*0}e^{+}e^{-}$ decays

Author: LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Bellee, V., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Godinez, J. Castro, Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Epple, E., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Garau, M., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Gomes, A., Fernandez, S. Gomez, Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guerry, L., Guittiere, M., Guliaeva, V., Günther, P. 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F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Santos, D. Martinez, Vidal, F. Martinez, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Nolte, N. S., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. 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Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Lamas, M. Romero, Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Sellam, S., Semennikov, A., Senger, T., Soares, M. 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W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, D., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.
Subjects: High Energy Physics - Experiment
Abstract: An angular analysis of $B^0\rightarrow K^{*0}e^{+}e^{-}$ decays is presented using proton-proton collision data collected by the LHCb experiment at centre-of-mass energies of 7, 8 and 13 TeV, corresponding to an integrated luminosity of 9 fb$^{-1}$. The analysis is performed in the region of the dilepton invariant mass squared of 1.1-6.0 GeV$^{2}/c^{4}$. In addition, a test of lepton flavour universality is performed by comparing the obtained angular observables with those measured in $B^0\rightarrow K^{*0}\mu^{+}\mu^{-}$ decays. In general, the angular observables are found to be consistent with the Standard Model expectations as well as with global analyses of other $b \rightarrow s \ell^{+} \ell^{-}$ processes, where $\ell$ is either a muon or an electron. No sign of lepton-flavour-violating effects is observed., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/1628/ (LHCb public pages)
Published: 2025

24. Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model

Author: Ma, Guoqing, Huang, Haoyang, Yan, Kun, Chen, Liangyu, Duan, Nan, Yin, Shengming, Wan, Changyi, Ming, Ranchen, Song, Xiaoniu, Chen, Xing, Zhou, Yu, Sun, Deshan, Zhou, Deyu, Zhou, Jian, Tan, Kaijun, An, Kang, Chen, Mei, Ji, Wei, Wu, Qiling, Sun, Wen, Han, Xin, Wei, Yanan, Ge, Zheng, Li, Aojie, Wang, Bin, Huang, Bizhu, Wang, Bo, Li, Brian, Miao, Changxing, Xu, Chen, Wu, Chenfei, Yu, Chenguang, Shi, Dapeng, Hu, Dingyuan, Liu, Enle, Yu, Gang, Yang, Ge, Huang, Guanzhe, Yan, Gulin, Feng, Haiyang, Nie, Hao, Jia, Haonan, Hu, Hanpeng, Chen, Hanqi, Yan, Haolong, Wang, Heng, Guo, Hongcheng, Xiong, Huilin, Xiong, Huixin, Gong, Jiahao, Wu, Jianchang, Wu, Jiaoren, Wu, Jie, Yang, Jie, Liu, Jiashuai, Li, Jiashuo, Zhang, Jingyang, Guo, Junjing, Lin, Junzhe, Li, Kaixiang, Liu, Lei, Xia, Lei, Zhao, Liang, Tan, Liguo, Huang, Liwen, Shi, Liying, Li, Ming, Li, Mingliang, Cheng, Muhua, Wang, Na, Chen, Qiaohui, He, Qinglin, Liang, Qiuyan, Sun, Quan, Sun, Ran, Wang, Rui, Pang, Shaoliang, Yang, Shiliang, Liu, Sitong, Liu, Siqi, Gao, Shuli, Cao, Tiancheng, Wang, Tianyu, Ming, Weipeng, He, Wenqing, Zhao, Xu, Zhang, Xuelin, Zeng, Xianfang, Liu, Xiaojia, Yang, Xuan, Dai, Yaqi, Yu, Yanbo, Li, Yang, Deng, Yineng, Wang, Yingming, Wang, Yilei, Lu, Yuanwei, Chen, Yu, Luo, Yu, Luo, Yuchu, Yin, Yuhe, Feng, Yuheng, Yang, Yuxiang, Tang, Zecheng, Zhang, Zekai, Yang, Zidong, Jiao, Binxing, Chen, Jiansheng, Li, Jing, Zhou, Shuchang, Zhang, Xiangyu, Zhang, Xinhao, Zhu, Yibo, Shum, Heung-Yeung, and Jiang, Daxin
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract: We present Step-Video-T2V, a state-of-the-art text-to-video pre-trained model with 30B parameters and the ability to generate videos up to 204 frames in length. A deep compression Variational Autoencoder, Video-VAE, is designed for video generation tasks, achieving 16x16 spatial and 8x temporal compression ratios, while maintaining exceptional video reconstruction quality. User prompts are encoded using two bilingual text encoders to handle both English and Chinese. A DiT with 3D full attention is trained using Flow Matching and is employed to denoise input noise into latent frames. A video-based DPO approach, Video-DPO, is applied to reduce artifacts and improve the visual quality of the generated videos. We also detail our training strategies and share key observations and insights. Step-Video-T2V's performance is evaluated on a novel video generation benchmark, Step-Video-T2V-Eval, demonstrating its state-of-the-art text-to-video quality when compared with both open-source and commercial engines. Additionally, we discuss the limitations of current diffusion-based model paradigm and outline future directions for video foundation models. We make both Step-Video-T2V and Step-Video-T2V-Eval available at https://github.com/stepfun-ai/Step-Video-T2V. The online version can be accessed from https://yuewen.cn/videos as well. Our goal is to accelerate the innovation of video foundation models and empower video content creators., Comment: 36 pages, 14 figures
Published: 2025

25. Evidence of the P_ccbars(4459)0 in Upsilon(1S, 2S) inclusive decays at Belle

Author: Adachi, I., Aggarwal, L., Ahmed, H., Ahn, J. K., Aihara, H., Akopov, N., Alhakami, M., Aloisio, A., Althubiti, N., Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Baghel, N. K., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Bartl, 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., Bhardwaj, V., Bhuyan, B., Bianchi, F., Biswas, D., Bodrov, D., Bolz, A., Boschetti, A., Bozek, A., Bracko, 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., Cochran, J., Corona, L., Cui, J. X., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Capua, F., Dingfelder, J., Dolezal, Z., Jimenez, I. Domnguez, Dong, T. V., Dong, X., Dossett, D., Dugic, K., Dujany, G., Ecker, P., Eppelt, J., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Forti, F., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Gaudino, G., Gaur, V., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Glazov, A., Gobbo, B., Godang, R., Goldenzweig, P., Graziani, E., Greenwald, D., Gruberova, Z., Guan, Y., Gudkova, K., Haide, I., Han, Y., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernandez, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobi, D., Jacobs, W. W., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kaleta, M., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Ketter, C., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodys, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Krizan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, D., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lalwani, K., Lam, T., Lange, J. S., Lau, T. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Lewis, P. M., Li, C., Li, L. K., Li, Q. M., Li, W. Z., Li, Y., Li, Y. B., Liao, Y. P., Libby, J., Lin, J., Liu, M. H., Liu, Q. Y., Liu, Y., Liu, Z. Q., Liventsev, D., Longo, S., Lyu, C., Ma, Y., Madaan, C., Maggiora, M., Maharana, S. P., Maiti, R., 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., Maushart, M., McKenna, J. A., Mehta, R., Meier, F., Meleshko, D., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, H., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Nishida, S., Ogawa, S., Ono, H., Onuki, Y., Otani, F., Pakhlova, G., Pardi, S., Parham, K., Park, H., Park, J., Park, K., Park, S. -H., Paschen, B., Patra, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Ravindran, K., Rehman, J. U., Reif, M., Reiter, S., Remnev, M., Reuter, L., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schmitz, J., Schneider, S., Schnell, G., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Song, W., Staric, M., Stavroulakis, P., Stefkova, S., Stroili, R., Strube, J., 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., Tsaklidis, I., Uchida, M., Ueda, I., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Vossen, A., Wakai, M., Wallner, S., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yuan, J., Zani, L., Zeng, F., Zhang, B., Zhou, J. S., Zhou, Q. D., Zhu, L., Zhukova, V. I., Zlebck, R., and Zou, S.
Subjects: High Energy Physics - Experiment
Abstract: Using data samples of 102 million Upsilon(1S) events and 158 million Upsilon(2S) events collected by the Belle detector at the KEKB asymmetric-energy $e^+e^-$ collider, we search for [udsccbar] pentaquark states decaying to Jpsi Lambda. Using the first observations of Upsilon(1S, 2S) inclusive decays to Jpsi Lambda, we find evidence of the P_ccbars(4459)0 state with a significance of 3.3 standard deviations, including statistical and systematic uncertainties. We measure the mass and width of the Pccbars(4459)0 to be (4471.7 +- 4.8 +- 0.6) MeV/c2 and (21.9 +- 13.1 +- 2.7) MeV, respectively. The branching fractions for P_ccbars(4459)0 production are measured to be B[Upsilon(1S) -> P_ccbars(4459)0/ Pbar_ccbars(4459)0 + anything] = (3.5 +- 2.0 +- 0.2)*10-6 and B[Upsilin(2S) -> P_ccbars(4459)0/ Pbar_ccbars(4459)0 +anything] = (2.9 +- 1.7 +- 0.4)*10-6. The inclusive branching fractions of Upsilon(1S, 2S) -> Jpsi Lambda/Lambdabar are measured to be B[Upsilin(1S) -> Jpsi Lambda/Lambdabar + anything] = (36.9 +- 5.3 +- 2.4)*10-6 and B[Upsilon(2S) -> Jpsi Lambda/Lambdabar + anything] = (22.3 +- 5.7 +- 3.1)*10-6. We measure the visible cross section $\sigma(e^+e^- \to J/psi \Lambda/\bar\Lambda$ + anything) = (90 +- 14 +- 6) fb for the continuum production at $\sqrt{s} = 10.52$ GeV. In all cases, the first uncertainties are statistical and the second are systematic., Comment: 8 pages, 3 figures
Published: 2025

26. Precise Measurement of the $\chi_{c0}$ Resonance Parameters and Branching Fractions of $\chi_{c0,c2}\to\pi^+\pi^-/K^+K^-$

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, T. Y., 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, 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: By analyzing a $\psi(3686)$ data sample containing $(107.7\pm0.6)\times10^{6}$ events taken with the BESIII detector at the BEPCII storage ring in 2009, the $\chi_{c0}$ resonance parameters are precisely measured using $\chi_{c0,c2} \to \pi^+\pi^-/K^+K^-$ events. The mass of $\chi_{c0}$ is determined to be $M(\chi_{c0})=(3415.67\pm0.07\pm0.06\pm0.07$)~MeV/$c^2$, and its full width is $\Gamma(\chi_{c0})=(12.44\pm0.12\pm0.12)~{\rm MeV}$, where the first uncertainty is statistical, the second systematic, and the third for mass comes from $\chi_{c2}$ mass uncertainty. These measurements improve the precision of $\chi_{c0}$ mass by a factor of four and width by one order of magnitude over the previous individual measurements, and significantly boost our knowledge about the charmonium spectrum. Together with additional $(345.4\pm2.6)\times10^{6}$ $\psi(3686)$ data events taken in 2012, the decay branching fractions of $\chi_{c0,c2}\to\pi^+\pi^-/K^+K^-$ are measured as well, with precision improved by a factor of three compared to previous measurements. These $\chi_{c0}$ decay branching fractions provide important inputs for the study of glueballs., Comment: 9 pages, 1 figure
Published: 2025

27. Non-Reciprocal Current-Phase Relation and Superconducting Diode Effect in Topological-Insulator-Based Josephson Junctions

Author: Kudriashov, A., Zhou, X., Hovhannisyan, R. A., Frolov, A., Elesin, L., Wang, Y., Zharkova, E. V., Taniguchi, T., Watanabe, K., Yashina, L. A., Liu, Z., Zhou, Xin, Novoselov, K. S., and Bandurin, D. A.
Subjects: Condensed Matter - Superconductivity, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: Josephson junctions (JJ) are essential for superconducting quantum technologies and searches of self-conjugate quasiparticles, pivotal for fault-tolerant quantum computing. Measuring the current-phase relation (CPR) in JJ based on topological insulators (TI) can provide critical insights into unconventional phenomena in these systems, such as the presence of Majorana bound states (MBS) and the nature of non-reciprocal transport. However, reconstructing CPR as a function of magnetic field in such JJs has remained experimentally challenging. Here, we introduce a platform for precise CPR measurements in planar JJs composed of NbSe$_2$ and few layer thick Bi$_2$Se$_3$ (TI) as a function of magnetic field. When a single flux quantum $\Phi_\mathrm{0}$ threads the junction, we observe anomalous peak-dip-shaped CPR behaviour and non-reciprocal supercurrent flow. We demonstrate that these anomalies stem from the edge-amplified sloped supercurrent profile rather than MBS signatures often invoked to explain puzzles emerging near $\Phi_\mathrm{0}$ in TI-based JJ. Furthermore, we show that such a supercurrent profile gives rise to a previously overlooked, robust and tunable Josephson diode effect. These findings establish field-dependent CPR measurements as a critical tool for exploring topological superconducting devices and offer new design principles for non-reciprocal superconducting electronics., Comment: 10 pages, 5 figures
Published: 2025

28. Floquet-Volkov interference in a semiconductor

Author: Bao, Changhua, Zhong, Haoyuan, Fan, Benshu, Cai, Xuanxi, Wang, Fei, Zhou, Shaohua, Lin, Tianyun, Zhang, Hongyun, Yu, Pu, Tang, Peizhe, Duan, Wenhui, and Zhou, Shuyun
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science, Physics - Optics
Abstract: Intense light-field can dress both Bloch electrons inside crystals and photo-emitted free electrons in the vacuum, dubbed as Floquet and Volkov states respectively. These quantum states can further interfere coherently, modulating light-field dressed states. Here, we report experimental evidence of the Floquet-Volkov interference in a semiconductor - black phosphorus. A highly asymmetric modulation of the spectral weight is observed for the Floquet-Volkov states, and such asymmetry can be further controlled by rotating the pump polarization. Our work reveals the quantum interference between different light-field dressed electronic states, providing insights for material engineering on the ultrafast timescale.
Published: 2025
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29. Position reconstruction and surface background model for the PandaX-4T detector

Author: Qian, Zhicheng, Gu, Linhui, Cheng, Chen, Bo, Zihao, Chen, Wei, Chen, Xun, Chen, Yunhua, Cheng, Zhaokan, Cui, Xiangyi, Fan, Yingjie, Fang, Deqing, Gao, Zhixing, Geng, Lisheng, Giboni, Karl, Guo, Xunan, Guo, Xuyuan, Guo, Zichao, Han, Chencheng, Han, Ke, He, Changda, He, Jinrong, Huang, Di, Huang, Houqi, Huang, Junting, Hou, Ruquan, Hou, Yu, Ji, Xiangdong, Ji, Xiangpan, Ju, Yonglin, Li, Chenxiang, Li, Jiafu, Li, Mingchuan, Li, Shuaijie, Li, Tao, Li, Zhiyuan, Lin, Qing, Liu, Jianglai, Lu, Congcong, Lu, Xiaoying, Luo, Lingyin, Luo, Yunyang, Ma, Wenbo, Ma, Yugang, Mao, Yajun, Meng, Yue, Ning, Xuyang, Pang, Binyu, Qi, Ningchun, Ren, Xiangxiang, Shan, Dong, Shang, Xiaofeng, Shao, Xiyuan, Shen, Guofang, Shen, Manbin, Sun, Wenliang, Tao, Yi, Wang, Anqing, Wang, Guanbo, Wang, Hao, Wang, Jiamin, Wang, Lei, Wang, Meng, Wang, Qiuhong, Wang, Shaobo, Wang, Siguang, Wang, Wei, Wang, Xiuli, Wang, Xu, Wang, Zhou, Wei, Yuehuan, Wu, Weihao, Wu, Yuan, Xiao, Mengjiao, Xiao, Xiang, Xiong, Kaizhi, Xu, Yifan, Yao, Shunyu, Yan, Binbin, Yan, Xiyu, Yang, Yong, Ye, Peihua, Yu, Chunxu, Yuan, Ying, Yuan, Zhe, Yun, Youhui, Zeng, Xinning, Zhang, Minzhen, Zhang, Peng, Zhang, Shibo, Zhang, Shu, Zhang, Tao, Zhang, Wei, Zhang, Yang, Zhang, Yingxin, Zhang, Yuanyuan, Zhao, Li, Zhou, Jifang, Zhou, Jiaxu, Zhou, Jiayi, Zhou, Ning, Zhou, Xiaopeng, Zhou, Yubo, and Zhou, Zhizhen
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: We report the position reconstruction methods and surface background model for the PandaX-4T dark matter direct search experiment. This work develops two position reconstruction algorithms: template matching (TM) method and photon acceptance function (PAF) method. Both methods determine the horizontal position of events based on the light pattern of secondary scintillation collected by the light sensors. After a comprehensive evaluation of resolution, uniformity, and robustness, the PAF method was selected for position reconstruction, while the TM method was employed for verification. The PAF method achieves a bulk event resolution of 1.0 mm and a surface event resolution of 4.4 mm for a typical $S2$ signal with a bottom charge of 1500 PE (about 14 keV). The uniformity is around 20\%. Robustness studies reveal average deviations of 5.1 mm and 8.8 mm for the commissioning run (Run0) and the first science run (Run1), respectively, due to the deactivation of certain PMTs. A data-driven surface background model is developed based on the PAF method. The surface background is estimated to be $0.09 \pm 0.06$ events for Run0 (0.54 tonne$\cdot$year) and $0.17 \pm 0.11$ events for Run1 (1.00 tonne$\cdot$year)., Comment: 22 pages, 15 figures, 2 tables
Published: 2025

30. Search for $e^+e^-\to K_S^0 K_S^0 h_c$

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., 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, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., 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, 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, P., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., 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., Lan, W. N., 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, Lei, Li, M. H., Li, P. L., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y., 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, 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, 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, Q. M., Ma, R. Q., Ma, R. Y., 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, Y. H., 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. R., Qi, M., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., 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, J. L., Shi, J. Y., 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, 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., 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, Bo, Wang, C., Wang, D. Y., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., 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., 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, Lianjie, Wu, X., Wu, X. H., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., 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, M., Xu, Q. J., Xu, Q. N., 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, W. P., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, R. J., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Y. Z., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. 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., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Yue, Ying, 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., 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. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., 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, X. R., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., 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, W. J., Zhu, W. Z., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: Using $e^+e^-$ collision data at 13 center-of-mass energies ranging from 4.600 to 4.950 GeV collected with the BESIII detector, we search for the unmeasured $e^+e^-\to K_S^0 K_S^0 h_c$ process . No significant signal is observed, and the upper limits of the Born cross sections at each center-of-mass energy are presented.
Published: 2025

31. Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?

Author: Hou, Qingshan, Zhou, Yukun, Goh, Jocelyn Hui Lin, Zou, Ke, Yew, Samantha Min Er, Srinivasan, Sahana, Wang, Meng, Lo, Thaddaeus, Lei, Xiaofeng, Wagner, Siegfried K., Chia, Mark A., Yang, Dawei, Jiang, Hongyang, Ran, AnRan, Santos, Rui, Somfai, Gabor Mark, Zhou, Juan Helen, Chen, Haoyu, Chen, Qingyu, Cheung, Carol Yim-Lui, Keane, Pearse A., and Tham, Yih Chung
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: The advent of foundation models (FMs) is transforming medical domain. In ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1.4 million natural images and 1.6 million retinal images, has demonstrated high adaptability across clinical applications. Conversely, DINOv2, a general-purpose vision FM pre-trained on 142 million natural images, has shown promise in non-medical domains. However, its applicability to clinical tasks remains underexplored. To address this, we conducted head-to-head evaluations by fine-tuning RETFound and three DINOv2 models (large, base, small) for ocular disease detection and systemic disease prediction tasks, across eight standardized open-source ocular datasets, as well as the Moorfields AlzEye and the UK Biobank datasets. DINOv2-large model outperformed RETFound in detecting diabetic retinopathy (AUROC=0.850-0.952 vs 0.823-0.944, across three datasets, all P<=0.007) and multi-class eye diseases (AUROC=0.892 vs. 0.846, P<0.001). In glaucoma, DINOv2-base model outperformed RETFound (AUROC=0.958 vs 0.940, P<0.001). Conversely, RETFound achieved superior performance over all DINOv2 models in predicting heart failure, myocardial infarction, and ischaemic stroke (AUROC=0.732-0.796 vs 0.663-0.771, all P<0.001). These trends persisted even with 10% of the fine-tuning data. These findings showcase the distinct scenarios where general-purpose and domain-specific FMs excel, highlighting the importance of aligning FM selection with task-specific requirements to optimise clinical performance.
Published: 2025

32. Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

Author: Imran, Muhammad, Krebs, Jonathan R., Sivaraman, Vishal Balaji, Zhang, Teng, Kumar, Amarjeet, Ueland, Walker R., Fassler, Michael J., Huang, Jinlong, Sun, Xiao, Wang, Lisheng, Shi, Pengcheng, Rokuss, Maximilian, Baumgartner, Michael, Kirchhof, Yannick, Maier-Hein, Klaus H., Isensee, Fabian, Liu, Shuolin, Han, Bing, Nguyen, Bong Thanh, Shin, Dong-jin, Ji-Woo, Park, Choi, Mathew, Uhm, Kwang-Hyun, Ko, Sung-Jea, Lee, Chanwoong, Chun, Jaehee, Kim, Jin Sung, Zhang, Minghui, Zhang, Hanxiao, You, Xin, Gu, Yun, Pan, Zhaohong, Liu, Xuan, Liang, Xiaokun, Tiefenthaler, Markus, Almar-Munoz, Enrique, Schwab, Matthias, Kotyushev, Mikhail, Epifanov, Rostislav, Wodzinski, Marek, Muller, Henning, Qayyum, Abdul, Mazher, Moona, Niederer, Steven A., Wang, Zhiwei, Yang, Kaixiang, Ren, Jintao, Korreman, Stine Sofia, Gao, Yuchong, Zeng, Hongye, Zheng, Haoyu, Zheng, Rui, Yue, Jinghua, Zhou, Fugen, Liu, Bo, Cosman, Alexander, Liang, Muxuan, Zhao, Chang, Upchurch Jr., Gilbert R., Ma, Jun, Zhou, Yuyin, Cooper, Michol A., and Shao, Wei
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://aortaseg24.grand-challenge.org.
Published: 2025

33. AdaFlow: Efficient Long Video Editing via Adaptive Attention Slimming And Keyframe Selection

Author: Zhang, Shuheng, Liu, Yuqi, Zhou, Hongbo, Peng, Jun, Zhou, Yiyi, Sun, Xiaoshuai, and Ji, Rongrong
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Despite great progress, text-driven long video editing is still notoriously challenging mainly due to excessive memory overhead. Although recent efforts have simplified this task into a two-step process of keyframe translation and interpolation generation, the token-wise keyframe translation still plagues the upper limit of video length. In this paper, we propose a novel and training-free approach towards efficient and effective long video editing, termed AdaFlow. We first reveal that not all tokens of video frames hold equal importance for keyframe translation, based on which we propose an Adaptive Attention Slimming scheme for AdaFlow to squeeze the $KV$ sequence, thus increasing the number of keyframes for translations by an order of magnitude. In addition, an Adaptive Keyframe Selection scheme is also equipped to select the representative frames for joint editing, further improving generation quality. With these innovative designs, AdaFlow achieves high-quality long video editing of minutes in one inference, i.e., more than 1$k$ frames on one A800 GPU, which is about ten times longer than the compared methods, e.g., TokenFlow. To validate AdaFlow, we also build a new benchmark for long video editing with high-quality annotations, termed LongV-EVAL. Our code is released at: https://github.com/jidantang55/AdaFlow.
Published: 2025

34. Broadband $\gamma$-ray spectrum of supernova remnant Cassiopeia A

Author: Cao, Zhen, Aharonian, F., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Bian, W., Bukevich, A. V., Cai, C. M., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, H. X., Chen, Liang, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, X. B., Chen, X. J., Chen, Y., Cheng, N., Cheng, Y. D., Chu, M. C., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Diao, Y. X., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, J. H., Fang, K., Feng, C. F., Feng, H., Feng, L., Feng, S. H., Feng, X. T., Feng, Y., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Ge, T. T., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, J., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., Hannuksela, O. A., Hasan, M., He, H. H., He, H. N., He, J. Y., He, X. Y., He, Y., Hernández-Cadena, S., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, S. C., Huang, C., Huang, D. H., Huang, J. J., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Y. Y., Ji, X. L., Jia, H. Y., Jia, K., Jiang, H. B., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kaci, S., Kang, M. M., Karpikov, I., Khangulyan, D., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, Jian, Li, Jie, Li, K., Li, L., Li, R. L., Li, S. D., Li, T. Y., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, D. B., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. R., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, X., Liu, Y., Liu, Y. N., Lou, Y. Q., Luo, Q., Luo, Y., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mou, G. B., Mu, H. J., Nan, Y. C., Neronov, A., Ng, K. C. Y., Ni, M. Y., Nie, L., Ou, L. J., Pattarakijwanich, P., Pei, Z. Y., Qi, J. C., Qi, M. Y., Qin, J. J., Raza, A., Ren, C. Y., Ruffolo, D., Sáiz, A., Saeed, M., Semikoz, D., Shao, L., Shchegolev, O., Shen, Y. Z., Sheng, X. D., Shi, Z. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, D. X., Sun, H., Sun, Q. N., Sun, X. N., Sun, Z. B., Tabasam, N. H., Takata, J., Tam, P. H. T., Tan, H. B., Tang, Q. W., Tang, R., Tang, Z. B., Tian, W. W., Tong, C. N., Wan, L. H., Wang, C., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, Kai, Wang, L. P., Wang, L. Y., Wang, R., Wang, W., Wang, X. G., Wang, X. J., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Z. H., Wang, Z. X., Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Weng, S. S., Wu, C. Y., Wu, H. R., Wu, Q. W., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, Y. L., Xing, Y., Xiong, D. R., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, C. Y., Yang, F. F., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, W. X., Yao, Y. H., Yao, Z. G., Ye, X. A., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, B. T., Zhang, F., Zhang, H., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. R., Zhang, S. S., Zhang, W. Y., Zhang, X., Zhang, X. P., Zhang, Yi, Zhang, Yong, Zhang, Z. P., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zhao, X. H., Zhao, Z. H., Zheng, F., Zhong, W. J., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, B. Y., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., Zou, Y. C., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The core-collapse supernova remnant (SNR) Cassiopeia A (Cas A) is one of the brightest galactic radio sources with an angular radius of $\sim$ 2.5 $\arcmin$. Although no extension of this source has been detected in the $\gamma$-ray band, using more than 1000 days of LHAASO data above $\sim 0.8$ TeV, we find that its spectrum is significantly softer than those obtained with Imaging Air Cherenkov Telescopes (IACTs) and its flux near $\sim 1$ TeV is about two times higher. In combination with analyses of more than 16 years of \textit{Fermi}-LAT data covering $0.1 \, \mathrm{GeV} - 1 \, \mathrm{TeV}$, we find that the spectrum above 30 GeV deviates significantly from a single power-law, and is best described by a smoothly broken power-law with a spectral index of $1.90 \pm 0.15_\mathrm{stat}$ ($3.41 \pm 0.19_\mathrm{stat}$) below (above) a break energy of $0.63 \pm 0.21_\mathrm{stat} \, \mathrm{TeV}$. Given differences in the angular resolution of LHAASO-WCDA and IACTs, TeV $\gamma$-ray emission detected with LHAASO may have a significant contribution from regions surrounding the SNR illuminated by particles accelerated earlier, which, however, are treated as background by IACTs. Detailed modelling can be used to constrain acceleration processes of TeV particles in the early stage of SNR evolution.
Published: 2025

35. Overview of EXL-50 Research Progress and Future Plan

Author: Shi, Yuejiang, Wang, Yumin, Liu, Bing, Song, Xianming, Song, Shaodong, Jiang, Xinchen, Guo, Dong, Luo, Di, Gu, Xiang, Sun, Tiantian, Huang, Xianli, Li, Zhi, Dong, Lili, Wang, Xueyun, Yin, Gang, Wang, Mingyuan, Liu, Wenjun, Zhao, Hanyue, Xie, Huasheng, Yong, Liu, Qi, Dongkai, Xing, Bo, Ding, Jiangbo, Wu, Chao, Li, Lei, Zhang, Haijun, Yang, Yuanming, Zhao, Xin, Yang, Enwu, Luo, Wenwu, Zhou, Peihai, Han, Lei, Zhou, Qing, Wang, Hanqing, Dong, Jiaqi, Yuan, Baoshan, Peng, Y. -K. Martin., Liu, Minsheng, and Team, the EXL-50
Subjects: Physics - Plasma Physics
Abstract: XuanLong-50 (EXL-50) is the first medium-size spherical torus (ST) in China, with the toroidal field at major radius at 50 cm around 0.5T. CS-free and non-inductive current drive via electron cyclotron resonance heating (ECRH) was the main physics research issue for EXL-50. Discharges with plasma currents of 50 kA - 180 kA were routinely obtained in EXL-50, with the current flattop sustained for up to or beyond 2 s. The current drive effectiveness on EXL-50 was as high as 1 A/W for low-density discharges using 28GHz ECRH alone for heating power less than 200 kW. The plasma current reached Ip>80 kA for high-density (5*10e18m-2) discharges with 150 kW 28GHz ECRH. Higher performance discharge (Ip of about 120 kA and core density of about 1*10e19m-3) was achieved with 150 kW 50GHz ECRH. The plasma current in EXL-50 was mainly carried by the energetic electrons.Multi-fluid equilibrium model has been successfully applied to reconstruct the magnetic flux surface and the measured plasma parameters of the EXL-50 equilibrium. The physics mechanisms for the solenoid-free ECRH current drive and the energetic electrons has also been investigated. Preliminary experimental results show that 100 kW of lower hybrid current drive (LHCD) waves can drive 20 kA of plasma current. Several boron injection systems were installed and tested in EXL-50, including B2H6 gas puffing, boron powder injection, boron pellet injection. The research plan of EXL-50U, which is the upgrade machine of EXL-50, is also presented.
Published: 2025

36. Search for resonance-enhanced $CP$ and angular asymmetries in the $\Lambda^+_{c}\to p\mu^+\mu^-$ decay at LHCb

Author: LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Balboni, A., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bonacci, R. B., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bottalico, E., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Brandenburg, J. D., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonincontri, L., Marcos, M. Burgos, Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cesare, S., Chadwick, A. J., Chahrour, I., Chang, H., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collaviti, S., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Darze, G., Davidson, A., Davies, J. E., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Gardner, P., Garg, K. G., Garrido, L., Gaspar, C., Gavrikov, A., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Giugliano, C., Giza, M. A., Glaser, F. C., Gligorov, V. V., Göbel, C., Golinka-Bezshyyko, L., Golobardes, E., Golubkov, D., Golutvin, A., Fernandez, S. Gomez, Gomulka, W., Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Govorkova, E., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guerry, L., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Hallett, G., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Harris, T. H., Hartmann, M., Hashmi, S., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Idzik, M., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Gonzalez, P. Laguarta, Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Lanfranchi, G., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lee, H., Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Lesser, E. D., Leverington, B., Li, A., Li, C., Li, H., Li, K., Li, L., Li, M., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Linton, H., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, W., Liu, Y., Liu, Y. L., Ordonez, G. Loachamin, Salvia, A. Lobo, Loi, A., Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lu, Q., Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manganella, F. M., Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Gomez, D. Martinez, Santos, D. Martinez, Vidal, F. Martinez, Granollers, A. Martorell i, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Moeser, L., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morren, W., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Pan, X., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Parmar, D., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Peters, M. J., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pilato, R. N., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Galvez, M. Ribalda, Ribatti, R., Ricart, G., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Segal, I., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Sommerfeld, N. S., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stefaniak, M., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sundfeld, D., Sutcliffe, W., Swientek, K., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tang, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Tork, T., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Urbach, B., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Cadenas, V. Valcarce, Valenti, G., Canudas, N. Valls, van Eldik, J., Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. Vidrier, Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, H., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. J., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Winn, M., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, X., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xing, T. X., Xu, A., Xu, L., Xu, M., Xu, Z., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhang, Z., Zhao, Y., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.
Subjects: High Energy Physics - Experiment
Abstract: The first measurement of the $CP$ asymmetry of the decay rate ($A_{CP}$) and the $CP$ average ($\Sigma A_{\text{FB}}$) and $CP$ asymmetry ($\Delta A_{\text{FB}}$) of the forward-backward asymmetry in the muon system of $\mathit{\Lambda}^+_c\to p\mu^+\mu^-$ decays is reported. The measurement is performed using a data sample of proton-proton collisions, recorded by the LHCb experiment from 2016 to 2018 at a center-of-mass energy of 13$\text{ TeV}$, which corresponds to an integrated luminosity of 5.4$\text{ fb}^{-1}$. The asymmetries are measured in two regions of dimuon mass near the $\phi$-meson mass peak. The dimuon-mass integrated results are \begin{align*} A_{CP} &= (-1.1 \pm 4.0 \pm 0.5)\%,\\ \Sigma A_{\text{FB}} &= (\phantom{-}3.9 \pm 4.0 \pm 0.6)\%,\\ \Delta A_{\text{FB}} &= (\phantom{-}3.1 \pm 4.0 \pm 0.4)\%, \end{align*} where the first uncertainty is statistical and the second systematic. The results are consistent with the conservation of $CP$ symmetry and the Standard Model expectations., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3473/ (LHCb public pages)
Published: 2025

37. Observation of $D^+\to \bar K_1(1270)^0\mu^+\nu_\mu$ and $D^0\to K_1(1270)^-\mu^+\nu_\mu$

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: By analyzing 7.93 $\rm fb^{-1}$ of $e^+e^-$ collision data collected at the center-of-mass energy of 3.773 GeV with the BESIII detector operated at the BEPCII collider, we report the observation of the semimuonic decays of $D^+\to \bar K_1(1270)^0\mu^+\nu_\mu$ and $D^0\to K_1(1270)^-\mu^+\nu_\mu$ with statistical significances of $12.5\sigma$ and $6.0\sigma$, respectively. Their decay branching fractions are determined to be ${\mathcal B}[D^{+}\to \bar{K}_1(1270)^0 \mu^{+}\nu_{\mu}]=(2.36\pm0.20^{+0.18}_{-0.27}\pm 0.48)\times10^{-3}$ and ${\mathcal B}[D^{0}\to K_1(1270)^{-} \mu^{+}\nu_{\mu}]=(0.78\pm0.11^{+0.05}_{-0.09}\pm 0.15)\times10^{-3}$, where the first and second uncertainties are statistical and systematic, respectively, and the third originates from the input branching fraction of $\bar K_{1}(1270)^0\to K^- \pi^+\pi^0$ or $K_1(1270)^-\to K^-\pi^+\pi^-$. Combining our branching fractions with the previous measurements of ${\mathcal B}[D^+\to \bar K_1(1270)^0e^+\nu_{e}]$ and ${\mathcal B}[D^0\to K_1(1270)^-e^+\nu_{e}]$, we determine the branching fraction ratios to be ${\mathcal B}[D^+\to \bar K_1(1270)^0\mu^+\nu_{\mu}]/{\mathcal B}[D^+\to \bar K_1(1270)^0e^+\nu_{e}]=1.03 \pm 0.14 \substack{+0.11\\-0.15}$ and ${\mathcal B}[D^0\to K_1(1270)^-\mu^+\nu_{\mu}]/{\mathcal B}[D^0\to K_1(1270)^-e^+\nu_{e}]=0.74\pm 0.13 \substack{+0.08\\-0.13}$. Using the branching fractions measured in this work and the world-average lifetimes of the $D^+$ and $D^0$ mesons, we determine the semimuonic partial decay width ratio to be $\Gamma [D^+\to \bar K_1(1270)^0 \mu^+\nu_\mu]/\Gamma [D^0\to K_1(1270)^- \mu^+\nu_\mu]=1.22\pm 0.10\substack{+0.06\\-0.09}$, which is consistent with unity as predicted by isospin conservation., Comment: 10 pages, 2 figures
Published: 2025

38. Search for Double Beta Decay of $^{136}$Xe to the $0^+_1$ Excited State of $^{136}$Ba with PandaX-4T

Author: PandaX Collaboration, Luo, Lingyin, Bo, Zihao, Chen, Wei, Chen, Xun, Chen, Yunhua, Cheng, Zhaokan, Cui, Xiangyi, Fang, Yingji, Fang, Deqing, Gao, Zhixing, Geng, Lisheng, Giboni, Karl, Guo, Xunan, Guo, Xuyuan, Guo, Zichao, Han, Chencheng, Han, Ke, He, Changda, He, Jinrong, Huang, Di, Huang, Houqi, Huang, Junting, Hou, Ruquan, Hou, Yu, Ji, Xiangdong, Ji, Xiangpan, Ju, Yonglin, Li, Chenxiang, Li, Tao, Li, Jiafu, Li, Mingchuan, Li, Shuaijie, Li, Zhiyuan, Lin, Qing, Liu, Jianglai, Lu, Congcong, Lu, Xiaoying, Luo, Yunyang, Ma, Wenbo, Ma, Yugang, Mao, Yajun, Meng, Yue, Ning, Xuyang, Pang, Binyu, Qi, Ningchun, Qian, Zhicheng, Ren, Xiangxiang, Shan, Dong, Shang, Xiaofeng, Shao, Xiyuan, Shen, Guofang, Shen, Manbin, Sun, Wenliang, Tao, Yi, Wang, Anqing, Wang, Guanbo, Wang, Hao, Wang, Jiamin, Wang, Lei, Wang, Meng, Wang, Qiuhong, Wang, Shaobo, Wang, Siguang, Wang, Wei, Wang, Xiuli, Wang, Xu, Wang, Zhou, Wei, Yuehuan, Wu, Weihao, Wu, Yuan, Xiao, Mengjiao, Xiao, Xiang, Xiong, Kaizhi, Xu, Yifan, Yao, Shunyu, Yan, Binbin, Yan, Xiyu, Yang, Yong, Ye, Peihua, Yu, Chunxu, Yuan, Ying, Yuan, Zhe, Yun, Youhui, Zeng, Xinning, Zhang, Minzhen, Zhang, Peng, Zhang, Shibo, Zhang, Shu, Zhang, Tao, Zhang, Wei, Zhang, Yang, Zhang, Yingxin, Zhang, Yuanyuan, Zhao, Li, Zhou, Jifang, Zhou, Jiaxu, Zhou, Ning, Zhou, Xiaopeng, Zhou, Yubo, and Zhou, Zhizhen
Subjects: Nuclear Experiment
Abstract: We perform a search of double beta decay of $^{136}$Xe to the excited state, $0^+_1$, of $^{136}$Ba (2$\nu \beta \beta$-0$_1^+$), using the dual-phase xenon detector of PandaX-4T with the first 94.9-day commissioning data. The multi-site events are reconstructed up to the MeV energy scale, which helps to improve the background model significantly. The background contribution from the stainless steel platform outside PandaX-4T cryostat is evaluated for the first time. No significant evidence for 2$\nu \beta \beta$-0$_1^+$ is observed, resulting in a lower limit of $T_{1/2}^{2 \nu \beta \beta-0_1^+} > 7.5 \times 10^{22}$ yr at the 90% confidence level. This is the first experimental limit on such a rare decay in a natural xenon-based detector.
Published: 2025

39. LLM-KT: Aligning Large Language Models with Knowledge Tracing using a Plug-and-Play Instruction

Author: Wang, Ziwei, Zhou, Jie, Chen, Qin, Zhang, Min, Jiang, Bo, Zhou, Aimin, Bai, Qinchun, and He, Liang
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: The knowledge tracing (KT) problem is an extremely important topic in personalized education, which aims to predict whether students can correctly answer the next question based on their past question-answer records. Prior work on this task mainly focused on learning the sequence of behaviors based on the IDs or textual information. However, these studies usually fail to capture students' sufficient behavioral patterns without reasoning with rich world knowledge about questions. In this paper, we propose a large language models (LLMs)-based framework for KT, named \texttt{\textbf{LLM-KT}}, to integrate the strengths of LLMs and traditional sequence interaction models. For task-level alignment, we design Plug-and-Play instruction to align LLMs with KT, leveraging LLMs' rich knowledge and powerful reasoning capacity. For modality-level alignment, we design the plug-in context and sequence to integrate multiple modalities learned by traditional methods. To capture the long context of history records, we present a plug-in context to flexibly insert the compressed context embedding into LLMs using question-specific and concept-specific tokens. Furthermore, we introduce a plug-in sequence to enhance LLMs with sequence interaction behavior representation learned by traditional sequence models using a sequence adapter. Extensive experiments show that \texttt{\textbf{LLM-KT}} obtains state-of-the-art performance on four typical datasets by comparing it with approximately 20 strong baselines.
Published: 2025

40. Mitigating Hallucinations in Large Vision-Language Models with Internal Fact-based Contrastive Decoding

Author: Wang, Chao, Zhou, Xuancheng, Fu, Weiwei, and Zhou, Yang
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract: Large Visual Language Models (LVLMs) integrate visual and linguistic modalities, exhibiting exceptional performance across various multimodal tasks. Nevertheless, LVLMs remain vulnerable to the issue of object hallucinations. Previous efforts to mitigate this issue focus on supervised fine-tuning (SFT) or incorporating external knowledge, both of which entail significant costs related to training and the acquisition of external data. To address these challenges, we propose a novel model-agnostic approach termed Internal Fact-based Contrastive Decoding (IFCD), designed to mitigate and suppress hallucinations during the inference process of LVLMs by exploiting the LVLMs' own hallucinations. IFCD is grounded in experimental observations that alterations to the LVLMs' internal representations tend to amplify hallucinations caused by language bias. By contrasting disturbed distribution, IFCD calibrates the LVLMs' output and effectively removes the hallucinatory logits from the final predictions. Experimental results validate that IFCD significantly alleviates both object-level and attribute-level hallucinations while achieving an average 9% accuracy improvement on POPE and 8% accuracy improvement on MME object hallucinations subset compared with direct decoding, respectively.
Published: 2025

41. Continually Evolved Multimodal Foundation Models for Cancer Prognosis

Author: Peng, Jie, Zhou, Shuang, Yang, Longwei, Song, Yiran, Zhang, Mohan, Zhou, Kaixiong, Xie, Feng, Lin, Mingquan, Zhang, Rui, and Chen, Tianlong
Subjects: Computer Science - Machine Learning, I.2.7, J.3
Abstract: Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates. To enhance prediction accuracy, previous studies have integrated diverse data modalities, such as clinical notes, medical images, and genomic data, leveraging their complementary information. However, existing approaches face two major limitations. First, they struggle to incorporate newly arrived data with varying distributions into training, such as patient records from different hospitals, thus rendering sub-optimal generalizability and limited utility in real-world applications. Second, most multimodal integration methods rely on simplistic concatenation or task-specific pipelines, which fail to capture the complex interdependencies across modalities. To address these, we propose a continually evolving multi-modal foundation model. Extensive experiments on the TCGA dataset demonstrate the effectiveness of our approach, highlighting its potential to advance cancer prognosis by enabling robust and adaptive multimodal integration., Comment: 9 pages, 1 figure
Published: 2025

42. MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining

Author: Wu, Ruiqi, Su, Na, Zhang, Chenran, Ma, Tengfei, Zhou, Tao, Cui, Zhiting, Tang, Nianfeng, Mao, Tianyu, Zhou, Yi, Fan, Wen, Wu, Tianxing, Jing, Shenqi, and Fu, Huazhu
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Vision-language pretraining (VLP) has been investigated to generalize across diverse downstream tasks for fundus image analysis. Although recent methods showcase promising achievements, they significantly rely on large-scale private image-text data but pay less attention to the pretraining manner, which limits their further advancements. In this work, we introduce MM-Retinal V2, a high-quality image-text paired dataset comprising CFP, FFA, and OCT image modalities. Then, we propose a novel fundus vision-language pretraining model, namely KeepFIT V2, which is pretrained by integrating knowledge from the elite data spark into categorical public datasets. Specifically, a preliminary textual pretraining is adopted to equip the text encoder with primarily ophthalmic textual knowledge. Moreover, a hybrid image-text knowledge injection module is designed for knowledge transfer, which is essentially based on a combination of global semantic concepts from contrastive learning and local appearance details from generative learning. Extensive experiments across zero-shot, few-shot, and linear probing settings highlight the generalization and transferability of KeepFIT V2, delivering performance competitive to state-of-the-art fundus VLP models trained on large-scale private image-text datasets. Our dataset and model are publicly available via https://github.com/lxirich/MM-Retinal.
Published: 2025

43. Observation of $h_{c}$ radiative decays to multiple light hadrons and the tensor state $f_2(1270)$

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., 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, M. 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., Choi, S. K., Chu, X., 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., Ding, Y. X., 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, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., 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, Y. Y., Gao, Yang, Garbolino, S., Garzia, I., 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, K. D., 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, P., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. J., 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., Lan, Q., Lan, W. N., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, C. K., 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, Lei, Li, M. H., Li, M. R., Li, P. L., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y., 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, L. Q., 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. J., 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, W. T., Liu, X., Liu, X. Y., 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, Y., Lu, Y. H., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, J. S., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Lyu, Y. H., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, Q. M., Ma, R. Q., Ma, R. Y., 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, Y. H., 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., 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. R., Qi, M., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qin, J. J., Qin, J. L., Qin, L. Q., Qin, L. Y., Qin, P. B., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Rivetti, A., Rolo, M., Rong, G., Rong, S. S., 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, J. L., Shi, J. Y., Shi, S. Y., Shi, X., Song, H. L., 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, Y., Sun, Y. C., Sun, Y. H., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, L. F., Tang, M., Tang, Y. A., Tao, L. Y., Tat, M., Teng, J. X., Thoren, V., Tian, J. Y., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wang, B., Wang, Bo, Wang, C., Wang, D. Y., Wang, H. J., Wang, J. J., Wang, K., Wang, L. L., Wang, L. W., 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, Yuan, Wang, Z., Wang, Z. L., Wang, Z. Y., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, Lianjie, Wu, S. G., Wu, S. M., Wu, X., Wu, X. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, K. J., 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, M., Xu, Q. J., Xu, Q. N., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, H. Y., Yan, L., Yan, W. B., Yan, W. C., Yan, W. P., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, R. J., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. Q., Yang, Y. X., Yang, Y. Z., Ye, M., Ye, M. 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, H., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Yue, Ying, Zafar, A. A., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., 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, N., Zhang, P., Zhang, Q., 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, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., 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. L., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, X. R., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., 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, W. J., Zhu, W. Z., Zhu, Y. C., Zhu, Z. A., Zhuang, X. Y., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: Using $\psi(3686)\rightarrow \pi^{0} h_{c}$ decays from a data sample of $(27.12\pm0.14)\times10^{8}$ $\psi(3686)$ events collected by the BESIII detector at the BEPCII collider, $h_c$ radiative decays to $\gamma\pi^{+}\pi^{-},~\gamma\pi^{+}\pi^{-}\eta,~\gamma2(\pi^{+}\pi^{-})$, and $\gamma p\bar{p}$ are observed for the first time, each with a significance greater than $5\sigma$. The corresponding branching fractions are measured. Furthermore, intermediate states below 2.8 GeV/$c^{2}$ are investigated, leading to the first observation of the decay process of $h_c\rightarrow\gamma f_{2}(1270)\rightarrow\gamma\pi^{+}\pi^{-}$ with a significance of $5.5\,\sigma$. This observation represents the first instance of $h_c$ radiative decay to a tensor state.
Published: 2025

44. Baichuan-Omni-1.5 Technical Report

Author: Li, Yadong, Liu, Jun, Zhang, Tao, Chen, Song, Li, Tianpeng, Li, Zehuan, Liu, Lijun, Ming, Lingfeng, Dong, Guosheng, Pan, Da, Li, Chong, Fang, Yuanbo, Kuang, Dongdong, Wang, Mingrui, Zhu, Chenglin, Zhang, Youwei, Guo, Hongyu, Zhang, Fengyu, Wang, Yuran, Ding, Bowen, Song, Wei, Li, Xu, Huo, Yuqi, Liang, Zheng, Zhang, Shusen, Wu, Xin, Zhao, Shuai, Xiong, Linchu, Wu, Yozhen, Ye, Jiahui, Lu, Wenhao, Li, Bowen, Zhang, Yan, Zhou, Yaqi, Chen, Xin, Su, Lei, Zhang, Hongda, Chen, Fuzhong, Dong, Xuezhen, Nie, Na, Wu, Zhiying, Xiao, Bin, Li, Ting, Dang, Shunya, Zhang, Ping, Sun, Yijia, Wu, Jincheng, Yang, Jinjie, Lin, Xionghai, Ma, Zhi, Wu, Kegeng, li, Jia, Yang, Aiyuan, Liu, Hui, Zhang, Jianqiang, Chen, Xiaoxi, Ai, Guangwei, Zhang, Wentao, Chen, Yicong, Huang, Xiaoqin, Li, Kun, Luo, Wenjing, Duan, Yifei, Zhu, Lingling, Xiao, Ran, Su, Zhe, Pu, Jiani, Wang, Dian, Jia, Xu, Zhang, Tianyu, Ai, Mengyu, Wang, Mang, Qiao, Yujing, Zhang, Lei, Shen, Yanjun, Yang, Fan, Zhen, Miao, Zhou, Yijie, Chen, Mingyang, Li, Fei, Zhu, Chenzheng, Lu, Keer, Zhao, Yaqi, Liang, Hao, Li, Youquan, Qin, Yanzhao, Sun, Linzhuang, Xu, Jianhua, Sun, Haoze, Lin, Mingan, Zhou, Zenan, and Chen, Weipeng
Subjects: Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: We introduce Baichuan-Omni-1.5, an omni-modal model that not only has omni-modal understanding capabilities but also provides end-to-end audio generation capabilities. To achieve fluent and high-quality interaction across modalities without compromising the capabilities of any modality, we prioritized optimizing three key aspects. First, we establish a comprehensive data cleaning and synthesis pipeline for multimodal data, obtaining about 500B high-quality data (text, audio, and vision). Second, an audio-tokenizer (Baichuan-Audio-Tokenizer) has been designed to capture both semantic and acoustic information from audio, enabling seamless integration and enhanced compatibility with MLLM. Lastly, we designed a multi-stage training strategy that progressively integrates multimodal alignment and multitask fine-tuning, ensuring effective synergy across all modalities. Baichuan-Omni-1.5 leads contemporary models (including GPT4o-mini and MiniCPM-o 2.6) in terms of comprehensive omni-modal capabilities. Notably, it achieves results comparable to leading models such as Qwen2-VL-72B across various multimodal medical benchmarks.
Published: 2025

45. Evidence for $B^-\rightarrow D^{**0}\tau^-\overline{\nu_{\tau}}$ decays

Author: LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Balboni, A., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bonacci, R. B., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collaviti, S., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Darze, G., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Gardner, P., Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Fernandez, S. Gomez, Gomulka, W., Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guerry, L., Guittiere, M., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Hajheidari, M., Hallett, G., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Harris, T. H., Hartmann, M., Hashmi, S., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Idzik, M., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Gonzalez, P. Laguarta, Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Lanfranchi, G., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lee, H., Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Lesser, E. D., Leverington, B., Li, A., Li, C., Li, H., Li, K., Li, L., Li, M., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Linton, H., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, W., Liu, Y., Liu, Y. L., Salvia, A. Lobo, Loi, A., Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lu, Q., Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manganella, F. M., Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Gomez, D. Martinez, Santos, D. Martinez, Vidal, F. Martinez, Granollers, A. Martorell i, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morren, W., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Pan, X., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Parmar, D., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Peters, M. J., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Segal, I., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Sommerfeld, N. S., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sundfeld, D., Sutcliffe, W., Swallow, P. N., Swientek, K., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tang, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Urbach, B., Ursov, E., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Cadenas, V. Valcarce, Valenti, G., Canudas, N. Valls, Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. Vidrier, Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, H., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. J., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Winn, M., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, X., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.
Subjects: High Energy Physics - Experiment
Abstract: The first evidence for the decay $B^-\rightarrow D^{**0}\tau^-\overline{\nu_{\tau}}$ is obtained using proton-proton collision data collected by the LHCb experiment, corresponding to an integrated luminosity of 9 fb$^{-1}$ , at centre-of-mass energies of 7, 8 and 13 Tev. Here, the $D^{**0}$ meson represents any of the three excited charm mesons $D_{1}(2420)^{0}$, $D_{2}^{*}(2460)^{0}$, and $D_{1}^{'}(2400)^{0}$. The $B^-\rightarrow D^{**0}\tau^-\overline{\nu_{\tau}}$ signal is measured with a significance of 3.5 $\sigma$, including systematic uncertainties. The combined branching fraction $BR(B^-\rightarrow D^{**0}_{1,2}\tau^-\overline{\nu_{\tau}})\times BR(D^{**0}_{1,2}\rightarrow D^{*+}\pi^-)$, where $D^{**0}_{1,2}$ denotes both $D_{1}(2420)^{0}$ and $D_{2}^{*}(2460)^{0}$ contributions, is measured to be $(0.051\pm0.013(stat)\pm 0.006(syst)\pm 0.009(\rm{ext}) )\%$, where the last uncertainty reflects that of the branching fraction of the normalisation channel $B^-\rightarrow D^{**0}_{1,2}D_s^{(*)-}$. The ratio between the tauonic and muonic semileptonic $B$ decays, with the latter taken from world average values, is also determined and found to be ${\cal R}(D^{**0}_{1,2})=0.13\pm0.03(stat)\pm0.01(syst)\pm0.02\,(\rm{ext})$., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3300/ (LHCb public pages)
Published: 2025

46. Channel-wise Parallelizable Spiking Neuron with Multiplication-free Dynamics and Large Temporal Receptive Fields

Author: Xue, Peng, Fang, Wei, Ma, Zhengyu, Huang, Zihan, Zhou, Zhaokun, Tian, Yonghong, Masquelier, Timothée, and Zhou, Huihui
Subjects: Computer Science - Neural and Evolutionary Computing
Abstract: Spiking Neural Networks (SNNs) are distinguished from Artificial Neural Networks (ANNs) for their sophisticated neuronal dynamics and sparse binary activations (spikes) inspired by the biological neural system. Traditional neuron models use iterative step-by-step dynamics, resulting in serial computation and slow training speed of SNNs. Recently, parallelizable spiking neuron models have been proposed to fully utilize the massive parallel computing ability of graphics processing units to accelerate the training of SNNs. However, existing parallelizable spiking neuron models involve dense floating operations and can only achieve high long-term dependencies learning ability with a large order at the cost of huge computational and memory costs. To solve the dilemma of performance and costs, we propose the mul-free channel-wise Parallel Spiking Neuron, which is hardware-friendly and suitable for SNNs' resource-restricted application scenarios. The proposed neuron imports the channel-wise convolution to enhance the learning ability, induces the sawtooth dilations to reduce the neuron order, and employs the bit shift operation to avoid multiplications. The algorithm for design and implementation of acceleration methods is discussed meticulously. Our methods are validated in neuromorphic Spiking Heidelberg Digits voices, sequential CIFAR images, and neuromorphic DVS-Lip vision datasets, achieving the best accuracy among SNNs. Training speed results demonstrate the effectiveness of our acceleration methods, providing a practical reference for future research.
Published: 2025

47. Humanity's Last Exam

Author: Phan, Long, Gatti, Alice, Han, Ziwen, Li, Nathaniel, Hu, Josephina, Zhang, Hugh, Zhang, Chen Bo Calvin, Shaaban, Mohamed, Ling, John, Shi, Sean, Choi, Michael, Agrawal, Anish, Chopra, Arnav, Khoja, Adam, Kim, Ryan, Ren, Richard, Hausenloy, Jason, Zhang, Oliver, Mazeika, Mantas, Nguyen, Tung, Anderson, Daron, Shah, Imad Ali, Doroshenko, Mikhail, Stokes, Alun Cennyth, Mahmood, Mobeen, Lee, Jaeho, Pokutnyi, Oleksandr, Iskra, Oleg, Wang, Jessica P., Gerbicz, Robert, Levin, John-Clark, Popov, Serguei, Feng, Fiona, Feng, Steven Y., Zhao, Haoran, Yu, Michael, Gangal, Varun, Zou, Chelsea, Wang, Zihan, Kazakov, Mstyslav, Galgon, Geoff, Schmitt, Johannes, Sanchez, Alvaro, Lee, Yongki, Yeadon, Will, Sauers, Scott, Roth, Marc, Agu, Chidozie, Riis, Søren, Giska, Fabian, Utpala, Saiteja, Cheatom, Antrell, Giboney, Zachary, Goshu, Gashaw M., Crowson, Sarah-Jane, Naiya, Mohinder Maheshbhai, Burns, Noah, Finke, Lennart, Cheng, Zerui, Park, Hyunwoo, Fournier-Facio, Francesco, Zampese, Jennifer, Wydallis, John B., Hoerr, Ryan G., Nandor, Mark, Gehrunger, Tim, Cai, Jiaqi, McCarty, Ben, Nam, Jungbae, Taylor, Edwin, Jin, Jun, Loume, Gautier Abou, Cao, Hangrui, Garretson, Alexis C, Sileo, Damien, Ren, Qiuyu, Cojoc, Doru, Arkhipov, Pavel, Qazi, Usman, Bacho, Aras, Li, Lianghui, Motwani, Sumeet, de Witt, Christian Schroeder, Kopylov, Alexei, Veith, Johannes, Singer, Eric, Rissone, Paolo, Jin, Jaehyeok, Shi, Jack Wei Lun, Willcocks, Chris G., Prabhu, Ameya, Tang, Longke, Zhou, Kevin, Santos, Emily de Oliveira, Maksimov, Andrey Pupasov, Vendrow, Edward, Zenitani, Kengo, Robinson, Joshua, Mikov, Aleksandar, Guillod, Julien, Li, Yuqi, Pageler, Ben, Vendrow, Joshua, Kuchkin, Vladyslav, Marion, Pierre, Efremov, Denis, Lynch, Jayson, Liang, Kaiqu, Gritsevskiy, Andrew, Martinez, Dakotah, Crispino, Nick, Zvonkine, Dimitri, Fraga, Natanael Wildner, Soori, Saeed, Press, Ori, Tang, Henry, Salazar, Julian, Green, Sean R., Brüssel, Lina, Twayana, Moon, Dieuleveut, Aymeric, Rogers, T. Ryan, Zhang, Wenjin, Finocchio, Ross, Li, Bikun, Yang, Jinzhou, Rao, Arun, Loiseau, Gabriel, Kalinin, Mikhail, Lukas, Marco, Manolescu, Ciprian, Stambaugh, Nate, Mishra, Subrata, Kamdoum, Ariel Ghislain Kemogne, Hogg, Tad, Jin, Alvin, Bosio, Carlo, Sun, Gongbo, Coppola, Brian P, Heidinger, Haline, Sayous, Rafael, Ivanov, Stefan, Cavanagh, Joseph M, Shen, Jiawei, Imperial, Joseph Marvin, Schwaller, Philippe, Senthilkuma, Shaipranesh, Bran, Andres M, Algaba, Andres, Verbeken, Brecht, Houte, Kelsey Van den, Van Der Sypt, Lynn, Noever, David, Schut, Lisa, Sucholutsky, Ilia, Zheltonozhskii, Evgenii, Yuan, Qiaochu, Lim, Derek, Stanley, Richard, Sivarajan, Shankar, Yang, Tong, Maar, John, Wykowski, Julian, Oller, Martí, Sandlin, Jennifer, Sahu, Anmol, Ardito, Cesare Giulio, Hu, Yuzheng, Dias, Felipe Meneguitti, Kreiman, Tobias, Rawal, Kaivalya, Vilchis, Tobias Garcia, Zu, Yuexuan, Lackner, Martin, Koppel, James, Nguyen, Jeremy, Antonenko, Daniil S., Chern, Steffi, Zhao, Bingchen, Arsene, Pierrot, Ivanov, Sergey, Poświata, Rafał, Wang, Chenguang, Li, Daofeng, Crisostomi, Donato, Dehghan, Ali, Achilleos, Andrea, Ambay, John Arnold, Myklebust, Benjamin, Sen, Archan, Perrella, David, Kaparov, Nurdin, Inlow, Mark H, Zang, Allen, Ramakrishnan, Kalyan, Orel, Daniil, Poritski, Vladislav, Ben-David, Shalev, Berger, Zachary, Whitfill, Parker, Foster, Michael, Munro, Daniel, Ho, Linh, Hava, Dan Bar, Kuchkin, Aleksey, Lauff, Robert, Holmes, David, Sommerhage, Frank, Zhang, Anji, Moat, Richard, Schneider, Keith, Pyda, Daniel, Kazibwe, Zakayo, Singh, Mukhwinder, Clarke, Don, Kim, Dae Hyun, Fish, Sara, Elser, Veit, Vilchis, Victor Efren Guadarrama, Klose, Immo, Demian, Christoph, Anantheswaran, Ujjwala, Zweiger, Adam, Albani, Guglielmo, Li, Jeffery, Daans, Nicolas, Radionov, Maksim, Rozhoň, Václav, Ginis, Vincent, Ma, Ziqiao, Stump, Christian, Platnick, Jacob, Nevirkovets, Volodymyr, Basler, Luke, Piccardo, Marco, Cohen, Niv, Singh, Virendra, Tkadlec, Josef, Rosu, Paul, Goldfarb, Alan, Padlewski, Piotr, Barzowski, Stanislaw, Montgomery, Kyle, Menezes, Aline, Patel, Arkil, Wang, Zixuan, Tucker-Foltz, Jamie, Stade, Jack, Grabb, Declan, Goertzen, Tom, Kazemi, Fereshteh, Milbauer, Jeremiah, Shukla, Abhishek, Elgnainy, Hossam, Labrador, Yan Carlos Leyva, He, Hao, Zhang, Ling, Givré, Alan, Wolff, Hew, Demir, Gözdenur, Aziz, Muhammad Fayez, Kaddar, Younesse, Ängquist, Ivar, Chen, Yanxu, Thornley, Elliott, Zhang, Robin, Pan, Jiayi, Terpin, Antonio, Muennighoff, Niklas, Schoelkopf, Hailey, Zheng, Eric, Carmi, Avishy, Shah, Jainam, Brown, Ethan D. L., Zhu, Kelin, Bartolo, Max, Wheeler, Richard, Ho, Andrew, Barkan, Shaul, Wang, Jiaqi, Stehberger, Martin, Kretov, Egor, Bradshaw, Peter, Heimonen, JP, Sridhar, Kaustubh, Hossain, Zaki, Akov, Ido, Makarychev, Yury, Tam, Joanna, Hoang, Hieu, Cunningham, David M., Goryachev, Vladimir, Patramanis, Demosthenes, Krause, Michael, Redenti, Andrew, Aldous, David, Lai, Jesyin, Coleman, Shannon, Xu, Jiangnan, Lee, Sangwon, Magoulas, Ilias, Zhao, Sandy, Tang, Ning, Cohen, Michael K., Carroll, Micah, Paradise, Orr, Kirchner, Jan Hendrik, Steinerberger, Stefan, Ovchynnikov, Maksym, Matos, Jason O., Shenoy, Adithya, Wang, Michael, Nie, Yuzhou, Giordano, Paolo, Petersen, Philipp, Sztyber-Betley, Anna, Faraboschi, Paolo, Riblet, Robin, Crozier, Jonathan, Halasyamani, Shiv, Pinto, Antonella, Verma, Shreyas, Joshi, Prashant, Meril, Eli, Yong, Zheng-Xin, Tee, Allison, Andréoletti, Jérémy, Weller, Orion, Singhal, Raghav, Zhang, Gang, Ivanov, Alexander, Khoury, Seri, Gustafsson, Nils, Mostaghimi, Hamid, Thaman, Kunvar, Chen, Qijia, Khánh, Tran Quoc, Loader, Jacob, Cavalleri, Stefano, Szlyk, Hannah, Brown, Zachary, Narayan, Himanshu, Roberts, Jonathan, Alley, William, Sun, Kunyang, Stendall, Ryan, Lamparth, Max, Reuel, Anka, Wang, Ting, Xu, Hanmeng, Hernández-Cámara, Pablo, Martin, Freddie, Preu, Thomas, Korbak, Tomek, Abramovitch, Marcus, Williamson, Dominic, Bosio, Ida, Chen, Ziye, Bálint, Biró, Lo, Eve J. Y., Nunes, Maria Inês S., Jiang, Yibo, Bari, M Saiful, Kassani, Peyman, Wang, Zihao, Ansarinejad, Behzad, Sun, Yewen, Durand, Stephane, Douville, Guillaume, Tordera, Daniel, Balabanian, George, Anderson, Earth, Kvistad, Lynna, Moyano, Alejandro José, Milliron, Hsiaoyun, Sakor, Ahmad, Eron, Murat, McAlister, Isaac C., O., Andrew Favre D., Shah, Shailesh, Zhou, Xiaoxiang, Kamalov, Firuz, Clark, Ronald, Abdoli, Sherwin, Santens, Tim, Wang, Harrison K, Chen, Evan, Tomasiello, Alessandro, De Luca, G. Bruno, Looi, Shi-Zhuo, Le, Vinh-Kha, Kolt, Noam, Mündler, Niels, Semler, Avi, Rodman, Emma, Drori, Jacob, Fossum, Carl J, Gloor, Luk, Jagota, Milind, Pradeep, Ronak, Fan, Honglu, Shah, Tej, Eicher, Jonathan, Chen, Michael, Thaman, Kushal, Merrill, William, Firsching, Moritz, Harris, Carter, Ciobâcă, Stefan, Gross, Jason, Pandey, Rohan, Gusev, Ilya, Jones, Adam, Agnihotri, Shashank, Zhelnov, Pavel, Usawasutsakorn, Siranut, Mofayezi, Mohammadreza, Piperski, Alexander, Carauleanu, Marc, Zhang, David K., Dobarskyi, Kostiantyn, Ler, Dylan, Leventov, Roman, Soroko, Ignat, Jansen, Thorben, Creighton, Scott, Lauer, Pascal, Duersch, Joshua, Taamazyan, Vage, Bezzi, Dario, Morak, Wiktor, Ma, Wenjie, Held, William, Huy, Tran Đuc, Xian, Ruicheng, Zebaze, Armel Randy, Mohamed, Mohanad, Leser, Julian Noah, Yuan, Michelle X, Yacar, Laila, Lengler, Johannes, Olszewska, Katarzyna, Shahrtash, Hossein, Oliveira, Edson, Jackson, Joseph W., Gonzalez, Daniel Espinosa, Zou, Andy, Chidambaram, Muthu, Manik, Timothy, Haffenden, Hector, Stander, Dashiell, Dasouqi, Ali, Shen, Alexander, Duc, Emilien, Golshani, Bita, Stap, David, Uzhou, Mikalai, Zhidkovskaya, Alina Borisovna, Lewark, Lukas, Rodriguez, Miguel Orbegozo, Vincze, Mátyás, Wehr, Dustin, Tang, Colin, Phillips, Shaun, Samuele, Fortuna, Muzhen, Jiang, Ekström, Fredrik, Hammon, Angela, Patel, Oam, Farhidi, Faraz, Medley, George, Mohammadzadeh, Forough, Peñaflor, Madellene, Kassahun, Haile, Friedrich, Alena, Sparrow, Claire, Perez, Rayner Hernandez, Sakal, Taom, Dhamane, Omkar, Mirabadi, Ali Khajegili, Hallman, Eric, Okutsu, Kenchi, Battaglia, Mike, Maghsoudimehrabani, Mohammad, Amit, Alon, Hulbert, Dave, Pereira, Roberto, Weber, Simon, Handoko, Peristyy, Anton, Malina, Stephen, Albanie, Samuel, Cai, Will, Mehkary, Mustafa, Aly, Rami, Reidegeld, Frank, Dick, Anna-Katharina, Friday, Cary, Sidhu, Jasdeep, Shapourian, Hassan, Kim, Wanyoung, Costa, Mariana, Gurdogan, Hubeyb, Weber, Brian, Kumar, Harsh, Jiang, Tong, Agarwal, Arunim, Ceconello, Chiara, Vaz, Warren S., Zhuang, Chao, Park, Haon, Tawfeek, Andrew R., Aggarwal, Daattavya, Kirchhof, Michael, Dai, Linjie, Kim, Evan, Ferret, Johan, Wang, Yuzhou, Yan, Minghao, Burdzy, Krzysztof, Zhang, Lixin, Franca, Antonio, Pham, Diana T., Loh, Kang Yong, Jackson, Abram, Gul, Shreen, Chhablani, Gunjan, Du, Zhehang, Cosma, Adrian, Colino, Jesus, White, Colin, Votava, Jacob, Vinnikov, Vladimir, Delaney, Ethan, Spelda, Petr, Stritecky, Vit, Shahid, Syed M., Mourrat, Jean-Christophe, Vetoshkin, Lavr, Sponselee, Koen, Bacho, Renas, de la Rosa, Florencia, Li, Xiuyu, Malod, Guillaume, Lang, Leon, Laurendeau, Julien, Kazakov, Dmitry, Adesanya, Fatimah, Portier, Julien, Hollom, Lawrence, Souza, Victor, Zhou, Yuchen Anna, Degorre, Julien, Yalın, Yiğit, Obikoya, Gbenga Daniel, Arnaboldi, Luca, Rai, Bigi, Filippo, Boscá, M. C., Shumar, Oleg, Bacho, Kaniuar, Clavier, Pierre, Recchia, Gabriel, Popescu, Mara, Shulga, Nikita, Tanwie, Ngefor Mildred, Peskoff, Denis, Lux, Thomas C. H., Rank, Ben, Ni, Colin, Brooks, Matthew, Yakimchyk, Alesia, Huanxu, Liu, Häggström, Olle, Verkama, Emil, Gundlach, Hans, Brito-Santana, Leonor, Amaro, Brian, Vajipey, Vivek, Grover, Rynaa, Fan, Yiyang, Silva, Gabriel Poesia Reis e, Xin, Linwei, Kratish, Yosi, Łucki, Jakub, Li, Wen-Ding, Gopi, Sivakanth, Caciolai, Andrea, Xu, Justin, Scaria, Kevin Joseph, Vargus, Freddie, Habibi, Farzad, Long, Lian, Rodolà, Emanuele, Robins, Jules, Cheng, Vincent, Fruhauff, Tony, Raynor, Brad, Qi, Hao, Jiang, Xi, Segev, Ben, Fan, Jingxuan, Martinson, Sarah, Wang, Erik Y., Hausknecht, Kaylie, Brenner, Michael P., Mao, Mao, Zhang, Xinyu, Avagian, David, Scipio, Eshawn Jessica, Ragoler, Alon, Tan, Justin, Sims, Blake, Plecnik, Rebeka, Kirtland, Aaron, Bodur, Omer Faruk, Shinde, D. P., Adoul, Zahra, Zekry, Mohamed, Karakoc, Ali, Santos, Tania C. B., Shamseldeen, Samir, Karim, Loukmane, Liakhovitskaia, Anna, Resman, Nate, Farina, Nicholas, Gonzalez, Juan Carlos, Maayan, Gabe, Hoback, Sarah, Pena, Rodrigo De Oliveira, Sherman, Glen, Kelley, Elizabeth, Mariji, Hodjat, Pouriamanesh, Rasoul, Wu, Wentao, Mendoza, Sandra, Alarab, Ismail, Cole, Joshua, Ferreira, Danyelle, Johnson, Bryan, Safdari, Mohammad, Dai, Liangti, Arthornthurasuk, Siriphan, Pronin, Alexey, Fan, Jing, Ramirez-Trinidad, Angel, Cartwright, Ashley, Pottmaier, Daphiny, Taheri, Omid, Outevsky, David, Stepanic, Stanley, Perry, Samuel, Askew, Luke, Rodríguez, Raúl Adrián Huerta, Minissi, Ali M. R., Ali, Sam, Lorena, Ricardo, Iyer, Krishnamurthy, Fasiludeen, Arshad Anil, Salauddin, Sk Md, Islam, Murat, Gonzalez, Juan, Ducey, Josh, Somrak, Maja, Mavroudis, Vasilios, Vergo, Eric, Qin, Juehang, Borbás, Benjámin, Chu, Eric, Lindsey, Jack, Radhakrishnan, Anil, Jallon, Antoine, McInnis, I. M. J., Kumar, Pawan, Goswami, Laxman Prasad, Bugas, Daniel, Heydari, Nasser, Jeanplong, Ferenc, Apronti, Archimedes, Galal, Abdallah, Ze-An, Ng, Singh, Ankit, Xavier, Joan of Arc, Agarwal, Kanu Priya, Berkani, Mohammed, Junior, Benedito Alves de Oliveira, Malishev, Dmitry, Remy, Nicolas, Hartman, Taylor D., Tarver, Tim, Mensah, Stephen, Gimenez, Javier, Montecillo, Roselynn Grace, Campbell, Russell, Sharma, Asankhaya, Meer, Khalida, Alapont, Xavier, Patil, Deepakkumar, Maheshwari, Rajat, Dendane, Abdelkader, Shukla, Priti, Bogdanov, Sergei, Möller, Sören, Siddiqi, Muhammad Rehan, Saxena, Prajvi, Gupta, Himanshu, Enyekwe, Innocent, P V, Ragavendran, EL-Wasif, Zienab, Maksapetyan, Aleksandr, Rossbach, Vivien, Harjadi, Chris, Bahaloohoreh, Mohsen, Bian, Song, Lai, John, Uro, Justine Leon, Bateman, Greg, Sayed, Mohamed, Menshawy, Ahmed, Duclosel, Darling, Jain, Yashaswini, Aaron, Ashley, Tiryakioglu, Murat, Siddh, Sheeshram, Krenek, Keith, Hoover, Alex, McGowan, Joseph, Patwardhan, Tejal, Yue, Summer, Wang, Alexandr, and Hendrycks, Dan
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 27 pages, 6 figures
Published: 2025

48. Dynamic Operation and Control of a Multi-Stack Alkaline Water Electrolysis System with Shared Gas Separators and Lye Circulation: A Model-Based Study

Author: Qiu, Yiwei, Li, Jiatong, Zeng, Yangjun, Zhou, Yi, Chen, Shi, Qiu, Xiaoyan, Zhou, Buxiang, He, Ge, Ji, Xu, and Li, Wenying
Subjects: Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Systems and Control
Abstract: An emerging approach for large-scale hydrogen production using renewable energy is to integrate multiple alkaline water electrolysis (AWE) stacks into a single balance of plant (BoP) system, sharing components such as gas-lye separation and lye circulation. This configuration, termed the $N$-in-1 AWE system, packs $N$ stacks into a modular system, reducing land requirements, the complexity of plant topology, and overall capital costs. However, the coupling of these stacks through the shared BoP introduces challenges in dynamic operation under varying energy inputs, making their performance unclear compared to traditional 1-in-1 systems. To address this, we develop a state-space model of the $N$-in-1 AWE system, capturing the dynamic behaviors of lye circulation, temperature, and HTO impurity, and their impact on energy conversion efficiency. We then propose a nonlinear model predictive controller (NMPC) to coordinately optimize inter-stack electrolytic current distribution, lye flow, and cooling, enabling the system to dynamically track varying load commands while maximizing efficiency, stabilizing temperature, and limiting HTO impurity accumulation. Simulation studies on a 4,000 Nm$^3$/h-rated 4-in-1 system verify the proposed controller under dynamic operation. Comparison with 4 independent 1-in-1 systems reveals that, with proper control, the $N$-in-1 configuration offers comparable flexibility in accommodating real-world wind power inputs. The average differences in the root-mean-square errors (RMSEs) for load-tracking and stack temperature stabilization, and specific energy consumption are below 0.014 MW, 2.356 K, and 0.003 kWh/Nm$^3$.
Published: 2025

49. A Semiparametric Bayesian Method for Instrumental Variable Analysis with Partly Interval-Censored Time-to-Event Outcome

Author: Cui, Elvis Han, Lu, Xuyang, Zhou, Jin, Zhou, Hua, and Li, Gang
Subjects: Statistics - Methodology, Computer Science - Machine Learning, Statistics - Applications, Statistics - Computation, Statistics - Machine Learning
Abstract: This paper develops a semiparametric Bayesian instrumental variable analysis method for estimating the causal effect of an endogenous variable when dealing with unobserved confounders and measurement errors with partly interval-censored time-to-event data, where event times are observed exactly for some subjects but left-censored, right-censored, or interval-censored for others. Our method is based on a two-stage Dirichlet process mixture instrumental variable (DPMIV) model which simultaneously models the first-stage random error term for the exposure variable and the second-stage random error term for the time-to-event outcome using a bivariate Gaussian mixture of the Dirichlet process (DPM) model. The DPM model can be broadly understood as a mixture model with an unspecified number of Gaussian components, which relaxes the normal error assumptions and allows the number of mixture components to be determined by the data. We develop an MCMC algorithm for the DPMIV model tailored for partly interval-censored data and conduct extensive simulations to assess the performance of our DPMIV method in comparison with some competing methods. Our simulations revealed that our proposed method is robust under different error distributions and can have superior performance over its parametric counterpart under various scenarios. We further demonstrate the effectiveness of our approach on an UK Biobank data to investigate the causal effect of systolic blood pressure on time-to-development of cardiovascular disease from the onset of diabetes mellitus.
Published: 2025

50. Top Ten Challenges Towards Agentic Neural Graph Databases

Author: Bai, Jiaxin, Wang, Zihao, Zhou, Yukun, Yin, Hang, Fei, Weizhi, Hu, Qi, Deng, Zheye, Cheng, Jiayang, Zheng, Tianshi, Tsang, Hong Ting, Gao, Yisen, Xie, Zhongwei, Li, Yufei, Fan, Lixin, Yuan, Binhang, Wang, Wei, Chen, Lei, Zhou, Xiaofang, and Song, Yangqiu
Subjects: Computer Science - Artificial Intelligence, Computer Science - Databases, Computer Science - Machine Learning
Abstract: Graph databases (GDBs) like Neo4j and TigerGraph excel at handling interconnected data but lack advanced inference capabilities. Neural Graph Databases (NGDBs) address this by integrating Graph Neural Networks (GNNs) for predictive analysis and reasoning over incomplete or noisy data. However, NGDBs rely on predefined queries and lack autonomy and adaptability. This paper introduces Agentic Neural Graph Databases (Agentic NGDBs), which extend NGDBs with three core functionalities: autonomous query construction, neural query execution, and continuous learning. We identify ten key challenges in realizing Agentic NGDBs: semantic unit representation, abductive reasoning, scalable query execution, and integration with foundation models like large language models (LLMs). By addressing these challenges, Agentic NGDBs can enable intelligent, self-improving systems for modern data-driven applications, paving the way for adaptable and autonomous data management solutions., Comment: 12 Pages
Published: 2025

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