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

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1. Host Identity Determines the Bacterial and Fungal Community and Network Structures in the Phyllosphere of Plant Species in a Temperate Steppe

Author: Chunyan Guo, An Yang, and Wen-Hao Zhang
Subjects: bacterial and fungal community, Illumina MiSeq sequencing, network structure, phyllosphere, temperate grasslands, Plant culture, SB1-1110, Microbial ecology, QR100-130, Plant ecology, QK900-989
Abstract: Elucidating the plant−microbiome network is of importance in understanding species coexistence in natural ecosystems. The phyllosphere, which is the aerial parts of terrestrial plants, is inhabited by diverse microbes. However, few studies have focused on the phyllosphere microbiome and plant-microbiome network in temperate grasslands. In this study, we explored the diversity, community structure, and network architecture of phyllosphere bacteria and fungi in 19 plant species native to the temperate grassland in Inner Mongolia, China. We obtained 3,313 and 758 phyllosphere bacterial and fungal operational taxonomic units, respectively, and found that the bacterial community was dominated by Proteobacteria, Actinobacteriota, and Firmicutes, whereas the fungal community was dominated by Ascomycota and Basidiomycota. Plant identity exerted significant impacts on α-diversities of both bacterial and fungal communities. The composition of bacterial and fungal communities differed among plant species. Plant identity had a greater effect on fungal than bacterial communities. Both bacterial and fungal network structures were characterized by specialized, modular, lowly connected, and no nested properties. The plant−fungal network had a high level of specification, modularity, antinestedness, and connectance compared with the plant−bacterial network. Our results suggest more intimate relationships between plants and phyllosphere fungi than between plants and phyllosphere bacteria and also that the phyllosphere fungal community in the temperate grassland ecosystem is more resistant to environmental disturbance than the phyllosphere bacterial community. These findings may contribute to our understanding of the mechanisms by which species coexist and communities stabilize in grassland ecosystems.
Published: 2024
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2. Comparative transcriptome analysis reveals key genes and pathways in response to Alternaria alternata apple pathotype infection

Author: Kai Liu, Zhaolin Liang, An Yang, Jiadi Yan, Peihua Cong, Xiaolei Han, and Caixia Zhang
Subjects: Apple, Transcriptome, Alternaria alternata apple pathotype, Infection, Defense response, Plant culture, SB1-1110
Abstract: Apple leaf spot, caused by the Alternaria alternata apple pathotype (AAAP), is an important fungal disease of apple. To understand the molecular basis of resistance and pathogenesis in apple leaf spot, the transcriptomes of two apple cultivars ‘Hanfu’ (HF) (resistant) and ‘Golden Delicious’(GD) (susceptible) were analyzed at 0, 6, 18, 24 and 48 h after AAAP inoculation by RNA-Seq. At each time point, a large number of significantly differentially expressed genes (DEGs) were screened between AAAP-inoculated and uninoculated apple leaves. Analysis of the common DEGs at four time points revealed significant differences in the resistance of ‘HF’ and ‘GD’ apple to AAAP infection. RLP, RNL, and JA signal-related genes were upregulated in both cultivars to restrict AAAP development. However, genes encoding CNLs, TNLs, WRKYs, and AP2s were only activated in ‘HF’ as part of the resistance response, of which, some play major roles in the regulation of ET and SA signal transduction. Further analysis showed that many DEGs with opposite expression trends in the two hosts may play important regulatory roles in response to AAAP infection. Transient expression of one such gene MdERF110 in ‘GD’ apple leaves improved AAAP resistance. Collectively, this study highlights the reasons for differential resistance to AAAP infection between ‘HF’ and ‘GD’ apples which can theoretically assist the molecular breeding of disease-resistant apple crops.
Published: 2024
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3. Feasibility of Carbon Dioxide as Cushion Gas in Depleted Gas Reservoirs: An Experiment Study on CO2–CH4 Dispersion during Flow Alternation

Author: Jinhui Yang, Binshan Ju, An Yang, Zixian Cui, Meng Wang, Yapeng Tian, and Hengchen Qi
Subjects: carbon dioxide, methane, cushion gas, depleted gas reservoir, dispersion, Technology
Abstract: This study investigates the feasibility of utilizing carbon dioxide (CO2) as a cushion gas in depleted reservoirs for enhanced gas storage efficiency and carbon sequestration against the backdrop of rising natural gas stable supply demand and climate change concerns. Simulations of gas storage reservoir scenarios require accurate dispersion parameters at flow alternation conditions to quantify the size of the miscible displacement front. Several experimental studies using core-flooding equipment aimed at measuring related parameters have been reported over the last decade but did not take flow alternation into consideration. We simulated directionally variable displacements to mimic the cyclic injection and extraction processes in gas storage, focusing on the dispersion characteristics of CO2 and methane (CH4) during flow alternation. Key findings were observed using Nuclear Magnetic Resonance (NMR) imaging, which provided real-time data on the spatial distribution and temporal changes of CH4 signals in rock cores. The results revealed that dispersion, influenced predominantly by dispersion coefficients rather than molecular diffusion, was significantly higher during alternating flow compared to concurrent displacement. Additionally, CO2 exhibited a greater dispersion effect when displacing CH4 than the reverse. This enhanced mixing efficiency during flow alternation supports the potential of CO2 as a cost-effective and efficient cushion gas, offering both improved storage performance and the added benefit of CO2 sequestration. These findings contribute valuable insights for the numerical simulation and operational adaptation of CO2 in gas storage reservoirs, emphasizing the importance of understanding fluid interactions under varying flow conditions to optimize storage efficiency and environmental benefits.
Published: 2024
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4. Atmospheric nitrogen deposition affects forest plant and soil system carbon:nitrogen:phosphorus stoichiometric flexibility: A meta-analysis

Author: Xiyan Jiang, Xiaojing Wang, Yaqi Qiao, Yi Cao, Yan Jiao, An Yang, Mengzhou Liu, Lei Ma, Mengya Song, and Shenglei Fu
Subjects: C:N:P stoichiometry, Meta-analysis, Forest ecosystem, Nitrogen addition form, Nutrient cycles, Ecology, QH540-549.5
Abstract: Background: Nitrogen (N) deposition affects forest stoichiometric flexibility through changing soil nutrient availability to influence plant uptake. However, the effect of N deposition on the flexibility of carbon (C), N, and phosphorus (P) in forest plant-soil-microbe systems remains unclear. Methods: We conducted a meta-analysis based on 751 pairs of observations to evaluate the responses of plant, soil and microbial biomass C, N and P nutrients and stoichiometry to N addition in different N intensity (0–50, 50–100, >100 kg⋅ha−1⋅year−1 of N), duration (0–5, >5 year), method (understory, canopy), and matter (ammonium N, nitrate N, organic N, mixed N). Results: N addition significantly increased plant N:P (leaf: 14.98%, root: 13.29%), plant C:P (leaf: 6.8%, root: 25.44%), soil N:P (13.94%), soil C:P (10.86%), microbial biomass N:P (23.58%), microbial biomass C:P (12.62%), but reduced plant C:N (leaf: 6.49%, root: 9.02%). Furthermore, plant C:N:P stoichiometry changed significantly under short-term N inputs, while soil and microorganisms changed drastically under high N addition. Canopy N addition primarily affected plant C:N:P stoichiometry through altering plant N content, while understory N inputs altered more by influencing soil C and P content. Organic N significantly influenced plant and soil C:N and C:P, while ammonia N changed plant N:P. Plant C:P and soil C:N were strongly correlated with mean annual precipitation (MAT), and the C:N:P stoichiometric flexibility in soil and plant under N addition connected with soil depth. Besides, N addition decoupled the correlations between soil microorganisms and the plant. Conclusions: N addition significantly increased the C:P and N:P in soil, plant, and microbial biomass, reducing plant C:N, and aggravated forest P limitations. Significantly, these impacts were contingent on climate types, soil layers, and N input forms. The findings enhance our comprehension of the plant-soil system nutrient cycling mechanisms in forest ecosystems and plant strategy responses to N deposition.
Published: 2024
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5. Development of allosteric and selective CDK2 inhibitors for contraception with negative cooperativity to cyclin binding

Author: Erik B. Faber, Luxin Sun, Jian Tang, Emily Roberts, Sornakala Ganeshkumar, Nan Wang, Damien Rasmussen, Abir Majumdar, Laura E. Hirsch, Kristen John, An Yang, Hira Khalid, Jon E. Hawkinson, Nicholas M. Levinson, Vargheese Chennathukuzhi, Daniel A. Harki, Ernst Schönbrunn, and Gunda I. Georg
Subjects: Science
Abstract: Abstract Compared to most ATP-site kinase inhibitors, small molecules that target an allosteric pocket have the potential for improved selectivity due to the often observed lower structural similarity at these distal sites. Despite their promise, relatively few examples of structurally confirmed, high-affinity allosteric kinase inhibitors exist. Cyclin-dependent kinase 2 (CDK2) is a target for many therapeutic indications, including non-hormonal contraception. However, an inhibitor against this kinase with exquisite selectivity has not reached the market because of the structural similarity between CDKs. In this paper, we describe the development and mechanism of action of type III inhibitors that bind CDK2 with nanomolar affinity. Notably, these anthranilic acid inhibitors exhibit a strong negative cooperative relationship with cyclin binding, which remains an underexplored mechanism for CDK2 inhibition. Furthermore, the binding profile of these compounds in both biophysical and cellular assays demonstrate the promise of this series for further development into a therapeutic selective for CDK2 over highly similar kinases like CDK1. The potential of these inhibitors as contraceptive agents is seen by incubation with spermatocyte chromosome spreads from mouse testicular explants, where they recapitulate Cdk2 -/- and Spdya -/- phenotypes.
Published: 2023
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6. STEM Pushout and Redirection of HMoob American College Students at a Predominantly White Institution. WCER Working Paper No. 2024-4

Author: University of Wisconsin-Madison, Wisconsin Center for Education Research (WCER), Bailey B. Smolarek, Matthew Wolfgram, Chundou Her, Lena Lee, Stacey J. Lee, Geboli Long, Payeng Moua, Kong Pheng Pha, Ariana Thao, Mai See Thao, Mai Neng Vang, Susan Vang, Chee Meng Xiong, Choua Xiong, Edward Xiong, Odyssey Xiong, Pa Kou Xiong, Ying Yang Youa Xiong, Kayeng Yang, Lisa Yang, Mai Chong Yang, Scy Yang, and Steven Yang
Abstract: Asian Americans as a group are overrepresented among STEM college graduates and have the highest average college enrollment rate of any racial or ethnic category. Thus, Asian Americans are typically excluded from educational interventions directed at improving STEM education for Students of Color because they are not considered to be underrepresented minorities. However, statistics obscure the individual needs of the more than 20 ethnic subgroups that fall under the umbrella term Asian Americans. Using a participatory action research approach, this paper documents the institutional and sociocultural factors that push out HMoob (or Hmong) American college students from STEM programs at one large, predominantly White university; and the coordinate processes of gatekeeping and transactional advising that either redirect those students toward non-STEM programs or force them out of the university completely.
Published: 2024

7. Gallai-Schur Triples and Related Problems

Author: Mao, Yaping, Robertson, Aaron, Wang, Jian, Yang, Chenxu, and Yang, Gang
Subjects: Mathematics - Combinatorics, 05D10
Abstract: Schur's Theorem states that, for any $r \in \mathbb{Z}^+$, there exists a minimum integer $S(r)$ such that every $r$-coloring of $\{1,2,\dots,S(r)\}$ admits a monochromatic solution to $x+y=z$. Recently, Budden determined the related Gallai-Schur numbers; that is, he determined the minimum integer $GS(r)$ such that every $r$-coloring of $\{1,2,\dots,GS(r)\}$ admits either a rainbow or monochromatic solution to $x+y=z$. In this article we consider problems that have been solved in the monochromatic setting under a monochromatic-rainbow paradigm. In particular, we investigate Gallai-Schur numbers when $x \neq y$, we consider $x+y+b=z$ and $x+y
Published: 2025

8. Improved measurement of absolute branching fraction of the inclusive decay $\Lambda_{c}^{+} \to K_{S}^{0} X$

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, 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. 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, 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, B. Y., 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., 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., 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, 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, 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., 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., Mao, Y. J., Mao, Z. P., Marcello, S., Melendi, F. M., 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., 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., Rosini, F., Rosner, Ch., Ruan, M. Q., 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. 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, L. F., Tang, M., 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. 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., Wei, H. R., 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, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., 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. 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, 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. Q., 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: By analyzing $4.5$ fb$^{-1}$ of $e^{+}e^{-}$ collision data accumulated with the BESIII detector at center-of-mass energies ranging from $4599.53$ MeV to $4698.82$ MeV, we report the measurement of the absolute branching fraction (BF) of the inclusive decay $\Lambda_{c}^{+} \to K_{S}^{0} X$ using the double-tag technique. The result is $\mathcal{B}(\Lambda_{c}^{+} \to K_{S}^{0} X)=(10.9\pm0.2\pm0.1)\%$, where the first uncertainty is statistical and the second is systematic. This result indicates that there are still undiscovered decay channels containing $K_{S}^{0}$ in the final state with a combined BF of $(3.1\pm0.4)\%$. The BF of the inclusive decay $\Lambda_{c}^{+} \to \overline{K}^{0} / K^{0} X$ is calculated to be $\mathcal{B}(\Lambda_{c}^{+} \to \overline{K}^{0} / K^{0} X)=(21.8 \pm0.4 \pm0.2 \pm1.1)\%$, where the third uncertainty accounts for a possible difference between $\mathcal{B}(\Lambda_{c}^{+} \to K_{S}^{0} X)$ and $\mathcal{B}(\Lambda_{c}^{+} \to K_{L}^{0} X)$. The result is in agreement with the prediction of the statistical isospin model.
Published: 2025

9. Enhancing the coherence time of a neutral atom by an optical quartic trap

Author: Chang, Haobo, Tian, Zhuangzhuang, Lv, Xin, Yang, Mengna, Wang, Zhihui, Guo, Qi, Yang, Pengfei, Zhang, Pengfei, Li, Gang, and Zhang, Tiancai
Subjects: Quantum Physics, Physics - Atomic Physics
Abstract: The coherence time of an optically trapped neutral atom is a crucial parameter for quantum technologies. We found that optical dipole traps with higher-order spatial forms inherently offer lower decoherence rates compared to those with lower-order spatial forms. We formulated the decoherence rate caused by the variance of the differential energy shift and photon jumping rate. Then, we constructed blue-detuned harmonic and quartic optical dipole traps, and experimentally investigated the coherence time of a trapped single cesium atom. The experimental results qualitatively verified our theory. Our approach provides a novel method to enhance the coherence time of optically trapped neutral atoms., Comment: 4 pages, 3 figures
Published: 2025

10. Development and characterization of the JNE concentrator

Author: Ouyang, Shuai, Yang, Yuzi, Zhang, Yang, Zhang, Aiqiang, Yang, Haoyan, Wei, Changxu, Liu, Yuhao, Wang, Zhe, Xue, Tao, Wang, Zongyi, and Chen, Shaomin
Subjects: High Energy Physics - Experiment
Abstract: The Jinping Neutrino Experiment (JNE) will utilize approximately 3000 8-inch MCP-PMTs identified as GDB-6082 from North Night Vision Technology to detect neutrinos. To enhance the effective coverage of the JNE detector, mounting a custom-designed light concentrator on each photomultiplier tube (PMT) is a practical and economical approach. We measured angular responses of the concentration factor at four wavelengths in air medium for the concentrator with the selected cutoff angle of 70 degree. The measurements align with the Monte Carlo simulations. Furthermore, our results indicate that these concentrators can improve the efficiency of light collection by 40 % under parallel illumination conditions. However, this enhancement results in a slight increase in transit-time spread, with the full width at half maximum (FWHM) increasing by less than 0.3 ns. We conclude that the developed light concentrators are highly suitable for the JNE.
Published: 2025

11. LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversation

Author: Li, Haitao, Chen, Yifan, Hu, Yiran, Ai, Qingyao, Chen, Junjie, Yang, Xiaoyu, Yang, Jianhui, Wu, Yueyue, Liu, Zeyang, and Liu, Yiqun
Subjects: Computer Science - Computation and Language, Computer Science - Information Retrieval
Abstract: Retrieval-augmented generation (RAG) has proven highly effective in improving large language models (LLMs) across various domains. However, there is no benchmark specifically designed to assess the effectiveness of RAG in the legal domain, which restricts progress in this area. To fill this gap, we propose LexRAG, the first benchmark to evaluate RAG systems for multi-turn legal consultations. LexRAG consists of 1,013 multi-turn dialogue samples and 17,228 candidate legal articles. Each sample is annotated by legal experts and consists of five rounds of progressive questioning. LexRAG includes two key tasks: (1) Conversational knowledge retrieval, requiring accurate retrieval of relevant legal articles based on multi-turn context. (2) Response generation, focusing on producing legally sound answers. To ensure reliable reproducibility, we develop LexiT, a legal RAG toolkit that provides a comprehensive implementation of RAG system components tailored for the legal domain. Additionally, we introduce an LLM-as-a-judge evaluation pipeline to enable detailed and effective assessment. Through experimental analysis of various LLMs and retrieval methods, we reveal the key limitations of existing RAG systems in handling legal consultation conversations. LexRAG establishes a new benchmark for the practical application of RAG systems in the legal domain, with its code and data available at https://github.com/CSHaitao/LexRAG., Comment: 10 pages
Published: 2025

12. SSD: A State-based Stealthy Backdoor Attack For Navigation System in UAV Route Planning

Author: Wang, Zhaoxuan, Li, Yang, Zhang, Jie, Han, Xingshuo, Liu, Kangbo, Yang, Lyu, Zhou, yuan, Zhang, Tianwei, and Pan, Quan
Subjects: Computer Science - Cryptography and Security
Abstract: Unmanned aerial vehicles (UAVs) are increasingly employed to perform high-risk tasks that require minimal human intervention. However, UAVs face escalating cybersecurity threats, particularly from GNSS spoofing attacks. While previous studies have extensively investigated the impacts of GNSS spoofing on UAVs, few have focused on its effects on specific tasks. Moreover, the influence of UAV motion states on the assessment of network security risks is often overlooked. To address these gaps, we first provide a detailed evaluation of how motion states affect the effectiveness of network attacks. We demonstrate that nonlinear motion states not only enhance the effectiveness of position spoofing in GNSS spoofing attacks but also reduce the probability of speed-related attack detection. Building upon this, we propose a state-triggered backdoor attack method (SSD) to deceive GNSS systems and assess its risk to trajectory planning tasks. Extensive validation of SSD's effectiveness and stealthiness is conducted. Experimental results show that, with appropriately tuned hyperparameters, SSD significantly increases positioning errors and the risk of task failure, while maintaining 100% stealth across three state-of-the-art detectors., Comment: 13 pages
Published: 2025

13. Fundamental Physics and Cosmology with TianQin

Author: Luo, Jun, An, Haipeng, Bian, Ligong, Cai, Rong-Gen, Cao, Zhoujian, Han, Wenbiao, He, Jianhua, Hendry, Martin A., Hu, Bin, Hu, Yi-Ming, Huang, Fa Peng, Huang, Shun-Jia, Kim, Sang Pyo, Li, En-Kun, Liu, Yu-Xiao, Milyukov, Vadim, Pi, Shi, Postnov, Konstantin, Sasaki, Misao, Shao, Cheng-Gang, Shao, Lijing, Shi, Changfu, Sun, Shuo, Wang, Anzhong, Wang, Pan-Pan, Wang, Sai, Wang, Shao-Jiang, Xianyu, Zhong-Zhi, Yang, Huan, Yang, Tao, Zhang, Jian-dong, Zhang, Xin, Zhao, Wen, Zhu, Liang-Gui, and Mei, Jianwei
Subjects: General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Theory
Abstract: The exploration of the surrounding world and the universe is an important theme in the legacy of humankind. The detection of gravitational waves is adding a new dimension to this grand effort. What are the fundamental physical laws governing the dynamics of the universe? What is the fundamental composition of the universe? How has the universe evolved in the past and how will it evolve in the future? These are the basic questions that press for answers. The space-based gravitational wave detector TianQin will tune in to gravitational waves in the millihertz frequency range ($10^{-4} \sim 1$ Hz, to be specific), opening a new gravitational wave spectrum window to explore many of the previously hidden sectors of the universe. TianQin will discover many astrophysical systems, populating the universe at different redshifts: some will be of new types that have never been detected before, some will have very high signal-to-noise ratios, and some will have very high parameter estimation precision. The plethora of information collected will bring us to new fronts on which to search for the breaking points of general relativity, the possible violation of established physical laws, the signature of possible new gravitational physics and new fundamental fields, and to improve our knowledge on the expansion history of the universe. In this white paper, we highlight the advances that TianQin can bring to fundamental physics and cosmology., Comment: 113 pages
Published: 2025

14. New Dataset and Methods for Fine-Grained Compositional Referring Expression Comprehension via Specialist-MLLM Collaboration

Author: Yang, Xuzheng, Liu, Junzhuo, Wang, Peng, Wang, Guoqing, Yang, Yang, and Shen, Heng Tao
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Referring Expression Comprehension (REC) is a foundational cross-modal task that evaluates the interplay of language understanding, image comprehension, and language-to-image grounding. To advance this field, we introduce a new REC dataset with two key features. First, it is designed with controllable difficulty levels, requiring fine-grained reasoning across object categories, attributes, and relationships. Second, it incorporates negative text and images generated through fine-grained editing, explicitly testing a model's ability to reject non-existent targets, an often-overlooked yet critical challenge in existing datasets. To address fine-grained compositional REC, we propose novel methods based on a Specialist-MLLM collaboration framework, leveraging the complementary strengths of them: Specialist Models handle simpler tasks efficiently, while MLLMs are better suited for complex reasoning. Based on this synergy, we introduce two collaborative strategies. The first, Slow-Fast Adaptation (SFA), employs a routing mechanism to adaptively delegate simple tasks to Specialist Models and complex tasks to MLLMs. Additionally, common error patterns in both models are mitigated through a target-refocus strategy. The second, Candidate Region Selection (CRS), generates multiple bounding box candidates based on Specialist Model and uses the advanced reasoning capabilities of MLLMs to identify the correct target. Extensive experiments on our dataset and other challenging compositional benchmarks validate the effectiveness of our approaches. The SFA strategy achieves a trade-off between localization accuracy and efficiency, and the CRS strategy greatly boosts the performance of both Specialist Models and MLLMs. We aim for this work to offer valuable insights into solving complex real-world tasks by strategically combining existing tools for maximum effectiveness, rather than reinventing them.
Published: 2025

15. LongRoPE2: Near-Lossless LLM Context Window Scaling

Author: Shang, Ning, Zhang, Li Lyna, Wang, Siyuan, Zhang, Gaokai, Lopez, Gilsinia, Yang, Fan, Chen, Weizhu, and Yang, Mao
Subjects: Computer Science - Computation and Language
Abstract: LongRoPE2 is a novel approach that extends the effective context window of pre-trained large language models (LLMs) to the target length, while preserving the performance on the original shorter context window. This is achieved by three contributions: (1) a hypothesis that insufficient training in higher RoPE dimensions contributes to the persistent out-of-distribution (OOD) issues observed in existing methods; (2) an effective RoPE rescaling algorithm that adopts evolutionary search guided by "needle-driven" perplexity to address the insufficient training problem; (3) a mixed context window training approach that fine-tunes model weights to adopt rescaled RoPE for long-context sequences while preserving the short-context performance with the original RoPE. Extensive experiments on LLaMA3-8B and Phi3-mini-3.8B across various benchmarks validate the hypothesis and demonstrate the effectiveness of LongRoPE2. Remarkably, LongRoPE2 extends LLaMA3-8B to achieve a 128K effective context length while retaining over 98.5% of short-context performance, using only 10B tokens -- 80x fewer than Meta's approach, which fails to reach the target effective context length. Code will be available at https://github.com/microsoft/LongRoPE.
Published: 2025

16. Multi-Keypoint Affordance Representation for Functional Dexterous Grasping

Author: Yang, Fan, Luo, Dongsheng, Chen, Wenrui, Lin, Jiacheng, Cai, Junjie, Yang, Kailun, Li, Zhiyong, and Wang, Yaonan
Subjects: Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: Functional dexterous grasping requires precise hand-object interaction, going beyond simple gripping. Existing affordance-based methods primarily predict coarse interaction regions and cannot directly constrain the grasping posture, leading to a disconnection between visual perception and manipulation. To address this issue, we propose a multi-keypoint affordance representation for functional dexterous grasping, which directly encodes task-driven grasp configurations by localizing functional contact points. Our method introduces Contact-guided Multi-Keypoint Affordance (CMKA), leveraging human grasping experience images for weak supervision combined with Large Vision Models for fine affordance feature extraction, achieving generalization while avoiding manual keypoint annotations. Additionally, we present a Keypoint-based Grasp matrix Transformation (KGT) method, ensuring spatial consistency between hand keypoints and object contact points, thus providing a direct link between visual perception and dexterous grasping actions. Experiments on public real-world FAH datasets, IsaacGym simulation, and challenging robotic tasks demonstrate that our method significantly improves affordance localization accuracy, grasp consistency, and generalization to unseen tools and tasks, bridging the gap between visual affordance learning and dexterous robotic manipulation. The source code and demo videos will be publicly available at https://github.com/PopeyePxx/MKA., Comment: The source code and demo videos will be publicly available at https://github.com/PopeyePxx/MKA
Published: 2025

17. Precision measurement of the branching fraction for the decay $\psi(2S)\rightarrow\tau^{+}\tau^{-}$

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, 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. 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, 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. 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., Wei, H. R., 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., 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. 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 $(2259.3 \pm 11.1)\times10^{6}$ $\psi(2S)$ events acquired with the BESIII detector, the branching fraction of $\psi(2S)\rightarrow\tau^{+}\tau^{-}$ is measured with improved precision to be $\mathcal{B}_{\psi(2S)\rightarrow\tau^{+}\tau^{-}}=(3.240~\pm~0.023~\pm~0.081)\times 10^{-3}$, where the first and second uncertainties are statistical and systematic, respectively, which is consistent with the world average value within one standard deviation. This value, along with those for the branching fractions of the $\psi(2S)$ decaying into $e^{+}e^{-}$ and $\mu^{+}\mu^{-}$, is in good agreement with the relation predicted by the sequential lepton hypothesis. Combining the branching fraction values with the leptonic width of the $\psi(2S)$, the total width of the $\psi(2S)$ is determined to be (287 $\pm$ 9) keV., Comment: 10 page, 5 figures
Published: 2025

18. Twofold Debiasing Enhances Fine-Grained Learning with Coarse Labels

Author: Zhao, Xin-yang, Jin, Jian, Li, Yang-yang, and Yao, Yazhou
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: The Coarse-to-Fine Few-Shot (C2FS) task is designed to train models using only coarse labels, then leverages a limited number of subclass samples to achieve fine-grained recognition capabilities. This task presents two main challenges: coarse-grained supervised pre-training suppresses the extraction of critical fine-grained features for subcategory discrimination, and models suffer from overfitting due to biased distributions caused by limited fine-grained samples. In this paper, we propose the Twofold Debiasing (TFB) method, which addresses these challenges through detailed feature enhancement and distribution calibration. Specifically, we introduce a multi-layer feature fusion reconstruction module and an intermediate layer feature alignment module to combat the model's tendency to focus on simple predictive features directly related to coarse-grained supervision, while neglecting complex fine-grained level details. Furthermore, we mitigate the biased distributions learned by the fine-grained classifier using readily available coarse-grained sample embeddings enriched with fine-grained information. Extensive experiments conducted on five benchmark datasets demonstrate the efficacy of our approach, achieving state-of-the-art results that surpass competitive methods.
Published: 2025

19. Multidimensional Monotonicity and Economic Applications

Author: Yang, Frank and Yang, Kai Hao
Subjects: Economics - Theoretical Economics
Abstract: We characterize the extreme points of multidimensional monotone functions from $[0,1]^n$ to $[0,1]$, as well as the extreme points of the set of one-dimensional marginals of these functions. These characterizations lead to new results in various mechanism design and information design problems, including public good provision with interdependent values; interim efficient bilateral trade mechanisms; asymmetric reduced form auctions; and optimal private private information structure. As another application, we also present a mechanism anti-equivalence theorem for two-agent, two-alternative social choice problems: A mechanism is payoff-equivalent to a deterministic DIC mechanism if and only if they are ex-post equivalent., Comment: 70 pages, 7 figures
Published: 2025

20. PCL: Prompt-based Continual Learning for User Modeling in Recommender Systems

Author: Yang, Mingdai, Yang, Fan, Guo, Yanhui, Xu, Shaoyuan, Zhou, Tianchen, Chen, Yetian, Shao, Simone, Liu, Jia, and Gao, Yan
Subjects: Computer Science - Information Retrieval
Abstract: User modeling in large e-commerce platforms aims to optimize user experiences by incorporating various customer activities. Traditional models targeting a single task often focus on specific business metrics, neglecting the comprehensive user behavior, and thus limiting their effectiveness. To develop more generalized user representations, some existing work adopts Multi-task Learning (MTL)approaches. But they all face the challenges of optimization imbalance and inefficiency in adapting to new tasks. Continual Learning (CL), which allows models to learn new tasks incrementally and independently, has emerged as a solution to MTL's limitations. However, CL faces the challenge of catastrophic forgetting, where previously learned knowledge is lost when the model is learning the new task. Inspired by the success of prompt tuning in Pretrained Language Models (PLMs), we propose PCL, a Prompt-based Continual Learning framework for user modeling, which utilizes position-wise prompts as external memory for each task, preserving knowledge and mitigating catastrophic forgetting. Additionally, we design contextual prompts to capture and leverage inter-task relationships during prompt tuning. We conduct extensive experiments on real-world datasets to demonstrate PCL's effectiveness., Comment: 5 pages. Accepted by www'25 as short paper
Published: 2025
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21. Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs

Author: Yang, Dayu, Liu, Tianyang, Zhang, Daoan, Simoulin, Antoine, Liu, Xiaoyi, Cao, Yuwei, Teng, Zhaopu, Qian, Xin, Yang, Grey, Luo, Jiebo, and McAuley, Julian
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Software Engineering
Abstract: In large language models (LLMs), code and reasoning reinforce each other: code offers an abstract, modular, and logic-driven structure that supports reasoning, while reasoning translates high-level goals into smaller, executable steps that drive more advanced code intelligence. In this study, we examine how code serves as a structured medium for enhancing reasoning: it provides verifiable execution paths, enforces logical decomposition, and enables runtime validation. We also explore how improvements in reasoning have transformed code intelligence from basic completion to advanced capabilities, enabling models to address complex software engineering tasks through planning and debugging. Finally, we identify key challenges and propose future research directions to strengthen this synergy, ultimately improving LLM's performance in both areas., Comment: Project Repo: https://github.com/dayuyang1999/Awesome-Code-Reasoning
Published: 2025

22. Enhanced deep-freezing magneto- and elasto-caloric effects by modifying lattice anharmonicity and electronic structures

Author: Huang, Xiao-Ming, Zhao, Ying, Hao, Xiaowen, Xiang, Hua-You, Yang, Jin-Han, Wang, Chin-Wei, Yang, Wenyun, Zhang, Cuiping, Zhao, Binru, Ma, Jie, Li, Zongbin, Kuang, Yafei, Zuo, Liang, Tong, Xin, Yan, Hai-Le, and Ren, Qingyong
Subjects: Condensed Matter - Materials Science
Abstract: Designing the high performance magneto or elastocaloric effect in NiMnIn alloys with spin-lattice coupling in a deep freezing temperature range of 200 K to 255 K is challenging due to the limited lattice entropy change and large negative contribution of magnetic entropy change during phase transitions. In this work, we systematically study the first order magneto-structural transition in NiMnIn based alloys by in-situ microstructural characterizations, physical property measurements, and first principles calculations. A multi element alloying strategy involving Cu and Ga co doping is proposed to manipulate the phase transition. The co doping reduces the lattice anharmonicity and thermal expansion coefficient of the martensitic phase, leading to an increase in the unit cell volume change and lattice entropy change. It also modifies the electronic density of states, causing a decrease in the magnetization change .The relief of the lattice mismatch reduces hysteresis losses in the refrigeration cycle. These synergetic effects yield excellent magneto and elastocaloric effects,with the effective magnetocaloric refrigeration capacity reaching up to 182 J/kg under the magnetic field of 5 T or an adiabatic temperature change of -4 K under a low field of 1.5 T and the elastocaloric coefficient of performance to 30 or an adiabatic temperature change of -7 K with the strain of 5% at 230 K, offering a potential solution for solid-state deep-freezing refrigeration.
Published: 2025

23. Observation of a new charmed baryon decaying to $\Xi_c^+ \pi^- \pi^+$

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., 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., Bezshyyko, O., 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., Coronel, C., 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., 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., Faghih, S., 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., Korchin, A., 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., Kushnir, V., Kutsenko, B. K., Kyryllin, I., 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, J., 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, D., 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., 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., Mytrochenko, V., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Nekrasova, E., 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., 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., 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., 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., Singha, M., Skidmore, N., Skwarnicki, T., Slater, M. W., 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., Upadhyay, A., 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., Vivacqua, B., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, A., 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., Zhu, Y., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.
Subjects: High Energy Physics - Experiment
Abstract: The $\Xi_c^+ \pi^- \pi^+$ spectrum is investigated using proton-proton collisions at a center-of-mass energy of 13TeV, corresponding to an integrated luminosity of 5.4fb$^{-1}$, collected by the LHCb experiment during 2016--2018. Four states are observed with high significance, and their masses and widths are measured to be \begin{align*} m[\Xi_c(2815)^{+}] &= 2816.65 \pm 0.03 \pm 0.03 \pm 0.23 ~\text{MeV}, \Gamma[\Xi_c(2815)^{+}] &= 2.07 \pm 0.08 \pm 0.12~\text{MeV},\\[5pt] m[\Xi_c(2923)^{+}] &= 2922.8 \pm 0.3 \pm 0.5 \pm 0.2~\text{MeV}, \Gamma[\Xi_c(2923)^{+}] &= 5.3 \pm 0.9 \pm 1.4~\text{MeV},\\[5pt] m[\Xi_c(2970)^{+}] &= 2968.6 \pm 0.5 \pm 0.5 \pm 0.2~\text{MeV}, \Gamma[\Xi_c(2970)^{+}] &= 31.7 \pm 1.7 \pm 1.9~\text{MeV},\\[5pt] m[\Xi_c(3080)^{+}] &= 3076.8 \pm 0.7 \pm 1.3 \pm 0.2~\text{MeV}, \Gamma[\Xi_c(3080)^{+}] &= 6.8 \pm 2.3 \pm 0.9~\text{MeV}, \end{align*} where the uncertainties are statistical, systematic, and due to the limited precision on the $\Xi_c^+$ mass, respectively. The $\Xi_c(2923)^{+}$ baryon is observed for the first time, and is consistent with being the isospin partner of the previously observed $\Xi_c(2923)^{0}$ state. Most of the measured parameters are more precise than existing world averages., 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/3080/ (LHCb public pages)
Published: 2025

24. Training Large Recommendation Models via Graph-Language Token Alignment

Author: Yang, Mingdai, Liu, Zhiwei, Yang, Liangwei, Liu, Xiaolong, Wang, Chen, Peng, Hao, and Yu, Philip S.
Subjects: Computer Science - Information Retrieval
Abstract: Recommender systems (RS) have become essential tools for helping users efficiently navigate the overwhelming amount of information on e-commerce and social platforms. However, traditional RS relying on Collaborative Filtering (CF) struggles to integrate the rich semantic information from textual data. Meanwhile, large language models (LLMs) have shown promising results in natural language processing, but directly using LLMs for recommendation introduces challenges, such as ambiguity in generating item predictions and inefficiencies in scalability. In this paper, we propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment. By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs. Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction, eliminating ambiguity in the free-form text as recommendation results. Extensive experiments on three benchmark datasets demonstrate the effectiveness of GLTA, with ablation studies validating each component., Comment: 5 pages. Accepted by www'25 as short paper
Published: 2025
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25. WIMP Dark Matter Search using a 3.1 tonne $\times$ year Exposure of the XENONnT Experiment

Author: Aprile, E., Aalbers, J., Abe, K., Maouloud, S. Ahmed, Althueser, L., Andrieu, B., Angelino, E., Martin, D. Antón, Armbruster, S. R., Arneodo, F., Baudis, L., Bazyk, M., Bellagamba, L., Biondi, R., Bismark, A., Boese, K., Brown, A., Bruno, G., Budnik, R., Cai, C., Capelli, C., Cardoso, J. M. R., Chávez, A. P. Cimental, Colijn, A. P., Conrad, J., Cuenca-García, J. J., D'Andrea, V., Garcia, L. C. Daniel, Decowski, M. P., Deisting, A., Di Donato, C., Di Gangi, P., Diglio, S., Eitel, K., Morabit, S. el, Elykov, A., Ferella, A. D., Ferrari, C., Fischer, H., Flehmke, T., Flierman, M., Fuchs, D., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Gao, F., Ghosh, S., Giacomobono, R., Girard, F., Glade-Beucke, R., Grandi, L., Grigat, J., Guan, H., Guida, M., Gyorgy, P., Hammann, R., Higuera, A., Hils, C., Hoetzsch, L., Hood, N. F., Iacovacci, M., Itow, Y., Jakob, J., Joerg, F., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Kharbanda, P., Kobayashi, M., Koke, D., Kooshkjalali, K., Kopec, A., Landsman, H., Lang, R. F., Levinson, L., Li, I., Li, S., Liang, S., Liang, Z., Lin, Y. -T., Lindemann, S., Lindner, M., Liu, K., Liu, M., Loizeau, J., Lombardi, F., Long, J., Lopes, J. A. M., Lucchetti, G. M., Luce, T., Ma, Y., Macolino, C., Mahlstedt, J., Mancuso, A., Manenti, L., Marignetti, F., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Mastroianni, S., Melchiorre, A., Merz, J., Messina, M., Michael, A., Miuchi, K., Molinario, A., Moriyama, S., Morå, K., Mosbacher, Y., Murra, M., Müller, J., Ni, K., Oberlack, U., Paetsch, B., Pan, Y., Pellegrini, Q., Peres, R., Peters, C., Pienaar, J., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qin, J., García, D. Ramírez, Rajado, M., Ravindran, A., Razeto, A., Singh, R., Sanchez, L., Santos, J. M. F. dos, Sarnoff, I., Sartorelli, G., Schreiner, J., Schulte, P., Eißing, H. Schulze, Schumann, M., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Shi, S., Shi, J., Silva, M., Simgen, H., Stevens, A., Szyszka, C., Takeda, A., Takeuchi, Y., Tan, P. -L., Thers, D., Trinchero, G., Tunnell, C. D., Tönnies, F., Valerius, K., Vecchi, S., Vetter, S., Solar, F. I. Villazon, Volta, G., Weinheimer, C., Weiss, M., Wenz, D., Wittweg, C., Wu, V. H. S., Xing, Y., Xu, D., Xu, Z., Yamashita, M., Yang, J., Yang, L., Ye, J., Yuan, L., Zavattini, G., Zhao, Y., and Zhong, M.
Subjects: High Energy Physics - Experiment, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Phenomenology, Physics - Instrumentation and Detectors
Abstract: We report on a search for weakly interacting massive particle (WIMP) dark matter (DM) via elastic DM-xenon-nucleus interactions in the XENONnT experiment. We combine datasets from the first and second science campaigns resulting in a total exposure of $3.1\;\text{tonne}\times\text{year}$. In a blind analysis of nuclear recoil events with energies above $3.8\,\mathrm{keV_{NR}}$, we find no significant excess above background. We set new upper limits on the spin-independent WIMP-nucleon scattering cross-section for WIMP masses above $10\,\mathrm{GeV}/c^2$ with a minimum of $1.7\,\times\,10^{-47}\,\mathrm{cm^2}$ at $90\,\%$ confidence level for a WIMP mass of $30\,\mathrm{GeV}/c^2$. We achieve a best median sensitivity of $1.4\,\times\,10^{-47}\,\mathrm{cm^2}$ for a $41\,\mathrm{GeV}/c^2$ WIMP. Compared to the result from the first XENONnT science dataset, we improve our sensitivity by a factor of up to 1.8., Comment: Limits are included in the submission file
Published: 2025

26. Photoexcitation-induced Stacking Transition Assisted by Intralayer Reconstruction in Charge-Density-Wave Materials

Author: Zhang, Jin, Yang, Yang, Zhang, Jia, Guan, Mengxue, Xu, Jiyu, Yang, Kun, Shi, Xinghua, and Meng, Sheng
Subjects: Condensed Matter - Materials Science
Abstract: Laser excitation has emerged as an effective tool for probing microscopic interactions and manipulating phases of matter. Among charge density wave (CDW) materials, 1T-TaS2 has garnered significant attention due to its diverse stacking orders and photoexcited responses. However, the mechanisms driving transitions among different stacking orders and the microscopic out-of-equilibrium dynamics remain unclear. We elucidate that photoexcitation can introduce interlayer stacking order transitions facilitated by laser-induced intralayer reconstruction in 1T-TaS2. Importantly, our finding reveals a novel pathway to introduce different phases through laser excitations, apparently distinct from thermally-induced phase transitions via interlayer sliding. In particular, photoexcitation is able to considerably change potential energy surfaces and evoke collective lattice dynamics. Consequently, the laser-induced intralayer reconstruction plays a crucial role in interlayer stacking-order transition, offering a new method to create exotic stackings and quantum phases. The exploration opens up great opportunities for manipulating CDW phases and electronic properties on the femtosecond timescale., Comment: 10 pages, 4 figures
Published: 2025

27. Zero-shot Load Forecasting for Integrated Energy Systems: A Large Language Model-based Framework with Multi-task Learning

Author: Li, Jiaheng, Li, Donghe, Yang, Ye, Xi, Huan, Xiao, Yu, Sun, Li, An, Dou, and Yang, Qingyu
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: The growing penetration of renewable energy sources in power systems has increased the complexity and uncertainty of load forecasting, especially for integrated energy systems with multiple energy carriers. Traditional forecasting methods heavily rely on historical data and exhibit limited transferability across different scenarios, posing significant challenges for emerging applications in smart grids and energy internet. This paper proposes the TSLLM-Load Forecasting Mechanism, a novel zero-shot load forecasting framework based on large language models (LLMs) to address these challenges. The framework consists of three key components: a data preprocessing module that handles multi-source energy load data, a time series prompt generation module that bridges the semantic gap between energy data and LLMs through multi-task learning and similarity alignment, and a prediction module that leverages pre-trained LLMs for accurate forecasting. The framework's effectiveness was validated on a real-world dataset comprising load profiles from 20 Australian solar-powered households, demonstrating superior performance in both conventional and zero-shot scenarios. In conventional testing, our method achieved a Mean Squared Error (MSE) of 0.4163 and a Mean Absolute Error (MAE) of 0.3760, outperforming existing approaches by at least 8\%. In zero-shot prediction experiments across 19 households, the framework maintained consistent accuracy with a total MSE of 11.2712 and MAE of 7.6709, showing at least 12\% improvement over current methods. The results validate the framework's potential for accurate and transferable load forecasting in integrated energy systems, particularly beneficial for renewable energy integration and smart grid applications.
Published: 2025

28. VideoGrain: Modulating Space-Time Attention for Multi-grained Video Editing

Author: Yang, Xiangpeng, Zhu, Linchao, Fan, Hehe, and Yang, Yi
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Recent advancements in diffusion models have significantly improved video generation and editing capabilities. However, multi-grained video editing, which encompasses class-level, instance-level, and part-level modifications, remains a formidable challenge. The major difficulties in multi-grained editing include semantic misalignment of text-to-region control and feature coupling within the diffusion model. To address these difficulties, we present VideoGrain, a zero-shot approach that modulates space-time (cross- and self-) attention mechanisms to achieve fine-grained control over video content. We enhance text-to-region control by amplifying each local prompt's attention to its corresponding spatial-disentangled region while minimizing interactions with irrelevant areas in cross-attention. Additionally, we improve feature separation by increasing intra-region awareness and reducing inter-region interference in self-attention. Extensive experiments demonstrate our method achieves state-of-the-art performance in real-world scenarios. Our code, data, and demos are available at https://knightyxp.github.io/VideoGrain_project_page/, Comment: ICLR 2025, code and demos are available at https://knightyxp.github.io/VideoGrain_project_page/
Published: 2025

29. Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metric

Author: Yang, Yuming, Nan, Yang, Ye, Junjie, Dou, Shihan, Wang, Xiao, Li, Shuo, Lv, Huijie, Wu, Mingqi, Gui, Tao, Zhang, Qi, and Huang, Xuanjing
Subjects: Computer Science - Computation and Language
Abstract: Data diversity is crucial for the instruction tuning of large language models. Existing studies have explored various diversity-aware data selection methods to construct high-quality datasets and enhance model performance. However, the fundamental problem of precisely defining and measuring data diversity remains underexplored, limiting clear guidance for data engineering. To address this, we systematically analyze 11 existing diversity measurement methods by evaluating their correlation with model performance through extensive fine-tuning experiments. Our results indicate that a reliable diversity measure should properly account for both inter-sample differences and the information distribution in the sample space. Building on this, we propose NovelSum, a new diversity metric based on sample-level "novelty." Experiments on both simulated and real-world data show that NovelSum accurately captures diversity variations and achieves a 0.97 correlation with instruction-tuned model performance, highlighting its value in guiding data engineering practices. With NovelSum as an optimization objective, we further develop a greedy, diversity-oriented data selection strategy that outperforms existing approaches, validating both the effectiveness and practical significance of our metric., Comment: 16 pages. The related codes and resources will be released later. Project page: https://github.com/UmeanNever/NovelSum
Published: 2025

30. Diffusion Models for Tabular Data: Challenges, Current Progress, and Future Directions

Author: Li, Zhong, Huang, Qi, Yang, Lincen, Shi, Jiayang, Yang, Zhao, van Stein, Niki, Bäck, Thomas, and van Leeuwen, Matthijs
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: In recent years, generative models have achieved remarkable performance across diverse applications, including image generation, text synthesis, audio creation, video generation, and data augmentation. Diffusion models have emerged as superior alternatives to Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) by addressing their limitations, such as training instability, mode collapse, and poor representation of multimodal distributions. This success has spurred widespread research interest. In the domain of tabular data, diffusion models have begun to showcase similar advantages over GANs and VAEs, achieving significant performance breakthroughs and demonstrating their potential for addressing unique challenges in tabular data modeling. However, while domains like images and time series have numerous surveys summarizing advancements in diffusion models, there remains a notable gap in the literature for tabular data. Despite the increasing interest in diffusion models for tabular data, there has been little effort to systematically review and summarize these developments. This lack of a dedicated survey limits a clear understanding of the challenges, progress, and future directions in this critical area. This survey addresses this gap by providing a comprehensive review of diffusion models for tabular data. Covering works from June 2015, when diffusion models emerged, to December 2024, we analyze nearly all relevant studies, with updates maintained in a \href{https://github.com/Diffusion-Model-Leiden/awesome-diffusion-models-for-tabular-data}{GitHub repository}. Assuming readers possess foundational knowledge of statistics and diffusion models, we employ mathematical formulations to deliver a rigorous and detailed review, aiming to promote developments in this emerging and exciting area.
Published: 2025

31. Neutron multiplicity measurement in muon capture on oxygen nuclei in the Gd-loaded Super-Kamiokande detector

Author: Collaboration, The Super-Kamiokande, Miki, S., Abe, K., Abe, S., Asaoka, Y., Bronner, C., Harada, M., Hayato, Y., Hiraide, K., Hosokawa, K., Ieki, K., Ikeda, M., Kameda, J., Kanemura, Y., Kaneshima, R., Kashiwagi, Y., Kataoka, Y., Mine, S., Miura, M., Moriyama, S., Nakahata, M., Nakayama, S., Noguchi, Y., Okamoto, K., Pronost, G., Sato, K., Sekiya, H., Shiba, H., Shimizu, K., Shiozawa, M., Sonoda, Y., Suzuki, Y., Takeda, A., Takemoto, Y., Takenaka, A., Tanaka, H., Watanabe, S., Yano, T., Kajita, T., Okumura, K., Tashiro, T., Tomiya, T., Wang, X., Yoshida, S., Megias, G. D., Fernandez, P., Labarga, L., Ospina, N., Zaldivar, B., Pointon, B. W., Yanagisawa, C., Kearns, E., Raaf, J. L., Wan, L., Wester, T., Bian, J., Cortez, B., Griskevich, N. J., Locke, S., Smy, M. B., Sobel, H. W., Takhistov, V., Yankelevich, A., Hill, J., Jang, M. C., Lee, S. H., Moon, D. H., Park, R. G., Yang, B. S., Bodur, B., Scholberg, K., Walter, C. W., Beauchêne, A., Drapier, O., Ershova, A., Giampaolo, A., Mueller, Th. A., Santos, A. D., Paganini, P., Quach, C., Quilain, B., Rogly, R., Nakamura, T., Jang, J. S., Litchfield, R. P., Machado, L. N., Soler, F. J. P., Learned, J. G., Choi, K., Iovine, N., Cao, S., Anthony, L. H. V., Martin, D., Prouse, N. W., Scott, M., Sztuc, A. A., Uchida, Y., Berardi, V., Calabria, N. F., Catanesi, M. G., Radicioni, E., Langella, A., De Rosa, G., Collazuol, G., Feltre, M., Iacob, F., Lamoureux, M., Mattiazzi, M., Ludovici, L., Gonin, M., Périssé, L., Fujisawa, C., Horiuchi, S., Kobayashi, M., Liu, Y. M., Maekawa, Y., Nishimura, Y., Okazaki, R., Akutsu, R., Friend, M., Hasegawa, T., Ishida, T., Kobayashi, T., Jakkapu, M., Matsubara, T., Nakadaira, T., Nakamura, K., Oyama, Y., Yrey, A. Portocarrero, Sakashita, K., Sekiguchi, T., Tsukamoto, T., Bhuiyan, N., Burton, G. T., Di Lodovico, F., Gao, J., Goldsack, A., Katori, T., Migenda, J., Ramsden, R. M., Xie, Z., Zsoldos, S., Kotsar, Y., Ozaki, H., Suzuki, A. T., Takagi, Y., Takeuchi, Y., Zhong, H., Feng, J., Feng, L., Han, S., Hu, J. R., Hu, Z., Kawaue, M., Kikawa, T., Mori, M., Nakaya, T., Ngoc, T. V., Wendell, R. A., Yasutome, K., Jenkins, S. J., McCauley, N., Mehta, P., Tarrant, A., Wilking, M. J., Fukuda, Y., Itow, Y., Menjo, H., Ninomiya, K., Yoshioka, Y., Lagoda, J., Mandal, M., Mijakowski, P., Prabhu, Y. S., Zalipska, J., Jia, M., Jiang, J., Jung, C. K., Shi, W., Hino, Y., Ishino, H., Kitagawa, H., Koshio, Y., Nakanishi, F., Sakai, S., Tada, T., Tano, T., Ishizuka, T., Barr, G., Barrow, D., Cook, L., Samani, S., Wark, D., Holin, A., Nova, F., Jung, S., Yang, J. Y., Yoo, J., Fannon, J. E. P., Kneale, L., Malek, M., McElwee, J. M., Peacock, T., Stowell, P., Thiesse, M. D., Thompson, L. F., Wilson, S. T., Okazawa, H., Lakshmi, S. M., Kim, S. B., Kwon, E., Lee, M. W., Seo, J. W., Yu, I., Ichikawa, A. K., Nakamura, K. D., Tairafune, S., Nishijima, K., Eguchi, A., Goto, S., Mizuno, Y., Muro, T., Nakagiri, K., Nakajima, Y., Shima, S., Taniuchi, N., Watanabe, E., Yokoyama, M., de Perio, P., Fujita, S., Jesús-Valls, C., Martens, K., Marti, Ll., Tsui, K. M., Vagins, M. R., Xia, J., Kuze, M., Izumiyama, S., Matsumoto, R., Terada, K., Asaka, R., Ishitsuka, M., Ito, H., Kinoshita, T., Ommura, Y., Shigeta, N., Shinoki, M., Suganuma, T., Yamauchi, K., Yoshida, T., Martin, J. F., Tanaka, H. A., Towstego, T., Nakano, Y., Cormier, F., Gaur, R., Gousy-Leblanc, V., Hartz, M., Konaka, A., Li, X., Smithers, B. R., Chen, S., Wu, Y., Xu, B. D., Zhang, A. Q., Zhang, B., Girgus, M., Govindaraj, P., Posiadala-Zezula, M., Boyd, S. B., Edwards, R., Hadley, D., Nicholson, M., O'Flaherty, M., Richards, B., Ali, A., Jamieson, B., Amanai, S., Minamino, A., Pintaudi, G., Sano, S., Shibayama, R., Shimamura, R., Suzuki, S., and Wada, K.
Subjects: High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: In recent neutrino detectors, neutrons produced in neutrino reactions play an important role. Muon capture on oxygen nuclei is one of the processes that produce neutrons in water Cherenkov detectors. We measured neutron multiplicity in the process using cosmic ray muons that stop in the gadolinium-loaded Super-Kamiokande detector. For this measurement, neutron detection efficiency is obtained with the muon capture events followed by gamma rays to be $50.2^{+2.0}_{-2.1}\%$. By fitting the observed multiplicity considering the detection efficiency, we measure neutron multiplicity in muon capture as $P(0)=24\pm3\%$, $P(1)=70^{+3}_{-2}\%$, $P(2)=6.1\pm0.5\%$, $P(3)=0.38\pm0.09\%$. This is the first measurement of the multiplicity of neutrons associated with muon capture without neutron energy threshold.
Published: 2025

32. Room-temperature field-tunable radiofrequency rectification in epitaxial SrIrO3 films

Author: Zhou, Liang, Du, Zongzheng, Wang, Jinhua, Chen, Pingbo, Ye, Bicong, Feng, Tao, Yang, Jiahao, Xiao, Zehao, Yang, Meng, Li, Junxue, Zhang, Wenqing, Lu, Hai-zhou, and He, Hongtao
Subjects: Condensed Matter - Materials Science, Physics - Applied Physics
Abstract: Although significant advancements have been made in wireless technologies and portable devices, it remains a challenge for high-frequency and nanowatt-level radiofrequency rectification. In this work, we report a pronounced radiofrequency rectification up to 37 GHz in nominally centrosymmetric SrIrO3 epitaxial films, with the minimum detectable power as low as ~300 nanowatts. Strikingly, the SrIrO3 rectifier is highly field-tunable and exhibits a strong in-plane field anisotropy, thus showing a unique advantage in broad-band radiofrequency rectification. The rectification effect can persist up to at least 360 K and shows a sensitive temperature dependence including a sign inversion. By a systematic study of the nonlinear transport properties of SrIrO3, it is further revealed that the radiofrequency rectification originates from the nonlinear Hall effect with the dominant contribution from field-induced Berry curvature dipole. Our work demonstrates the superior performance of the field-tunable SrIrO3 rectifiers, unleashing the great application potential of centrosymmetric materials in harvesting and detecting ambient electromagnetic energy., Comment: 4 figures
Published: 2025

33. Multi-Teacher Knowledge Distillation with Reinforcement Learning for Visual Recognition

Author: Yang, Chuanguang, Yu, Xinqiang, Yang, Han, An, Zhulin, Yu, Chengqing, Huang, Libo, and Xu, Yongjun
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Multi-teacher Knowledge Distillation (KD) transfers diverse knowledge from a teacher pool to a student network. The core problem of multi-teacher KD is how to balance distillation strengths among various teachers. Most existing methods often develop weighting strategies from an individual perspective of teacher performance or teacher-student gaps, lacking comprehensive information for guidance. This paper proposes Multi-Teacher Knowledge Distillation with Reinforcement Learning (MTKD-RL) to optimize multi-teacher weights. In this framework, we construct both teacher performance and teacher-student gaps as state information to an agent. The agent outputs the teacher weight and can be updated by the return reward from the student. MTKD-RL reinforces the interaction between the student and teacher using an agent in an RL-based decision mechanism, achieving better matching capability with more meaningful weights. Experimental results on visual recognition tasks, including image classification, object detection, and semantic segmentation tasks, demonstrate that MTKD-RL achieves state-of-the-art performance compared to the existing multi-teacher KD works., Comment: AAAI-2025
Published: 2025

34. Revealing Microscopic Objects in Fluorescence Live Imaging by Video-to-video Translation Based on A Spatial-temporal Generative Adversarial Network

Author: Jiao, Yang, Yang, Mei, and Weng, Mo
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: In spite of being a valuable tool to simultaneously visualize multiple types of subcellular structures using spectrally distinct fluorescent labels, a standard fluoresce microscope is only able to identify a few microscopic objects; such a limit is largely imposed by the number of fluorescent labels available to the sample. In order to simultaneously visualize more objects, in this paper, we propose to use video-to-video translation that mimics the development process of microscopic objects. In essence, we use a microscopy video-to-video translation framework namely Spatial-temporal Generative Adversarial Network (STGAN) to reveal the spatial and temporal relationships between the microscopic objects, after which a microscopy video of one object can be translated to another object in a different domain. The experimental results confirm that the proposed STGAN is effective in microscopy video-to-video translation that mitigates the spectral conflicts caused by the limited fluorescent labels, allowing multiple microscopic objects be simultaneously visualized.
Published: 2025

35. Prompt as Knowledge Bank: Boost Vision-language model via Structural Representation for zero-shot medical detection

Author: Yang, Yuguang, Chen, Tongfei, Huang, Haoyu, Yang, Linlin, Xie, Chunyu, Leng, Dawei, Cao, Xianbin, and Zhang, Baochang
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Zero-shot medical detection can further improve detection performance without relying on annotated medical images even upon the fine-tuned model, showing great clinical value. Recent studies leverage grounded vision-language models (GLIP) to achieve this by using detailed disease descriptions as prompts for the target disease name during the inference phase. However, these methods typically treat prompts as equivalent context to the target name, making it difficult to assign specific disease knowledge based on visual information, leading to a coarse alignment between images and target descriptions. In this paper, we propose StructuralGLIP, which introduces an auxiliary branch to encode prompts into a latent knowledge bank layer-by-layer, enabling more context-aware and fine-grained alignment. Specifically, in each layer, we select highly similar features from both the image representation and the knowledge bank, forming structural representations that capture nuanced relationships between image patches and target descriptions. These features are then fused across modalities to further enhance detection performance. Extensive experiments demonstrate that StructuralGLIP achieves a +4.1\% AP improvement over prior state-of-the-art methods across seven zero-shot medical detection benchmarks, and consistently improves fine-tuned models by +3.2\% AP on endoscopy image datasets., Comment: Accepted as ICLR 2025 conference paper
Published: 2025

36. The surface binding and energy issues in rational design of the separation membrane of Li||S batteries

Author: Cheng, Shuyu, Wu, Lijing Wang Chao, Yang, Sheng, Liu, Yang, Zhao, Yi, Cui, Dandan, Zhang, Shaowei, Dou, Shixue, Du, Hongfang, and Lin, Liangxu
Subjects: Condensed Matter - Materials Science, Physics - Applied Physics
Abstract: Lithium-sulfur batteries (LSBs) represent one of the most promising next-generation energy storage technologies, offering exceptionally high energy densities. However, their widespread adoption remains hindered by challenges such as sluggish conversion reactions and the dissolution of lithium polysulfides, which lead to poor cycling stability and reduced performance. While significant efforts have been made to address these limitations, the energy storage capabilities of LSBs in practical devices remain far from achieving their full potential. This report delves into recent advancements in the rational design of separation membranes for LSBs, focusing on addressing fundamental issues related to surface binding and surface energy interactions within materials science. By examining the functionalization and optimization of separation membranes, we aim to highlight strategies that can guide the development of more robust and efficient LSBs, bringing them closer to practical implementation., Comment: 40 pages, 9 figures
Published: 2025

37. Advancing C-C Coupling of Electrocatalytic CO2 Reduction Reaction for C2+ Products

Author: Liang, Guangyuan, Yang, Sheng, Wu, Chao, Liu, Yang, Zhao, Yi, Huang, Liang, Zhang, Shaowei, Dou, Shixue, Du, Hongfang, Cui, Dandan, and Lin, Liangxu
Subjects: Physics - Chemical Physics, Condensed Matter - Materials Science
Abstract: The production of multicarbon (C2+) products through electrocatalytic CO2 reduction reaction (CO2RR) is crucial to addressing global environmental challenges and advancing sustainable energy solutions. However, efficiently producing these high-value chemicals via C-C coupling reactions is a significant challenge. This requires catalysts with optimized surface configurations and electronic properties capable of breaking the scaling relations among various intermediates. In this report, we introduce the fundamentals of electrocatalytic CO2RR and the mechanism of C-C coupling. We examine the effects of catalytic surface interactions with key intermediates and reaction pathways, and discuss emerging strategies for enhancing C-C coupling reactions toward C2+ products. Despite varieties of these strategies, we summarize direct clues for the proper design of the catalyst for the electrocatalytic CO2RR towards C2+ products, aiming to provide valuable insights to broad readers in the field., Comment: 39 pages, 9 figures
Published: 2025

38. Single Inclusive $\pi^\pm$ and $K^\pm$ Production in $e^+e^-$ Annihilation at center-of-mass Energies from 2.000 to 3.671GeV

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, J., Gao, X. B., Gao, Y., Gao, Y. N., Gao, Y. Y., Garbolino, S., Garzia, I., Ge, L. 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. 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, 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. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Malik, Q. A., Mao, H. X., Mao, Y. J., Mao, Z. P., Marcello, S., Marshall, A., Melendi, F. M., 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., Normand, C., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, X. J., Peng, Y. Y., Peters, K., Petridis, 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., Rademacker, J., Redmer, C. F., Rivetti, A., Rolo, M., Rong, G., Rong, S. S., Rosini, F., Rosner, Ch., Ruan, M. Q., 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, X. M., 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., Wei, H. R., 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, H. X., 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., 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 data samples with a total integrated luminosity of 253 $\rm pb^{-1}$ collected by the BESIII detector operating at the BEPCII collider, the differential cross-sections of inclusive $\pi^\pm$ and $K^\pm$ production, as a function of momentum and normalized by the total hadronic cross-section, are measured at center-of-mass energies from 2.000 to 3.671 GeV. The measured $\pi^{\pm}$ cross sections are consistent with the previously reported $\pi^{0}$ cross-sections by BESIII, while the $K^{\pm}$ cross sections are systematically higher than the $K^0_S$ cross sections by a factor of approximately 1.4. These new results are in agreement with state-of-the-art QCD analyses at next-to-next-to-leading order accuracy, particularly in the large hadron momentum region at energy scales down to 3 GeV. These findings support the validity of isospin symmetry in parton fragmentation processes.
Published: 2025

39. Category-free Out-of-Distribution Node Detection with Feature Resonance

Author: Yang, Shenzhi, Zhao, Junbo, Yang, Shouqing, Li, Yixuan, Yang, Dingyu, Zhang, Xiaofang, and Wang, Haobo
Subjects: Computer Science - Machine Learning
Abstract: Detecting out-of-distribution (OOD) nodes in the graph-based machine-learning field is challenging, particularly when in-distribution (ID) node multi-category labels are unavailable. Thus, we focus on feature space rather than label space and find that, ideally, during the optimization of known ID samples, unknown ID samples undergo more significant representation changes than OOD samples, even if the model is trained to fit random targets, which we called the Feature Resonance phenomenon. The rationale behind it is that even without gold labels, the local manifold may still exhibit smooth resonance. Based on this, we further develop a novel graph OOD framework, dubbed Resonance-based Separation and Learning (RSL), which comprises two core modules: (i) a more practical micro-level proxy of feature resonance that measures the movement of feature vectors in one training step. (ii) integrate with synthetic OOD nodes strategy to train an effective OOD classifier. Theoretically, we derive an error bound showing the superior separability of OOD nodes during the resonance period. Empirically, RSL achieves state-of-the-art performance, reducing the FPR95 metric by an average of 18.51% across five real-world datasets.
Published: 2025

40. AI Models Still Lag Behind Traditional Numerical Models in Predicting Sudden-Turning Typhoons

Author: Xu, Daosheng, Lu, Zebin, Leung, Jeremy Cheuk-Hin, Zhao, Dingchi, Li, Yi, Shi, Yang, Chen, Bin, Nie, Gaozhen, Wu, Naigeng, Tian, Xiangjun, Yang, Yi, Zhang, Shaoqing, and Zhang, Banglin
Subjects: Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning
Abstract: Given the interpretability, accuracy, and stability of numerical weather prediction (NWP) models, current operational weather forecasting relies heavily on the NWP approach. In the past two years, the rapid development of Artificial Intelligence (AI) has provided an alternative solution for medium-range (1-10 days) weather forecasting. Bi et al. (2023) (hereafter Bi23) introduced the first AI-based weather prediction (AIWP) model in China, named Pangu-Weather, which offers fast prediction without compromising accuracy. In their work, Bi23 made notable claims regarding its effectiveness in extreme weather predictions. However, this claim lacks persuasiveness because the extreme nature of the two tropical cyclones (TCs) examples presented in Bi23, namely Typhoon Kong-rey and Typhoon Yutu, stems primarily from their intensities rather than their moving paths. Their claim may mislead into another meaning which is that Pangu-Weather works well in predicting unusual typhoon paths, which was not explicitly analyzed. Here, we reassess Pangu-Weather's ability to predict extreme TC trajectories from 2020-2024. Results reveal that while Pangu-Weather overall outperforms NWP models in predicting tropical cyclone (TC) tracks, it falls short in accurately predicting the rarely observed sudden-turning tracks, such as Typhoon Khanun in 2023. We argue that current AIWP models still lag behind traditional NWP models in predicting such rare extreme events in medium-range forecasts.
Published: 2025

41. 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., 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., Dai, Y. D. Cui 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., 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., 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., 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., 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, Liu, E. W. Liang Y. F. Liang S. J. Lin 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, Q. N., Sun, X. N. Sun Z. B., Takata, J., Tan, P. H. T. Tam H. B., Tang, Q. W., Tang, R., Tang, Z. B., Tian, W. W., Tong, C. N., Wang, L. H. Wan 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, 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., Comment: Corrected spelling errors in several author names
Published: 2025
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42. AttentionEngine: A Versatile Framework for Efficient Attention Mechanisms on Diverse Hardware Platforms

Author: Chen, Feiyang, Cheng, Yu, Wang, Lei, Xia, Yuqing, Miao, Ziming, Ma, Lingxiao, Yang, Fan, Xue, Jilong, Yang, Zhi, Yang, Mao, and Chen, Haibo
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Performance
Abstract: Transformers and large language models (LLMs) have revolutionized machine learning, with attention mechanisms at the core of their success. As the landscape of attention variants expands, so too do the challenges of optimizing their performance, particularly across different hardware platforms. Current optimization strategies are often narrowly focused, requiring extensive manual intervention to accommodate changes in model configurations or hardware environments. In this paper, we introduce AttentionEngine, a comprehensive framework designed to streamline the optimization of attention mechanisms across heterogeneous hardware backends. By decomposing attention computation into modular operations with customizable components, AttentionEngine enables flexible adaptation to diverse algorithmic requirements. The framework further automates kernel optimization through a combination of programmable templates and a robust cross-platform scheduling strategy. Empirical results reveal performance gains of up to 10x on configurations beyond the reach of existing methods. AttentionEngine offers a scalable, efficient foundation for developing and deploying attention mechanisms with minimal manual tuning. Our code has been open-sourced and is available at https://github.com/microsoft/AttentionEngine., Comment: 15 pages
Published: 2025

43. New insight into the Rapid Burster by Insight-HXMT

Author: Chen, Y. P., Zhang, S., Zhang, S. N., Ji, L., Kong, L. D., Wang, P. J., Tao, L., Ge, M. Y., Liu, C. Z., Lu, F. J., Qu, J. L., Li, T. P., Xu, Y. P., Cao, X. L., Chen, Y., Bu, Q. C., Cai, C., Chang, Z., Chen, G., Chen, L., Chen, T. X., Cui, W. W., Du, Y. Y., Gao, G. H., Gao, H., Gao, M., Gu, Y. D., Guan, J., Guo, C. C., Han, D. W., Huang, Y., Huo, J., Jia, S. M., Jiang, W. C., Jin, J., Li, B., Li, C. K., Li, G., Li, W., Li, X., Li, X. B., Li, X. F., Li, Z. W., Liang, X. H., Liao, J. Y., Liu, B. S., Liu, H. W., Liu, H. X., Liu, X. J., Lu, X. F., Luo, Q., Luo, T., Ma, R. C., Ma, X., Meng, B., Nang, Y., Nie, J. Y., Ou, G., Sai, N., Song, L. M., Song, X. Y., Sun, L., Tan, Y., Tuo, Y. L., Wang, C., Wang, L. J., Wang, W. S., Wang, Y. S., Wen, X. Y., Wu, B. B., Wu, B. Y., Wu, M., Xiao, G. C., Xiao, S., Xiong, S. L., Yang, R. J., Yang, S., Yang, Y. J., Yi, Q. B., Yin, Q. Q., You, Y., Zhang, F., Zhang, H. M., Zhang, J., Zhang, W. C., Zhang, W., Zhang, Y., Zhang, Y. F., Zhang, Y. H., Zhao, H. S., Zhao, X. F., Zheng, S. J., Zheng, Y. G., and Zhou, D. K.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We report the timing and spectral analyses upon of the type II X-ray bursts from the Rapid Burster (MXB 1730--335) observed by Insight-HXMT and Swift/XRT. By stacking the long-duration bursts, we find for the first time that the hard X-rays are lagging than the soft X-rays by 3 seconds. However, such a lag is not visible for the short-duration bursts, probably because of the poor statistics. For all bursts the energy spectrum is found to be non-thermal, thanks to the broad band coverage of Insight-HXMT. These findings put new insights into the type-II bursts and require a temporally showing-up corona for possible interpretation.
Published: 2025
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44. External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation

Author: Liang, Mingfu, Liu, Xi, Jin, Rong, Liu, Boyang, Suo, Qiuling, Zhou, Qinghai, Zhou, Song, Chen, Laming, Zheng, Hua, Li, Zhiyuan, Jiang, Shali, Yang, Jiyan, Xia, Xiaozhen, Yang, Fan, Badr, Yasmine, Wen, Ellie, Xu, Shuyu, Chen, Hansey, Zhang, Zhengyu, Nie, Jade, Yang, Chunzhi, Zeng, Zhichen, Zhang, Weilin, Huang, Xingliang, Li, Qianru, Wang, Shiquan, Lyu, Evelyn, Lu, Wenjing, Zhang, Rui, Wang, Wenjun, Rudy, Jason, Hang, Mengyue, Wang, Kai, Ma, Yinbin, Wang, Shuaiwen, Zeng, Sihan, Tang, Tongyi, Wei, Xiaohan, Jin, Longhao, Zhang, Jamey, Chen, Marcus, Zhang, Jiayi, Huang, Angie, Zhang, Chi, Zhao, Zhengli, Yang, Jared, Jin, Qiang, Chen, Xian, Amlesahwaram, Amit Anand, Song, Lexi, Luo, Liang, Hao, Yuchen, Xiao, Nan, Yetim, Yavuz, Pan, Luoshang, Liu, Gaoxiang, Hu, Yuxi, Huang, Yuzhen, Xu, Jackie, Zhu, Rich, Zhang, Xin, Liu, Yiqun, Yin, Hang, Chen, Yuxin, Zhang, Buyun, Liu, Xiaoyi, Wang, Xingyuan, Mao, Wenguang, Li, Zhijing, Huang, Qin, Sun, Chonglin, Mao, Shupin, Au, Benjamin, Qin, Jingzheng, Yao, Peggy, Choi, Jae-Woo, Gao, Bin, Wang, Ernest, Zhang, Lei, Chen, Wen-Yen, Lee, Ted, Zha, Jay, Meng, Yi, Gong, Alex, Gao, Edison, Vahdatpour, Alireza, Han, Yiping, Yao, Yantao, Kureha, Toshinari, Chang, Shuo, Sultan, Musharaf, Bocharov, John, Chordia, Sagar, Gan, Xiaorui, Sun, Peng, Liu, Rocky, Long, Bo, Chen, Wenlin, Kolay, Santanu, and Li, Huayu
Subjects: Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Ads recommendation is a prominent service of online advertising systems and has been actively studied. Recent studies indicate that scaling-up and advanced design of the recommendation model can bring significant performance improvement. However, with a larger model scale, such prior studies have a significantly increasing gap from industry as they often neglect two fundamental challenges in industrial-scale applications. First, training and inference budgets are restricted for the model to be served, exceeding which may incur latency and impair user experience. Second, large-volume data arrive in a streaming mode with data distributions dynamically shifting, as new users/ads join and existing users/ads leave the system. We propose the External Large Foundation Model (ExFM) framework to address the overlooked challenges. Specifically, we develop external distillation and a data augmentation system (DAS) to control the computational cost of training/inference while maintaining high performance. We design the teacher in a way like a foundation model (FM) that can serve multiple students as vertical models (VMs) to amortize its building cost. We propose Auxiliary Head and Student Adapter to mitigate the data distribution gap between FM and VMs caused by the streaming data issue. Comprehensive experiments on internal industrial-scale applications and public datasets demonstrate significant performance gain by ExFM., Comment: Accepted by the ACM Web Conference (WWW) 2025 Industrial Track as Oral Presentation
Published: 2025

45. Optimizing Singular Spectrum for Large Language Model Compression

Author: Li, Dengjie, Shen, Tiancheng, Zhou, Yao, Yang, Baisong, Liu, Zhongying, Yang, Masheng, Ghanem, Bernard, Yang, Yibo, Zhong, Yujie, and Yang, Ming-Hsuan
Subjects: Computer Science - Computation and Language
Abstract: Large language models (LLMs) have demonstrated remarkable capabilities, yet prohibitive parameter complexity often hinders their deployment. Existing singular value decomposition (SVD) based compression methods simply deem singular values as importance scores of decomposed components. However, this importance ordered by singular values does not necessarily correlate with the performance of a downstream task. In this work, we introduce SoCo (Singular spectrum optimization for large language model Compression), a novel compression framework that learns to rescale the decomposed components of SVD in a data-driven manner. Concretely, we employ a learnable diagonal matrix to assign importance scores for singular spectrum and develop a three-stage training process that progressively refines these scores from initial coarse compression to fine-grained sparsification-thereby striking an effective balance between aggressive model compression and performance preservation. Thanks to the learnable singular spectrum, SoCo adaptively prunes components according to the sparsified importance scores, rather than relying on the fixed order of singular values. More importantly, the remaining components with amplified importance scores can compensate for the loss of the pruned ones. Experimental evaluations across multiple LLMs and benchmarks demonstrate that SoCo surpasses the state-of-the-art methods in model compression.
Published: 2025

46. Benchmarking Multimodal RAG through a Chart-based Document Question-Answering Generation Framework

Author: Yang, Yuming, Zhong, Jiang, Jin, Li, Huang, Jingwang, Gao, Jingpeng, Liu, Qing, Bai, Yang, Zhang, Jingyuan, Jiang, Rui, and Wei, Kaiwen
Subjects: Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: Multimodal Retrieval-Augmented Generation (MRAG) enhances reasoning capabilities by integrating external knowledge. However, existing benchmarks primarily focus on simple image-text interactions, overlooking complex visual formats like charts that are prevalent in real-world applications. In this work, we introduce a novel task, Chart-based MRAG, to address this limitation. To semi-automatically generate high-quality evaluation samples, we propose CHARt-based document question-answering GEneration (CHARGE), a framework that produces evaluation data through structured keypoint extraction, crossmodal verification, and keypoint-based generation. By combining CHARGE with expert validation, we construct Chart-MRAG Bench, a comprehensive benchmark for chart-based MRAG evaluation, featuring 4,738 question-answering pairs across 8 domains from real-world documents. Our evaluation reveals three critical limitations in current approaches: (1) unified multimodal embedding retrieval methods struggles in chart-based scenarios, (2) even with ground-truth retrieval, state-of-the-art MLLMs achieve only 58.19% Correctness and 73.87% Coverage scores, and (3) MLLMs demonstrate consistent text-over-visual modality bias during Chart-based MRAG reasoning. The CHARGE and Chart-MRAG Bench are released at https://github.com/Nomothings/CHARGE.git.
Published: 2025

47. SurveyX: Academic Survey Automation via Large Language Models

Author: Liang, Xun, Yang, Jiawei, Wang, Yezhaohui, Tang, Chen, Zheng, Zifan, Song, Shichao, Lin, Zehao, Yang, Yebin, Niu, Simin, Wang, Hanyu, Tang, Bo, Xiong, Feiyu, Mao, Keming, and li, Zhiyu
Subjects: Computer Science - Computation and Language
Abstract: Large Language Models (LLMs) have demonstrated exceptional comprehension capabilities and a vast knowledge base, suggesting that LLMs can serve as efficient tools for automated survey generation. However, recent research related to automated survey generation remains constrained by some critical limitations like finite context window, lack of in-depth content discussion, and absence of systematic evaluation frameworks. Inspired by human writing processes, we propose SurveyX, an efficient and organized system for automated survey generation that decomposes the survey composing process into two phases: the Preparation and Generation phases. By innovatively introducing online reference retrieval, a pre-processing method called AttributeTree, and a re-polishing process, SurveyX significantly enhances the efficacy of survey composition. Experimental evaluation results show that SurveyX outperforms existing automated survey generation systems in content quality (0.259 improvement) and citation quality (1.76 enhancement), approaching human expert performance across multiple evaluation dimensions. Examples of surveys generated by SurveyX are available on www.surveyx.cn, Comment: 15 pages, 16 figures
Published: 2025

48. Multi-Agent Coordination across Diverse Applications: A Survey

Author: Sun, Lijun, Yang, Yijun, Duan, Qiqi, Shi, Yuhui, Lyu, Chao, Chang, Yu-Cheng, Lin, Chin-Teng, and Shen, Yang
Subjects: Computer Science - Multiagent Systems, Computer Science - Artificial Intelligence
Abstract: Multi-agent coordination studies the underlying mechanism enabling the trending spread of diverse multi-agent systems (MAS) and has received increasing attention, driven by the expansion of emerging applications and rapid AI advances. This survey outlines the current state of coordination research across applications through a unified understanding that answers four fundamental coordination questions: (1) what is coordination; (2) why coordination; (3) who to coordinate with; and (4) how to coordinate. Our purpose is to explore existing ideas and expertise in coordination and their connections across diverse applications, while identifying and highlighting emerging and promising research directions. First, general coordination problems that are essential to varied applications are identified and analyzed. Second, a number of MAS applications are surveyed, ranging from widely studied domains, e.g., search and rescue, warehouse automation and logistics, and transportation systems, to emerging fields including humanoid and anthropomorphic robots, satellite systems, and large language models (LLMs). Finally, open challenges about the scalability, heterogeneity, and learning mechanisms of MAS are analyzed and discussed. In particular, we identify the hybridization of hierarchical and decentralized coordination, human-MAS coordination, and LLM-based MAS as promising future directions., Comment: 23 pages, 4 figures, 2 tables
Published: 2025

49. 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, Kaixin, 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, Pang, Tianyang, Peng, Zhongyuan, Peng, Zifan, Qi, Qige, Qiu, Shi, Qu, Xingwei, Quan, Shanghaoran, 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, Jinyang, 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

50. ATRI: Mitigating Multilingual Audio Text Retrieval Inconsistencies by Reducing Data Distribution Errors

Author: Yin, Yuguo, Xie, Yuxin, Yang, Wenyuan, Yang, Dongchao, Ru, Jinghan, Zhuang, Xianwei, Liang, Liming, and Zou, Yuexian
Subjects: Computer Science - Sound, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Multilingual audio-text retrieval (ML-ATR) is a challenging task that aims to retrieve audio clips or multilingual texts from databases. However, existing ML-ATR schemes suffer from inconsistencies for instance similarity matching across languages. We theoretically analyze the inconsistency in terms of both multilingual modal alignment direction error and weight error, and propose the theoretical weight error upper bound for quantifying the inconsistency. Based on the analysis of the weight error upper bound, we find that the inconsistency problem stems from the data distribution error caused by random sampling of languages. We propose a consistent ML-ATR scheme using 1-to-k contrastive learning and audio-English co-anchor contrastive learning, aiming to mitigate the negative impact of data distribution error on recall and consistency in ML-ATR. Experimental results on the translated AudioCaps and Clotho datasets show that our scheme achieves state-of-the-art performance on recall and consistency metrics for eight mainstream languages, including English. Our code will be available at https://github.com/ATRI-ACL/ATRI-ACL.
Published: 2025

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