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1. Preliminary Sensitivity Study for a Gravitational Redshift Measurement with China's Lunar Exploration Project

Author

Qin, Cheng-Gang, Liu, Tong, Dai, Xiao-Yi, Guo, Peng-Bin, Huang, Weisheng, Liu, Xiang-Pei, Tan, Yu-Jie, and Shao, Cheng-Gang

Subjects
General Relativity and Quantum Cosmology
Abstract

General relativity (GR) is a highly successful theory that describes gravity as a geometric phenomenon. The gravitational redshift, a classic test of GR, can potentially be violated in alternative gravity theories, and experimental tests on this effect are crucial for our understanding of gravity. In this paper, considering the space-ground clock comparisons with free-space links, we discuss a high-precision Doppler cancellation-based measurement model for testing gravitational redshift. This model can effectively reduce various sources of error and noise, reducing the influences of the first-order Doppler effect, atmospheric delay, Shapiro delay, etc. China's Lunar Exploration Project (CLEP) is proposed to equip the deep-space H maser with a daily stability of $2\times10^{-15}$, which provides an approach for testing gravitational redshift. Based on the simulation, we analyze the space-ground clock comparison experiments of the CLEP experiment, and simulation analysis demonstrates that under ideal condition of high-precision measurement of the onboard H-maser frequency offset and drift, the CLEP experiment may reach the uncertainty of $3.7\times10^{-6}$ after a measurement session of 60 days. Our results demonstrate that if the issue of frequency offset and drift is solved, CLEP missions have a potential of testing the gravitational redshift with high accuracy. This manuscript has been accepted for publication in Classical and Quantum Gravity. DOI 10.1088/1361-6382/ad4ae2, Comment: 27 pages, 4 figures, 1 table

Published
2025
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2. Memory Helps, but Confabulation Misleads: Understanding Streaming Events in Videos with MLLMs

Author

Zhang, Gengyuan, Ding, Mingcong, Liu, Tong, Zhang, Yao, and Tresp, Volker

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Multimodal large language models (MLLMs) have demonstrated strong performance in understanding videos holistically, yet their ability to process streaming videos-videos are treated as a sequence of visual events-remains underexplored. Intuitively, leveraging past events as memory can enrich contextual and temporal understanding of the current event. In this paper, we show that leveraging memories as contexts helps MLLMs better understand video events. However, because such memories rely on predictions of preceding events, they may contain misinformation, leading to confabulation and degraded performance. To address this, we propose a confabulation-aware memory modification method that mitigates confabulated memory for memory-enhanced event understanding., Comment: Short paper (5 pages)

Published
2025

3. AVD2: Accident Video Diffusion for Accident Video Description

Author

Li, Cheng, Zhou, Keyuan, Liu, Tong, Wang, Yu, Zhuang, Mingqiao, Gao, Huan-ang, Jin, Bu, and Zhao, Hao

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Traffic accidents present complex challenges for autonomous driving, often featuring unpredictable scenarios that hinder accurate system interpretation and responses. Nonetheless, prevailing methodologies fall short in elucidating the causes of accidents and proposing preventive measures due to the paucity of training data specific to accident scenarios. In this work, we introduce AVD2 (Accident Video Diffusion for Accident Video Description), a novel framework that enhances accident scene understanding by generating accident videos that aligned with detailed natural language descriptions and reasoning, resulting in the contributed EMM-AU (Enhanced Multi-Modal Accident Video Understanding) dataset. Empirical results reveal that the integration of the EMM-AU dataset establishes state-of-the-art performance across both automated metrics and human evaluations, markedly advancing the domains of accident analysis and prevention. Project resources are available at https://an-answer-tree.github.io, Comment: ICRA 2025, Project Page: https://an-answer-tree.github.io/

Published
2025

4. PerfSeer: An Efficient and Accurate Deep Learning Models Performance Predictor

Author

Zhao, Xinlong, Sun, Jiande, Zhang, Jia, Hou, Sujuan, Li, Shuai, Liu, Tong, and Liu, Ke

Subjects
Computer Science - Performance
Abstract

Predicting the performance of deep learning (DL) models, such as execution time and resource utilization, is crucial for Neural Architecture Search (NAS), DL cluster schedulers, and other technologies that advance deep learning. The representation of a model is the foundation for its performance prediction. However, existing methods cannot comprehensively represent diverse model configurations, resulting in unsatisfactory accuracy. To address this, we represent a model as a graph that includes the topology, along with the node, edge, and global features, all of which are crucial for effectively capturing the performance of the model. Based on this representation, we propose PerfSeer, a novel predictor that uses a Graph Neural Network (GNN)-based performance prediction model, SeerNet. SeerNet fully leverages the topology and various features, while incorporating optimizations such as Synergistic Max-Mean aggregation (SynMM) and Global-Node Perspective Boost (GNPB) to capture the critical performance information more effectively, enabling it to predict the performance of models accurately. Furthermore, SeerNet can be extended to SeerNet-Multi by using Project Conflicting Gradients (PCGrad), enabling efficient simultaneous prediction of multiple performance metrics without significantly affecting accuracy. We constructed a dataset containing performance metrics for 53k+ model configurations, including execution time, memory usage, and Streaming Multiprocessor (SM) utilization during both training and inference. The evaluation results show that PerfSeer outperforms nn-Meter, Brp-NAS, and DIPPM.

Published
2025

5. Variabilities driven by satellite black hole migration in AGN discs

Author

Xing, Jing-Tong, Liu, Tong, Huang, Bao-Quan, and Sun, Mouyuan

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

The physical origin of active galactic nucleus (AGN) variability remains unclear. Here we propose that the magnetic reconnection driven by the migration of satellite black holes (sBHs) in the AGN disc can be a new plausible mechanism for AGN short-term variability. During the sBH migration, the co-moving plasmas surrounding the sBH could influence the large-scale magnetic field of the AGN disk and trigger the magnetic reconnections to contribute to AGN UV/optical variability. Meanwhile, the plasma, which is accelerated by the magnetic reconnection, will successfully escape from the disc at Alfven velocity and cause a secondary magnetic reconnection in the corona. For a $\sim 10^{2}-10^{3}~{M_\mathrm{\odot}}$ sBH (including its binding gas) in the inner regions of the disc surrounding a supermassive black hole with $\sim 10^{7}~{M_\mathrm{\odot}}$, the reconnection process occurred in the space out of the disc can produce X-ray emission, which can last $\sim 10^4-10^6~\rm s$ with the luminosity $\sim 10^{39}- 10^{43}~\rm{erg ~s^{-1}}$., Comment: 7 pages, 2 figures, submitted

Published
2025

6. Graph Neural Networks for Travel Distance Estimation and Route Recommendation Under Probabilistic Hazards

Author

Liu, Tong and Meidani, Hadi

Subjects
Computer Science - Machine Learning
Abstract

Estimating the shortest travel time and providing route recommendation between different locations in a city or region can quantitatively measure the conditions of the transportation network during or after extreme events. One common approach is to use Dijkstra's Algorithm, which produces the shortest path as well as the shortest distance. However, this option is computationally expensive when applied to large-scale networks. This paper proposes a novel fast framework based on graph neural networks (GNNs) which approximate the single-source shortest distance between pairs of locations, and predict the single-source shortest path subsequently. We conduct multiple experiments on synthetic graphs of different size to demonstrate the feasibility and computational efficiency of the proposed model. In real-world case studies, we also applied the proposed method of flood risk analysis of coastal urban areas to calculate delays in evacuation to public shelters during hurricanes. The results indicate the accuracy and computational efficiency of the GNN model, and its potential for effective implementation in emergency planning and management., Comment: 17 pages, 11 figures

Published
2025

7. Bridging conformal field theory and parton approaches to SU(n)_k chiral spin liquids

Author

Liu, Tong, Wu, Ying-Hai, Tu, Hong-Hao, and Xiang, Tao

Subjects
Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Statistical Mechanics, High Energy Physics - Theory, Quantum Physics
Abstract

We employ the SU(n)_k Wess-Zumino-Witten (WZW) model in conformal field theory to construct lattice wave functions in both one and two dimensions. It is unveiled that these wave functions can be reinterpreted as parton states, which enables efficient conversion to matrix product states such that many physical properties can be evaluated directly. In one dimension, these wave functions describe critical spin chains whose universality classes are in one-to-one correspondence with the WZW models used in the construction. In two dimensions, our constructions yield model wave functions for chiral spin liquids, and we show how to find all topological sectors of them in a systematic way. Using the null vectors of Kac-Moody algebras, parent Hamiltonians of the SU(3)_k series are derived. The SU(3)_k chiral spin liquids are lattice analogs of non-Abelian spin-singlet fractional quantum Hall states, and the k = 2 member hosts Fibonacci anyons., Comment: 14 pages, 6 figures

Published
2025

8. Multi-Class Traffic Assignment using Multi-View Heterogeneous Graph Attention Networks

Author

Liu, Tong and Meidani, Hadi

Subjects
Computer Science - Machine Learning
Abstract

Solving traffic assignment problem for large networks is computationally challenging when conventional optimization-based methods are used. In our research, we develop an innovative surrogate model for a traffic assignment when multi-class vehicles are involved. We do so by employing heterogeneous graph neural networks which use a multiple-view graph attention mechanism tailored to different vehicle classes, along with additional links connecting origin-destination pairs. We also integrate the node-based flow conservation law into the loss function. As a result, our model adheres to flow conservation while delivering highly accurate predictions for link flows and utilization ratios. Through numerical experiments conducted on urban transportation networks, we demonstrate that our model surpasses traditional neural network approaches in convergence speed and predictive accuracy in both user equilibrium and system optimal versions of traffic assignment., Comment: 16 pages, 5 figures

Published
2025

9. FocalPO: Enhancing Preference Optimizing by Focusing on Correct Preference Rankings

Author

Liu, Tong, Yu, Xiao, Zhou, Wenxuan, Gu, Jindong, and Tresp, Volker

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

Efficient preference optimization algorithms such as Direct Preference Optimization (DPO) have become a popular approach in aligning large language models (LLMs) with human preferences. These algorithms implicitly treat the LLM as a reward model, and focus on training it to correct misranked preference pairs. However, recent work~\citep{chen2024preference} empirically finds that DPO training \textit{rarely improves these misranked preference pairs}, despite its gradient emphasizing on these cases. We introduce FocalPO, a DPO variant that instead \textit{down-weighs} misranked preference pairs and prioritizes enhancing the model's understanding of pairs that it can already rank correctly. Inspired by Focal Loss used in vision tasks, FocalPO achieves this by adding a modulating factor to dynamically scale DPO loss. Our experiment demonstrates that FocalPO surpasses DPO and its variants on popular benchmarks like Alpaca Eval 2.0 using Mistral-Base-7B and Llama-3-Instruct-8B. Additionally, we empirically reveals how FocalPO affects training on correct and incorrect sample groups, further underscoring its effectiveness.

Published
2025

10. Polarization Profiles of Globular Cluster Pulsars from FAST I. 25 Profiles from Previously Known Pulsars

Author

Liu, Tong, Wang, Lin, and Pan, Zhichen

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract

Pulsar polarization profiles are critical for understanding their magnetospheric structures and radiation mechanisms. We present polarization profile measurements for 25 pulsars in globular clusters (GCs) from the observation of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The diversity of polarization profiles shows complex magnetic structure and emission pattern of pulsars. Among these, the polarization profiles of 15 pulsars were firstly measured. M53A present a 57\% linear polarization ratio, being highest among these 25 GC pulsars. M15H present a 42\% circular polarization ratio, which is the highest among these 25 pulsars. The average ratios of circular and absolute circular polarization of these GC pulsars are -1\% and 10\% respectively, lower than normal pulsars measured with Parkes (\citealt{Oswald2023}), which are 5\% and 32\%, respectively. The Rotation Measure (RM) values of each pulsar were measured, giving the range of pulsar(s) from M3, M5, M15, M71, M53, NGC6517, NGC6539, NGC6760 being 8(1) to 16(1) rad $m^{-2}$, -1(2) to -4(6) rad $m^{-2}$, -70(2) to -76(1) rad $m^{-2}$, -480(14) rad $m^{-2}$ (M71A only), -2(1) rad $m^{-2}$ (M53A only), 187(1) to 212(2) rad $m^{-2}$, 109(1) rad $m^{-2}$ (NGC6539A only), and 102(1) to 129(4) rad $m^{-2}$. The GCs closer to the Galactic plane (GP) tend to have larger RM. This is consistent with previous study (\citealt{Hutschenreuter2022})., Comment: 13 pages, 4 figues, 2 tables, submitted to RAA, comments are always welcomed!

Published
2025

11. Oblique rotational axis detection using elliptical optical vortex based on rotational Doppler effect

Author

Zhu, Xiangyang, Ren, Yuan, Fan, Yaohui, Wen, Xinyi, Chen, Xiaocen, Tang, Ruoyu, Ding, You, Liu, Zhengliang, and Liu, Tong

Subjects
Physics - Optics
Abstract

The rotational Doppler effect (RDE) of structured light carrying orbital angular momentum (OAM) has attracted widespread attention for applications in optical sensors and OAM spectrum detection. These studies, however, based on RDE, are mostly focused on the motion parameters of rotating objects; other equally important attitude characteristics, e.g., the tilt angle of the axis of rotation, have rarely been considered. We observed an interesting phenomenon in the experiments: the rotational Doppler spectral distribution varies with the ellipticity of the elliptical optical vortex (EOV) and the tilt angle between the rotational axis and optical axis, which inspired us to wonder if it is possible to detect oblique rotational axis or compensate the rotational Doppler broadening effect induced by oblique incidence by utilizing the EOV. Here, we reveal the RDE quantitative relationship with tilt angle and ellipticity for the first time and report a novel approach for tilt angle measurement. By employing a series of EOV with periodically varying ellipticity to illuminate a rotating object and analyze the time-frequency spectral distribution of scattered light associated with ellipticity and tilt angle, the tilt angle can be acquired accurately based on the specific relationship between the tilt angle and ellipticity of the EOV. Furthermore, the spectrum broadening effect arising from oblique incidence in the actual scenario may be addressed through our scheme. The method may find applications in industrial manufacturing and target attitude measurement, and our results provide new insights for obtaining more information about objects.

Published
2025

14. Correction: CEPC Technical Design Report: Accelerator

Author

Abdallah, Waleed, de Freitas, Tiago CarlosAdorno, Afanaciev, Konstantin, Ahmad, Shakeel, Ahmed, Ijaz, Ai, Xiaocong, Aleem, Abid, Altmannshofer, Wolfgang, Alves, Fabio, An, Weiming, An, Rui, Anderle, Daniele Paolo, Antusch, Stefan, Arai, Yasuo, Arbuzov, Andrej, Arhrib, Abdesslam, Ashry, Mustafa, Bai, Sha, Bai, Yu, Bai, Yang, Bairathi, Vipul, Balazs, Csaba, Bambade, Philip, Ban, Yong, Bandyopadhyay, Triparno, Bao, Shou-Shan, Barber, Desmond P., Bat, Ays¸e, Batozskaya, Varvara, Behera, Subash Chandra, Belyaev, Alexander, Bertucci, Michele, Bi, Xiao-Jun, Bi, Yuanjie, Bian, Tianjian, Bianchi, Fabrizio, Bieko¨tter, Thomas, Biglietti, Michela, Bilanishvili, Shalva, Binglin, Deng, Bodrov, Denis, Bogomyagkov, Anton, Bondarenko, Serge, Boogert, Stewart, Boonekamp, Maarten, Borri, Marcello, Bosotti, Angelo, Boudry, Vincent, Boukidi, Mohammed, Boyko, Igor, Bozovic, Ivanka, Bozzi, Giuseppe, Brient, Jean-Claude, Budzinskaya, Anastasiia, Bukhari, Masroor, Bytev, Vladimir, Cacciapaglia, Giacomo, Cai, Hua, Cai, Wenyong, Cai, Wujun, Cai, Yijian, Cai, Yizhou, Cai, Yuchen, Cai, Haiying, Cai, Huacheng, Calibbi, Lorenzo, Cang, Junsong, Cao, Guofu, Cao, Jianshe, Chance, Antoine, Chang, Xuejun, Chang, Yue, Chang, Zhe, Chang, Xinyuan, Chao, Wei, Chatrabhuti, Auttakit, Che, Yimin, Che, Yuzhi, Chen, Bin, Chen, Danping, Chen, Fuqing, Chen, Fusan, Chen, Gang, Chen, Guoming, Chen, Hua-Xing, Chen, Huirun, Chen, Jinhui, Chen, Ji-Yuan, Chen, Kai, Chen, Mali, Chen, Mingjun, Chen, Mingshui, Chen, Ning, Chen, Shanhong, Chen, Shanzhen, Chen, Shao-Long, Chen, Shaomin, Chen, Shiqiang, Chen, Tianlu, Chen, Wei, Chen, Xiang, Chen, Xiaoyu, Chen, Xin, Chen, Xun, Chen, Xurong, Chen, Ye, Chen, Ying, Chen, Yukai, Chen, Zelin, Chen, Zilin, Chen, Gang, Chen, Boping, Chen, Chunhui, Cheng, Hok Chuen, Cheng, Huajie, Cheng, Shan, Cheng, Tongguang, Chi, Yunlong, Chimenti, Pietro, Chiu, Wen Han, Cho, Guk, Chu, Ming-Chung, Chu, Xiaotong, Chu, Ziliang, Coloretti, Guglielmo, Crivellin, Andreas, Cui, Hanhua, Cui, Xiaohao, Cui, Zhaoyuan, D’Anzi, Brunella, Dai, Ling-Yun, Dai, Xinchen, Dai, Xuwen, De Maria, Antonio, De Filippis, Nicola, De La Taille, Christophe, De Mori, Francesca, De Sio, Chiara, Del Core, Elisa, Deng, Shuangxue, Deng, Wei-Tian, Deng, Zhi, Deng, Ziyan, Dev, Bhupal, Dewen, Tang, Di Micco, Biagio, Ding, Ran, Ding, Siqin, Ding, Yadong, Dong, Haiyi, Dong, Jianing, Dong, Jing, Dong, Lan, Dong, Mingyi, Dong, Xu, Dong, Yipei, Dong, Yubing, Dordevic, Milos, Drewes, Marco, Du, Mingxuan, Du, Mingxuan, Du, Qianqian, Du, Xiaokang, Du, Yanyan, Du, Yong, Du, Yunfei, Duan, Chun-Gui, Duan, Zhe, Dydyshka, Yahor, Egede, Ulrik, Elmetenawee, Walaa, Eo, Yun, Fan, Ka Yan, Fan, Kuanjun, Fan, Yunyun, Fang, Bo, Fang, Shuangshi, Fang, Yuquan, Farilla, Ada, Farinelli, Riccardo, Farooq, Muhammad, Golfe, Angeles Faus, Fazliakhmetov, Almaz, Fei, Rujun, Feng, Bo, Feng, Chong, Feng, Junhua, Feng, Xu, Feng, Zhuoran, ZhuoranFeng, Castillo, Luis Roberto Flores, Forest, Etienne, Fowlie, Andrew, Fox, Harald, Fu, Hai-Bing, Fu, Jinyu, Fuks, Benjamin, Funakoshi, Yoshihiro, Gabrielli, Emidio, Gan, Nan, Gang, Li, Gao, Jie, Gao, Meisen, Gao, Wenbin, Gao, Wenchun, Gao, Yu, Gao, Yuanning, Gao, Zhanxiang, Gao, Yanyan, Ge, Kun, Ge, Shao-Feng, Ge, Zhenwu, Geng, Li-Sheng, Geng, Qinglin, Geng, Chao-Qiang, Ghosh, Swagata, Gioiosa, Antonio, Gladilin, Leonid, Gong, Ti, Gori, Stefania, Gou, Quanbu, Grinstein, Sebastian, Gu, Chenxi, Guillermo, Gerardo, da Costa, Joao Guimaraes, Guo, Dizhou, Guo, Fangyi, Guo, Jiacheng, Guo, Jun, Guo, Lei, Guo, Lei, Guo, Xia, Guo, Xin-Heng, Guo, Xinyang, Guo, Yun, Guo, Yunqiang, Guo, Yuping, Guo, Zhi-Hui, Gutie´rrez-Rodríguez, Alejandro, Ha, Seungkyu, Habib, Noman, Hajer, Jan, Hammer, Francois, Han, Chengcheng, Han, Huayong, Han, Jifeng, Han, Liang, Han, Liangliang, Han, Ruixiong, Han, Yang, Han, Yezi, Han, Yuanying, Han, Tao, Hao, Jiankui, Hao, Xiqing, XiqingHao, He, Chuanqi, He, Dayong, He, Dongbing, He, Guangyuan, He, Hong-Jian, He, Jibo, He, Jun, He, Longyan, He, Xiang, He, Xiao-Gang, He, Zhenqiang, Heinemann, Klaus, Heinemeyer, Sven, Heng, Yuekun, Herna´ndez-Ruíz, María A., Hong, Jiamin, Hor, Yuenkeung, Hou, George W. S., Hou, Xiantao, Hou, Xiaonan, Hou, Zhilong, Hou, Suen, Hu, Caishi, Hu, Chen, Hu, Dake, Hu, Haiming, Hu, Jiagen, Hu, Jun, Hu, Kun, Hu, Shouyang, Hu, Yongcai, Hu, Yu, Hu, Zhen, Hua, Zhehao, Hua, Jianfei, Huang, Chao-Shang, Huang, Fa Peng, Huang, Guangshun, Huang, Jinshu, Huang, Ke, Huang, Liangsheng, Huang, Shuhui, Huang, Xingtao, Huang, Xu-Guang, Huang, Yanping, Huang, Yonggang, Huang, Yongsheng, Huang, Zimiao, Huanyuan, Chen, Huh, Changgi, Hui, Jiaqi, Huo, Lihua, Hussain, Talab, Hwang, Kyuyeong, Ioannisian, Ara, Iqbal, Munawar, Jackson, Paul, Jafarzade, Shahriyar, Jang, Haeun, Jang, Seoyun, Ji, Daheng, Ji, Qingping, Ji, Quan, Ji, Xiaolu, Jia, Jingguang, Jia, Jinsheng, Jia, Xuewei, Jia, Zihang, Jiang, Cailian, Jiang, Han Ren, Jiang, Houbing, Jiang, Jun, Jiang, Xiaowei, Jiang, Xin, Jiang, Xuhui, Jiang, Yongcheng, Jiang, Zhongjian, Jiang, Cheng, Jiao, Ruiqi, Jin, Dapeng, Jin, Shan, Jin, Song, Jin, Yi, Jis, Junji, Jung, Sunghoon, Kacarevic, Goran, Kajfasz, Eric, Kalinovskaya, Lidia, Kampf, Aleksei, Kang, Wen, Kang, Xian-Wei, Kang, Xiaolin, Karmakar, Biswajit, Ke, Zhiyong, Keloth, Rijeesh, Khan, Alamgir, Khanpour, Hamzeh, Khosonthongkee, Khanchai, KhanchaiKhosonthongkee, Kim, Bobae, Kim, Dongwoon, Kim, Mi Ran, Kim, Minsuk, Kim, Sungwon, Kim, On, Klasen, Michael, Ko, Sanghyun, Koop, Ivan, Kornienko, Vitaliy, Kortman, Bryan, Kozlov, Gennady, Kuang, Shiqing, Kumar, Mukesh, Kuo, Chia Ming, Kwok, Tsz Hong, Lagarde, Franc¸ois Sylvain Ren, Lai, Pei-Zhu, Laktineh, Imad, Lan, Xiaofei, Lan, Zuxiu, Lavezzi, Lia, Lee, Justin, Lee, Junghyun, Lee, Sehwook, Lei, Ge, Lemmon, Roy, Leng, Yongxiang, Leung, Sze Ching, Li, Hai Tao, Li, Bingzhi, Li, Bo, Li, Bo, Li, Changhong, Li, Chao, Li, Cheng, Li, Cheng, Li, Chunhua, Li, Cui, Li, Dazhang, Li, Dikai, Li, Fei, Li, Gang, Li, Gang, Li, Gang, Li, Gaosong, Li, Haibo, Li, Haifeng, Li, Hai-Jun, Li, Haotian, Li, Hengne, Li, Honglei, Li, Huijing, Li, Jialin, Li, Jingyi, Li, Jinmian, Li, Jun, Li, Leyi, Li, Liang, Li, Ling, Li, Mei, Li, Meng, Li, Minxian, Li, Pei-Rong, Li, Qiang, Li, Shaopeng, Li, Shenghe, Li, Shu, Li, Shuo, Li, Teng, Li, Tiange, Li, Tong, Li, Weichang, Li, Weidong, Li, Wenjun, Li, Xiaoling, Li, Xiaomei, Li, Xiaonan, Li, Xiaoping, Li, Xiaoting, Li, Xin, Li, Xinqiang, Li, Xuekang, Li, Yang, Li, Yanwei, Li, Yiming, Li, Ying, Li, Ying-Ying, Li, Yonggang, Li, Yonglin, Li, Yufeng, Li, Yuhui, Li, Zhan, Li, Zhao, Li, Zhiji, Li, Tong, Li, Lingfeng, Li, Fei, Liang, Jing, Liang, Jinhan, Liang, Zhijun, Liao, Guangrui, Liao, Hean, Liao, Jiajun, Liao, Libo, Liao, Longzhou, Liao, Yi, Liao, Yipu, Limphirat, Ayut, AyutLimphirat, Lin, Tao, Lin, Weiping, Lin, Yufu, Lin, Yugen, Liu, Beijiang, Liu, Bo, Liu, Danning, Liu, Dong, Liu, Fu-Hu, Liu, Hongbang, Liu, Huangcheng, Liu, Hui, Liu, Huiling, Liu, Jia, Liu, Jia, Liu, Jiaming, Liu, Jianbei, Liu, Jianyi, Liu, Jingdong, Liu, Jinhua, Liu, Kai, Liu, Kang, Liu, Kun, Liu, Mengyao, Liu, Peng, Liu, Pengcheng, Liu, Qibin, Liu, Shan, Liu, Shidong, Liu, Shuang, Liu, Shubin, Liu, Tao, Liu, Tao, Liu, Tong, Liu, Wei, Liu, Xiang, Liu, Xiao-Hai, Liu, Xiaohui, Liu, Xiaoyu, Liu, Xin, Liu, Xinglin, Liu, Xingquan, Liu, Yang, Liu, Yanlin, Liu, Yao-Bei, Liu, Yi, Liu, Yiming, Liu, Yong, Liu, Yonglu, Liu, Yu, Liu, Yubin, Liu, Yudong, Liu, Yulong, Liu, Zhaofeng, Liu, Zhen, Liu, Zhenchao, Liu, Zhi, Liu, Zhi-Feng, Liu, Zhiqing, Liu, Zhongfu, Liu, Zuowei, Liu, Mia, Liu, Zhen, Liu, Xiaoyang, Lou, Xinchou, Lu, Cai-Dian, Lu, Jun-Xu, Lu, Qiu Zhen, Lu, Shang, Lu, Shang, Lu, Wenxi, Lu, Xiaohan, Lu, Yunpeng, Lu, Zhiyong, Lu, Xianguo, Lu, Wei, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Lukanov, Arslan, Luo, Jinliang, Luo, Tao, Luo, xiaoan, Luo, Xiaofeng, Luo, Xiaolan, Lv, Jindong, Lyu, Feng, Lyu, Xiao-Rui, Lyu, Kun-Feng, Ma, Ande, Ma, Hong-Hao, Ma, Jun-Li, Ma, Kai, Ma, Lishuang, Ma, Na, Ma, Renjie, Ma, Weihu, Ma, Xinpeng, Ma, Yanling, Ma, Yan-Qing, Ma, Yongsheng, Ma, Zhonghui, Ma, Zhongjian, Ma, Yang, Maity, Mousam, Mao, Lining, Mao, Yanmin, Mao, Yaxian, Martens, Aure´lien, Maria, Caccia Massimo Luigi, Matsumoto, Shigeki, Mellado, Bruce, Meloni, Davide, Men, Lingling, Meng, Cai, Meng, Lingxin, Mi, Zhenghui, Miao, Yuhui, Migliorati, Mauro, Ming, Lei, Mitsou, Vasiliki A., Monaco, Laura, Moraes, Arthur, Mosala, Karabo, Moursy, Ahmad, Mu, Lichao, Mu, Zhihui, Muchnoi, Nickolai, Muenstermann, Daniel, Muenstermann, Daniel, Munbodh, Pankaj, Murray, William John, Nanni, Jérôme, Nanzanov, Dmitry, Nie, Changshan, Nikitin, Sergei, Ning, Feipeng, Ning, Guozhu, Niu, Jia-Shu, Niu, Juan-Juan, Niu, Yan, Nkadimeng, Edward Khomotso, Ohmi, Kazuhito, Oide, Katsunobu, Okawa, Hideki, Ouchemhou, Mohamed, Ouyang, Qun, Paesani, Daniele, Pagani, Carlo, Paganis, Stathes, Pakuza, Collette, Pan, Jiangyang, Pan, Juntong, Pan, Tong, Pan, Xiang, Panda, Papia, Pandey, Saraswati, Pandurovic, Mila, Paparella, Rocco, Pasechnik, Roman, Passemar, Emilie, Pei, Hua, Peng, Xiaohua, Peng, Xinye, Peng, Yuemei, Ping, Jialun, Ping, Ronggang, Adhya, Souvik Priyam, Qi, Baohua, Qi, Hang, Qi, Huirong, Qi, Ming, Qian, Sen, Qian, Zhuoni, Qiao, Congfeng, Qin, Guangyou, Qin, Jiajia, Qin, Laishun, Qin, Liqing, Qin, Qin, Qin, Xiaoshuai, Qin, Zhonghua, Qu, Guofeng, Racioppi, Antonio, Ramsey-Musolf, Michael, Raza, Shabbar, Rekovic, Vladimir, Ren, Jing, Reuter, Ju¨rgen, Robens, Tania, Rossi, Giancarlo, Ruan, Manqi, Ruan, Manqi, Rumyantsev, Leonid, Ryu, Min Sang, Sadykov, Renat, Sang, Minjing, Sanz-Cillero, Juan Jose´, Saur, Miroslav, Savla, Nishil, Schmidt, Michael A., Sertore, Daniele, Settles, Ron, Sha, Peng, Shao, Ding-Yu, Shao, Ligang, Shao, Hua-Sheng, She, Xin, Shen, Chuang, Shen, Hong-Fei, Shen, Jian-Ming, Shen, Peixun, Shen, Qiuping, Shen, Zhongtao, Sheng, Shuqi, Shi, Haoyu, Shi, Hua, Shi, Qi, Shi, Shusu, Shi, Xiaolei, Shi, Xin, Shi, Yukun, Shi, Zhan, Shipsey, Ian, Shiu, Gary, Shu, Chang, Si, Zong-Guo, Sidorenkov, Andrei, Smiljanić, Ivan, Song, Aodong, Song, Huayang, Song, Jiaojiao, Song, Jinxing, Song, Siyuan, Song, Weimin, Song, Weizheng, Song, Zhi, Sourav, Shashwat, Spruzzola, Paolo, Su, Feng, Su, Shengsen, Su, Wei, Su, Shufang, Sui, Yanfeng, Sui, Zexuan, Sullivan, Michael, Sun, Baiyang, Sun, Guoqiang, Sun, Hao, Sun, Hao-Kai, Sun, Junfeng, Sun, Liang, Sun, Mengcheng, Sun, Pengfei, Sun, Sichun, Sun, Xianjing, Sun, Xiaohu, Sun, Xilei, Sun, Xingyang, Sun, Xin-Yuan, Sun, Yanjun, Sun, Yongzhao, Sun, Yue, Sun, Zheng, Sun, Zheng, Suwonjandee, Narumon, Eldin, Elsayed Tag, Tan, Biao, Tang, Bo, Tang, Chuanxiang, Tang, Gao, Tang, Guangyi, Tang, Jian, Tang, Jingyu, Tang, Liang, Tang, Ying’Ao, Tao, Junquan, Tawfik, Abdel Nasser, Taylor, Geoffrey, Telnov, Valery, Tian, Saike, Torre, Riccardo, Trzaska, Wladyslaw Henryk, Tsybychev, Dmitri, Tu, Yanjun, Tuo, Shengquan, Tytgat, Michael, Islam, Ghalib Ul, Ushakov, Nikita, Valencia, German, Velthuis, Jaap, Vicini, Alessandro, Vickey, Trevor, Vidakovic, Ivana, Videau, Henri, Volkas, Raymond, Voronin, Dmitry, Vukasinovic, Natasa, Wan, Xia, Wan, Xuying, Wang, Xiao, Wang, Anqing, Wang, Bin, Wang, Chengtao, Wang, Chuanye, Wang, Ci, Wang, Dayong, Wang, Dou, Wang, En, Wang, Fei, Wang, Fei, Wang, Guanwen, Wang, Guo-Li, Wang, Haijing, Wang, Haolin, Wang, Jia, Wang, Jian, Wang, Jianchun, Wang, Jianli, Wang, Jiawei, Wang, Jin, Wang, Jin-Wei, Wang, Joseph, Wang, Kechen, Wang, Lechun, Wang, Lei, Wang, Liguo, Wang, Lijiao, Wang, Lu, Wang, Meng, Wang, Na, Wang, Pengcheng, Wang, Qian, Wang, Qun, Wang, Shu Lin, Wang, Shudong, Wang, Taofeng, Wang, Tianhong, Wang, Tianyang, Wang, Tong, Wang, Wei, Wang, Wei, Wang, Xiaolong, Wang, Xiaolong, Wang, Xiaoning, Wang, Xiao-Ping, Wang, Xiongfei, Wang, Xujian, Wang, Yaping, Wang, Yaqian, Wang, Yi, Wang, Yiao, Wang, Yifang, Wang, Yilun, Wang, Yiwei, Wang, You-Kai, Wang, Yuanping, Wang, Yuexin, Wang, Yuhao, Wang, Yu-Ming, Wang, Yuting, Wang, Zhen, Wang, Zhigang, Wang, Weiping, Wang, Zeren Simon, Wang, Biao, Wang, Hui, Wang, Lian-Tao, Wang, Zihui, Wang, Zirui, Wang, Jia, Wang, Tong, Wei, Daihui, Wei, Shujun, Wei, Wei, Wei, Xiaomin, Wei, Yuanyuan, Wei, Yingjie, Wen, Liangjian, Wen, Xuejun, Wen, Yufeng, White, Martin, Williams, Peter, Wolffs, Zef, Womersley, William John, Wu, Baona, Wu, Bobing, Wu, Guanjian, Wu, Jinfei, Wu, Lei, Wu, Lina, Wu, Linghui, Wu, Minlin, Wu, Peiwen, Wu, Qi, Wu, Qun, Wu, Tianya, Wu, Xiang, Wu, Xiaohong, Wu, Xing-Gang, Wu, Xuehui, Wu, Yaru, Wu, Yongcheng, Wu, Yuwen, Wu, Zhi, Wu, Xin, Xia, Lei, Xia, Ligang, Xia, Shang, Xiang, Benhou, Xiang, Dao, Xiang, Zhiyu, Xiao, Bo-Wen, Xiao, Chu-Wen, Xiao, Dong, Xiao, Guangyan, Xiao, Han, Xiao, Meng, Xiao, Ouzheng, Xiao, Rui-Qing, Xiao, Xiang, Xiao, Yichen, Xiao, Ying, Xiao, Yu, Xiao, Yunlong, Xiao, Zhenjun, Xiao, Hengyuan, Xie, Nian, Xie, Yuehong, Xin, Tianmu, Xing, Ye, Xing, Zhizhong, Xu, Da, Xu, Fang, Xu, Fanrong, Xu, Haisheng, Xu, Haocheng, Xu, Ji, Xu, Miaofu, Xu, Qingjin, Xu, Qingnian, Xu, Wei, Xu, Wei, Xu, Weixi, Xu, Xinping, Xu, Zhen, Xu, Zijun, Xu, Zehua, Xu, Yaoyuan, Xue, Feifei, Yan, Baojun, Yan, Bin, Yan, Fen, Yan, Fucheng, Yan, Jiaming, Yan, Liang, Yan, Luping, Yan, Qi-Shu, Yan, Wenbiao, Yan, Yupeng, Yan, Luping, Yan, Haoyue, Yang, Dong, Yang, Fengying, Yang, Guicheng, Yang, Haijun, Yang, Jin Min, Yang, Jing, Yang, Lan, Yang, Li, Yang, Li Lin, Yang, Lili, Yang, Litao, Yang, Mei, Yang, Qiaoli, Yang, Tiansen, Yang, Xiaochen, Yang, Yingjun, Yang, Yueling, Yang, Zhengyong, Yang, Zhenwei, Yang, Youhua, Yang, Xiancong, Yao, De-Liang, Yao, Shi, Ye, Lei, Ye, Lingxi, Ye, Mei, Ye, Rui, Ye, Rui, Ye, Yecheng, Yermolchyk, Vitaly, Yi, Kai, Yi, Li, Yi, Yang, Yin, Di, Yin, Peng-Fei, Yin, Shenghua, Yin, Ze, Yin, Zhongbao, Yinhong, Zhang, Yoo, Hwi Dong, You, Zhengyun, Young, Charles, Yu, Boxiang, Yu, Chenghui, Yu, Fusheng, Yu, Jie-Sheng, Yu, Jinqing, Yu, Lingda, Yu, Zhao-Huan, Yu, Felix, Yu, Bingrong, Yuan, Changzheng, Yuan, Li, Yuan, Xing-Bo, Yuan, Youjin, Yue, Junhui, Yue, Qian, Yue, Baobiao, Zaib, Un Nisa, Zanzottera, Riccardo, Zeng, Hao, Zeng, Ming, Zhai, Jian, Zhai, Jiyuan, Zhai, Xin Zhe, Zhan, Xi-Jie, Zhang, Ben-Wei, Zhang, Bolun, Zhang, Di, Zhang, Guangyi, Zhang, Hao, Zhang, Hong-Hao, Zhang, Huaqiao, Zhang, Hui, Zhang, Jialiang, Zhang, Jianyu, Zhang, Jianzhong, Zhang, Jiehao, Zhang, Jielei, Zhang, Jingru, Zhang, Jinxian, Zhang, Junsong, Zhang, Junxing, Zhang, Lei, Zhang, Lei, Zhang, Liang, Zhang, Licheng, Zhang, Liming, Zhang, Linhao, Zhang, Luyan, Zhang, Mengchao, Zhang, Rao, Zhang, Shulei, Zhang, Wan, Zhang, Wenchao, Zhang, Xiangzhen, Zhang, Xiaomei, Zhang, Xiaoming, Zhang, Xiaoxu, Zhang, Xiaoyu, Zhang, Xuantong, Zhang, Xueyao, Zhang, Yang, Zhang, Yang, Zhang, Yanxi, Zhang, Yao, Zhang, Ying, Zhang, Yixiang, Zhang, Yizhou, Zhang, Yongchao, Zhang, Yu, Zhang, Yuan, Zhang, Yujie, Zhang, Yulei, Zhang, Yumei, Zhang, Yunlong, Zhang, Zhandong, Zhang, Zhaoru, Zhang, Zhen-Hua, Zhang, Zhenyu, Zhang, Zhichao, Zhang, Zhi-Qing, Zhang, Zhuo, Zhang, Zhiqing, Zhang, Cong, Zhang, Tianliang, Zhang, Luyan, Zhao, Guang, Zhao, Hongyun, Zhao, Jie, Zhao, Jingxia, Zhao, Jingyi, Zhao, Ling, Zhao, Luyang, Zhao, Mei, Zhao, Minggang, Zhao, Mingrui, Zhao, Qiang, Zhao, Ruiguang, Zhao, Tongxian, Zhao, Yaliang, Zhao, Ying, Zhao, Yue, Zhao, Zhiyu, Zhao, Zhuo, Zhemchugov, Alexey, Zheng, Hongjuan, Zheng, Jinchao, Zheng, Liang, Zheng, Ran, zheng, shanxi, Zheng, Xu-Chang, Zhile, Wang, Zhong, Weicai, Zhong, Yi-Ming, Zhou, Chen, Zhou, Daicui, Zhou, Jianxin, Zhou, Jing, Zhou, Jing, Zhou, Ning, Zhou, Qi-Dong, Zhou, Shiyu, Zhou, Shun, Zhou, Sihong, Zhou, Xiang, Zhou, Xingyu, Zhou, Yang, Zhou, Yong, Zhou, Yu-Feng, Zhou, Zusheng, Zhou, Demin, Zhu, Dechong, Zhu, Hongbo, Zhu, Huaxing, Zhu, Jingya, Zhu, Kai, Zhu, Pengxuan, Zhu, Ruilin, Zhu, Xianglei, Zhu, Yingshun, Zhu, Yongfeng, Zhuang, Xiao, Zhuang, Xuai, Zobov, Mikhail, Zong, Zhanguo, Zou, Cong, and Zou, Hongying

Published
2025
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18. ADSL promotes autophagy and tumor growth through fumarate-mediated Beclin1 dimethylation

Author

Wang, Lei, Shi, Runze, Wang, Shuo, Duan, Yuran, Wang, Zheng, Zheng, Peixiang, Sun, Xue, Chen, Xiaohan, Ji, Guimei, Shen, Yuli, Dong, Bofei, Lin, Yanni, Wen, Ting, Tian, Qi, Guo, Zhanpeng, Hou, Yueru, Wu, Shiqi, Xiao, Ling, Li, Min, Xiao, Liwei, Wu, Qingang, Meng, Ying, Liu, Guijun, Duan, Sofie, Bai, Xueli, Liu, Tong, Zhang, Zhiren, Zhan, Peng, Lu, Zhimin, and Xu, Daqian

Published
2025
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24. EDeRF: Updating Local Scenes and Editing Across Fields for Real-Time Dynamic Reconstruction of Road Scene

Author

Liang, Zhaoxiang, Guo, Wenjun, Yang, Yi, Liu, Tong, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cho, Minsu, editor, Laptev, Ivan, editor, Tran, Du, editor, Yao, Angela, editor, and Zha, Hongbin, editor

Published
2025
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25. Decoupled DETR for Few-Shot Object Detection

Author

Shangguan, Zeyu, Huai, Lian, Liu, Tong, Liu, Yuyu, Jiang, Xingqun, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cho, Minsu, editor, Laptev, Ivan, editor, Tran, Du, editor, Yao, Angela, editor, and Zha, Hongbin, editor

Published
2025
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26. The Influence of Rain-Type on the Seepage and Stability of Purple Silty Cohesive Soil Slope

Author

Jiang, Ou, Yang, Shiyi, Yan, Xuancheng, Yang, Can, Liu, Tong, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Lu, Xinzheng, Series Editor, Wang, Weiqiang, editor, Wang, Chengzhi, editor, and Lu, Yang, editor

Published
2025
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27. Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector

Author

Fu, Yuqian, Wang, Yu, Pan, Yixuan, Huai, Lian, Qiu, Xingyu, Shangguan, Zeyu, Liu, Tong, Fu, Yanwei, Van Gool, Luc, Jiang, Xingqun, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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28. Research on the Effect of Aero-Engine Connection Interface on Acoustic Emission Signals

Author

Liu, Tong, Liu, Chao, Jiang, Dongxiang, Ceccarelli, Marco, Series Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Wang, Zuolu, editor, Zhang, Kai, editor, Feng, Ke, editor, Xu, Yuandong, editor, and Yang, Wenxian, editor

Published
2025
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37. Clinical Treatment Status of Papillary Thyroid Microcarcinoma

Author

Liu Tong, Meng Kexin, Zhu Ying, Fan Fengjing, Wang Bei

Subjects
thyroid gland, papillary microcarcinoma, active surveillance, thermal ablation, Medical technology, R855-855.5, Medicine
Abstract

Papillary thyroid microcarcinoma (PTMC) is a subtype of papillary thyroid carcinoma (PTC) characterized by a diameter of less than 10 mm. While its incidence is on the rise, PTMC generally carries a favorable prognosis. Traditional surgical intervention remains the primary treatment method, widely recognized for its effectiveness. However, surgical procedures can lead to postoperative scarring and complications, posing challenges for patients. For some low-risk PTMC cases that exhibit long periods of non-progression, active surveillance has emerged as a viable treatment option. Thermal ablation technology, guided by ultrasound, has demonstrated comparable short-term efficacy to surgery but with smaller incisions and reduced costs, offering a new alternative for PTMC patients. Currently, the management strategies for PTMC exhibit considerable diversity, contributing to ongoing debates in treatment approaches. This paper provides a comprehensive summary and review of the primary therapies available today.

Published
2025
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