11,264 results on '"LI Xiaoyu"'
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2. Design and process optimization of binary low eutectic fatty acid phase change microcapsules
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YUAN Hongping, HONG Shu, DENG Junqian, WU Xinyu, LI Xiaoyu, XIAO Jun, and LIAN Hailan
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lauric acid ,myristic acid ,binary low eutectic fatty acid ,phase change microcapsule ,cosmo-rs ,Materials of engineering and construction. Mechanics of materials ,TA401-492 - Abstract
The phase transition temperature of single fatty acids is generally high and prone to leakage， which cannot meet the demand for using them as phase change materials for regulating the energy consumption of building air conditioning in summer.Based on conductor-like screening model for real solvents （COSMO-RS），136 binary low eutectic fatty acids of 7 medium chain fatty acids and 10 long chain fatty acids were designed and calculated by using COSMOthermX software，and the eutectic temperature and molar ratio of binary low eutectic fatty acids were predicted.Furthermore， the optimal combination was used as the core material， and melamine-urea-formaldehyde （MUF） resin was used as the wall material to prepare phase change microcapsules through in-situ polymerization. The effects of different process conditions （core-to-wall ratio， reaction temperature， reaction time， reaction speed，etc.） on the thermal and physical properties of the microcapsules were systematically discussed. The results show that the COSMO-RS model can judge the relationship between hydrogen bond donor （HBD） and hydrogen bond acceptor （HBA） more intuitively. The eutectic temperature （33.25 ℃） in theory is 98.08% similar to the experimental temperature （33.1 ℃） of lauric acid （LA）-myristic acid （MA） （LM） with the optimal combination of LA and MA with a molar ratio of 0.66 to 0.34. Under the conditions of a core-to-wall ratio of 2∶1， reaction time of 3 h， reaction temperature of 80 ℃， and stirring speed of 200 r/min， the encapsulation efficiency of MUF on the core material LM is 61.37%， which effectively solves the leakage problem and has potential application value in reducing the energy consumption of building air conditioning refrigeration.
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- 2024
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3. A Frequency Regulation Method of Energy Storage System Based on Adaptive Adjustment of Virtual Synchronous Generator Control Parameters
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ZHANG Chong, LI Bo, LI Xiaoyu, LIU Hongbo, and LIU Yongfa
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double carbon ,energy storage ,new energy ,wind power ,virtual synchronous generator ,frequency response ,frequency regulation ,Applications of electric power ,TK4001-4102 ,Production of electric energy or power. Powerplants. Central stations ,TK1001-1841 ,Science - Abstract
ObjectivesThe large-scale penetration of wind power has reduced the frequency regulation capability of the power system to a certain extent.As a relatively mature and effective technical means, the energy storage system is widely used in power grid frequency regulation. Therefore, the response process and optimal configuration of energy storage system (ESS) participating in power grid frequency regulation under the control of virtual synchronous generator were studied.MethodsBased on the simulation software DIgSILENT/PowerFactory, an ESS control model and typical power system were constructed to analyze the frequency response characteristics of the grid before and after the ESS participation. Furthermore, considering the reserve capacity of wind turbines under different output modes, the configuration results of the ESS were optimized by dividing the wind speed range and determining the wind turbine power reserve coefficient, so as to realize the adaptive adjustment of the frequency regulation coefficient of the ESS.ResultsThe investment of the ESS can effectively improve the frequency response and reduce wind curtailment of the system. By reserving the frequency regulation capacity of wind turbines reasonably, the ESS can provide reliable power support for the power grid.ConclusionsThe self-adaptive adjustment method of the frequency regulation coefficient of the ESS based on the wind speed and the output power of fans can effectively reduce the overshoot and the output power of the ESS while meeting the frequency regulation requirements, thereby extending the working time.
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- 2024
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4. Unleashing the potential: super-resolution microscopy as the key to advanced mitochondrial research
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Li Xiaoyu, He Miao, and Huang Xiaoshuai
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super resolution microscopy ,mitochondria ,fluorescence microscopy ,Medicine - Abstract
Investigating the fine structure of mitochondria and their dynamic interactions with other organelles is crucial for unraveling the mechanisms underlying mitochondrial-related diseases. The development of super-resolution techniques has provided powerful visualization tools for mitochondrial research, which is significant for investigating mitochondrial cristae structure, the localization of mitochondrial-related protein complex, and the interactions between mitochondria and other organelles. In this perspective, we introduce several advanced super-resolution techniques and their applications in mitochondrial research, and discuss the potential roles these techniques may play in future studies of mitochondria.
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- 2024
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5. ADCY5 act as a putative tumor suppressor in glioblastoma: An integrated analysis
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Wang Can, Wen Yan, Huang Luo, Zhang Xin, Luo Yan, Liu Deqing, Tu Honglei, Li Xiaoyu, Sui Jiangdong, Xie Yue, and Li Jing
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ADCY5 ,GBM ,Proliferation ,Migration ,Bioinformatics analysis ,Prognostic value ,Science (General) ,Q1-390 ,Social sciences (General) ,H1-99 - Abstract
Background: Adenylyl cyclase (AC) isoforms played a key role in the multiple cancer pathology, However, the expression, prognostic value and function of ADCY5 in Glioblastoma (GBM) have not been reported yet. This research intends to discover the expression, epigenetic alteration and biological function of ADCY5 in GBM and its value on patients' prognosis. Methods: ① Transcriptional level, epigenetic alteration, prognostic value and molecular network of ADCY5 were analyzed by using of public online datasets. ② The mRNA expression profile of ADCY5 was explored by using GEPIA database and protein expression levels were detected by HPA Database. ③ The prognostic value of ADCY5 was determined by Kaplan-Meier Plotter, GEPIA and CGGA database. ④ The epigenetic characteristics of ADCY5 were determined by DiseaseMeth database. ⑤ Identification of genes co-expressed with ADCY5 and potential mechanism analyses were performed by using DAVID cBioPorta and STRING. ⑥ Reverse transcription-polymerase chain reaction (RT-PCR), cell counting kit-8 (CCK-8), colony formation, wound-healing scratch and transwell assay were applied to detect relative mRNA expression and biological function of ADCY5 in GMB cells. Results: ADCY5 mRNA and protein were downregulated in GBM compared with normal tissues. Analysis of the genetics and epigenetics of ADCY5 suggested that its expression was negatively correlated with DNA methylation. High expression of ADCY5 was significantly associated with age, grade, IDH mutation, 1p19q_codeletion, radiotherapy and chemotherapy and acted as an independent prognostic factor in GBM. ADCY5 mRNA also down-expressed in GBM cell lines and re-expressed of ADCY5 could inhibit cell proliferation, viability, migration/invasion and epithelial-mesenchymal transition (EMT) in vitro. In the analysis of genes co-expressed with ADCY5, we found that cAMP/AKT pathway, cGMP-PKG pathway, Wnts pathway were dissimilarly enriched. Conclusion: Our study indicated that ADCY5 could act as an epigenetic biomarker in GBM, as well as a prognosis target in patients with GBM.
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- 2024
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6. The Global Cancer Statistics Report in 2022: A Narrow Spectrum Summary and Outlook
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LI Xiaoyu, HUANG Qing, WU Yumeng, and HU Sheng
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cancer ,epidemiology ,prevention ,economic burden ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
According to the global cancer statistics in 2022 updated by the International Agency for Research on Cancer (IARC), there were nearly 20 million new cases of cancer and 9.7 million deaths. Lung cancer was the most commonly diagnosed cancer, accounting for nearly 2.5 million new cases (12.4% of all global cancers), followed by female breast cancer (11.6%), colorectal cancer (9.6%), prostate cancer (7.3%), and stomach cancer (4.9%). Lung cancer was also the leading cause of cancer deaths, with an estimated 1.8 million deaths (18.7%), followed by colorectal cancer (9.3%), liver cancer (7.8%), female breast cancer (6.9%), and stomach cancer (6.8%). Population-based projections suggest that the number of new cancer cases will reach 35 million by 2050. Increasing the investment in prevention and control measures targeting key cancer risk factors, including smoking, obesity, and infections, could save many lives globally and bring significant economic and social returns to countries in the coming decades.
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- 2024
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7. Construction and Verification of A Nomogram Model for Predicting Invasive Risk of Ground Glass Nodules
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LI Xiaoyu, LIU Zhiliang, JIN Bingji, and MIAO Ye
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ground glass nodules ,biomarkers ,nomogram model ,ct signs ,pathological subtype ,invasive adenocarcinoma of lung ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Objective To investigate the importance of a nomogram model based on biomarkers and CT signs in the prediction of the invasive risk of ground glass nodules. Methods A total of 322 patients with ground glass nodule, including 240 and 82 patients in the model and verification groups, respectively, were retrospectively analyzed. Independent risk factors for the invasive risk of ground glass nodules were screened out after using single and multiple Logistic analysis. R software was used to construct the nomogram model, and clinical decision curve analysis (DCA), receiver operating curve (ROC), and calibration curve were used for internal and external verification of the model. Results In this study, the independent risk factors for the invasive risk of ground glass nodules included systemic immune-inflammation index (SII), CYFRA21-1, edge, vascular cluster sign, and nodular consolidation tumor ratio (CTR). The area under the ROC curve of the constructed nomogram model had a value of 0.946, and that of the external validation group reached 0.932, which suggests the good capability of the model in predicting the invasive risk of ground glass nodules. The model was internally verified through drawing of calibration curves of Bootstrap 1000 automatic sampling. The results showed that the consistency index between the model and actual curves reached 0.955, with a small absolute error and good fit. The DCA curve revealed a good clinical practicability. In addition, nodule margin, vascular cluster sign, and CTR were correlated with the grade of pathological subtype of invasive adenocarcinoma. Conclusion A nomogram model based on biomarkers and CT signs has good value and clinical practicability in the prediction of the invasive risk of ground glass nodules.
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- 2024
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8. The Spatiotemporal Changes and Influencing Factors of Vegetation NDVI in the Hehuang Valley of Qinghai Province from 2000 to 2020
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LI Xiaoyu, XIN Zhongbao, YANG Junliu, and LIU Jinhao
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hehuang valley ,ndvi ,spatiotemporal variations ,climate change ,human activity ,geographic detector ,Environmental sciences ,GE1-350 ,Agriculture - Abstract
[Objective] This study is aimed to understand the spatiotemporal changes in vegetation in the Qinghai Hehuang Valley and clarify the effects of climate change, land development and utilization and human activities on vegetation change. [Methods] The MODIS NDVI dataset from 2000 to 2020 was used to characterize vegetation changes. Based on Theil-Sen Median trend test, partial correlation analysis, geographical detectors and other methods, this paper explored the spatiotemporal changes of NDVI in Qinghai Hehuang Valley and its relationship with temperature, precipitation, slope, soil type, human activities and other influencing factors. [Results] (1) In the past 20 years, the vegetation NDVI in the Hehuang Valley region had shown a fluctuating growth trend, with a significant increase in the area of 2.21×104 km2 (p
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- 2024
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9. Mine external fire monitoring method using the fusion of visible visual features
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Fan Weiqiang, Li Xiaoyu, Liu Yi, and Weng Zhi
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external fire ,fire monitoring ,static features ,dynamic features ,feature fusion ,bp neural networks ,Engineering geology. Rock mechanics. Soil mechanics. Underground construction ,TA703-712 ,Mining engineering. Metallurgy ,TN1-997 - Abstract
In order to overcome the problems of poor real-time performance, high false alarm rate and underreport alarm rate of mine external fire monitoring, a method of fire monitoring using the fusion of visible visual features is proposed.Firstly, the visual features corresponding to the video images of fire sources in different monitoring environments are analyzed, and the extraction methods of fire source texture, sharp corners, similarity coefficient and flicker frequency are designed.Then, an improved seed region growth algorithm is used to segment the suspected fire area, and different feature extraction methods are used to calculate the dynamic and static characteristics of the suspected fire area.Secondly, the extracted dynamic and static features are used to construct fire feature vectors.Finally, a fire monitoring model using BP neural network is constructed, and monitoring model is verified.The results show that the proposed fire monitoring method can effectively detect mine external fire in different scenes and distances.The correct rate and detection rate are 98.60% and 99.06%, respectively, the false detection rate is as low as 2.00%.It has strong anti-interference ability and robustness.
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- 2023
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10. Digital Twin for Agricultural Machinery: From Concept to Application
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GUO Dafang, DU Yuefeng, WU Xiuheng, HOU Siyu, LI Xiaoyu, ZHANG Yan'an, and CHEN Du
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agricultural machinery ,digital twin ,information technology ,virtual simulation ,smart agriculture ,Agriculture (General) ,S1-972 ,Technology (General) ,T1-995 - Abstract
SignificanceAgricultural machinery serves as the fundamental support for implementing advanced agricultural production concepts. The key challenge for the future development of smart agriculture lies in how to enhance the design, manufacturing, operation, and maintenance of these machines to fully leverage their capabilities. To address this, the concept of the digital twin has emerged as an innovative approach that integrates various information technologies and facilitates the integration of virtual and real-world interactions. By providing a deeper understanding of agricultural machinery and its operational processes, the digital twin offers solutions to the complexity encountered throughout the entire lifecycle, from design to recycling. Consequently, it contributes to an all-encompassing enhancement of the quality of agricultural machinery operations, enabling them to better meet the demands of agricultural production. Nevertheless, despite its significant potential, the adoption of the digital twin for agricultural machinery is still at an early stage, lacking the necessary theoretical guidance and methodological frameworks to inform its practical implementation.ProgressDrawing upon the successful experiences of the author's team in the digital twin for agricultural machinery, this paper presents an overview of the research progress made in digital twin. It covers three main areas: The digital twin in a general sense, the digital twin in agriculture, and the digital twin for agricultural machinery. The digital twin is conceptualized as an abstract notion that combines model-based system engineering and cyber-physical systems, facilitating the integration of virtual and real-world environments. This paper elucidates the relevant concepts and implications of digital twin in the context of agricultural machinery. It points out that the digital twin for agricultural machinery aims to leverage advanced information technology to create virtual models that accurately describe agricultural machinery and its operational processes. These virtual models act as a carrier, driven by data, to facilitate interaction and integration between physical agricultural machinery and their digital counterparts, consequently yielding enhanced value. Additionally, it proposes a comprehensive framework comprising five key components: Physical entities, virtual models, data and connectivity, system services, and business applications. Each component's functions operational mechanism, and organizational structure are elucidated. The development of the digital twin for agricultural machinery is still in its conceptual phase, and it will require substantial time and effort to gradually enhance its capabilities. In order to advance further research and application of the digital twin in this domain, this paper integrates relevant theories and practical experiences to propose an implementation plan for the digital twin for agricultural machinery. The macroscopic development process encompasses three stages: Theoretical exploration, practical application, and summarization. The specific implementation process entails four key steps: Intelligent upgrading of agricultural machinery, establishment of information exchange channels, construction of virtual models, and development of digital twin business applications. The implementation of digital twin for agricultural machinery comprises four stages: Pre-research, planning, implementation, and evaluation. The digital twin serves as a crucial link and bridge between agricultural machinery and the smart agriculture. It not only facilitates the design and manufacturing of agricultural machinery, aligning them with the realities of agricultural production and supporting the advancement of advanced manufacturing capabilities, but also enhances the operation, maintenance, and management of agricultural production to better meet practical requirements. This, in turn, expedites the practical implementation of smart agriculture. To fully showcase the value of the digital twin for agricultural machinery, this paper addresses the existing challenges in the design, manufacturing, operation, and management of agricultural machinery. It expounds the methods by which the digital twin can address these challenges and provides a technical roadmap for empowering the design, manufacturing, operation, and management of agricultural machinery through the use of the digital twin. In tackling the critical issue of leveraging the digital twin to enhance the operational quality of agricultural machinery, this paper presents two research cases focusing on high-powered tractors and large combine harvesters. These cases validate the feasibility of the digital twin in improving the quality of plowing operations for high-powered tractors and the quality of grain harvesting for large combine harvesters.Conclusions and ProspectsThis paper serves as a reference for the development of research on digital twin for agricultural machinery, laying a theoretical foundation for empowering smart agriculture and intelligent equipment with the digital twin. The digital twin provides a new approach for the transformation and upgrade of agricultural machinery, offering a new path for enhancing the level of agricultural mechanization and presenting new ideas for realizing smart agriculture. However, existing digital twin for agricultural machinery is still in its early stages, and there are a series of issues that need to be explored. It is necessary to involve more professionals from relevant fields to advance the research in this area.
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- 2023
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11. A new intake distortion design system for research
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CUI Jiahang, LI Jianghong, LI Xiaoyu, DONG Suyan, CAI Feichao, and FAN Wei
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航空发动机 ,畸变模拟网 ,fluent ,多孔介质模型 ,稳态总压测试系统 ,Motor vehicles. Aeronautics. Astronautics ,TL1-4050 - Abstract
The intake distortion experiment is one of the important methods for aero-engine aerodynamic stability assessment. In this paper, in order to overcome the shortcomings of traditional design methods such as low accuracy in approximating the target distortion map and many experimental iterations, a design system is proposed to design the target map distortion network using Fluent based on the mapping relationship between screen distance and porous media. Finally, the steady-state total pressure test system is designed, and the physical design of the distortion generator and wind tunnel test are completed, with the error of distortion index of less than 1.5%. The test results show that the design system can simulate the distortion requirements with high accuracy and efficiency.
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- 2023
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12. The Hippo signaling pathway: from multiple signals to the hallmarks of cancers
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Zhu Ning, Yang Ruizeng, Wang Xiaodong, Yuan Liang, Li Xiaoyu, Wei Fang, and Zhang Lei
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Hippo ,YAP ,TAZ ,TEAD ,signal transduction ,cancer ,hallmark ,Biochemistry ,QD415-436 ,Genetics ,QH426-470 - Abstract
Evolutionarily conserved, the Hippo signaling pathway is critical in regulating organ size and tissue homeostasis. The activity of this pathway is tightly regulated under normal circumstances, since its physical function is precisely maintained to control the rate of cell proliferation. Failure of maintenance leads to a variety of tumors. Our understanding of the mechanism of Hippo dysregulation and tumorigenesis is becoming increasingly precise, relying on the emergence of upstream inhibitor or activator and the connection linking Hippo target genes, mutations, and related signaling pathways with phenotypes. In this review, we summarize recent reports on the signaling network of the Hippo pathway in tumorigenesis and progression by exploring its critical mechanisms in cancer biology and potential targeting in cancer therapy.
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- 2023
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13. Study on load current characteristics of scraper conveyor under vertical impact
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SI Lei, LI Jiahao, TAN Chao, WANG Zhongbin, LI Xiaoyu, HUO Xiaoquan, and YUAN Zengyun
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scraper conveyor ,vertical impact ,load current characteristics ,bending angle of middle slot ,middle slot clearance ,Mining engineering. Metallurgy ,TN1-997 - Abstract
During the operation of the mine scraper conveyor, it is easy to be affected by the uneven movement of hydraulic support and the uneven cutting bottom of shearer, bearing various types of impact, of which the impact in the vertical direction is the most obvious. This phenomenon will lead to serious wear of the transmission system and unbalanced power of the driving motor. In this article, the formation mechanism of vertical impact of scraper conveyor is studied and the running resistance of the scraper conveyor under different vertical impact effects is calculated. The vertical impact simulation model of the scraper conveyor transmission system is established. The load conditions of scraper conveyor under different working conditions of middle slot clearance, convex and concave of middle slot are simulated and analyzed. The simulation results show that when the middle slot clearance increases, the impact between the middle groove and the middle plate will be stronger, and the impact on the drive system of the scraper conveyor will be more obvious. The peak value of load current produced by vertical impact is positively correlated with the degree of concave or convex bending of the middle groove. The maximum bending angle of adjacent middle slots is the main factor determining the sudden peak value of impact load and the load increment after impact stability of scraper conveyor. The impact caused by the convex and concave bending of the middle groove has a maximum impact on the running resistance of about 12%, which is the main factor affecting the motor power imbalance of the drive system. In the coal mining face, the impact load monitoring platform of scraper conveyor is built, and some impact load monitoring experiments under different vertical bending angles are carried out. The comparison results show that different degrees of vertical impact make the load current fluctuate obviously, and in a short time, the peak value of the impact current can reach 1.5 times of the normal working current, causing serious damage to the chain, motor and other parts of the scraper conveyor. The change trend of the motor current curve after impact is basically consistent with the simulation curve, and the average absolute error range is about 0.435A ~ 1.342A, which verifies the correctness and rationality of the vertical impact simulation model of the scraper conveyor. The research results provide a theoretical basis for improving the intelligent control level of scraper conveyor and ensuring the load stability and power balance of coal mine transportation equipment, so as to reduce the transportation cost of fully mechanized mining face and prolong the service life of transportation equipment.
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- 2023
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14. Vancomycin associated acute kidney injury in patients with infectious endocarditis: a large retrospective cohort study
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Pan Kunming, Huang Ying, Xu Chenqi, Chen Zhangzhang, Ding Xiaoqiang, Li Xiaoyu, Xu Xialian, and Lv Qianzhou
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vancomycin ,acute kidney injury ,infectious endocarditis ,risk factors ,duration of therapy ,Therapeutics. Pharmacology ,RM1-950 - Abstract
Background: Vancomycin remains the cornerstone antibiotic for the treatment of infective endocarditis (IE). Vancomycin has been associated with significant nephrotoxicity. However, vancomycin associated acute kidney injury (AKI) has not been evaluated in patients with IE. We conducted this large retrospective cohort study to reveal the incidence, risk factors, and prognosis of vancomycin-associated acute kidney injury (VA-AKI) in patients with IE.Methods: Adult patients diagnosed with IE and receiving vancomycin were included. The primary outcome was VA-AKI.Results: In total, 435 of the 600 patients were enrolled. Of these, 73.6% were male, and the median age was 52 years. The incidence of VA-AKI was 17.01% (74). Only 37.2% (162) of the patients received therapeutic monitoring of vancomycin, and 30 (18.5%) patients had reached the target vancomycin trough concentration. Multiple logistic regression analysis revealed that body mass index [odds ratio (OR) 1.088, 95% CI 1.004, 1.179], duration of vancomycin therapy (OR 1.030, 95% CI 1.003, 1.058), preexisting chronic kidney disease (OR 2.291, 95% CI 1.018, 5.516), admission to the intensive care unit (OR 2.291, 95% CI 1.289, 3.963) and concomitant radiocontrast agents (OR 2.085, 95% CI 1.093, 3.978) were independent risk factors for VA-AKI. Vancomycin variety (Lai Kexin vs. Wen Kexin, OR 0.498, 95% CI 0.281, 0.885) were determined to be an independent protective factor for VI-AKI. Receiver operator characteristic curve analysis revealed that duration of therapy longer than 10.75 days was associated with a significantly increased risk of VA-AKI (HR 1.927). Kidney function was fully or partially recovered in 73.0% (54) of patients with VA-AKI.Conclusion: The incidence of VA-AKI in patients with IE was slightly higher than in general adult patients. Concomitant contrast agents were the most alarmingly nephrotoxic in patients with IE, adding a 2-fold risk of VA-AKI. In patients with IE, a course of vancomycin therapy longer than 10.75 days was associated with a significantly increased risk of AKI. Thus, closer monitoring of kidney function and vancomycin trough concentrations was recommended in patients with concurrent contrast or courses of vancomycin longer than 10.75 days.
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- 2023
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15. Mine infrared image enhancement algorithm based on dual domain and ILoG-CLAHE
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FAN Weiqiang, LI Xiaoyu, WENG Zhi, LIU Bin, and YANG Kun
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mine infrared image ,dual domain decomposition ,noise suppression ,edge sharpening ,brightness adjustment ,reconstructed image ,Mining engineering. Metallurgy ,TN1-997 - Abstract
The complex working environment of mine leads to the degradation of the infrared image. The existing infrared image enhancement algorithm is easy to lose the scene details or causes the target edge blur while improving the signal-to-noise ratio and contrast. In order to solve the above problems, a mine infrared image enhancement algorithm based on dual domain decomposition coupling improved Gaussian Laplacian (ILoG) factor and contrast limited adaptive histogram equalization (CLAHE) (ILoG-CLAHE) is proposed. Firstly, the dual domain decomposition model is used to decompose the mine infrared image into a detailed sub-images containing high-frequency information and a basic sub-images containing low-frequency information. Secondly, the CLAHE algorithm is used to improve the brightness, contrast and definition of the basic sub-images to highlight the general features of the monitoring scene. The constructed ILoG operator is used to suppress noise and sharpen edges of detail sub-images and eliminate gradient inversion. Thirdly, the reconstructed image with improved image quality is obtained through the basic sub-image and detail sub-image after reconstruction processing. Finally, a Gamma correction function of gray level redistribution is designed to adjust the brightness of the reconstructed image. The mine infrared-enhanced image is obtained. The performance of the algorithm is analyzed by subjective vision and objective indicators. The results show that the overall visual effect and objective index of the mine infrared image enhanced by the mine infrared image enhancement algorithm based on dual domain and ILoG-CLAHE have been greatly improved. The comprehensive enhancement performance and robustness are better. Compared with the original mine infrared image and the six comparison algorithms, the comprehensive evaluation index values of this algorithm are increased by 0.28, 0.11, 0.23, 0.38, 0.57, 0.04, and 0.10 respectively. The six algorithms include CLAHE algorithm, bilateral filtering(BF) decomposition and CLAHE enhancement of basic sub-images (BF-CLAHE) algorithm, BF decomposition and Gamma transform (BF-Gamma) algorithm, guided filtering and Gamma transform (GF-Gamma) algorithm, adaptive histogram equalization(AHE) coupled Laplacian transform (AHE-LP) algorithm, and un-sharp mask(UM) based layer fusion (LF-UM) algorithm. The brightness, clarity and contrast of images are greatly improved, and noise suppression and edge sharpening are realized. It shows that the algorithm is suitable for the enhancement of infrared images in the complex working environment of mine.
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- 2023
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16. Variations in Foreign Labor Policies: A Comparative Analysis of Malaysia and Singapore
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Li Xiaoyu
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Social Sciences - Abstract
This comparative study delves into the foreign labor policies of Malaysia and Singapore, two prominent host countries for foreign workers in Southeast Asia. Despite their historical, cultural, and developmental similarities, these nations exhibit variations in the efficiency of their foreign labor policies. Employing the Most Similar System Design (MSSD) approach, this research explores three key explanatory variables: economic factors related to industrial structures, differences in governance systems, and cultural backgrounds. The study finds that these factors significantly shape the formulation and effectiveness of foreign labor policies. The analysis reveals that Singapore outperforms Malaysia in leveraging foreign worker policies to drive economic transformation, enhance competitiveness, and manage the impact of foreign labor. Factors contributing to this discrepancy include the divergence in industrial structures, variations in governance systems, and the influence of cultural backgrounds. Singapore's unitary system, focus on high-skilled foreign workers, and non-racial political landscape contribute to its policy efficiency, while Malaysia's federal structure, lowerskilled labor demand, and communal politics impact its policy effectiveness.
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- 2024
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17. Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study
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Ke, Yekun, Li, Xiaoyu, Liang, Yingyu, Shi, Zhenmei, and Song, Zhao
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Mathematics - Numerical Analysis - Abstract
Recent empirical studies have identified fixed point iteration phenomena in deep neural networks, where the hidden state tends to stabilize after several layers, showing minimal change in subsequent layers. This observation has spurred the development of practical methodologies, such as accelerating inference by bypassing certain layers once the hidden state stabilizes, selectively fine-tuning layers to modify the iteration process, and implementing loops of specific layers to maintain fixed point iterations. Despite these advancements, the understanding of fixed point iterations remains superficial, particularly in high-dimensional spaces, due to the inadequacy of current analytical tools. In this study, we conduct a detailed analysis of fixed point iterations in a vector-valued function modeled by neural networks. We establish a sufficient condition for the existence of multiple fixed points of looped neural networks based on varying input regions. Additionally, we expand our examination to include a robust version of fixed point iterations. To demonstrate the effectiveness and insights provided by our approach, we provide case studies that looped neural networks may exist $2^d$ number of robust fixed points under exponentiation or polynomial activation functions, where $d$ is the feature dimension. Furthermore, our preliminary empirical results support our theoretical findings. Our methodology enriches the toolkit available for analyzing fixed point iterations of deep neural networks and may enhance our comprehension of neural network mechanisms.
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- 2024
18. Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent
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Chen, Bo, Li, Xiaoyu, Liang, Yingyu, Shi, Zhenmei, and Song, Zhao
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
In-context learning has been recognized as a key factor in the success of Large Language Models (LLMs). It refers to the model's ability to learn patterns on the fly from provided in-context examples in the prompt during inference. Previous studies have demonstrated that the Transformer architecture used in LLMs can implement a single-step gradient descent update by processing in-context examples in a single forward pass. Recent work has further shown that, during in-context learning, a looped Transformer can implement multi-step gradient descent updates in forward passes. However, their theoretical results require an exponential number of in-context examples, $n = \exp(\Omega(T))$, where $T$ is the number of loops or passes, to achieve a reasonably low error. In this paper, we study linear looped Transformers in-context learning on linear vector generation tasks. We show that linear looped Transformers can implement multi-step gradient descent efficiently for in-context learning. Our results demonstrate that as long as the input data has a constant condition number, e.g., $n = O(d)$, the linear looped Transformers can achieve a small error by multi-step gradient descent during in-context learning. Furthermore, our preliminary experiments validate our theoretical analysis. Our findings reveal that the Transformer architecture possesses a stronger in-context learning capability than previously understood, offering new insights into the mechanisms behind LLMs and potentially guiding the better design of efficient inference algorithms for LLMs.
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- 2024
19. Fine-grained Attention I/O Complexity: Comprehensive Analysis for Backward Passes
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Li, Xiaoyu, Liang, Yingyu, Shi, Zhenmei, Song, Zhao, and Zhou, Yufa
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computational Complexity ,Computer Science - Computation and Language - Abstract
Large Language Models (LLMs) have demonstrated remarkable capabilities in processing long-context information. However, the quadratic complexity of attention computation with respect to sequence length poses significant computational challenges, and I/O aware algorithms have been proposed. This paper presents a comprehensive analysis of the I/O complexity for attention mechanisms, focusing on backward passes by categorizing into small and large cache scenarios. Using the red-blue pebble game framework, we establish tight bounds on I/O complexity across all cache sizes. We confirm that the de facto standard I/O aware algorithm FlashAttention is optimal for both forward and backward passes for the large cache size scenario. For small cache sizes, we provide an algorithm that improves over existing methods and achieves the tight bounds. Additionally, we extend our analysis to sparse attention, a mainstream speeding-up approach, deriving fine-grained lower bounds for both forward and backward passes and both small and large caches. Our findings complete the theoretical foundation for I/O complexity in attention mechanisms, offering insights for designing efficient algorithms of LLM training and inference.
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- 2024
20. Observation of an axial-vector state in the study of $\psi(3686) \to \phi \eta \eta'$ decay
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Chang, W. L., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., 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., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fischer, K., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guan, Z. L., 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., 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, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Hussain, T., Hölzken, F., Hüsken, N, der Wiesche, N. in, Irshad, M., Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Larin, P., Lavezzi, L., Lei, T. T., Lei, Z. H., Leithoff, H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, Ke, Li, L. J, Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, Xiaoyu, Li, Y. G., Li, Z. J., Li, Z. X., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Liao, Y. P., Libby, J., Limphirat, A., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F. H., Liu, Fang, Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. M., Liu, Huanhuan, Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mangoni, A., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Patteri, P., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, S. Q., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J, Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, R. S., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, Meng, Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. P., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, Tao, Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Yifan, Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y, Zhang, Shuihan, Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Yao, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, Lei, Zhao, Ling, Zhao, M. G., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. J., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
- Subjects
High Energy Physics - Experiment - Abstract
Using (2712.4 $\pm$ 14.3)$\times 10^{6}$ $\psi(3686)$ events collected with the BESIII detector at BEPCII, a partial wave analysis of the decay $\psi(3686) \to \phi \eta \eta' $ is performed with the covariant tensor approach. An axial-vector state with a mass near 2.3 $\rm GeV/c^2$ is observed for the first time. Its mass and width are measured to be 2316 $\pm 9_{\mathrm{stat}} \pm 30_{\mathrm{syst}}\,\rm MeV/c^2$ and 89 $\pm 15_{\mathrm{stat}} \pm 26_{\mathrm{syst}}\,\rm MeV$, respectively. The product branching fractions of $\mathcal{B}(\psi(3686) \to X(2300) \eta') \mathcal{B}(X(2300)\to \phi \eta)$ and $\mathcal{B}(\psi(3686) \to X(2300) \eta)\mathcal{B}(X(2300)\to \phi \eta')$ are determined to be (4.8 $\pm 1.3_{\mathrm{stat}} \pm 0.7_{\mathrm{syst}})\times 10^{-6}$ and (2.2 $\pm 0.7_{\mathrm{stat}} \pm 0.7_{\mathrm{syst}})\times 10^{-6}$, respectively. The branching fraction $\mathcal{B}(\psi(3686) \to \phi \eta \eta')$ is measured for the first time to be (3.14$\pm0.17_{\mathrm{stat}}\pm0.24_{\mathrm{syst}})\times10^{-5}$. The first uncertainties are statistical and the second are systematic.
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- 2024
21. Array2BR: An End-to-End Noise-immune Binaural Audio Synthesis from Microphone-array Signals
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Chi, Cheng, Li, Xiaoyu, Li, Andong, Ke, Yuxuan, Li, Xiaodong, and Zheng, Chengshi
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Computer Science - Sound ,Computer Science - Artificial Intelligence ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
Telepresence technology aims to provide an immersive virtual presence for remote conference applications, and it is extremely important to synthesize high-quality binaural audio signals for this aim. Because the ambient noise is often inevitable in practical application scenarios, it is highly desired that binaural audio signals without noise can be obtained from microphone-array signals directly. For this purpose, this paper proposes a new end-to-end noise-immune binaural audio synthesis framework from microphone-array signals, abbreviated as Array2BR, and experimental results show that binaural cues can be correctly mapped and noise can be well suppressed simultaneously using the proposed framework. Compared with existing methods, the proposed method achieved better performance in terms of both objective and subjective metric scores.
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- 2024
22. Efficient PAC Learning of Halfspaces with Constant Malicious Noise Rate
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Shen, Jie and Li, Xiaoyu
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Computer Science - Machine Learning ,Computer Science - Data Structures and Algorithms ,Statistics - Machine Learning - Abstract
Understanding noise tolerance of learning algorithms under certain conditions is a central quest in learning theory. In this work, we study the problem of computationally efficient PAC learning of halfspaces in the presence of malicious noise, where an adversary can corrupt both instances and labels of training samples. The best-known noise tolerance either depends on a target error rate under distributional assumptions or on a margin parameter under large-margin conditions. In this work, we show that when both types of conditions are satisfied, it is possible to achieve {\em constant} noise tolerance by minimizing a reweighted hinge loss. Our key ingredients include: 1) an efficient algorithm that finds weights to control the gradient deterioration from corrupted samples, and 2) a new analysis on the robustness of the hinge loss equipped with such weights., Comment: author list in contribution order
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- 2024
23. RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking Framework
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Li, Xiaoyu, Li, Peidong, Zhao, Lijun, Liu, Dedong, Gao, Jinghan, Wu, Xian, Wu, Yitao, and Cui, Dixiao
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Robotics - Abstract
3D Multi-Object Tracking (MOT) obtains significant performance improvements with the rapid advancements in 3D object detection, particularly in cost-effective multi-camera setups. However, the prevalent end-to-end training approach for multi-camera trackers results in detector-specific models, limiting their versatility. Moreover, current generic trackers overlook the unique features of multi-camera detectors, i.e., the unreliability of motion observations and the feasibility of visual information. To address these challenges, we propose RockTrack, a 3D MOT method for multi-camera detectors. Following the Tracking-By-Detection framework, RockTrack is compatible with various off-the-shelf detectors. RockTrack incorporates a confidence-guided preprocessing module to extract reliable motion and image observations from distinct representation spaces from a single detector. These observations are then fused in an association module that leverages geometric and appearance cues to minimize mismatches. The resulting matches are propagated through a staged estimation process, forming the basis for heuristic noise modeling. Additionally, we introduce a novel appearance similarity metric for explicitly characterizing object affinities in multi-camera settings. RockTrack achieves state-of-the-art performance on the nuScenes vision-only tracking leaderboard with 59.1% AMOTA while demonstrating impressive computational efficiency., Comment: RockTrack establishes a new state-of-the-art with 59.1% AMOTA on the nuScenes vision-only test leaderboard with ResNet50-level backbone
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- 2024
24. StereoCrafter: Diffusion-based Generation of Long and High-fidelity Stereoscopic 3D from Monocular Videos
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Zhao, Sijie, Hu, Wenbo, Cun, Xiaodong, Zhang, Yong, Li, Xiaoyu, Kong, Zhe, Gao, Xiangjun, Niu, Muyao, and Shan, Ying
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Graphics ,I.3.0 ,I.4.0 - Abstract
This paper presents a novel framework for converting 2D videos to immersive stereoscopic 3D, addressing the growing demand for 3D content in immersive experience. Leveraging foundation models as priors, our approach overcomes the limitations of traditional methods and boosts the performance to ensure the high-fidelity generation required by the display devices. The proposed system consists of two main steps: depth-based video splatting for warping and extracting occlusion mask, and stereo video inpainting. We utilize pre-trained stable video diffusion as the backbone and introduce a fine-tuning protocol for the stereo video inpainting task. To handle input video with varying lengths and resolutions, we explore auto-regressive strategies and tiled processing. Finally, a sophisticated data processing pipeline has been developed to reconstruct a large-scale and high-quality dataset to support our training. Our framework demonstrates significant improvements in 2D-to-3D video conversion, offering a practical solution for creating immersive content for 3D devices like Apple Vision Pro and 3D displays. In summary, this work contributes to the field by presenting an effective method for generating high-quality stereoscopic videos from monocular input, potentially transforming how we experience digital media., Comment: 11 pages, 10 figures
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- 2024
25. DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos
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Hu, Wenbo, Gao, Xiangjun, Li, Xiaoyu, Zhao, Sijie, Cun, Xiaodong, Zhang, Yong, Quan, Long, and Shan, Ying
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Graphics - Abstract
Despite significant advancements in monocular depth estimation for static images, estimating video depth in the open world remains challenging, since open-world videos are extremely diverse in content, motion, camera movement, and length. We present DepthCrafter, an innovative method for generating temporally consistent long depth sequences with intricate details for open-world videos, without requiring any supplementary information such as camera poses or optical flow. DepthCrafter achieves generalization ability to open-world videos by training a video-to-depth model from a pre-trained image-to-video diffusion model, through our meticulously designed three-stage training strategy with the compiled paired video-depth datasets. Our training approach enables the model to generate depth sequences with variable lengths at one time, up to 110 frames, and harvest both precise depth details and rich content diversity from realistic and synthetic datasets. We also propose an inference strategy that processes extremely long videos through segment-wise estimation and seamless stitching. Comprehensive evaluations on multiple datasets reveal that DepthCrafter achieves state-of-the-art performance in open-world video depth estimation under zero-shot settings. Furthermore, DepthCrafter facilitates various downstream applications, including depth-based visual effects and conditional video generation., Comment: Project webpage: https://depthcrafter.github.io
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- 2024
26. ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
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Yu, Wangbo, Xing, Jinbo, Yuan, Li, Hu, Wenbo, Li, Xiaoyu, Huang, Zhipeng, Gao, Xiangjun, Wong, Tien-Tsin, Shan, Ying, and Tian, Yonghong
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Despite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. In this work, we propose \textbf{ViewCrafter}, a novel method for synthesizing high-fidelity novel views of generic scenes from single or sparse images with the prior of video diffusion model. Our method takes advantage of the powerful generation capabilities of video diffusion model and the coarse 3D clues offered by point-based representation to generate high-quality video frames with precise camera pose control. To further enlarge the generation range of novel views, we tailored an iterative view synthesis strategy together with a camera trajectory planning algorithm to progressively extend the 3D clues and the areas covered by the novel views. With ViewCrafter, we can facilitate various applications, such as immersive experiences with real-time rendering by efficiently optimizing a 3D-GS representation using the reconstructed 3D points and the generated novel views, and scene-level text-to-3D generation for more imaginative content creation. Extensive experiments on diverse datasets demonstrate the strong generalization capability and superior performance of our method in synthesizing high-fidelity and consistent novel views., Comment: Project page: https://drexubery.github.io/ViewCrafter/
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- 2024
27. Physiological and ecological characteristics and reproductive responses of Phragmites australis to dry-wet conditions in inland saline marshes of Northeast China
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Cui Mingyang, Du Zhixin, Li Xiaoyu, and Chen Junze
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Phragmites australis ,Hydrological variation ,Root structure ,Reproductive characteristics ,Plant physiology ,Medicine ,Biology (General) ,QH301-705.5 - Abstract
Inland saline marshes in northeastern China have unique soil characteristics and population distribution features. Hydrological change is a critical environmental factor causing wetland degradation and soil salinization in this region. The growth and reproductive responses of typical wetland plants to dry-wet alternations are essential for restoring inland saline marshes. A pot experiment was conducted to study the growth and reproductive responses of Phragmites australis populations to three hydrological treatments simulating drought degradation (drought), permanent inundation restoration (flooding), and seasonal inundation restoration (dry-wet). The species showed different growth and reproductive responses to the three treatments. After 120 d, the drought conditions induced a lower biomass, root length and root surface area of P. australis, but with higher root diameter, soluble sugar, and Na+ ion contents. Flooding and alternating dry-wet treatments induced the opposite responses. Alternating dry-wet treatments can be considered a better solution to effectively conserve water and meet the water needs of P. australis in the current growing season. The biomass under the alternating wet and dry treatment was the same as that under flooding, but the number of rhizome shoots was lower. The alternating dry-wet treatments was able to recover the growth of P. australis in the current season, but the potential for asexual reproduction of the species was insufficient.
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- 2022
- Full Text
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28. MagicMan: Generative Novel View Synthesis of Humans with 3D-Aware Diffusion and Iterative Refinement
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He, Xu, Li, Xiaoyu, Kang, Di, Ye, Jiangnan, Zhang, Chaopeng, Chen, Liyang, Gao, Xiangjun, Zhang, Han, Wu, Zhiyong, and Zhuang, Haolin
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Existing works in single-image human reconstruction suffer from weak generalizability due to insufficient training data or 3D inconsistencies for a lack of comprehensive multi-view knowledge. In this paper, we introduce MagicMan, a human-specific multi-view diffusion model designed to generate high-quality novel view images from a single reference image. As its core, we leverage a pre-trained 2D diffusion model as the generative prior for generalizability, with the parametric SMPL-X model as the 3D body prior to promote 3D awareness. To tackle the critical challenge of maintaining consistency while achieving dense multi-view generation for improved 3D human reconstruction, we first introduce hybrid multi-view attention to facilitate both efficient and thorough information interchange across different views. Additionally, we present a geometry-aware dual branch to perform concurrent generation in both RGB and normal domains, further enhancing consistency via geometry cues. Last but not least, to address ill-shaped issues arising from inaccurate SMPL-X estimation that conflicts with the reference image, we propose a novel iterative refinement strategy, which progressively optimizes SMPL-X accuracy while enhancing the quality and consistency of the generated multi-views. Extensive experimental results demonstrate that our method significantly outperforms existing approaches in both novel view synthesis and subsequent 3D human reconstruction tasks., Comment: Project Page: https://thuhcsi.github.io/MagicMan
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- 2024
29. Quantum Speedups for Approximating the John Ellipsoid
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Li, Xiaoyu, Song, Zhao, and Yu, Junwei
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Computer Science - Data Structures and Algorithms - Abstract
In 1948, Fritz John proposed a theorem stating that every convex body has a unique maximal volume inscribed ellipsoid, known as the John ellipsoid. The John ellipsoid has become fundamental in mathematics, with extensive applications in high-dimensional sampling, linear programming, and machine learning. Designing faster algorithms to compute the John ellipsoid is therefore an important and emerging problem. In [Cohen, Cousins, Lee, Yang COLT 2019], they established an algorithm for approximating the John ellipsoid for a symmetric convex polytope defined by a matrix $A \in \mathbb{R}^{n \times d}$ with a time complexity of $O(nd^2)$. This was later improved to $O(\text{nnz}(A) + d^\omega)$ by [Song, Yang, Yang, Zhou 2022], where $\text{nnz}(A)$ is the number of nonzero entries of $A$ and $\omega$ is the matrix multiplication exponent. Currently $\omega \approx 2.371$ [Alman, Duan, Williams, Xu, Xu, Zhou 2024]. In this work, we present the first quantum algorithm that computes the John ellipsoid utilizing recent advances in quantum algorithms for spectral approximation and leverage score approximation, running in $O(\sqrt{n}d^{1.5} + d^\omega)$ time. In the tall matrix regime, our algorithm achieves quadratic speedup, resulting in a sublinear running time and significantly outperforming the current best classical algorithms.
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- 2024
30. A Tighter Complexity Analysis of SparseGPT
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Li, Xiaoyu, Liang, Yingyu, Shi, Zhenmei, and Song, Zhao
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Computer Science - Data Structures and Algorithms ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Computer Science - Machine Learning - Abstract
In this work, we improved the analysis of the running time of SparseGPT [Frantar, Alistarh ICML 2023] from $O(d^{3})$ to $O(d^{\omega} + d^{2+a+o(1)} + d^{1+\omega(1,1,a)-a})$ for any $a \in [0, 1]$, where $\omega$ is the exponent of matrix multiplication. In particular, for the current $\omega \approx 2.371$ [Alman, Duan, Williams, Xu, Xu, Zhou 2024], our running time boils down to $O(d^{2.53})$. This running time is due to the analysis of the lazy update behavior in iterative maintenance problems such as [Deng, Song, Weinstein 2022; Brand, Song, Zhou ICML 2024].
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- 2024
31. Fast John Ellipsoid Computation with Differential Privacy Optimization
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Gu, Jiuxiang, Li, Xiaoyu, Liang, Yingyu, Shi, Zhenmei, Song, Zhao, and Yu, Junwei
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Computer Science - Data Structures and Algorithms ,Computer Science - Cryptography and Security ,Computer Science - Machine Learning - Abstract
Determining the John ellipsoid - the largest volume ellipsoid contained within a convex polytope - is a fundamental problem with applications in machine learning, optimization, and data analytics. Recent work has developed fast algorithms for approximating the John ellipsoid using sketching and leverage score sampling techniques. However, these algorithms do not provide privacy guarantees for sensitive input data. In this paper, we present the first differentially private algorithm for fast John ellipsoid computation. Our method integrates noise perturbation with sketching and leverage score sampling to achieve both efficiency and privacy. We prove that (1) our algorithm provides $(\epsilon,\delta)$-differential privacy, and the privacy guarantee holds for neighboring datasets that are $\epsilon_0$-close, allowing flexibility in the privacy definition; (2) our algorithm still converges to a $(1+\xi)$-approximation of the optimal John ellipsoid in $O(\xi^{-2}(\log(n/\delta_0) + (L\epsilon_0)^{-2}))$ iterations where $n$ is the number of data point, $L$ is the Lipschitz constant, $\delta_0$ is the failure probability, and $\epsilon_0$ is the closeness of neighboring input datasets. Our theoretical analysis demonstrates the algorithm's convergence and privacy properties, providing a robust approach for balancing utility and privacy in John ellipsoid computation. This is the first differentially private algorithm for fast John ellipsoid computation, opening avenues for future research in privacy-preserving optimization techniques.
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- 2024
32. Head360: Learning a Parametric 3D Full-Head for Free-View Synthesis in 360{\deg}
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He, Yuxiao, Zhuang, Yiyu, Wang, Yanwen, Yao, Yao, Zhu, Siyu, Li, Xiaoyu, Zhang, Qi, Cao, Xun, and Zhu, Hao
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Creating a 360{\deg} parametric model of a human head is a very challenging task. While recent advancements have demonstrated the efficacy of leveraging synthetic data for building such parametric head models, their performance remains inadequate in crucial areas such as expression-driven animation, hairstyle editing, and text-based modifications. In this paper, we build a dataset of artist-designed high-fidelity human heads and propose to create a novel parametric 360{\deg} renderable parametric head model from it. Our scheme decouples the facial motion/shape and facial appearance, which are represented by a classic parametric 3D mesh model and an attached neural texture, respectively. We further propose a training method for decompositing hairstyle and facial appearance, allowing free-swapping of the hairstyle. A novel inversion fitting method is presented based on single image input with high generalization and fidelity. To the best of our knowledge, our model is the first parametric 3D full-head that achieves 360{\deg} free-view synthesis, image-based fitting, appearance editing, and animation within a single model. Experiments show that facial motions and appearances are well disentangled in the parametric space, leading to SOTA performance in rendering and animating quality. The code and SynHead100 dataset are released at https://nju-3dv.github.io/projects/Head360., Comment: ECCV 2024
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- 2024
33. SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq
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Li, Xiaoyu, Zhu, Fangfang, and Min, Wenwen
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Quantitative Biology - Genomics - Abstract
The rapid development of spatial transcriptomics (ST) technologies is revolutionizing our understanding of the spatial organization of biological tissues. Current ST methods, categorized into next-generation sequencing-based (seq-based) and fluorescence in situ hybridization-based (image-based) methods, offer innovative insights into the functional dynamics of biological tissues. However, these methods are limited by their cellular resolution and the quantity of genes they can detect. To address these limitations, we propose SpaDiT, a deep learning method that utilizes a diffusion generative model to integrate scRNA-seq and ST data for the prediction of undetected genes. By employing a Transformer-based diffusion model, SpaDiT not only accurately predicts unknown genes but also effectively generates the spatial structure of ST genes. We have demonstrated the effectiveness of SpaDiT through extensive experiments on both seq-based and image-based ST data. SpaDiT significantly contributes to ST gene prediction methods with its innovative approach. Compared to eight leading baseline methods, SpaDiT achieved state-of-the-art performance across multiple metrics, highlighting its substantial bioinformatics contribution.
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- 2024
34. Observation of the Electromagnetic Dalitz Transition $h_c \rightarrow e^+e^-\eta_c$
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Ahmed, S., Albrecht, M., Aliberti, R., Amoroso, A., An, M. R., An, Q., Bai, X. H., Bai, Y., Bakina, O., Ferroli, R. Baldini, Balossino, I., Ban, Y., Begzsuren, K., Berger, N., Bertani, M., Bettoni, D., Bianchi, F., Bloms, J., Bortone, A., Boyko, I., Briere, R. A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Chang, W. L., Chelkov, G., Chen, D. Y., Chen, G., Chen, H. S., Chen, M. L., Chen, S. J., Chen, X. R., Chen, Y. B., Chen, Z. J, Cheng, W. S., Cibinetto, G., Cossio, F., Cui, X. F., Dai, H. L., Dai, X. C., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, Y., Dong, C., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, S. X., Fan, Y. L., Fang, J., Fang, S. S., Fang, Y., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Fritsch, M., Fu, C. D., Gao, Y., Gao, Y. G., Garzia, I., Ge, P. T., Geng, C., Gersabeck, E. M., Gilman, A, Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Greco, M., Gu, L. M., Gu, M. H., Gu, S., Gu, Y. T., Guan, C. Y, Guo, A. Q., Guo, L. B., Guo, R. P., Guo, Y. P., Guskov, A., Han, T. T., Han, W. Y., Hao, X. Q., Harris, F. A., Hüsken, N, He, K. L., Heinsius, F. H., Heinz, C. H., Held, T., Heng, Y. K., Herold, C., Himmelreich, M., Holtmann, T., Hou, Y. R., Hou, Z. L., Hu, H. M., Hu, J. F., Hu, T., Hu, Y., Huang, G. S., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Z., Hussain, T., Andersson, W. Ikegami, Imoehl, W., Irshad, M., Jaeger, S., Janchiv, S., Ji, Q., Ji, Q. P., Ji, X. B., Ji, X. L., Ji, Y. Y., Jiang, H. B., Jiang, X. S., Jiao, J. B., Jiao, Z., Jin, S., Jin, Y., Johansson, T., Kalantar-Nayestanaki, N., Kang, X. S., Kappert, R., Kavatsyuk, M., Ke, B. C., Keshk, I. K., Khoukaz, A., Kiese, P., Kiuchi, R., Kliemt, R., Koch, L., Kolcu, O. B., Kopf, B., Kuemmel, M., Kuessner, M., Kupsc, A., Kurth, M. G., Kühn, W., Lane, J. J., Lange, J. S., Larin, P., Lavania, A., Lavezzi, L., Lei, Z. H., Leithoff, H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H., Li, H. B., Li, H. J., Li, J. L., Li, J. Q., Li, J. S., Li, Ke, Li, L. K., Li, Lei, Li, P. R., Li, S. Y., Li, W. D., Li, W. G., Li, X. H., Li, X. L., Li, Xiaoyu, Li, Z. Y., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Libby, J., Lin, C. X., Liu, B. J., Liu, C. X., Liu, D., Liu, F. H., Liu, Fang, Liu, Feng, Liu, H. B., Liu, H. M., Liu, Huanhuan, Liu, Huihui, Liu, J. B., Liu, J. L., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, Shuai, Liu, T., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. D., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Luo, C. L., Luo, M. X., Luo, P. W., Luo, T., Luo, X. L., Lusso, S., Lyu, X. R., Ma, F. C., Ma, H. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, R. T., Ma, X. X., Ma, X. Y., Maas, F. E., Maggiora, M., Maldaner, S., Malde, S., Malik, Q. A., Mangoni, A., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Min, T. J., Mitchell, R. E., Mo, X. H., Mo, Y. J., Muchnoi, N. Yu., Muramatsu, H., Nakhoul, S., Nefedov, Y., Nerling, F., Nikolaev, I. B., Ning, Z., Nisar, S., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Patteri, P., Pelizaeus, M., Peng, H. P., Peters, K., Pettersson, J., Ping, J. L., Ping, R. G., Poling, R., Prasad, V., Qi, H., Qi, H. R., Qi, K. H., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qian, Z., Qiao, C. F., Qin, L. Q., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, S. Q., Rashid, K. H., Ravindran, K., Redmer, C. F., Rivetti, A., Rodin, V., Rolo, M., Rong, G., Rosner, Ch., Rump, M., Sang, H. S., Sarantsev, A., Schelhaas, Y., Schnier, C., Schoenning, K., Scodeggio, M., Shan, D. C., Shan, W., Shan, X. Y., Shangguan, J. F., Shao, M., Shen, C. P., Shen, P. X., Shen, X. Y., Shi, H. C., Shi, R. S., Shi, X., Shi, X. D, Song, J. J., Song, W. M., Song, Y. X., Sosio, S., Spataro, S., Su, K. X., Su, P. P., Sui, F. F., Sun, G. X., Sun, H. K., Sun, J. F., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, X, Sun, Y. J., Sun, Y. K., Sun, Y. Z., Sun, Z. T., Tan, Y. H., Tan, Y. X., Tang, C. J., Tang, G. Y., Tang, J., Teng, J. X., Thoren, V., Tian, Y. T., Uman, I., Wang, B., Wang, C. W., Wang, D. Y., Wang, H. J., Wang, H. P., Wang, K., Wang, L. L., Wang, M., Wang, M. Z., Wang, Meng, Wang, W., Wang, W. H., Wang, W. P., Wang, X., Wang, X. F., Wang, X. L., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. Q., Wang, Y. Y., Wang, Z., Wang, Z. Y., Wang, Ziyi, Wang, Zongyuan, Wei, D. H., Weidenkaff, P., Weidner, F., Wen, S. P., White, D. J., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, Z., Xia, L., Xiao, H., Xiao, S. Y., Xiao, Z. J., Xie, X. H., Xie, Y. G., Xie, Y. H., Xing, T. Y., Xu, G. F., Xu, Q. J., Xu, W., Xu, X. P., Xu, Y. C., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, Xu, Yang, H. J., Yang, H. X., Yang, L., Yang, S. L., Yang, Y. X., Yang, Yifan, Yang, Zhi, Ye, M., Ye, M. H., Yin, J. H., You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yuan, C. Z., Yuan, L., Yuan, X. Q., Yuan, Y., Yuan, Z. Y., Yue, C. X., Yuncu, A., Zafar, A. A., Zeng, Y., Zhang, B. X., Zhang, Guangyi, Zhang, H., Zhang, H. H., Zhang, H. Y., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. W., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, Jiawei, Zhang, L. M., Zhang, L. Q., Zhang, Lei, Zhang, S., Zhang, S. F., Zhang, Shulei, Zhang, X. D., Zhang, X. Y., Zhang, Y., Zhang, Y. H., Zhang, Y. T., Zhang, Yan, Zhang, Yao, Zhang, Yi, Zhang, Z. H., Zhang, Z. Y., Zhao, G., Zhao, J., Zhao, J. Y., Zhao, J. Z., Zhao, Lei, Zhao, Ling, Zhao, M. G., Zhao, Q., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, J. P., Zheng, W. J., Zheng, Y., Zheng, Y. H., Zhong, B., Zhong, C., Zhou, L. P., Zhou, Q., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, S. H., Zhu, T. J., Zhu, W. J., Zhu, Y. C., Zhu, Z. A., Zou, B. S., and Zou, J. H.
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High Energy Physics - Experiment - Abstract
Using $(27.12\pm 0.14)\times10^8$ $\psi(3686)$ decays and data samples of $e^+e^-$ collisions with $\sqrt{s}$ from 4.130 to 4.780~GeV collected with the BESIII detector, we report the first observation of the electromagnetic Dalitz transition $h_c\to e^+e^-\eta_c$ with a statistical significance of $5.4\sigma$. We measure the ratio of the branching fractions $\frac{\mathcal{B}(h_c\rightarrow e^+e^-\eta_c)}{\mathcal{B}(h_c\rightarrow \gamma \eta_c)}$ separately for the $h_c$ samples produced via $\psi(3686)\to\pi^0h_c$ and $e^+e^-\to\pi^+\pi^-h_c$. The average ratio is determined to be $(0.59\pm0.10(\text{stat.})\pm0.04(\text{syst.}))\%$, where the uncertainty includes both statistical and systematic components.
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- 2024
35. COT: A Generative Approach for Hate Speech Counter-Narratives via Contrastive Optimal Transport
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Zhang, Linhao, Jin, Li, Xu, Guangluan, Li, Xiaoyu, and Sun, Xian
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Computer Science - Computation and Language ,68U15 ,I.2.7 - Abstract
Counter-narratives, which are direct responses consisting of non-aggressive fact-based arguments, have emerged as a highly effective approach to combat the proliferation of hate speech. Previous methodologies have primarily focused on fine-tuning and post-editing techniques to ensure the fluency of generated contents, while overlooking the critical aspects of individualization and relevance concerning the specific hatred targets, such as LGBT groups, immigrants, etc. This research paper introduces a novel framework based on contrastive optimal transport, which effectively addresses the challenges of maintaining target interaction and promoting diversification in generating counter-narratives. Firstly, an Optimal Transport Kernel (OTK) module is leveraged to incorporate hatred target information in the token representations, in which the comparison pairs are extracted between original and transported features. Secondly, a self-contrastive learning module is employed to address the issue of model degeneration. This module achieves this by generating an anisotropic distribution of token representations. Finally, a target-oriented search method is integrated as an improved decoding strategy to explicitly promote domain relevance and diversification in the inference process. This strategy modifies the model's confidence score by considering both token similarity and target relevance. Quantitative and qualitative experiments have been evaluated on two benchmark datasets, which demonstrate that our proposed model significantly outperforms current methods evaluated by metrics from multiple aspects., Comment: IEEE jounrnals
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- 2024
36. Effects of TGF-β1 on the migration of oral cancer-associated fibroblasts in two and three dimensional co-culture models
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YANG Jin, WU Feifei, GAO Qinghong, LI Xiaoyu, MANABU Kato, CHENG Ran, and ZHOU Hongmei
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carcinoma-associated fibroblasts， ,migration， ,three-dimensional cell culture model， ,transforming growth factor-β1， ,oral carcinoma， ,Medicine - Abstract
Objective To observe the effect of transforming growth factor-β1 (TGF-β1) on the migration of oral carcinoma associated fibroblasts (CAFs) with two-dimensional culture model and three-dimensional model.Methods Under two-dimensional culture conditions, CAFs stimulated by TGF-β1 with the addition of 10 ng/mL medium were used as the experimental group, and untreated CAFs were used as the control group. The migration of CAFs with the stimulation of TGF-β1 was measured by cell scratch assay and transwell assay. CAFs positive for green fluorescent protein (GFP) were cultured by retrovirus transfection. Human tongue squamous cell carcinoma cells SCC25, GFP(+) CAFs and CAFs with three-dimensional cell co-culture models were established. The three-dimensional model cultured under the stimulation of TGF-β1 with 10 ng/mL medium was used as the experimental group, and the three-dimensional model without TGF-β1 was used as the control group. The migration of CAFs with the stimulation of TGF-β1 was also measured by the three-dimensional models.Results It was verified that 10 ng/mL TGF-β1 promoted the migration of CAFs in the two-dimensional culture model. The three-dimensional co-culture models of SCC25, GFP(+) CAFs and CAFs were successfully established. The migration of SCC25 and CAFs was detected in the three-dimensional model. However, 10 ng/mL TGF-β1 had little effect on their migration.Conclusion The effect of TGF-β1 in vitro on the migration of oral CAFs was associated with different culture models in two and three dimensions.
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- 2020
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37. Beneficial effects of baicalein on a model of allergic rhinitis
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Liu Tao, Xu Jing, Wu Yungang, Li Xiaoxia, Ding Detao, Ma Dengdian, Yao Mengwei, Wei Wenzhong, Zhang Wei, Wang Shaohua, Yao Jing, and Li Xiaoyu
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baicalein ,ovalbumin ,allergic rhinitis ,inflammatory factors ,p-stat3 ,Pharmaceutical industry ,HD9665-9675 - Abstract
Allergic rhinitis (AR) is a common disease that causes severe inflammation and even disabilities. Previous studies have reported baicalein to have an anti-inflammatory effect. However, the pharmacological action of baicalein on anaphylaxis has not been clarified yet. This study assessed the in vivo protective effect of baicalein post-treatment in an ameliorating ovalbumin (OVA)-sensitized AR rat model. Baicalein attenuated histological alterations, aberrant tissue repair and inflammation after OVA-induced AR. Baicalein reduced the frequency of nasal/ear rubs and sneezes in rats, and inhibited generation of several inflammatory cytokines (TNF-α, IL-1β, and IL-6) in both blood and nasal lavage of rats. Infiltrations of eosinophils, lymphocyte, and neutrophils were decreased in baicalein-administered rats. Furthermore, baicalein inhibited the expression of STAT3 phosphorylation in the nasal mucosa. In summary, baicalein attenuated OVA-induced AR and inflammation, which suggests it as a promising therapeutic agent for the alleviation of AR-associated inflammation and pathology.
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- 2020
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38. Detection of MSX1 gene mutations in patients with congenital tooth loss in Van der Woude syndrome
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DU Xinya, LI Xiaoyu, XIE Chun, WU Bin, SONG Guangbao, and DU Ye
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van der woude syndrome， ,congenital missing teeth， ,lower lip fistula， ,cleft lip， ,cleft palate， ,msx1， ,pathogenic gene， ,gene polymorphism， ,genetic mutations， ,Medicine - Abstract
Objective To explore the relationship between MSX1 gene detection and tooth loss in a Van der Woude syndrome (VWS) family. Methods DNA was extracted from the venous blood of 2 patients with dental hy⁃ podontia in the 9th family of Van der Woude syndrome (VWS) families and 62 controls with complete dentition. Primers were designed for the MSXl gene. The coding regions of exons 1 and 2 of the MSX1 gene were amplified by PCR. The purified products of exons 1 and 2 of the MSX1 gene were sequenced and analyzed by sequence alignment. Results The ivs2+68 C>T polymorphism in the MSX1 gene was found in the VWS9 members with tooth loss, and the VWS pa⁃ tients with IRF6 gene mutations had increased tooth loss. Conclusion Congenital tooth loss in the patients with con⁃ genital missing teeth in VWS family 9 may be related to the ivs2 + 68 C> T polymorphism of the MSX1 gene.
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- 2020
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39. Research on quantitative performance model of GPU graphics processing based on unified rendering architecture
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Ma Chengcheng, Tian Ze, Li Xiaoyu, and Sun Linna
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GPU ,graphics processing ,unified rendering architecture ,performance model ,Electronics ,TK7800-8360 - Abstract
The unified rendering architecture GPU provides a rich graphics computing and storage resources and puts forward higher requirements for software optimization. To design and optimize performance effectively for the unified rendering architecture GPU，on the basis of research on the unified rendering architecture GPU architecture and working principle, this paper analyses the various factors influencing the graphics: graphic instruction generation, host interface data transfer, graphic instruction parsing, graphics processing pipeline and unified rendering array processing capability. In the process of development of GPU, using this method to design each part of the performance indicators, through the simulation shows that the model assessment graphics performance and the measured phase, the error is less than 7.5 percent.
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- 2019
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40. Pharmacokinetic study of ranolazine in Chinese healthy volunteers
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YU Mingjie, XIANG Rongfeng, XIONG Lirong, LI Xiaoyu, DAI Qing, and CHEN Yongchuan
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ranolazine ,sustained release tablets ,hplc-ms-ms ,pharmacokinetics ,Medicine (General) ,R5-920 - Abstract
Objective To evaluate pharmacokinetics after administration of ranolazine sustained release tablets at a single and multiple dose to healthy Chinese volunteers. Methods A self-controlled trial design was used. In the single-time group, 10 healthy volunteers, half male and half female, were included, and 500, 1 000 and 1 500 mg ranolazine sustained-release tablets were taken sequentially in 3 cycles. For the multiple-dose group, 10 individuals of both sexes were enrolled, and 500 and 1 000 mg ranolazine sustained-release tablets were sequentially administered in 2 cycles. The concentration of ranolazine in plasma samples was determined by LC-MS-MS, and the main pharmacokinetic parameters were calculated. Results The linear concentration of ranolazine in plasma ranged from 40 to 4 000 μg/L, and the LLOQ was 40 μg/L. The intra-assay and inter-assay precision were less than 15%. After single dose, the main parameters in doses 500, 1 000, 1 500 mg were Cmax: 774±157, 1 818±554 and 2 762±1 099 μg/L, respectively; tmax: 4.9±1.8, 4.9±2.6 and 4.9±1.5 h, respectively; t1/2z: 8.2±6.8, 10.3±8.7 and 9.7±12.0 h, respectively; AUC0-t: 8 470±2 765, 20 936±6 957 and 32 554±17 780 μg·h/L, respectively. After multiple dose, the main parameters in doses 500, 1 000 mg were Cmaxss: 1 826±904 and 3 635±620 μg/L; Cminss: 1 022±3 and 2 282±163 μg/L; Cav: 1 253±634 and 2 615±536 μg/L; AUC0-t: 22 452±12 359 and 53 232±15 909 μg·h/L; MRT0-t: 8.7±2.3 and 10.6±2.6; RAUC: 3.11±2.60 and 2.86±1.54. Conclusion In a single administration trial, Cmax and AUC are increased proportionally to the dose, and there is a good linear relationship between the three doses. Multiple dose tests have found that ranolazine has a certain accumulation in the body.
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- 2019
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41. Effect of construction orientation on the microstructure and properties of SLM Ti alloy clasps
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XIE Wenq⁃ iang, WANG Jieqi, ZHUANG Peilin, LI Xiaoyu, ZHENG Meihua, ZHANG Wen, and WEI Peiling
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Selective laser melting， ,Ti⁃6Al⁃4V clasps， ,Construction orientation， ,Anisotropy， ,Metallography， ,Medicine - Abstract
Objective To investigate the physical properties of Ti⁃6Al⁃4V clasps generated by selective laser melt⁃ ing (SLM) with different construction directions and to compare these clasps with cast clasps, which could provide a ba⁃ sis for fabricating SLM clasps with high precision and excellent mechanical properties. Methods Ti⁃6Al⁃4V clasps were fabricated by SLM at 0 degrees (SLM0 group), 45 degrees (SLM45 group) and 90 degrees (SLM90 group) (n = 12). Twelve clasps were cast by the casting method as the control group. Meanwhile, four metal abutments were cast random⁃ ly as the abutments of the four groups. X⁃ray was used to detect cracks in the clasps of each group. The roughness of the clasps was measured by confocal microscopy, the fitness tests between clasps and abutment were processed by stereomi⁃ croscopy, and the microstructure of clasps in each group was observed under a metallographic microscope to evaluate the physical properties. Results There were 0⁃8 visible cracks in the casting group but no obvious defects in the SLM groups. The maximum surface roughness was observed in the cast group (18.102 ± 3.762) μm, while the minimum roughness was observed in the SLM90 group (5.942 ± 1.486) μm (P < 0.05). There was no statistically significant differ⁃ ence in surface roughness between the SLM0 group [(8.711 ± 2.378) μm] and the SLM45 group [(8.513 ± 1.161) μm]. Fitness was worst in the casting group [(68.445 ± 14.876) μm] and best in the SLM90 group [(33.417 ± 5.880) μm] (P < 0.05). There was no statistically significant difference in fitness between the SLM0 group [(52.917 ± 12.102) μm] and the SLM45 group [(50.889 ± 7.011) μm]. In addition, the growth direction of the β grains was roughly parallel to the build direction, and acicular α grains were present between β grains. SLM was composed of fine grains, while the cast group had large grains. Conclusions Specimens generated by SLM had finer grains than cast specimens. In addi⁃ tion, SLM90 clasps had the highest fitness and the lowest surface roughness.
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- 2019
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42. A sparsity adaptive multi-user detection algorithm for SIMO-NOMA systerms
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Zhao Xiaojuan, Yang Shouyi, Zhang Aihua, and Li Xiaoyu
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compressive sensing ,multi-user detection ,SIMO-NOMA ,sparsity adaptive ,hard fusion ,Electronics ,TK7800-8360 - Abstract
Non-orthogonal multiple access(NOMA) can improve spectrum efficiency and support massive connectivity by the use of resources in non-orthogonal way, which is expected to become one of the key technologies of 5G. Considering the situation that the base station(BS) is equipped with multiple antennas，this paper proposes a compressive sensing(CS) based sparsity adaptive matching pursuit hard fusion algorithm(SAMP-HFA) to realize multi-user detection(MUD) for uplink grant-free single-input multiple-output non-orthogonal multiple access(SIMO-NOMA) systems where the number of active user is unknown. The proposed algorithm consists of three steps. Firstly, it detects the user activity information by conventional SAMP algorithm at each antenna, and then amalgamates the detected user activity information to obtain a common active user set. Finally, the users′ data can be detected by the obtained active user set. The results show that the proposed SAMP-HFA demonstrates significant performance gain in terms of bit error rate(BER) with the number of antennas increases.
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- 2019
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43. CV-VAE: A Compatible Video VAE for Latent Generative Video Models
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Zhao, Sijie, Zhang, Yong, Cun, Xiaodong, Yang, Shaoshu, Niu, Muyao, Li, Xiaoyu, Hu, Wenbo, and Shan, Ying
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Spatio-temporal compression of videos, utilizing networks such as Variational Autoencoders (VAE), plays a crucial role in OpenAI's SORA and numerous other video generative models. For instance, many LLM-like video models learn the distribution of discrete tokens derived from 3D VAEs within the VQVAE framework, while most diffusion-based video models capture the distribution of continuous latent extracted by 2D VAEs without quantization. The temporal compression is simply realized by uniform frame sampling which results in unsmooth motion between consecutive frames. Currently, there lacks of a commonly used continuous video (3D) VAE for latent diffusion-based video models in the research community. Moreover, since current diffusion-based approaches are often implemented using pre-trained text-to-image (T2I) models, directly training a video VAE without considering the compatibility with existing T2I models will result in a latent space gap between them, which will take huge computational resources for training to bridge the gap even with the T2I models as initialization. To address this issue, we propose a method for training a video VAE of latent video models, namely CV-VAE, whose latent space is compatible with that of a given image VAE, e.g., image VAE of Stable Diffusion (SD). The compatibility is achieved by the proposed novel latent space regularization, which involves formulating a regularization loss using the image VAE. Benefiting from the latent space compatibility, video models can be trained seamlessly from pre-trained T2I or video models in a truly spatio-temporally compressed latent space, rather than simply sampling video frames at equal intervals. With our CV-VAE, existing video models can generate four times more frames with minimal finetuning. Extensive experiments are conducted to demonstrate the effectiveness of the proposed video VAE., Comment: Project Page: https://ailab-cvc.github.io/cvvae/index.html
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- 2024
44. Mani-GS: Gaussian Splatting Manipulation with Triangular Mesh
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Gao, Xiangjun, Li, Xiaoyu, Zhuang, Yiyu, Zhang, Qi, Hu, Wenbo, Zhang, Chaopeng, Yao, Yao, Shan, Ying, and Quan, Long
- Subjects
Computer Science - Graphics ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Neural 3D representations such as Neural Radiance Fields (NeRF), excel at producing photo-realistic rendering results but lack the flexibility for manipulation and editing which is crucial for content creation. Previous works have attempted to address this issue by deforming a NeRF in canonical space or manipulating the radiance field based on an explicit mesh. However, manipulating NeRF is not highly controllable and requires a long training and inference time. With the emergence of 3D Gaussian Splatting (3DGS), extremely high-fidelity novel view synthesis can be achieved using an explicit point-based 3D representation with much faster training and rendering speed. However, there is still a lack of effective means to manipulate 3DGS freely while maintaining rendering quality. In this work, we aim to tackle the challenge of achieving manipulable photo-realistic rendering. We propose to utilize a triangular mesh to manipulate 3DGS directly with self-adaptation. This approach reduces the need to design various algorithms for different types of Gaussian manipulation. By utilizing a triangle shape-aware Gaussian binding and adapting method, we can achieve 3DGS manipulation and preserve high-fidelity rendering after manipulation. Our approach is capable of handling large deformations, local manipulations, and soft body simulations while keeping high-quality rendering. Furthermore, we demonstrate that our method is also effective with inaccurate meshes extracted from 3DGS. Experiments conducted demonstrate the effectiveness of our method and its superiority over baseline approaches., Comment: Project page here: https://gaoxiangjun.github.io/mani_gs/
- Published
- 2024
45. From Text to Blueprint: Leveraging Text-to-Image Tools for Floor Plan Creation
- Author
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Li, Xiaoyu, Benjamin, Jonathan, and Zhang, Xin
- Subjects
Computer Science - Human-Computer Interaction - Abstract
Artificial intelligence is revolutionizing architecture through text-to-image synthesis, converting textual descriptions into detailed visual representations. We explore AI-assisted floor plan design, focusing on technical background, practical methods, and future directions. Using tools like, Stable Diffusion, AI leverages models such as Generative Adversarial Networks and Variational Autoencoders to generate complex and functional floorplans designs. We evaluates these AI models' effectiveness in generating residential floor plans from text prompts. Through experiments with reference images, text prompts, and sketches, we assess the strengths and limitations of current text-to-image technology in architectural visualization. Architects can use these AI tools to streamline design processes, create multiple design options, and enhance creativity and collaboration. We highlight AI's potential to drive smarter, more efficient floorplan design, contributing to ongoing discussions on AI integration in the design profession and its future impact., Comment: 13 pages, 7 figures
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- 2024
46. Precision measurement of the branching fraction of \boldmath $J/\psi\rightarrow K^+K^-$ via $\psi(2S)\rightarrow \pi^+\pi^-J/\psi$
- Author
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Ai, X. C., Aliberti, R., Amoroso, A., An, M. R., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Chang, W. L., Che, G. R., Chelkov, G., Chen, C., Chen, Chao, Chen, G., Chen, H. S., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Choi, S. K., Chu, X., Cibinetto, G., Coen, S. C., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fischer, K, Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A, Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y, Guan, Z. L., 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., Han, W. Y., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H. 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, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Hussain, T., Hüsken, N, der Wiesche, N. in, Irshad, M., Jackson, J., Jaeger, S., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., K., X., 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., Kupsc, A., Kühn, W., Lane, J. J., Larin, P., Lavezzi, L., Lei, T. T., Lei, Z. H., Leithoff, H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, J. W., Li, Ke, Li, L. J, Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. X., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X. H., Li, X. L., Li, Xiaoyu, Li, Y. G., Li, Z. J., Li, Z. X., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Liao, Y. P., Libby, J., Limphirat, A., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F. H., Liu, Fang, Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. M., Liu, Huanhuan, Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mangoni, A., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Patteri, P., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qin, J. J., Qin, L. Q., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, S. Q., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., 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. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, R. S., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. T., Tan, Y. X., Tang, C. J., Tang, G. Y., Tang, J., Tang, Y. A., Tao, L. Y, Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, C. W., Wang, D. Y., Wang, F., Wang, H. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, Meng, 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, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D., Wei, D. H., Weidner, F., Wen, S. P., Wenzel, C. W., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. P., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, Tao, Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Yifan, Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yuan, C. Z., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, L. Q., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, Shuihan, Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Yan, Zhang, Yao, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, Lei, Zhao, Ling, Zhao, M. G., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, L. P., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. J., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
- Subjects
High Energy Physics - Experiment - Abstract
Using a sample of $448.1 \times 10^6$ $\psi(2S)$ events collected with the BESIII detector, we perform a study of the decay $J/\psi\rightarrow K^+K^-$ via $\psi(2S)\rightarrow \pi^+\pi^-J/\psi$. The branching fraction of $J/\psi\rightarrow K^+K^-$ is determined to be $\mathcal{B}_{K^+K^-}=(3.072\pm 0.023({\rm stat.})\pm 0.050({\rm syst.}))\times 10^{-4}$, which is consistent with previous measurements but with significantly improved precision., Comment: to be submitted to PRD
- Published
- 2024
47. Search for the leptonic decays $D^{*+}\to e^+\nu_e$ and $D^{*+}\to \mu^+\nu_\mu$
- Author
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Albrecht, M., Aliberti, R., Amoroso, A., An, M. R., An, Q., Bai, Y., Bakina, O., Ferroli, R. Baldini, Balossino, I., Ban, Y., Batozskaya, V., Becker, D., Begzsuren, K., Berger, N., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bloms, J., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Chang, W. L., Che, G. R., Chelkov, G., Chen, C., Chen, Chao, Chen, G., Chen, H. S., Chen, M. L., Chen, S. J., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Z. J., Cheng, W. S., 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, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, S. X., Duan, Z. H., Egorov, P., Fan, Y. L., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Fischer, K, Fritsch, M., Fritzsch, C., Fu, C. D., Gao, H., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A, Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Greco, M., Gu, L. M., Gu, M. H., Gu, Y. T., Guan, C. Y, Guo, A. Q., Guo, L. B., Guo, R. P., Guo, Y. P., Guskov, A., Han, W. Y., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Hou, G. Y., Hou, Y. R., Hou, Z. L., Hu, H. M., Hu, J. F., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Z., Huang, Z. C., Hussain, T., Hüsken, N, Imoehl, W., Irshad, M., Jackson, J., Jaeger, S., Janchiv, S., Jang, E., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, Z. K., Jiang, P. C., Jiang, S. S., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kappert, R., Kavatsyuk, M., Ke, B. C., Keshk, I. K., Khoukaz, A., Kiuchi, R., Kliemt, R., Koch, L., Kolcu, O. B., Kopf, B., Kuemmel, M., Kuessner, M., Kupsc, A., Kühn, W., Lane, J. J., Lange, J. S., Larin, P., Lavania, A., Lavezzi, L., Lei, T. T., Lei, Z. H., Leithoff, H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. Q., Li, J. S., Li, J. W., Li, Ke, Li, L. J, Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, S. X., Li, S. Y., Li, T., Li, W. D., Li, W. G., Li, X. H., Li, X. L., Li, Xiaoyu, Li, Y. G., Li, Z. X., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Libby, J., Limphirat, A., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, C., Liu, C. X., Liu, D., Liu, F. H., Liu, Fang, Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. M., Liu, Huanhuan, Liu, Huihui, Liu, J. B., Liu, J. L., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, R. T., Ma, X. Y., Ma, Y., Maas, F. E., Maggiora, M., Maldaner, S., Malde, S., Malik, Q. A., Mangoni, A., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Muchnoi, N. Yu., Nefedov, Y., Nerling, F., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Pogodin, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qian, Z., Qiao, C. F., Qin, J. J., Qin, L. Q., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, S. Q., Rashid, K. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rodin, V., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Sarantsev, A., Schelhaas, Y., Schnier, C., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., 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. C., Shi, J. Y., Shi, q. q., Shi, R. S., Shi, X., Song, J. J., Song, W. M., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, P. P., Su, Y. J., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y. J., Sun, Y. Z., Sun, Z. T., Tan, Y. X., Tang, C. J., Tang, G. Y., Tang, J., Tao, L. Y, Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Uman, I., Wang, B., Wang, B. L., Wang, C. W., Wang, D. Y., Wang, F., Wang, H. J., Wang, H. P., Wang, K., Wang, L. L., Wang, M., Wang, Meng, Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. H., Wang, W. P., Wang, X., Wang, X. F., Wang, X. L., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. Q., Wang, Yaqian, Wang, Z., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., White, D. J., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. J, Wu, Z., Xia, L., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, Q. J., Xu, X. P., Xu, Y. C., Xu, Z. P., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yang, H. J., Yang, H. L., Yang, H. X., Yang, Tao, Yang, Y. F., Yang, Y. X., Yang, Yifan, Ye, M., Ye, M. H., Yin, J. H., You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, T., Yu, X. D., Yuan, C. Z., Yuan, L., Yuan, S. C., Yuan, X. Q., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, X., Zeng, Y., 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. H., Zhang, H. Q., Zhang, H. Y., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, Jiawei, Zhang, L. M., Zhang, L. Q., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, Shuihan, Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Yan, Zhang, Yao, Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhao, G., Zhao, J., Zhao, J. Y., Zhao, J. Z., Zhao, Lei, Zhao, Ling, Zhao, M. G., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, J. P., Zheng, Y. H., Zhong, B., Zhong, C., Zhong, X., Zhou, H., Zhou, L. P., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. J., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
- Subjects
High Energy Physics - Experiment - Abstract
We present the first search for the leptonic decays $D^{*+}\to e^+\nu_e$ and $D^{*+}\to \mu^+\nu_\mu$ by analyzing a data sample of electron-positron collisions recorded with the BESIII detector at center-of-mass energies between 4.178 and 4.226 GeV, corresponding to an integrated luminosity of 6.32~fb$^{-1}$. No significant signal is observed. The upper limits on the branching fractions for $D^{*+}\to e^+\nu_e$ and $D^{*+}\to \mu^+\nu_\mu$ are set to be $1.1 \times 10^{-5}$ and $4.3 \times 10^{-6}$ at 90\% confidence level, respectively., Comment: 14 pages, 7 figures
- Published
- 2024
48. Search for the radiative transition $\chi_{c1}(3872)\to\gamma \psi_2(3823)$
- Author
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, M. R., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guan, Z. L., 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., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Hussain, T., Hölzken, F., Hüsken, N, der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Larin, P., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, Ke, Li, L. J, Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Z., Li, Xiaoyu, Li, Y. G., Li, Z. J., Li, Z. X., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F. H., Liu, Fang, Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. M., Liu, Huanhuan, Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Patteri, P., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J, Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, Meng, Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. P., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, Tao, Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Yifan, Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Y. J., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y, Zhang, Shuihan, Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Yao, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, Lei, Zhao, Ling, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
- Subjects
High Energy Physics - Experiment - Abstract
Using 9.0 $\rm fb^{-1}$ of $e^+e^-$ collision data collected at center-of-mass energies from 4.178 to 4.278 GeV with the BESIII detector at the BEPCII collider, we perform the first search for the radiative transition $\chi_{c1}(3872)\to\gamma \psi_2(3823)$. No $\chi_{c1}(3872)\to\gamma \psi_2(3823)$ signal is observed. The upper limit on the ratio of branching fractions $\mathcal{B}(\chi_{c1}(3872)\to\gamma \psi_2(3823), \psi_2(3823)\to\gamma\chi_{c1})/\mathcal{B}(\chi_{c1}(3872)\to\pi^+\pi^- J/\psi)$ is set as 0.075 at the 90\% confidence level. Our result contradicts theoretical predictions under the assumption that the $\chi_{c1}(3872)$ is the pure charmonium state $\chi_{c1}(2P)$., Comment: 8 pages, 2 figures
- Published
- 2024
- Full Text
- View/download PDF
49. Measurement of $e^{+}e^{-}\to \omega\eta^{\prime}$ cross sections at $\sqrt{s}=$ 2.000 to 3.080 GeV
- Author
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Ai, X. C., Aliberti, R., Amoroso, A., An, M. R., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Chang, T. T., Chang, W. L., Che, G. R., Chelkov, G., Chen, C., Chen, Chao, Chen, G., Chen, H. S., Chen, M. L., Chen, S. J., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Cheng, W. S., Choi, S. K., Chu, X., Cibinetto, G., Coen, S. C., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Z. H., Egorov, P., Fan, Y. H. Y., Fan, Y. L., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Fischer, K, Fritsch, M., Fritzsch, C., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A, Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Guan, C. Y, Guan, Z. L., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Han, T. T., Han, W. Y., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H. 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, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Hussain, T., Hüsken, N, Imoehl, W., Jackson, J., Jaeger, S., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, H. J., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Johansson, T., K., X., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kappert, R., Kavatsyuk, M., Ke, B. C., Khoukaz, A., Kiuchi, R., Kliemt, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kupsc, A., Kühn, W., Lane, J. J., Larin, P., Lavania, A., Lavezzi, L., Lei, T. T., Lei, Z. H., Leithoff, H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, J. W., Li, K. L., Li, Ke, Li, L. J, Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. X., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X. H., Li, X. L., Li, Xiaoyu, Li, Y. G., Li, Z. J., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Liao, Y. P., Libby, J., Limphirat, A., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F. H., Liu, Fang, Liu, Feng, Liu, G. M., Liu, H., Liu, H. M., Liu, Huanhuan, Liu, Huihui, Liu, J. B., Liu, J. L., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H. L., Ma, J. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, R. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Malik, Q. A., Mangoni, A., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Patteri, P., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Pogodin, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qin, J. J., Qin, L. Q., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, S. Q., Redmer, C. F., Ren, K. J., Rivetti, A., Rodin, V., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., 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. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, R. S., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. T., Tan, Y. X., Tang, C. J., Tang, G. Y., Tang, J., Tang, Y. A., Tao, L. Y, Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, C. W., Wang, D. Y., Wang, F., Wang, H. J., Wang, H. P., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, Meng, Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D., Wei, D. H., Weidner, F., Wen, S. P., Wenzel, C. W., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. P., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, Tao, Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Yifan, Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yuan, C. Z., Yuan, L., Yuan, S. C., Yuan, X. Q., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. H., Zhang, H. Q., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, Jiawei, Zhang, L. M., Zhang, L. Q., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, Shuihan, Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Xuyan, Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Yan, Zhang, Yao, Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhao, G., Zhao, J., Zhao, J. Y., Zhao, J. Z., Zhao, Lei, Zhao, Ling, Zhao, M. G., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, L. P., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. J., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
- Subjects
High Energy Physics - Experiment - Abstract
The Born cross sections for the process $e^{+}e^{-}\to \omega\eta^{\prime}$ are measured at 22 center-of-mass energies from 2.000 to 3.080 GeV using data collected with the BESIII detector at the BEPCII collider. A resonant structure is observed with a statistical significance of 9.6$\sigma$. A Breit-Wigner fit determines its mass to be $M_R=(2153\pm30\pm31)~{\rm{MeV}}/c^{2}$ and its width to be $\Gamma_{R}=(167\pm77\pm7)~\rm{MeV}$, where the first uncertainties are statistical and the second are systematic.
- Published
- 2024
50. Measurement of the Born cross section for $e^{+}e^{-}\to \eta h_c $ at center-of-mass energies between 4.1 and 4.6\,GeV
- Author
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guan, Z. L., 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., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Hussain, T., Hölzken, F., Hüsken, N, der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Larin, P., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, Ke, Li, L. J, Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Z., Li, Xiaoyu, Li, Y. G., Li, Z. J., Li, Z. X., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F. H., Liu, Fang, Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. M., Liu, Huanhuan, Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Patteri, P., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J, Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, Meng, Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. P., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, Tao, Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Yifan, Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Y. J., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y, Zhang, Shuihan, Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Yao, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, Lei, Zhao, Ling, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
- Subjects
High Energy Physics - Experiment - Abstract
We measure the Born cross section for the reaction $e^{+}e^{-} \rightarrow \eta h_c$ from $\sqrt{s} = 4.129$ to $4.600$~GeV using data sets collected by the BESIII detector running at the BEPCII collider. A resonant structure in the cross section line shape near 4.200~GeV is observed with a statistical significance of 7$\sigma$. The parameters of this resonance are measured to be \MeasMass\ and \MeasWidth, where the first uncertainties are statistical and the second systematic.
- Published
- 2024
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