1,704,030 results on '"Fan A"'
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2. Research on Dynamic Characteristics of Monopile Offshore Wind Turbine Tower Under Different Wind Speed Conditions
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FAN Ang, LI Luping, LIU Rui, OUYANG Minnan, and CHEN Shangnian
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offshore wind turbine ,tower ,wind speed ,blade rotation speed ,soil-structure interaction ,dynamic characteristics ,Applications of electric power ,TK4001-4102 ,Production of electric energy or power. Powerplants. Central stations ,TK1001-1841 ,Science - Abstract
Based on the ANSYS finite element software, the NREL 5 MW monopile offshore wind turbine tower system was modeled in 3D. The influence of different wind speeds on the dynamic characteristics of the tower structure was simulated and analyzed under the full consideration of the soil structure coupling effect and the impeller rotation. The six average wind speeds and the corresponding impeller speeds in the interval between the cut-in wind speed of 3 m/s and the cut-out wind speed of 25 m/s were selected as simulation conditions. The analysis results show that, with the increase of wind speed and impeller speed, the modal frequency of the tower structure gradually increases, and the first two modal frequencies change most obviously. Moreover, the peak value of the tower displacement increases as the wind speed and impeller speed increases, but the amplitude decreases. The displacement shows a non-linear growth trend. The equivalent stress value of the tower increases as the wind speed and impeller speed increases, but the change trend is different from the displacement, which is roughly linear. The fatigue life of the tower decreases sharply, but they are far more than the actual working hours, the fatigue damage and safety factor hazard values are mainly distributed at the bottom of the tower and at the single pile, consistent with the maximum equivalent stress distribution position.
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- 2024
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3. Luminescence spectrum characteristics and dating studies of archaeologically heated quartz
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WANG Chunxin, FAN Anchuan, LI Bo, YAN Zihan, and ZHANG Xiaolei
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archaeology ,pottery ,thermoluminescence ,spectrum ,chronology ,Nuclear engineering. Atomic power ,TK9001-9401 - Abstract
BackgroundLuminescence dating technology has made significant advancements in determining the chronology of archaeological materials subjected to low firing temperatures. However, the luminescence dating of archaeological materials subjected to high firing temperatures remains challenging.PurposeThis study aims to explore the luminescence emission spectrum characteristics and luminescence properties of high-firing temperature quartz to verify the feasibility of thermoluminescence (TL) signals from different bands in luminescence dating.MethodsFirstly, the high-firing temperature (about 950 °C) quartz extracted from pottery unearthed at the Lingjiatan archaeological site was taken as a case study, spectral measurement platform was established using a Risø DA-20 luminescence dating instrument coupled with an Andor spectrometer and a charge-coupled device camera to analyze the luminescence spectral properties of archaeological quartz with high firing temperatures. Then, five filter combinations and two photomultiplier tubes (PMTs) were used to compare the TL and isothermal thermoluminescence (ITL) sensitivities of blue and red emissions. Kinetic parameters for Blue TL and Red TL were determined by deconvolving the glow curves with the general-order equation. Finally, exposure experiments were conducted on the Blue and Red TL using a solar simulator. The single aliquot regenerative dose (SAR) protocol was implemented to assess the applicability of the Blue TL-SAR, Blue ITL-SAR, Red TL-SAR, Red ITL-SAR, and optically stimulated luminescence (OSL)-SAR methods for dating archaeological quartz exposed to high temperatures during production or use.ConclusionsThe spectral analysis reveals that the archaeological quartz subjected to high firing temperature exhibits significant Red TL emissions at approximately 620 nm, which is correlated with the TL peak at 375 °C. This Red TL at 375 °C exhibits a marked insensitivity to light. The multi-wavelength TL, multiwavelength ITL, and conventional OSL dating results are consistent with the known radiocarbon age within the error range. This study demonstrates the potential feasibility of using luminescence signals of different wavelengths for chronological studies of archaeological materials subjected to high firing temperatures.
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- 2024
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4. Pyrroline-5-carboxylate reductase 1 reprograms proline metabolism to drive breast cancer stemness under psychological stress
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Bai Cui, Bin He, Yanping Huang, Cenxin Wang, Huandong Luo, Jinxin Lu, Keyu Su, Xiaoyu Zhang, Yuanyuan Luo, Zhuoran Zhao, Yuqing Yang, Yunkun Zhang, Fan An, Hong Wang, Eric W.-F. Lam, Keith W. Kelley, Ling Wang, Quentin Liu, and Fei Peng
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Cytology ,QH573-671 - Abstract
Abstract Cancer stem-like cells (CSCs) contribute to cancer metastasis, drug resistance and tumor relapse, yet how amino acid metabolism promotes CSC maintenance remains exclusive. Here, we identify that proline synthetase PYCR1 is critical for breast cancer stemness and tumor growth. Mechanistically, PYCR1-synthesized proline activates cGMP-PKG signaling to enhance cancer stem-like traits. Importantly, cGMP-PKG signaling mediates psychological stress-induced cancer stem-like phenotypes and tumorigenesis. Ablation of PYCR1 markedly reverses psychological stress-induced proline synthesis, cGMP-PKG signaling activation and cancer progression. Clinically, PYCR1 and cGMP-PKG signaling components are highly expressed in breast tumor specimens, conferring poor survival in breast cancer patients. Targeting proline metabolism or cGMP-PKG signaling pathway provides a potential therapeutic strategy for breast patients undergoing psychological stress. Collectively, our findings unveil that PYCR1-enhanced proline synthesis displays a critical role in maintaining breast cancer stemness.
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- 2023
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5. Correlation between systemic inflammatory response index and thyroid function: 2009-2012 NHANES results
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Yuze Zhai, Benjun Wang, Weiwei Han, Bianfang Yu, Jichen Ci, and Fan An
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thyroid hormone ,systemic inflammatory response index ,thyroid function ,positive correlation ,national health and nutrition examination survey ,Diseases of the endocrine glands. Clinical endocrinology ,RC648-665 - Abstract
AimsThis study investigates the relationship between the Systemic Inflammatory Response Index (SIRI) and thyroid function.MethodsUtilizing data from the National Health and Nutrition Examination Survey (NHANES) 2009-2012, we excluded participants lacking SIRI or thyroid function data, those under 20 years, and pregnant individuals. SIRI was determined using blood samples. We conducted weighted multivariate regression and subgroup analyses to discern the independent relationship between SIRI and thyroid function.ResultsThe study included 1,641 subjects, with an average age of 47.26±16.77 years, including 48.65% males and 51.35% females. The population was divided into three SIRI-based groups (Q1-Q3). Q3, compared to Q1, exhibited higher age-at-onset, greater male prevalence, and increased levels of FT3, FT4, TT4, leukocytes, and triglycerides. This group also showed a higher incidence of diabetes, hypertension, and smoking. Notably, Q1 had lower LDL and HDL levels. SIRI maintained a positive association with FT4 (β = 0.01, 95% CI = 0.00-0.03, P for trend = 0.0071), TT4 (β = 0.20, 95% CI = 0.10, 0.31, P for trend=0.0001), and TPOAb (β = 8.0, 95% CI = 1.77-14.30, P for trend = 0.0120), indicating that each quartile increase in SIRI corresponded to a 0.01 ng/dL increase in FT4, a 0.2 g/dL increase in TT4, and an 8.03 IU/mL rise in TPOAb. The subgroup analysis suggested the SIRI-thyroid function correlation was influenced by hypertension.ConclusionInflammation may impact the development and progression of thyroid function disorders. Proactive anti-inflammatory treatment might mitigate thyroid abnormalities.
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- 2024
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6. IL-33 Contributes to the Pathological Changes of Hair Follicles in Psoriasis: A Potential Target for Psoriatic Alopecia
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Dai C, Chen H, Jiao M, Zhang N, Tang X, Fan A, Liu S, Qian Z, Wang C, Xu Y, Tan Z, Zeng F, and Zheng F
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il-33 ,psoriatic alopecia ,hair follicle ,keratinocytes ,Dermatology ,RL1-803 - Abstract
Chan Dai,1 Huoying Chen,2 Mengya Jiao,1 Na Zhang,1 Xuhuan Tang,1 Anqi Fan,3 Shiwang Liu,1 Zhigang Qian,1 Chenchen Wang,1 Yong Xu,1 Zheng Tan,1,4 Fanfan Zeng,5 Fang Zheng1,4 1Department of Immunology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People’s Republic of China; 2Department of Laboratory Medicine, The Second Affiliated Hospital of Guilin Medical University, Guilin, Guizhou, People’s Republic of China; 3College of Life Science, Yangtze University, Jingzhou, Hubei, People’s Republic of China; 4Key Laboratory of Organ Transplantation, Ministry of Education, NHC Key Laboratory of Organ Transplantation, Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, Hubei, People’s Republic of China; 5Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People’s Republic of ChinaCorrespondence: Fang Zheng, Department of Immunology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, Hubei, People’s Republic of China, Email zhengfangtj@hust.edu.cn Fanfan Zeng, Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, Hubei, People’s Republic of China, Email zengfanfan@126.comPurpose: IL-33 is constitutively expressed in skin tissues. Alopecia, a T cells-driven disorder of the hair follicles (HFs), is a common complication in the development of psoriasis. However, the role of IL-33 in psoriatic alopecia remains uncovered. Here, we investigated the roles of IL-33 in inducing pathological changes of hair follicles in psoriasis.Patients and Methods: Clinical samples and imiquimod (IMQ)-induced psoriatic mice samples were used to investigate the pathological changes and T-cell infiltration of HFs. By using immunohistochemistry staining, the distribution and expression alteration of IL-33 in HFs were determined. Next, by using IL-33 and ST2 knockout mice, we investigated the role of IL-33/ST2 axis in the pathological changes of HFs in psoriasis. Meanwhile, recombinant IL-33 protein was subcutaneous injected to confirm its effect. Finally, RNA sequencing was used to clarify the genes and signaling pathways that involved in this process. Differentially expressed genes were further verified by RT-PCR in cultured HFs in vitro.Results: We found that the pathological changes of HFs and T cells infiltration in imiquimod-induced psoriatic mice were similar to that in psoriasis patients. The IL-33 positive keratinocytes in the outer root sheath of HFs were increased in both psoriasis patients and psoriatic model mice compared with the controls. By using gene knockout mice, we found that the pathological changes and T cell infiltration were attenuated in IL-33−/− and ST2−/− psoriatic model mice. In addition, subcutaneous injection of recombinant IL-33 exacerbated the pathological changes of HFs and T cell infiltration. RNA sequencing and RT-RCR revealed that IL-33 upregulated the transcription of genes related to keratinocytes proliferation and T lymphocytes chemotaxis.Conclusion: Our study identifies that IL-33 promotes the pathological changes of HFs in psoriasis, which contributes to psoriatic alopecia. Inhibition of IL-33 may be a potential therapeutic approach for psoriatic alopecia.Keywords: IL-33, psoriatic alopecia, hair follicle, keratinocytes
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- 2023
7. Predicting Potential Molecular Mechanisms of Crataegi Fructus in the Treatment of Coronary Heart Disease Based on Network Pharmacology and Molecular Docking Technology
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YANG Min, MENG Fan-ying, SUN Hui, YANG Bo-rong, LIU Meng-xing, CHEN Jie, FAN Ao, and LIU Yuan
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crataegi fructus ,coronary heart disease ,network pharmacology ,molecular docking technology ,mechanism ,Food processing and manufacture ,TP368-456 ,Nutrition. Foods and food supply ,TX341-641 - Abstract
This study explored the active ingredients and mechanism of action of Crataegi Fructus in the treatment of coronary heart disease through network pharmacology and molecular docking technology. With the help of TCMSP, Gene Cards, OMIM and other databases to collect target information, the STRING database was used to construct a PPI network diagram, perform GO and KEGG pathway enrichment analysis on the common target, and finally molecularly dock the active ingredient with the core target to initially verify the network pharmacology results. In this study, a total of 6 Crataegi Fructus active ingredients (sitosterol, kaempferol, stigmasterol, quercetin, ent-Epicatechin, isorhamnetin) and 10 Crataegi Fructus core targets for the treatement of coronary heart disease (JUN, AKT1, TNF, MAPK1, TP53, RELA, IL6, MAPK8, MAPK14, EGFR) were screened; KEGG pathway enrichment results showed that Crataegi Fructus prevention and treatment of coronary heart disease pathway involved pathway in cancer, AGE-RAGE signaling pathway in diabetic complications, hepatitis B, MAPK signaling pathway, etc.; Molecular docking results showed that quercetin, isorhamnetin and kaempferol all had good binding to the core target. It is speculated that these components may be the main active components for the treatment of coronary heart disease. This study revealed that Crataegi Fructus may treat coronary heart disease through multiple components (isorhamnetin, kaempferol, quercetin), acting on key targets such as MAPK8, MAPK1, RELA, and regulating multiple signaling pathways such as MAPK. It preliminarily revealed the potential mechanism of Crataegi Fructus in the treatment of coronary heart disease.
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- 2022
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8. Investigation on mental health status of front-line anti-epidemic medical staff during the COVID-19 outbreak
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Zhu Juhong, Yang Bin, Fan Ajiao, Ma Xiuyun, and Dong Qiangli
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covid-19 ,medical staff ,anxiety ,depression ,Psychology ,BF1-990 ,Psychiatry ,RC435-571 - Abstract
ObjectiveTo investigate the mental health status of the front-line anti-epidemic medical staff during the COVID-19 outbreak， so as to provide references for the targeted psychological intervention and improvement of mental health status.MethodsA total of 162 front-line medical staff who worked in Lanzhou Heavy Particles Hospital of Gansu Province from October to December 2021 were were enrolled， and assessed using self-designed general information questionnaire， Self-rating Anxiety Scale （SAS） and Self-rating Depression Scale （SDS）.ResultsA total of 144 medical staff completed the valid questionnaire survey， and 17 （11.81%） and 19 （13.19%） cases were found to have anxiety and depression， respectively. The detection rate of anxiety yielded statistical difference among medical staff with different anti-epidemic working hours （χ2=10.602， P
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- 2022
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9. Nuclear Aurora kinase A switches m6A reader YTHDC1 to enhance an oncogenic RNA splicing of tumor suppressor RBM4
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SiSi Li, YangFan Qi, JiaChuan Yu, YuChao Hao, Bin He, MengJuan Zhang, ZhenWei Dai, TongHui Jiang, SuYi Li, Fang Huang, Ning Chen, Jing Wang, MengYing Yang, DaPeng Liang, Fan An, JinYao Zhao, WenJun Fan, YuJia Pan, ZiQian Deng, YuanYuan Luo, Tao Guo, Fei Peng, ZhiJie Hou, ChunLi Wang, FeiMeng Zheng, LingZhi Xu, Jie Xu, QingPing Wen, BiLian Jin, Yang Wang, and Quentin Liu
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Medicine ,Biology (General) ,QH301-705.5 - Abstract
Abstract Aberrant RNA splicing produces alternative isoforms of genes to facilitate tumor progression, yet how this process is regulated by oncogenic signal remains largely unknown. Here, we unveil that non-canonical activation of nuclear AURKA promotes an oncogenic RNA splicing of tumor suppressor RBM4 directed by m6A reader YTHDC1 in lung cancer. Nuclear translocation of AURKA is a prerequisite for RNA aberrant splicing, specifically triggering RBM4 splicing from the full isoform (RBM4-FL) to the short isoform (RBM4-S) in a kinase-independent manner. RBM4-S functions as a tumor promoter by abolishing RBM4-FL-mediated inhibition of the activity of the SRSF1-mTORC1 signaling pathway. Mechanistically, AURKA disrupts the binding of SRSF3 to YTHDC1, resulting in the inhibition of RBM4-FL production induced by the m6A-YTHDC1-SRSF3 complex. In turn, AURKA recruits hnRNP K to YTHDC1, leading to an m6A-YTHDC1-hnRNP K-dependent exon skipping to produce RBM4-S. Importantly, the small molecules that block AURKA nuclear translocation, reverse the oncogenic splicing of RBM4 and significantly suppress lung tumor progression. Together, our study unveils a previously unappreciated role of nuclear AURKA in m6A reader YTHDC1-dependent oncogenic RNA splicing switch, providing a novel therapeutic route to target nuclear oncogenic events.
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- 2022
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10. Antidepressant-Like Effect and Mechanism of Ginsenoside Rd on Rodent Models of Depression
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Li Y, Wang ML, Zhang B, Fan XX, Tang Q, Yu X, Li LN, Fan AR, Chang HS, and Zhang LZ
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ginsenoside rd ,antidepressant effect ,hif-1α-vegf signaling pathway ,vegfr-2 ,synaptic plasticity-related regulators ,molecular docking ,Therapeutics. Pharmacology ,RM1-950 - Abstract
Yu Li,1,* Mei-Ling Wang,1,* Bo Zhang,1 Xiao-Xu Fan,1 Qin Tang,1 Xue Yu,2 Li-Na Li,2 Ang-Ran Fan,2 Hong-Sheng Chang,1 Lan-Zhen Zhang1 1School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, People’s Republic of China; 2School of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102488, People’s Republic of China*These authors contributed equally to this workCorrespondence: Lan-Zhen Zhang; Hong-Sheng Chang, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Northeast Corner of the Intersection of Sunshine South Street and Baiyang East Road, Fangshan District, Beijing, 102488, People’s Republic of China, Tel +86 10 5391 2122, Email zhanglanzhen01@126.com; chs1971@sina.comBackground: There is growing evidence to suggest that ginsenoside Rd (GRd) has a therapeutic effect on depression, but the specific mechanisms behind its activity require further study.Objective: This study is designed to investigate the antidepressant-like effect and underlying mechanisms of GRd.Methods: In this study, the behavioral despair mouse model of depression and chronic unpredictable mild stress (CUMS) rat model of depression were established to explore the effects of GRd on depression-like behavior and its underlying mechanisms. Behavioral tests were used to evaluate the replication of animal models and depression-like behaviors. The hypoxia-inducible factor-1α (HIF-1α) blocker 2-methoxyestradiol (2-ME) was injected to determine the role of HIF-1α in the antidepressant-like effect of GRd. In addition, molecular biology techniques were used to determine the mRNA and protein expression of HIF-1ɑ signaling pathway and synaptic plasticity-related regulators, that is synapsin 1 (SYN 1) and postsynaptic density protein 95 (PSD 95). In silico binding interaction studies of GRd with focused target proteins were performed using molecular docking to predict the affinity and optimal binding mode between ligands and receptors.Results: Our data show that GRd significantly reversed depression-like behavior and promoted mRNA and protein expression of HIF-1ɑ signaling pathway and synaptic plasticity-related regulators. However, the antidepressant-like effect of GRd disappeared upon inhibition of HIF-1α expression following administration of 2-ME. Furthermore, molecular docking results showed that GRd possessed significant binding affinity for HIF-1α, VEGF, and VEGFR-2.Conclusion: Our results show that GRd exhibits significant antidepressant-like effect and that HIF-1α signaling pathway is a promising target for the treatment of depression.Keywords: ginsenoside Rd, antidepressant effect, HIF-1α-VEGF signaling pathway, VEGFR-2, synaptic plasticity-related regulators, molecular docking
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- 2022
11. A Review on Dynamic Characteristics and Life Loss of Large Wind Turbine Towers
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FAN Ang, LI Luping, ZHANG Shihai, OUYANG Minnan, WEN Xiankui, and CHEN Shangnian
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wind turbine ,tower ,dynamic characteristics ,life loss ,modal analysis ,dynamic response ,buckling ,fatigue ,Applications of electric power ,TK4001-4102 ,Production of electric energy or power. Powerplants. Central stations ,TK1001-1841 ,Science - Abstract
As an important component of the wind turbine, the tower has a harsh working environment and complex stress, and the load it bears is highly random and variable. Its dynamic characteristics directly affect the safe operation of the wind turbine. Higher requirements are placed on the dynamic characteristics, life loss characteristics and stability of tower. The engineering background of wind turbine tower dynamic characteristics and structural loss research were described, the tower structure and main load characteristics of large-scale wind turbines were introduced, the more common tower structure model establishment methods and working condition simulation methods were sorted out, and the research progress of tower structure modal analysis, dynamic response analysis, buckling stability analysis, and fatigue damage analysis were summarized. Additionally, the domestic and foreign numerical values of simulation technology and the research results were reviewed. A preliminary prospect for the future research direction of tower dynamic characteristics was also made, aiming to provide some references for the further development of this research field.
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- 2022
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12. JWST observations constrain the time evolution of fine structure constants and dark energy - electromagnetic coupling
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Wang, Ze-Fan, Lei, Lei, Feng, Lei, and Fan, Yi-Zhong
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Astrophysics - Cosmology and Nongalactic Astrophysics ,Astrophysics - Astrophysics of Galaxies ,Astrophysics - High Energy Astrophysical Phenomena - Abstract
It was hypothesized in the literature that some physical parameters may be time-evolving and the astrophysical data can serve as a probe. Recently, James Webb Space Telescope (JWST) have released its early observations. In this work, we select the JWST spectroscopic observations of the high redshift ($z>7.1$) galaxies with strong [OIII] ($\lambda=4959$ \AA \,and $5007$ \AA \,in the rest frame) emission lines to constraint the evolution of the fine structure constant ($\alpha$). With the spectra from two galaxies at redshifts of $7.19$ and $8.47$, the deviation of $\alpha$ to its fiducial value is found to be as small as $0.44^{+8.4+1.7}_{-8.3-1.7} \times 10^{-4}$ and $-10.0^{+18+1.5}_{-18-1.5} \times 10^{-4}$, respectively (the first error is statistical and the latter is systematic). The combination of our results with the previous data reveals that $\frac{1}{\alpha} \frac{d \alpha}{dt} = 0.30^{+4.5}_{-4.5} \times 10^{-17}~{\rm yr^{-1}}$. Clearly, there is no evidence for a cosmic evolution of $\alpha$. The prospect of further constraining the time evolution of $\alpha$ is also discussed. The scalar field of dark energy is hypothesized to drive the acceleration of the universe's expansion through an interaction with the electromagnetic field. By integrating the observational data of the fine-structure constant variation, $\frac{\Delta\alpha}{\alpha}(z)$, we have established a stringent upper limit on the coupling strength between dark energy and electromagnetism. Our analysis yields $\zeta \leq 3.92 \times 10^{-7}$ at the 95\% confidence level, representing the most stringent bound to date.
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- 2024
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13. From General to Specific: Utilizing General Hallucation to Automatically Measure the Role Relationship Fidelity for Specific Role-Play Agents
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Kong, Chuyi, Luo, Ziyang, Lin, Hongzhan, Fan, Zhiyuan, Fan, Yaxin, Sun, Yuxi, and Ma, Jing
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Computer Science - Computation and Language - Abstract
The advanced role-playing capabilities of Large Language Models (LLMs) have paved the way for developing Role-Playing Agents (RPAs). However, existing benchmarks, such as HPD, which incorporates manually scored character relationships into the context for LLMs to sort coherence, and SocialBench, which uses specific profiles generated by LLMs in the context of multiple-choice tasks to assess character preferences, face limitations like poor generalizability, implicit and inaccurate judgments, and excessive context length. To address the above issues, we propose an automatic, scalable, and generalizable paradigm. Specifically, we construct a benchmark by extracting relations from a general knowledge graph and leverage RPA's inherent hallucination properties to prompt it to interact across roles, employing ChatGPT for stance detection and defining relationship hallucination along with three related metrics. Extensive experiments validate the effectiveness and stability of our metrics. Our findings further explore factors influencing these metrics and discuss the trade-off between relationship hallucination and factuality.
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- 2024
14. Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning
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Shao, Zhiyu, Wu, Qiong, Fan, Pingyi, Wang, Kezhi, Fan, Qiang, Chen, Wen, and Letaief, Khaled B.
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Computer Science - Machine Learning ,Computer Science - Multiagent Systems ,Computer Science - Networking and Internet Architecture ,Electrical Engineering and Systems Science - Signal Processing - Abstract
This paper presents a semantic-aware multi-modal resource allocation (SAMRA) for multi-task using multi-agent reinforcement learning (MARL), termed SAMRAMARL, utilizing in platoon systems where cellular vehicle-to-everything (C-V2X) communication is employed. The proposed approach leverages the semantic information to optimize the allocation of communication resources. By integrating a distributed multi-agent reinforcement learning (MARL) algorithm, SAMRAMARL enables autonomous decision-making for each vehicle, channel assignment optimization, power allocation, and semantic symbol length based on the contextual importance of the transmitted information. This semantic-awareness ensures that both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications prioritize data that is critical for maintaining safe and efficient platoon operations. The framework also introduces a tailored quality of experience (QoE) metric for semantic communication, aiming to maximize QoE in V2V links while improving the success rate of semantic information transmission (SRS). Extensive simulations has demonstrated that SAMRAMARL outperforms existing methods, achieving significant gains in QoE and communication efficiency in C-V2X platooning scenarios., Comment: This paper has been submitted to IEEE Journal. The source code has been released at:https://github.com/qiongwu86/Semantic-Aware-Resource-Management-for-C-V2X-Platooning-via-Multi-Agent-Reinforcement-Learning
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- 2024
15. Detection of two TeV gamma-ray outbursts from NGC 1275 by LHAASO
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Cao, Zhen, Aharonian, F., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo., X.
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Astrophysics - High Energy Astrophysical Phenomena - Abstract
The Water Cherenkov Detector Array (WCDA) is one of the components of Large High Altitude Air Shower Observatory (LHAASO) and can monitor any sources over two-thirds of the sky for up to 7 hours per day with >98\% duty cycle. In this work, we report the detection of two outbursts of the Fanaroff-Riley I radio galaxy NGC 1275 that were detected by LHAASO-WCDA between November 2022 and January 2023 with statistical significance of 5.2~$\sigma$ and 8.3~$\sigma$. The observed spectral energy distribution in the range from 500 GeV to 3 TeV is fitted by a power-law with a best-fit spectral index of $\alpha=-3.37\pm0.52$ and $-3.35\pm0.29$, respectively. The outburst flux above 0.5~TeV was ($4.55\pm 4.21)\times~10^{-11}~\rm cm^{-2}~s^{-1}$ and ($3.45\pm 1.78)\times~10^{-11}~\rm cm^{-2}~s^{-1}$, corresponding to 60\%, 45\% of Crab Nebula flux. Variation analysis reveals the variability time-scale of days at the TeV energy band. A simple test by one-zone synchrotron self-Compton model reproduces the data in the gamma-ray band well., Comment: 11 pages, 8 figures, 3 tables
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- 2024
16. Multilayer Dataflow based Butterfly Sparsity Orchestration to Accelerate Attention Workloads
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Wu, Haibin, Li, Wenming, Yan, Kai, Fan, Zhihua, Liu, Tianyu, Liu, Yuqun, Liu, Yanhuan, Qiang, Ziqing, Ye, Xiaochun, and Fan, Dongrui
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Computer Science - Hardware Architecture - Abstract
Recent neural networks (NNs) with self-attention exhibit competitiveness across different AI domains, but the essential attention mechanism brings massive computation and memory demands. To this end, various sparsity patterns are introduced to reduce the quadratic computation complexity, among which the structured butterfly sparsity has been proven efficient in computation reduction while maintaining model accuracy. However, its complicated data accessing pattern brings utilization degradation and makes parallelism hard to exploit in general block-oriented architecture like GPU. Since the reconfigurable dataflow architecture is known to have better data reusability and architectural flexibility in general NN-based acceleration, we want to apply it to the butterfly sparsity for acquiring better computational efficiency for attention workloads. We first propose a hybrid butterfly-sparsity network to obtain better trade-offs between attention accuracy and performance. Next, we propose a scalable multilayer dataflow method supported by coarse-grained streaming parallelism designs, to orchestrate the butterfly sparsity computation on the dataflow array. The experiments show that compared with Jetson Xavier NX, our design has a speedup of up to $14.34\times$ ($9.29\times$ on average) as well as $11.14\times$ energy efficiency advancement in attention workloads. In comparison with SOTA attention accelerators of the same peak performance, our dataflow architecture acquires $2.38\times$-$4.7\times$ efficiency improvement as well as $6.60\times$-$15.37\times$ energy reduction with butterfly sparsity optimization., Comment: 9 pages, 17 figures, ICCAD 2024, 2024/07/05, Butterfly Sparsity Optimization Using Dataflow
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- 2024
17. GAFusion: Adaptive Fusing LiDAR and Camera with Multiple Guidance for 3D Object Detection
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Li, Xiaotian, Fan, Baojie, Tian, Jiandong, and Fan, Huijie
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Recent years have witnessed the remarkable progress of 3D multi-modality object detection methods based on the Bird's-Eye-View (BEV) perspective. However, most of them overlook the complementary interaction and guidance between LiDAR and camera. In this work, we propose a novel multi-modality 3D objection detection method, named GAFusion, with LiDAR-guided global interaction and adaptive fusion. Specifically, we introduce sparse depth guidance (SDG) and LiDAR occupancy guidance (LOG) to generate 3D features with sufficient depth information. In the following, LiDAR-guided adaptive fusion transformer (LGAFT) is developed to adaptively enhance the interaction of different modal BEV features from a global perspective. Meanwhile, additional downsampling with sparse height compression and multi-scale dual-path transformer (MSDPT) are designed to enlarge the receptive fields of different modal features. Finally, a temporal fusion module is introduced to aggregate features from previous frames. GAFusion achieves state-of-the-art 3D object detection results with 73.6$\%$ mAP and 74.9$\%$ NDS on the nuScenes test set.
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- 2024
18. Einstein Probe discovery of EP240408a: a peculiar X-ray transient with an intermediate timescale
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Zhang, Wenda, Yuan, Weimin, Ling, Zhixing, Chen, Yong, Rea, Nanda, Rau, Arne, Cai, Zhiming, Cheng, Huaqing, Zelati, Francesco Coti, Dai, Lixin, Hu, Jingwei, Jia, Shumei, Jin, Chichuan, Li, Dongyue, O'Brien, Paul, Shen, Rongfeng, Shu, Xinwen, Sun, Shengli, Sun, Xiaojin, Wang, Xiaofeng, Yang, Lei, Zhang, Bing, Zhang, Chen, Zhang, Shuang-Nan, Zhang, Yonghe, An, Jie, Buckley, David, Coleiro, Alexis, Cordier, Bertrand, Dou, Liming, Eyles-Ferris, Rob, Fan, Zhou, Feng, Hua, Fu, Shaoyu, Fynbo, Johan P. U., Galbany, Lluis, Jha, Saurabh W., Jiang, Shuaiqing, Kong, Albert, Kuulkers, Erik, Lei, Weihua, Li, Wenxiong, Liu, Bifang, Liu, Mingjun, Liu, Xing, Liu, Yuan, Liu, Zhu, Maitra, Chandreyee, Marino, Alessio, Monageng, Itumeleng, Nandra, Kirpal, Sanders, Jeremy, Soria, Roberto, Tao, Lian, Wang, Junfeng, Wang, Song, Wang, Tinggui, Wang, Zhongxiang, Wu, Qingwen, Wu, Xuefeng, Xu, Dong, Xu, Yanjun, Xue, Suijian, Xue, Yongquan, Zhang, Zijian, Zhu, Zipei, Zou, Hu, Bao, Congying, Chen, Fansheng, Chen, Houlei, Chen, Tianxiang, Chen, Wei, Chen, Yehai, Chen, Yifan, Cui, Chenzhou, Cui, Weiwei, Dai, Yanfeng, Fan, Dongwei, Guan, Ju, Han, Dawei, Hou, Dongjie, Hu, Haibo, Huang, Maohai, Huo, Jia, Jia, Zhenqing, Jiang, Bowen, Jin, Ge, Li, Chengkui, Li, Junfei, Li, Longhui, Li, Maoshun, Li, Wei, Li, Zhengda, Lian, Tianying, Liu, Congzhan, Liu, Heyang, Liu, Huaqiu, Lu, Fangjun, Luo, Laidan, Ma, Jia, Mao, Xuan, Pan, Haiwu, Pan, Xin, Song, Liming, Sun, Hui, Tan, Yunyin, Tang, Qingjun, Tao, Yihan, Wang, Hao, Wang, Juan, Wang, Lei, Wang, Wenxin, Wang, Yilong, Wang, Yusa, Wu, Qinyu, Xu, Haitao, Xu, Jingjing, Xu, Xinpeng, Xu, Yunfei, Xu, Zhao, Xue, Changbin, Xue, Yulong, Yan, Ailiang, Yang, Haonan, Yang, Xiongtao, Yang, Yanji, Zhang, Juan, Zhang, Mo, Zhang, Wenjie, Zhang, Zhen, Zhang, Ziliang, Zhao, Donghua, Zhao, Haisheng, Zhao, Xiaofan, Zhao, Zijian, Zhou, Hongyan, Zhou, Yilin, Zhu, Yuxuan, and Zhu, Zhencai
- Subjects
Astrophysics - High Energy Astrophysical Phenomena - Abstract
We report the discovery of a peculiar X-ray transient, EP240408a, by Einstein Probe (EP) and follow-up studies made with EP, Swift, NICER, GROND, ATCA and other ground-based multi-wavelength telescopes. The new transient was first detected with Wide-field X-ray Telescope (WXT) on board EP on April 8th, 2024, manifested in an intense yet brief X-ray flare lasting for 12 seconds. The flare reached a peak flux of 3.9x10^(-9) erg/cm2/s in 0.5-4 keV, about 300 times brighter than the underlying X-ray emission detected throughout the observation. Rapid and more precise follow-up observations by EP/FXT, Swift and NICER confirmed the finding of this new transient. Its X-ray spectrum is non-thermal in 0.5-10 keV, with a power-law photon index varying within 1.8-2.5. The X-ray light curve shows a plateau lasting for about 4 days, followed by a steep decay till becoming undetectable about 10 days after the initial detection. Based on its temporal property and constraints from previous EP observations, an unusual timescale in the range of 7-23 days is found for EP240408a, which is intermediate between the commonly found fast and long-term transients. No counterparts have been found in optical and near-infrared, with the earliest observation at 17 hours after the initial X-ray detection, suggestive of intrinsically weak emission in these bands. We demonstrate that the remarkable properties of EP240408a are inconsistent with any of the transient types known so far, by comparison with, in particular, jetted tidal disruption events, gamma-ray bursts, X-ray binaries and fast blue optical transients. The nature of EP240408a thus remains an enigma. We suggest that EP240408a may represent a new type of transients with intermediate timescales of the order of about 10 days. The detection and follow-ups of more of such objects are essential for revealing their origin., Comment: 25 pages, 11 figures
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- 2024
- Full Text
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19. One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation
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Wang, Zhendong, Li, Zhaoshuo, Mandlekar, Ajay, Xu, Zhenjia, Fan, Jiaojiao, Narang, Yashraj, Fan, Linxi, Zhu, Yuke, Balaji, Yogesh, Zhou, Mingyuan, Liu, Ming-Yu, and Zeng, Yu
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Computer Science - Robotics ,Computer Science - Machine Learning - Abstract
Diffusion models, praised for their success in generative tasks, are increasingly being applied to robotics, demonstrating exceptional performance in behavior cloning. However, their slow generation process stemming from iterative denoising steps poses a challenge for real-time applications in resource-constrained robotics setups and dynamically changing environments. In this paper, we introduce the One-Step Diffusion Policy (OneDP), a novel approach that distills knowledge from pre-trained diffusion policies into a single-step action generator, significantly accelerating response times for robotic control tasks. We ensure the distilled generator closely aligns with the original policy distribution by minimizing the Kullback-Leibler (KL) divergence along the diffusion chain, requiring only $2\%$-$10\%$ additional pre-training cost for convergence. We evaluated OneDP on 6 challenging simulation tasks as well as 4 self-designed real-world tasks using the Franka robot. The results demonstrate that OneDP not only achieves state-of-the-art success rates but also delivers an order-of-magnitude improvement in inference speed, boosting action prediction frequency from 1.5 Hz to 62 Hz, establishing its potential for dynamic and computationally constrained robotic applications. We share the project page at https://research.nvidia.com/labs/dir/onedp/.
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- 2024
20. Search for $\eta_c(2S)\to p\bar{p}$ and branching fraction measurements of $\chi_{cJ} \to p\bar{p}$ via $\psi(2S)$ radiative decays
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De~Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, P., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lan, W. N., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, Lei, Li, M. H., Li, P. L., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, Q. M., Ma, R. Q., Ma, R. Y., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Y. H., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H. R., Qi, M., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, Bo, Wang, C., Wang, D. Y., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, Lianjie, Wu, X., Wu, X. H., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, W. P., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, R. J., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Y. Z., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Yue, Ying, Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, X. R., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, W. Z., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
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High Energy Physics - Experiment - Abstract
Using $(27.12\pm0.14) \times 10^{8}$ $\psi(2S)$ events collected by the BESIII detector operating at BEPCII, we search for the decay $\eta_c(2S)\to p\bar{p}$ via the process $\psi(2S)\to \gamma\eta_c(2S)$, and only find a signal with a significance of $1.7\,\sigma$. The upper limit of the product branching fraction at the 90% confidence level is determined to be $\mathcal{B}(\psi(2S)\to \gamma\eta_c(2S))\times \mathcal{B}(\eta_c(2S)\to p\bar{p})<2.4\times 10^{-7}$. The branching fractions of $\chi_{cJ}\to p\bar{p}~(J=0,1,2)$ are also measured to be $\mathcal{B}(\chi_{c0}\to p\bar{p})=(2.51\pm0.02\pm0.08)\times 10^{-4}$, $\mathcal{B}(\chi_{c1}\to p\bar{p})=(8.16\pm0.09\pm0.25)\times 10^{-4}$, and $\mathcal{B}(\chi_{c2}\to p\bar{p})=(8.33\pm0.09\pm0.22)\times 10^{-4}$, where the first uncertainty is statistical and the second systematic.
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- 2024
21. Integrated spectrally multiplexed light-matter interface at telecom band
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Zhang, Xueying, Zhang, Bin, Wei, Shihai, Li, Hao, Liao, Jinyu, Zhou, Tao, Deng, Guangwei, Wang, You, Song, Haizhi, You, Lixing, Fan, Boyu, Fan, Yunru, Chen, Feng, Guo, Guangcan, and Zhou, Qiang
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Quantum Physics - Abstract
Light-matter interface is an important building block for long-distance quantum networks. Towards a scalable quantum network with high-rate quantum information processing, it requires to develop integrated light-matter interfaces with broadband and multiplexing capacities. Here we demonstrate a light-matter interface at telecom band in an integrated system. A five-spectral-channel atomic-frequency-comb photonic memory is prepared on a laser-written Er3+:LiNbO3 chip. The bandwidth of each channel is 4 GHz with a channel spacing of 15 GHz. The signal photons from time-bin entangled photon pairs at telecom band are sent into the on-chip memory and recalled after a storage time of 152 ns. The entanglement-preserving nature of our integrated quantum interface is assessed by an input/output fidelity of >92% for all the five spectral channels. Our light-matter interfaces constitute a notable step forward toward a high-rate quantum network involving integrated device.
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- 2024
22. Could the inter-band lag of active galactic nucleus vary randomly?
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Su, Zhen-Bo, Cai, Zhen-Yi, Wang, Jun-Xian, Wang, Tinggui, Xue, Yongquan, Cai, Min-Xuan, Fan, Lulu, Guo, Hengxiao, He, Zhicheng, He, Zizhao, Hu, Xu-Fan, Jiang, Ji-an, Jiang, Ning, Kang, Wen-Yong, Lei, Lei, Liu, Guilin, Liu, Teng, Liu, Zhengyan, Sheng, Zhenfeng, Sun, Mouyuan, and Zhao, Wen
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Astrophysics - Astrophysics of Galaxies ,Astrophysics - High Energy Astrophysical Phenomena - Abstract
The inter-band lags among the optical broad-band continua of active galactic nuclei (AGNs) have been intensively explored over the past decade. However, the nature of the lags remains under debate. Here utilizing two distinct scenarios for AGN variability, i.e., the thermal fluctuation of accretion disk and the reprocessing of both the accretion disk and clouds in the broad line region, we show that, owing to the random nature of AGN variability, the inter-band lags of an individual AGN would vary from one campaign with a finite baseline to another. Specifically, the thermal fluctuation scenario implies larger variations in the lags than the reprocessing scenario. Moreover, the former predicts a positive correlation between the lag and variation amplitude, while the latter does not result in such a correlation. For both scenarios, averaging the lags of an individual AGN measured with repeated and non-overlapping campaigns would give rise to a stable lag, which is larger for a longer baseline and gets saturation for a sufficiently long baseline. However, obtaining the stable lag for an individual AGN is very time-consuming. Alternatively, it can be equivalently inferred by averaging the lags of a sample of AGNs with similar physical properties, thus can be properly compared with predictions of AGN models. In addition, discussed are several new observational tests suggested by our simulations as well as the role of the deep high-cadence surveys of the Wide Field Survey Telescope in enriching our knowledge of the lags., Comment: 16 pages, 10 figures. Accepted for publication in Astrophysical Journal, comments are welcome!
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- 2024
23. SegGrasp: Zero-Shot Task-Oriented Grasping via Semantic and Geometric Guided Segmentation
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Li, Haosheng, Mao, Weixin, Deng, Weipeng, Meng, Chenyu, Zhang, Rui, Jia, Fan, Wang, Tiancai, Fan, Haoqiang, Wang, Hongan, and Deng, Xiaoming
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Computer Science - Robotics - Abstract
Task-oriented grasping, which involves grasping specific parts of objects based on their functions, is crucial for developing advanced robotic systems capable of performing complex tasks in dynamic environments. In this paper, we propose a training-free framework that incorporates both semantic and geometric priors for zero-shot task-oriented grasp generation. The proposed framework, SegGrasp, first leverages the vision-language models like GLIP for coarse segmentation. It then uses detailed geometric information from convex decomposition to improve segmentation quality through a fusion policy named GeoFusion. An effective grasp pose can be generated by a grasping network with improved segmentation. We conducted the experiments on both segmentation benchmark and real-world robot grasping. The experimental results show that SegGrasp surpasses the baseline by more than 15\% in grasp and segmentation performance., Comment: 7pages,6 figures
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- 2024
24. CCA-Secure Key-Aggregate Proxy Re-Encryption for Secure Cloud Storage
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Chen, Wei-Hao, Fan, Chun-I, and Tseng, Yi-Fan
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Computer Science - Cryptography and Security - Abstract
The development of cloud services in recent years has mushroomed, for example, Google Drive, Amazon AWS, Microsoft Azure. Merchants can easily use cloud services to open their online shops in a few seconds. Users can easily and quickly connect to the cloud in their own portable devices, and access their personal information effortlessly. Because users store large amounts of data on third-party devices, ensuring data confidentiality, availability and integrity become especially important. Therefore, data protection in cloud storage is the key to the survival of the cloud industry. Fortunately, Proxy Re-Encryption schemes enable users to convert their ciphertext into others ciphertext by using a re-encryption key. This method gracefully transforms the users computational cost to the server. In addition, with C-PREs, users can apply their access control right on the encrypted data. Recently, we lowered the key storage cost of C-PREs to constant size and proposed the first Key-Aggregate Proxy Re-Encryption scheme. In this paper, we further prove that our scheme is a CCA-secure Key-Aggregate Proxy Re-Encryption scheme in the adaptive model without using random oracle. Moreover, we also implement and analyze the Key Aggregate PRE application in the real world scenario.
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- 2024
25. A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning
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Zhang, Jingbo, Wu, Qiong, Fan, Pingyi, and Fan, Qiang
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Computer Science - Machine Learning - Abstract
Federated Edge Learning (FEL), an emerging distributed Machine Learning (ML) paradigm, enables model training in a distributed environment while ensuring user privacy by using physical separation for each user data. However, with the development of complex application scenarios such as the Internet of Things (IoT) and Smart Earth, the conventional resource allocation schemes can no longer effectively support these growing computational and communication demands. Therefore, joint resource optimization may be the key solution to the scaling problem. This paper simultaneously addresses the multifaceted challenges of computation and communication, with the growing multiple resource demands. We systematically review the joint allocation strategies for different resources (computation, data, communication, and network topology) in FEL, and summarize the advantages in improving system efficiency, reducing latency, enhancing resource utilization and enhancing robustness. In addition, we present the potential ability of joint optimization to enhance privacy preservation by reducing communication requirements, indirectly. This work not only provides theoretical support for resource management in federated learning (FL) systems, but also provides ideas for potential optimal deployment in multiple real-world scenarios. By thoroughly discussing the current challenges and future research directions, it also provides some important insights into multi-resource optimization in complex application environments., Comment: This paper has been submitted to CMC-Computers Materials & Continua
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- 2024
26. LHAASO detection of very-high-energy gamma-ray emission surrounding PSR J0248+6021
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Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, J. H., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., Zou, Y. C., and Zuo, X.
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Astrophysics - High Energy Astrophysical Phenomena - Abstract
We report the detection of an extended very-high-energy (VHE) gamma-ray source coincident with the locations of middle-aged (62.4~\rm kyr) pulsar PSR J0248+6021, by using the LHAASO-WCDA data of live 796 days and LHAASO-KM2A data of live 1216 days. A significant excess of \gray induced showers is observed both by WCDA in energy bands of 1-25~\rm TeV and KM2A in energy bands of $>$ 25~\rm TeV with 7.3 $\sigma$ and 13.5 $\sigma$, respectively. The best-fit position derived through WCDA data is R.A. = 42.06$^\circ \pm$ 0.12$^\circ$ and Dec. = 60.24$^\circ \pm $ 0.13$^\circ$ with an extension of 0.69$^\circ\pm$0.15$^\circ$ and that of the KM2A data is R.A.= 42.29$^\circ \pm $ 0.13$^\circ$ and Dec. = 60.38$^\circ \pm$ 0.07$^\circ$ with an extension of 0.37$^\circ\pm$0.07$^\circ$. No clear extended multiwavelength counterpart of this LHAASO source has been found from the radio band to the GeV band. The most plausible explanation of the VHE \gray emission is the inverse Compton process of highly relativistic electrons and positrons injected by the pulsar. These electrons/positrons are hypothesized to be either confined within the pulsar wind nebula or to have already escaped into the interstellar medium, forming a pulsar halo., Comment: 12 pages, 10 figures, Accepted by Sci. China-Phys. Mech. Astron
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- 2024
27. Data-Efficient Massive Tool Retrieval: A Reinforcement Learning Approach for Query-Tool Alignment with Language Models
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Zhang, Yuxiang, Fan, Xin, Wang, Junjie, Chen, Chongxian, Mo, Fan, Sakai, Tetsuya, and Yamana, Hayato
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Computer Science - Information Retrieval - Abstract
Recent advancements in large language models (LLMs) integrated with external tools and APIs have successfully addressed complex tasks by using in-context learning or fine-tuning. Despite this progress, the vast scale of tool retrieval remains challenging due to stringent input length constraints. In response, we propose a pre-retrieval strategy from an extensive repository, effectively framing the problem as the massive tool retrieval (MTR) task. We introduce the MTRB (massive tool retrieval benchmark) to evaluate real-world tool-augmented LLM scenarios with a large number of tools. This benchmark is designed for low-resource scenarios and includes a diverse collection of tools with descriptions refined for consistency and clarity. It consists of three subsets, each containing 90 test samples and 10 training samples. To handle the low-resource MTR task, we raise a new query-tool alignment (QTA) framework leverages LLMs to enhance query-tool alignment by rewriting user queries through ranking functions and the direct preference optimization (DPO) method. This approach consistently outperforms existing state-of-the-art models in top-5 and top-10 retrieval tasks across the MTRB benchmark, with improvements up to 93.28% based on the metric Sufficiency@k, which measures the adequacy of tool retrieval within the first k results. Furthermore, ablation studies validate the efficacy of our framework, highlighting its capacity to optimize performance even with limited annotated samples. Specifically, our framework achieves up to 78.53% performance improvement in Sufficiency@k with just a single annotated sample. Additionally, QTA exhibits strong cross-dataset generalizability, emphasizing its potential for real-world applications.
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- 2024
28. Extragalactic fast X-ray transient from a weak relativistic jet associated with a Type Ic-BL supernova
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Sun, H., Li, W. -X., Liu, L. -D., Gao, H., Wang, X. -F., Yuan, W., Zhang, B., Filippenko, A. V., Xu, D., An, T., Ai, S., Brink, T. G., Liu, Y., Liu, Y. -Q., Wang, C. -Y., Wu, Q. -Y., Wu, X. -F., Yang, Y., Zhang, B. -B., Zheng, W. -K., Ahumada, T., Dai, Z. -G., Delaunay, J., Elias-Rosa, N., Benetti, S., Fu, S. -Y., Howell, D. A., Huang, Y. -F., Kasliwal, M. M., Karambelkar, V., Stein, R., Lei, W. -H., Lian, T. -Y., Peng, Z. -K., Ridnaia, A. V., Svinkin, D. S., Wang, X. -Y., Wang, A. -L., Wei, D. -M., An, J., Andrews, M., Bai, J. -M, Dai, C. -Y., Ehgamberdiev, S. A., Fan, Z., Farah, J., Feng, H. -C., Fynbo, J. P. U., Guo, W. -J., Guo, Z., Hu, M. -K., Hu, J. -W., Jiang, S. -Q., Jin, J. -J., Li, A., Li, J. -D., Li, R. -Z., Liang, Y. -F., Ling, Z. -X., Liu, X., Mao, J. -R., McCully, C., Mirzaqulov, D., Newsome, M., Gonzalez, E. Padilla, Pan, X., Terreran, G., Tinyanont, S., Wang, B. -T., Wang, L. -Z., Wen, X. -D., Xiang, D. -F., Xue, S. -J., Yang, J., Zhu, Z. -P., Cai, Z. -M., Castro-Tirado, A. J., Chen, F. -S., Chen, H. -L., Chen, T. -X., Chen, W., Chen, Y. -H., Chen, Y. -F., Chen, Y., Cheng, H. -Q., Cordier, B., Cui, C. -Z., Cui, W. -W., Dai, Y. -F., Fan, D. -W., Feng, H., Guan, J., Han, D. -W., Hou, D. -J., Hu, H. -B., Huang, M. -H., Huo, J., Jia, S. -M., Jia, Z. -Q., Jiang, B. -W., Jin, C. -C., Jin, G., Kuulkers, E., Li, C. -K., Li, D. -Y., Li, J. -F., Li, L. -H., Li, M. -S., Li, W., Li, Z. -D., Liu, C. -Z, Liu, H. -Y., Liu, H. -Q., Liu, M. -J., Lu, F. -J., Luo, L. -D., Ma, J., Mao, X., Nandra, K., O'Brien, P., Pan, H. -W., Rau, A., Rea, N., Sanders, J., Song, L. -M., Sun, S. -L., Sun, X. -J., Tan, Y. -Y., Tang, Q. -J., Tao, Y. -H., Wang, H., Wang, J., Wang, L., Wang, W. -X., Wang, Y. -L., Wang, Y. -S., Xiong, D. -R., Xu, H. -T., Xu, J. -J., Xu, X. -P., Xu, Y. -F., Xu, Z., Xue, C. -B., Xue, Y. -L., Yan, A. -L., Yang, H. -N., Yang, X. -T., Yang, Y. -J., Zhang, C., Zhang, J., Zhang, M., Zhang, S. -N., Zhang, W. -D., Zhang, W. -J., Zhang, Y. -H., Zhang, Z., Zhang, Z. -L., Zhao, D. -H., Zhao, H. -S., Zhao, X. -F., Zhao, Z. -J., Zhou, Y. -L., Zhu, Y. -X., and Zhu, Z. -C.
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Astrophysics - High Energy Astrophysical Phenomena - Abstract
Massive stars end their life as core-collapse supernovae, amongst which some extremes are Type Ic broad-lined supernovae associated with long-duration gamma-ray bursts (LGRBs) having powerful relativistic jets. Their less-extreme brethren make unsuccessful jets that are choked inside the stars, appearing as X-ray flashes or low-luminosity GRBs. On the other hand, there exists a population of extragalactic fast X-ray transients (EFXTs) with timescales ranging from seconds to thousands of seconds, whose origins remain obscure. Known sources that contribute to the observed EFXT population include the softer analogs of LGRBs, shock breakouts of supernovae, or unsuccessful jets. Here, we report the discovery of the bright X-ray transient EP240414a detected by the Einstein Probe (EP), which is associated with the Type Ic supernova SN 2024gsa at a redshift of 0.401. The X-ray emission evolution is characterised by a very soft energy spectrum peaking at < 1.3 keV, which makes it distinct from known LGRBs, X-ray flashes, or low-luminosity GRBs. Follow-up observations at optical and radio bands revealed the existence of a weak relativistic jet that interacts with an extended shell surrounding the progenitor star. Located on the outskirts of a massive galaxy, this event reveals a new population of explosions of Wolf-Rayet stars characterised by a less powerful engine that drives a successful but weak jet, possibly owing to a progenitor star with a smaller core angular momentum than in traditional LGRB progenitors., Comment: 43 pages, 9 figures, 4 tables, submitted. Comments are welcome
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- 2024
29. GRB 240529A: A Tale of Two Shocks
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Sun, Tian-Rui, Geng, Jin-Jun, Yan, Jing-Zhi, Hu, You-Dong, Wu, Xue-Feng, Castro-Tirado, Alberto J., Yang, Chao, Ping, Yi-Ding, Hu, Chen-Ran, Xu, Fan, Gao, Hao-Xuan, Jiang, Ji-An, Zhu, Yan-Tian, Xue, Yongquan, Pérez-García, Ignacio, Wu, Si-Yu, Fernández-García, Emilio, Caballero-García, María D., Sánchez-Ramírez, Rubén, Guziy, Sergiy, Olivares, Ignacio, del Pulgar, Carlos Jesus Pérez, Castellón, A., Castillo, Sebastián, Xiong, Ding-Rong, Pandey, Shashi B., Hiriart, David, García-Segura, Guillermo, Lee, William H., Carrasco-García, I. M., Park, Il H., Meintjes, Petrus J., van Heerden, Hendrik J., Martín-Carrillo, Antonio, Hanlon, Lorraine, Zhang, Bin-Bin, Maury, Alain, Hernández-García, L., Gritsevich, Maria, Rossi, Andrea, Maiorano, Elisabetta, Cusano, Felice, D'Avanzo, Paolo, Ferro, Matteo, Melandri, Andrea, De Pasquale, Massimiliano, Brivio, Riccardo, Fang, Min, Fan, Lu-Lu, Hu, Wei-Da, Wan, Zhen, Hu, Lei, Zuo, Ying-Xi, Tang, Jin-Long, Zhang, Xiao-Ling, Zheng, Xian-Zhong, Li, Bin, Luo, Wen-Tao, Liu, Wei, Wang, Jian, Zhang, Hong-Fei, Liu, Hao, Gao, Jie, Liang, Ming, Wang, Hai-Ren, Yao, Da-Zhi, Cheng, Jing-Quan, Zhao, Wen, and Dai, Zi-Gao
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Astrophysics - High Energy Astrophysical Phenomena - Abstract
Thanks to the rapidly increasing time-domain facilities, we are entering a golden era of research on gamma-ray bursts (GRBs). In this Letter, we report our observations of GRB 240529A with the Burst Optical Observer and Transient Exploring System, the 1.5-meter telescope at Observatorio Sierra Nevada, the 2.5-meter Wide Field Survey Telescope of China, the Large Binocular Telescope, and the Telescopio Nazionale Galileo. The prompt emission of GRB 240529A shows two comparable energetic episodes separated by a quiescence time of roughly 400 s. Combining all available data on the GRB Coordinates Network, we reveal the simultaneous apparent X-ray plateau and optical re-brightening around $10^3-10^4$ s after the burst. Rather than the energy injection from the magnetar as widely invoked for similar GRBs, the multi-wavelength emissions could be better explained as two shocks launched from the central engine separately. The optical peak time and our numerical modeling suggest that the initial bulk Lorentz factor of the later shock is roughly 50, which indicates that the later jet should be accretion-driven and have a higher mass loading than a typical one. The quiescence time between the two prompt emission episodes may be caused by the transition between different accretion states of a central magnetar or black hole, or the fall-back accretion process. A sample of similar bursts with multiple emission episodes in the prompt phase and sufficient follow-up could help to probe the underlying physics of GRB central engines., Comment: Resubmitted to ApJL after addressing the referee's comments; comments are welcome
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- 2024
30. Optically Coherent Nitrogen-Vacancy Centers in HPHT Treated Diamonds
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Tang, Yuan-Han, Zhang, Xiaoran, Liu, Kang-Yuan, Xia, Fan, Zheng, Huijie, Liu, Xiaobing, Pan, Xin-Yu, Fan, Heng, and Liu, Gang-Qin
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Quantum Physics ,Condensed Matter - Mesoscale and Nanoscale Physics - Abstract
As a point defect with unique spin and optical properties, nitrogen-vacancy (NV) center in diamond has attracted much attention in the fields of quantum sensing, quantum simulation, and quantum networks. The optical properties of an NV center are crucial for all these quantum applications. However, NV centers fabricated by destructive methods such as electron irradiation or ion implantation usually exhibit poor optical coherence. In this work, we demonstrate a non-destructive method to fabricate optically coherent NV centers. High-purity single crystal diamonds are annealed under high pressure and high temperature (1700 $^{\circ}$C, 5.5 GPa), and individually resolvable NV centers with narrow PLE linewidth (<100 MHz) are produced. The high-pressure condition prevents the conversion of diamond to graphite during high-temperature annealing, significantly expanding the parameter space for creating high-performance artificial defects for quantum information science. These findings deepen our understanding of NV center formation in diamond and have implications for the optimization of color centers in solids, including silicon carbide and hexagonal boron nitride., Comment: 11 pages,4 figures
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- 2024
31. Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
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Wang, Peng, Bai, Shuai, Tan, Sinan, Wang, Shijie, Fan, Zhihao, Bai, Jinze, Chen, Keqin, Liu, Xuejing, Wang, Jialin, Ge, Wenbin, Fan, Yang, Dang, Kai, Du, Mengfei, Ren, Xuancheng, Men, Rui, Liu, Dayiheng, Zhou, Chang, Zhou, Jingren, and Lin, Junyang
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language - Abstract
We present the Qwen2-VL Series, an advanced upgrade of the previous Qwen-VL models that redefines the conventional predetermined-resolution approach in visual processing. Qwen2-VL introduces the Naive Dynamic Resolution mechanism, which enables the model to dynamically process images of varying resolutions into different numbers of visual tokens. This approach allows the model to generate more efficient and accurate visual representations, closely aligning with human perceptual processes. The model also integrates Multimodal Rotary Position Embedding (M-RoPE), facilitating the effective fusion of positional information across text, images, and videos. We employ a unified paradigm for processing both images and videos, enhancing the model's visual perception capabilities. To explore the potential of large multimodal models, Qwen2-VL investigates the scaling laws for large vision-language models (LVLMs). By scaling both the model size-with versions at 2B, 8B, and 72B parameters-and the amount of training data, the Qwen2-VL Series achieves highly competitive performance. Notably, the Qwen2-VL-72B model achieves results comparable to leading models such as GPT-4o and Claude3.5-Sonnet across various multimodal benchmarks, outperforming other generalist models. Code is available at https://github.com/QwenLM/Qwen2-VL ., Comment: Code is available at https://github.com/QwenLM/Qwen2-VL. arXiv admin note: text overlap with arXiv:2408.15262 by other authors
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- 2024
32. Fast magnetic reconnection in Kerr spacetime
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Fan, Zhong-Ying, Li, Yuehang, Zhou, Fan, and Guo, Minyong
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Astrophysics - High Energy Astrophysical Phenomena - Abstract
We develop a relativistic scenario of fast magnetic reconnection process, for general magnetohydrodynamical plasmas around Kerr black holes. Generalizing the Petschek model, we study various properties of the reconnection layer in distinct configurations. When current sheet forms in the zero-angular-momentum (ZAMO) frame which corotates with the black hole, the reconnection rate for both radial and azimuthal configurations is decreased by spacetime curvature. However, when the current sheet forms in a non-ZAMO frame, which rotates either faster or slower than the black hole, detail analysis establishes that for any given slow rotations (subrelativistic at most) and mildly relativistic inflow, the ZAMO observer will find asymmetric reconnection rates for radial configuration: it is decreased on one side of the current sheet and is increased on the other side in comparison to the unrotation limit. This is valid to both the Sweet-Parker and the Petschek scenario. The results clarify the effects of rotation on the reconnection layer in the laboratory frame in the flat spacetime limit., Comment: 25pages,7figures
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- 2024
33. Lactobacillus spp. create a protective micro-ecological environment through regulating the core fucosylation of vaginal epithelial cells against cervical cancer
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Qingjie Fan, Yuanhang Wu, Mechou Li, Fan An, Lulu Yao, Meixian Wang, Xiuying Wang, Jieli Yuan, Kui Jiang, Wenzhe Li, and Ming Li
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Cytology ,QH573-671 - Abstract
Abstract Vaginal dysbiosis often occurs in patients with cervical cancer. The fucosylation of mucosal epithelial cells is closely related to microbial colonization, and play an important role in protecting the vaginal mucosal epithelial cells. However, no reports on the relationship between vaginal dysbiosis and abnormal mucosal epithelial cell fucosylation, and their roles in the occurrence and development of cervical cancer are unavailable. Here we report that core fucosylation levels were significantly lower in the serum, exfoliated cervical cells and tumor tissue of cervical cancer patients. Core fucosyltransferase gene (Fut8) knockout promoted the proliferation and migration of cervical cancer cells. In patients with cervical cancer, the vaginal dysbiosis, and the abundance of Lactobacillus, especially L. iners, was significantly reduced. Meanwhile, the abundance of L.iners was positively correlated with core fucosylation levels. The L. iners metabolite lactate can activate the Wnt pathway through the lactate-Gpr81 complex, which increases the level of core fucosylation in epidermal cells, inhibiting the proliferation and migration of cervical cancer cells, and have application prospects in regulating the vaginal microecology and preventing cervical cancer.
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- 2021
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34. IL-25 Treatment Improves Metabolic Syndrome in High-Fat Diet and Genetic Models of Obesity
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Smith AD, Fan A, Qin B, Desai N, Zhao A, and Shea-Donohue T
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metabolic syndrome ,glucose ,ob/ob ,rodent ,Specialties of internal medicine ,RC581-951 - Abstract
Allen D Smith,1 Anya Fan,2 Bolin Qin,1 Neemesh Desai,2 Aiping Zhao,2 Terez Shea-Donohue3 1Diet, Genomics, and Immunology Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD, USA; 2Department of Radiation Oncology University of Maryland School of Medicine, Baltimore, MD, USA; 3Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, Bethesda, MD, USACorrespondence: Allen D SmithDiet, Genomics, and Immunology Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD, USATel +1 301-504-8577Fax +1- 301 504-9062Email allen.smith@usda.govIntroduction: Endemic obesity is considered the driving force for the dramatic increase in incidence of type 2 diabetes (T2D). There is mounting evidence that chronic, low-grade inflammation driven by Th1/Th17 cells and M1 macrophages, is a critical link between obesity and insulin resistance. IL-25 promotes development of a Th2 immune response and M2 macrophages that counteract the inflammation associated with obesity and T2D.Methods: Mice were fed a high-fat diet (HFD) for 16 weeks and then treated with IL-25 or BSA as a control for 21 days. Body weight, blood glucose levels, intraperitoneal glucose tolerance, and gene expression were evaluated in mice treated with BSA or IL-25. Ob/ob mice fed a normal control diet were also treated with BSA or IL-25 and body weight and blood glucose levels were measured. Transepithelial electrical resistance and sodium-linked glucose absorption were determined in muscle-free small intestinal tissue and glucose absorption assessed in vitro in intestinal epithelial and skeletal muscle cell lines.Results: Administration of IL-25 to HFD fed mice reversed glucose intolerance, an effect mediated in part by reduction in SGLT-1 activity and Glut2 expression. Importantly, the improved glucose tolerance in HFD mice treated with IL-25 was maintained for several weeks post-treatment indicating long-term changes in glucose metabolism in obese mice. Glucose intolerance was also reversed by IL-25 treatment in genetically obese ob/ob mice without inducing weight loss. In vitro studies demonstrated that glucose absorption was inhibited by IL-25 treatment in the epithelial IPEC-1 cells but increased glucose absorption in the L6 skeletal muscle cells. This supports a direct cell-specific effect of IL-25 on glucose metabolism.Conclusion: These results suggest that the IL-25 pathway may be a useful target for the treatment of metabolic syndrome.Keywords: metabolic syndrome, glucose, ob/ob, rodent
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- 2021
35. Nuclear Aurora kinase A triggers programmed death‐ligand 1‐mediated immune suppression by activating MYC transcription in triple‐negative breast cancer
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Shulan Sun, Wei Zhou, Xiaoxi Li, Fei Peng, Min Yan, Yajing Zhan, Fan An, Xiaoyan Li, Yunyong Liu, Quentin Liu, and Haozhe Piao
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Aurora kinase A ,immune evasion ,immunotherapy ,MYC ,programmed death‐ligand 1 ,triple‐negative breast cancer ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Abstract Background Increasing studies have reported that oncogenes regulate components of the immune system, suggesting that this is a mechanism for tumorigenesis. Aurora kinase A (AURKA), a serine/threonine kinase, is involved in cell mitosis and is essential for tumor cell proliferation, metastasis, and drug resistance. However, the mechanism by which AURKA is involved in immune response regulation is unclear. Therefore, this study aimed to investigate the role of AURKA in immune regulation in triple‐negative breast cancer (TNBC). Methods Peripheral blood mononuclear cells (PBMCs) were co‐cultured with TNBC cells. The xCELLigence Real‐Time Cell Analyzer‐MP system was used to detect the killing efficiency of immune cells on TNBC cells. The expression of immune effector molecules was tested by quantitative real‐time polymerase chain reaction (qRT‐PCR) to evaluate immune function. Furthermore, to validate AURKA‐regulated immune response in vivo, 4T1 murine breast cancer cell line with AURKA overexpression or downregulation was engrafted into BALB/c mice. The distribution and proportion of immune cells in tumors were further evaluated by immunohistochemistry and flow cytometry. Results Downregulation of AURKA in TNBC cells increased immune response by activating CD8+ T cell proliferation and activity. Nuclear rather than cytoplasmic AURKA‐derived programmed death‐ligand 1 (PD‐L1) expression was independent of its kinase activity. Mechanistic investigations showed that nuclear AURKA increased PD‐L1 expression via an MYC‐dependent pathway. PD‐L1 overexpression mostly reversed AURKA silencing‐induced expression of immune effector molecules, including interleukin‐ (IL‐2), interferon‐γ (IFN‐γ), and perforin. Moreover, AURKA expression was negatively correlated with the enrichment and activity of tumor‐infiltrating CD8+ T cells in 4T1 engrafted BALB/c mouse model. Conclusions Nuclear AURKA elevated PD‐L1 expression via an MYC‐dependent pathway and contributed to immune evasion in TNBC. Therapies targeting nuclear AURKA may restore immune responses against tumors.
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- 2021
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36. Nonlinearity-Driven Morphing and Control of Topological Modes in Non-Hermitian Systems
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Cai, Zhao-Fan, Wang, Yu-Chun, and Liu, Tao
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Quantum Physics ,Condensed Matter - Mesoscale and Nanoscale Physics ,Physics - Optics - Abstract
Non-Hermitian skin effect (NHSE) and nonlinearity can each delocalize topological zero modes (TZMs) from the boundary. To overcome the challenge of precise parameter tuning imposed by the NHSE-induced delocalization and to enhance the capacity of TZMs limited by nonlinearity-induced partial delocalization in Hermitian systems, we develop non-Hermitian nonlinear topological interface models. This model consists of both Hermitian and non-Hermitian Su-Schrieffer-Heeger (SSH) chains, incorporating nonreciprocal hopping and nonlinearity. When the nonlinearity is applied to both chains, the TZM becomes fully delocalized, extending across the entire lattice of two chains without the need for precise parameter tuning. By adjusting nonlinear coefficients in both chains, the wavefunction profile and plateaus across the entire lattice can be effectively controlled and customized through inherent configuration and intensity of the nonlinearity. Furthermore, the spectral localizer is utilized to demonstrate the topological protection of these extended non-Hermitian TZMs, confirming their robustness against disorder. Their dynamical stability under external pumping is also validated. Our findings provide a deeper insight into how nonlinearity and NHSE affect the behavior of topological modes, opening new possibilities for enhancing their capacity and performance in compact devices., Comment: 5 figures in the main text
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- 2024
37. Constraints on the photon polarisation in $b \to s \gamma$ transitions using $B_s^0 \rightarrow \phi e^+e^-$ decays
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LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Bellee, V., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Fernandez, S. Gomez, Gomulka, W., Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guerry, L., Guittiere, M., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Haimberger, J., Hajheidari, M., Hallett, G., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Hartmann, M., Hashmi, S., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Hill, D., Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Idzik, M., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Gonzalez, P. Laguarta, Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Lanfranchi, G., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lee, H., Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Lesser, E. D., Leverington, B., Li, A., Li, C., Li, H., Li, K., Li, L., Li, M., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Linton, H., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, W., Liu, Y., Liu, Y. L., Salvia, A. Lobo, Loi, A., Castro, J. Lomba, Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lu, Q., Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manganella, F. M., Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Gomez, D. Martinez, Santos, D. Martinez, Vidal, F. Martinez, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Sommerfeld, N. S., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sundfeld, D., Sutcliffe, W., Swallow, P. N., Swientek, K., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Ursov, E., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Cadenas, V. Valcarce, Valenti, G., Canudas, N. Valls, Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. Vidrier, Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vouters, G., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, H., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. J., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Winn, M., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, X., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, D., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.
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High Energy Physics - Experiment - Abstract
An angular analysis of the $B_s^0 \rightarrow \phi e^+e^-$ decay is performed using the proton-proton collision dataset collected between 2011 and 2018 by the LHCb experiment, corresponding to an integrated luminosity of $9\,{\rm fb}^{-1}$ at centre-of-mass energies of 7, 8 and $13\,{\rm TeV}$. The analysis is performed in the very low dielectron invariant mass-squared region between $0.0009$ and $0.2615\,{\rm GeV}^2\!/c^4$. The longitudinal polarisation fraction of the $\phi$ meson is measured to be less than $11.5\%$ at $90\%$ confidence level. The $A_{\mathrm{T}}^{\mathcal{R}e C\!P}$ observable, which is related to the lepton forward-backward asymmetry, is measured to be $0.116 \pm 0.155 \pm 0.006$, where the first uncertainty is statistical and the second systematic. The transverse asymmetries, $A_{\mathrm{T}}^{(2)}$ and $A_{\mathrm{T}}^{\mathcal{I}m C\!P}$ , which are sensitive to the virtual photon polarisation, are found to be $-0.045 \pm 0.235 \pm 0.014$ and $0.002 \pm 0.247 \pm 0.016$, respectively. The results are consistent with Standard Model predictions., Comment: 21 pages, 4 figures. All figures and tables, along with any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3433/(LHCb public pages)
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- 2024
38. SpecPCM: A Low-power PCM-based In-Memory Computing Accelerator for Full-stack Mass Spectrometry Analysis
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Fan, Keming, Moradifirouzabadi, Ashkan, Wu, Xiangjin, Li, Zheyu, Ponzina, Flavio, Persson, Anton, Pop, Eric, Rosing, Tajana, and Kang, Mingu
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Computer Science - Hardware Architecture ,Computer Science - Emerging Technologies ,Electrical Engineering and Systems Science - Signal Processing - Abstract
Mass spectrometry (MS) is essential for proteomics and metabolomics but faces impending challenges in efficiently processing the vast volumes of data. This paper introduces SpecPCM, an in-memory computing (IMC) accelerator designed to achieve substantial improvements in energy and delay efficiency for both MS spectral clustering and database (DB) search. SpecPCM employs analog processing with low-voltage swing and utilizes recently introduced phase change memory (PCM) devices based on superlattice materials, optimized for low-voltage and low-power programming. Our approach integrates contributions across multiple levels: application, algorithm, circuit, device, and instruction sets. We leverage a robust hyperdimensional computing (HD) algorithm with a novel dimension-packing method and develop specialized hardware for the end-to-end MS pipeline to overcome the non-ideal behavior of PCM devices. We further optimize multi-level PCM devices for different tasks by using different materials. We also perform a comprehensive design exploration to improve energy and delay efficiency while maintaining accuracy, exploring various combinations of hardware and software parameters controlled by the instruction set architecture (ISA). SpecPCM, with up to three bits per cell, achieves speedups of up to 82x and 143x for MS clustering and DB search tasks, respectively, along with a four-orders-of-magnitude improvement in energy efficiency compared with state-of-the-art CPU/GPU tools.
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- 2024
39. A Universal Circuit Set Using the $S_3$ Quantum Double
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Chen, Liyuan, Ren, Yuanjie, Fan, Ruihua, and Jaffe, Arthur
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Quantum Physics ,Condensed Matter - Strongly Correlated Electrons - Abstract
One potential route toward fault-tolerant universal quantum computation is to use non-Abelian topological codes. In this work, we investigate how to achieve this goal with the quantum double model $\mathcal{D}(S_3)$ -- a specific non-Abelian topological code. By embedding each on-site Hilbert space into a qubit-qutrit pair, we give an explicit construction of the circuits for creating, moving, and locally measuring all non-trivial anyons. We also design a specialized anyon interferometer to measure the total charge remotely for well-separated anyons; this avoids fusing them together. These protocols enable the implementation of a universal gate set proposed by Cui et al. and active quantum error correction of the circuit-level noise during the computation process. To further reduce the error rate and facilitate error correction, we encode each physical degree of freedom of $\mathcal{D}(S_3)$ into a novel, quantum, error-correcting code, enabling fault-tolerant realization, at the logical level, of all gates in the anyon manipulation circuits. Our proposal offers a promising path to realize universal topological quantum computation in the NISQ era., Comment: 17 pages
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- 2024
40. Asymptotics of Sum of Heavy-tailed Risks with Copulas
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Yang, Fan and Zhang, Yi
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Quantitative Finance - Risk Management - Abstract
We study the tail asymptotics of the sum of two heavy-tailed random variables. The dependence structure is modeled by copulas with the so-called tail order property. Examples are presented to illustrate the approach. Further for each example we apply the main results to obtain the asymptotic expansions for Value-at-Risk of aggregate risk.
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- 2024
41. NEP-MB-pol: A unified machine-learned framework for fast and accurate prediction of water's thermodynamic and transport properties
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Xu, Ke, Liang, Ting, Xu, Nan, Ying, Penghua, Chen, Shunda, Wei, Ning, Xu, Jianbin, and Fan, Zheyong
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Physics - Chemical Physics ,Condensed Matter - Materials Science ,Condensed Matter - Soft Condensed Matter ,Condensed Matter - Statistical Mechanics - Abstract
Water's unique hydrogen-bonding network and anomalous properties present significant challenges for accurately modeling its structural, thermodynamic, and transport behavior across varied conditions. Although machine-learned potentials have advanced the prediction of individual properties, a unified computational framework capable of simultaneously capturing water's complex and subtle properties with high accuracy has remained elusive. Here, we address this challenge by introducing NEP-MB-pol, a highly accurate and efficient neuroevolution potential trained on extensive MB-pol reference data with coupled-cluster-level accuracy, combined with path-integral molecular dynamics and quantum-correction techniques to incorporate nuclear quantum effects. This NEP-MB-pol framework reproduces experimentally measured structural, thermodynamic, and transport properties of water across a broad temperature range, achieving simultaneous, fast, and accurate prediction of self-diffusion coefficient, viscosity, and thermal conductivity. Our approach provides a unified and robust tool for exploring thermodynamic and transport properties of water under diverse conditions, with significant potential for broader applications across research fields., Comment: 12 pages, 4 figures in the main text; 8 figures in the SI
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- 2024
42. MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI
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Newlin, Nancy R., Schilling, Kurt, Koudoro, Serge, Chandio, Bramsh Qamar, Kanakaraj, Praitayini, Moyer, Daniel, Kelly, Claire E., Genc, Sila, Chen, Jian, Yang, Joseph Yuan-Mou, Wu, Ye, He, Yifei, Zhang, Jiawei, Zeng, Qingrun, Zhang, Fan, Adluru, Nagesh, Nath, Vishwesh, Pathak, Sudhir, Schneider, Walter, Gade, Anurag, Rathi, Yogesh, Hendriks, Tom, Vilanova, Anna, Chamberland, Maxime, Pieciak, Tomasz, Ciupek, Dominika, Vega, Antonio Tristán, Aja-Fernández, Santiago, Malawski, Maciej, Ouedraogo, Gani, Machnio, Julia, Ewert, Christian, Thompson, Paul M., Jahanshad, Neda, Garyfallidis, Eleftherios, and Landman, Bennett A.
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Physics - Medical Physics ,Computer Science - Machine Learning - Abstract
White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. There is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Submissions are evaluated on the reproducibility and comparability of cross-acquisition bundle-wise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024/019
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- 2024
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43. Image Regeneration: Evaluating Text-to-Image Model via Generating Identical Image with Multimodal Large Language Models
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Meng, Chutian, Ma, Fan, Miao, Jiaxu, Zhang, Chi, Yang, Yi, and Zhuang, Yueting
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Diffusion models have revitalized the image generation domain, playing crucial roles in both academic research and artistic expression. With the emergence of new diffusion models, assessing the performance of text-to-image models has become increasingly important. Current metrics focus on directly matching the input text with the generated image, but due to cross-modal information asymmetry, this leads to unreliable or incomplete assessment results. Motivated by this, we introduce the Image Regeneration task in this study to assess text-to-image models by tasking the T2I model with generating an image according to the reference image. We use GPT4V to bridge the gap between the reference image and the text input for the T2I model, allowing T2I models to understand image content. This evaluation process is simplified as comparisons between the generated image and the reference image are straightforward. Two regeneration datasets spanning content-diverse and style-diverse evaluation dataset are introduced to evaluate the leading diffusion models currently available. Additionally, we present ImageRepainter framework to enhance the quality of generated images by improving content comprehension via MLLM guided iterative generation and revision. Our comprehensive experiments have showcased the effectiveness of this framework in assessing the generative capabilities of models. By leveraging MLLM, we have demonstrated that a robust T2M can produce images more closely resembling the reference image.
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- 2024
44. Objective Moir\'e Pattern
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Feng, Fan
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Condensed Matter - Materials Science ,Condensed Matter - Mesoscale and Nanoscale Physics - Abstract
Moir\'e patterns, typically formed by overlaying two layers of two-dimensional materials, exhibit an effective long-range periodicity that depends on the short-range periodicity of each layer and their spatial misalignment. Here, we study moir\'e patterns in objective structures with symmetries different from those in conventional patterns such as twisted bilayer graphene. Specifically, the mathematical descriptions for ring patterns, 2D Bravais lattice patterns, and helical patterns are derived analytically as representative examples of objective moir\'e patterns, using an augmented Fourier approach. Our findings reveal that the objective moir\'e patterns retain the symmetries of their original structures but with different parameters. In addition, we present a non-objective case, conformal moir\'e patterns, to demonstrate the versatility of this approach. We hope this geometric framework will provide insights for solving more complex moir\'e patterns and facilitate the application of moir\'e patterns in X-ray diffractions, wave manipulations, molecular dynamics, and other fields., Comment: 14 pages, 5 figures
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- 2024
45. DarkSHINE Baseline Design Report: Physics Prospects and Detector Technologies
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Chen, Jing, Chen, Ji-Yuan, Chen, Jun-Feng, Chen, Xiang, Fu, Chang-Bo, Guo, Jun, Guo, Yi-Han, Khaw, Kim Siang, Li, Jia-Lin, Li, Liang, Li, Shu, Lin, Yu-ming, Liu, Dan-Ning, Liu, Kang, Liu, Kun, Liu, Qi-Bin, Liu, Zhi, Lu, Ze-Jia, Lv, Meng, Song, Si-Yuan, Sun, Tong, Tang, Jian-Nan, Wan, Wei-Shi, Wang, Dong, Wang, Xiao-Long, Wang, Yu-Feng, Wang, Zhen, Wang, Zi-Rui, Wu, Wei-Hao, Yang, Hai-Jun, Yang, Lin, Yang, Yong, Yu, Dian, Yuan, Rui, Zhang, Jun-Hua, Zhang, Yu-Lei, Zhang, Yun-Long, Zhao, Zhi-Yu, Zhou, Bai-Hong, Zhu, Chun-Xiang, Zhu, Xu-Liang, and Zhu, Yi-Fan
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Physics - Instrumentation and Detectors ,High Energy Physics - Experiment - Abstract
DarkSHINE is a newly proposed fixed-target experiment initiative to search for the invisible decay of Dark Photon via missing energy/momentum signatures, based on the high repetition rate electron beam to be deployed/delivered by the Shanghai High repetition rate XFEL and Extreme light facility (SHINE). This report elaborates the baseline design of DarkSHINE experiment by introducing the physics goals, experimental setups, details of each sub-detector system technical designs, signal and backgground modelings, expected search sensitivities and future prospects, which mark an important step towards the further prototyping and technical demonstrations.
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- 2024
46. Measurement of $\phi(1020)$ meson production in fixed-target $\textit{p}$Ne collisions at $\sqrt{s_{NN}}$ = 68.5 GeV
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LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Balboni, A., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bonacci, R. B., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collaviti, S., Collins, P., Colombo, T., Colonna, M. C., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Darze, G., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. 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R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morren, W., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Pan, X., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Parmar, D., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Peters, M. J., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Segal, I., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. 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K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. J., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Winn, M., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, X., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.
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High Energy Physics - Experiment ,Nuclear Experiment - Abstract
The first measurement of $\phi(1020)$ meson production in fixed-target $p$Ne collisions at $\sqrt{s_{NN}}=68.5$ GeV is presented. The $\phi(1020)$ mesons are reconstructed in their $K^{+}K^{-}$ decay in a data sample consisting of proton collisions on neon nuclei at rest, corresponding to an integrated luminosity of $21.7 \pm 1.4$ nb$^{-1}$, collected by the LHCb detector at CERN. The $\phi(1020)$ production cross-section in the centre-of-mass rapidity range of $-1.8
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- 2024
47. LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning
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Yang, Yahe, Tao, Chunliang, and Fan, Xiaojing
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Computer Science - Computation and Language - Abstract
Effective preference tuning is pivotal in aligning chatbot responses with human expectations, enhancing user satisfaction and engagement. Traditional approaches, notably Reinforcement Learning from Human Feedback (RLHF) as employed in advanced models like GPT-4, have demonstrated considerable success in this domain. However, RLHF methods are often computationally intensive and resource-demanding, limiting their scalability and accessibility for broader applications. To address these challenges, this study introduces LoRA-Lite Ensemble (LoRA-LiteE), an innovative framework that combines Supervised Fine-tuning (SFT) with Low-Rank Adaptation (LoRA) and Ensemble Learning techniques to effectively aggregate predictions of lightweight models, which aim to achieve a balance between the performance and computational cost. Utilizing the Chatbot Arena benchmark dataset, we conduct a comprehensive comparative analysis among our LoRA-LiteE model, corresponding base models at different scales, and GPT-4 trained with RLHF. Our empirical results demonstrate that the proposed LoRA-LiteE model achieves comparable performance to un-finetuned GPT-4 and outperforms the single larger-scale models under limited resource constraints. These findings highlight that our LoRA-LiteE provides a feasible and efficient methodology for human preference prediction in chatbot systems, enhancing scalability and accessibility, and thereby broadening the applicability of preference-tuned chatbots in resource-constrained environments.
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- 2024
48. Is Precise Recovery Necessary? A Task-Oriented Imputation Approach for Time Series Forecasting on Variable Subset
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Hao, Qi, Liang, Runchang, Gao, Yue, Dong, Hao, Fan, Wei, Jiang, Lu, and Wang, Pengyang
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Computer Science - Machine Learning - Abstract
Variable Subset Forecasting (VSF) refers to a unique scenario in multivariate time series forecasting, where available variables in the inference phase are only a subset of the variables in the training phase. VSF presents significant challenges as the entire time series may be missing, and neither inter- nor intra-variable correlations persist. Such conditions impede the effectiveness of traditional imputation methods, primarily focusing on filling in individual missing data points. Inspired by the principle of feature engineering that not all variables contribute positively to forecasting, we propose Task-Oriented Imputation for VSF (TOI-VSF), a novel framework shifts the focus from accurate data recovery to directly support the downstream forecasting task. TOI-VSF incorporates a self-supervised imputation module, agnostic to the forecasting model, designed to fill in missing variables while preserving the vital characteristics and temporal patterns of time series data. Additionally, we implement a joint learning strategy for imputation and forecasting, ensuring that the imputation process is directly aligned with and beneficial to the forecasting objective. Extensive experiments across four datasets demonstrate the superiority of TOI-VSF, outperforming baseline methods by $15\%$ on average.
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- 2024
49. Density-wave like behavior in a new Kagome material Ce$_{2}$Ru$_{3}$Si
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Wang, Jinhua, Fan, Shengtai, Li, Yiwen, Zhu, Xiyu, and Wen, Hai-hu
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Condensed Matter - Strongly Correlated Electrons ,Condensed Matter - Materials Science ,Condensed Matter - Superconductivity - Abstract
Kagome materials with inherent geometric frustration can produce many interesting physical properties, such as flat bands, quantum spin liquid, chiral magnetism, superconductivity and density-wave orders. Sometimes, the localized 4$f$ electrons from Ce atoms coupled with other conduction electrons would also give rise to the flat bands near the Fermi level, and results in the formation of heavy fermion. Thus, it is highly probable that kagome material incorporating Ce element will display nontrivial physical properties. In this study, we present a new Kagome material belonging to the trinary Laves phase, Ce$_{2}$Ru$_{3}$Si, in which kagome plane is formed by Ru atoms. Electrical transport and specific heat measurements reveal a density-wave like transition. A Curie-Weiss behavior is observed in low-temperature region. Meanwhile we also find a relatively large specific coefficient $\gamma_{n}(0)$. The calculated Wilson ratio $R_\mathrm{W}\propto{\chi(0)/\gamma_{n}}$ is approximately 3.1, indicating a moderate electron correlation effect. Chemical doping of Ir at the Ru site rapidly suppresses this density-wave like transition, while Mo doping leads to a gradual decrease in transition temperature. Theoretical calculation indicates both the Ce-4$f$ and Ru-4$d$ electronic bands cross the Fermi level, forming a Mexican-hat-shape Fermi surface close to the Fermi energy, potentially accounting for the observed density-wave like transition. Our findings provide an useful platform for investigating how hybridization between 4$f$ and 4$d$ electrons influences the electronic transport, and the relationship between the density-wave transition and kagome structure., Comment: 15pages, 4 figures,2 supplementary figures
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- 2024
50. The origin channels of hierarchical binary black hole mergers in the LIGO-Virgo-KAGRA O1, O2, and O3 runs
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Li, Guo-Peng and Fan, Xi-Long
- Subjects
Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Astrophysics of Galaxies ,Astrophysics - Solar and Stellar Astrophysics ,General Relativity and Quantum Cosmology - Abstract
We infer the origin channels of hierarchical mergers observed in the LIGO-Virgo-KAGRA (LVK) O1, O2, and O3 runs using a hierarchical Bayesian analysis under a parametric population model. By assuming the active galactic nucleus (ANG) disk and nuclear star cluster (NSC) channels, we find that NSCs likely dominate the hierarchical merger rate in the universe, corresponding to a fraction of $f_{\rm NSC}=0.87_{-0.29}^{+0.10}$ at 90\% credible intervals in our fiducial model; AGN disks may contribute up to nearly half of hierarchical mergers detectable with LVK, specifically $f_{\rm det,AGN}=0.34_{-0.26}^{+0.38}$. We investigate the impact of the escape speed, along with other population parameters on the branching fraction, suggesting that the mass, mass ratio, and spin of the sources play significant roles in population analysis. We show that hierarchical mergers constitute at least $\sim$$10\%$ of the gravitational wave events detected by LVK during the O1-O3 runs. Furthermore, we demonstrate that it is challenging to effectively infer detailed information about the host environment based solely on the distribution of black hole merger parameters if multiple formation channels are considered., Comment: 11 pages, 2 figures, 2 tables, and comments are welcome
- Published
- 2024
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