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4,379,879 results on '"An Zhao"'

1. NRF2 signaling plays an essential role in cancer progression through the NRF2-GPX2-NOTCH3 axis in head and neck squamous cell carcinoma

Author

Xiaoye Jin, Xiayuan Lou, Haoxiang Qi, Chao Zheng, Bo Li, Xuerong Siwu, Ren Liu, Qiaoli Lv, An Zhao, Jian Ruan, and Ming Jiang

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract The activation of nuclear factor erythroid 2–related factor 2 (NRF2) has been observed in various cancers. Yet its exact contribution to the development of head and neck squamous cell carcinoma (HNSCC) remains undetermined. We previously found that NRF2 signaling is critical for the differentiation of squamous basal progenitor cells, while disruption of NRF2 causes basal cell hyperplasia. In this study, we revealed a correlation between elevated NRF2 activity and poor outcomes in HNSCC patients. We demonstrated that NRF2 facilitates tumor proliferation, migration, and invasion, as evidenced by both in vitro and in vivo studies. Significantly, NRF2 augments the expression of the antioxidant enzyme GPX2, thereby enhancing the proliferative, migratory, and invasive properties of HNSCC cells. Activation of GPX2 is critical for sustaining cancer stem cells (CSCs) by up-regulating NOTCH3, a key driver of cancer progression. These results elucidate that NRF2 regulates HNSCC progression through the NRF2-GPX2-NOTCH3 axis. Our findings proposed that pharmacological targeting of the NRF2-GPX2-NOTCH3 axis could be a potential therapeutic approach against HNSCC.

Published
2024
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2. Interpolation-split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance

Author

Wing Keung Cheung, Ashkan Pakzad, Nesrin Mogulkoc, Sarah Helen Needleman, Bojidar Rangelov, Eyjolfur Gudmundsson, An Zhao, Mariam Abbas, Davina McLaverty, Dimitrios Asimakopoulos, Robert Chapman, Recep Savas, Sam M. Janes, Yipeng Hu, Daniel C. Alexander, John R. Hurst, and Joseph Jacob

Subjects
Computer engineering. Computer hardware, TK7885-7895, Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95
Abstract

Abstract The morphology and distribution of airway tree abnormalities enable diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity. Furthermore, the segmentation of a complete airway tree is challenging as the intensity, scale/size and shape of airway segments and their walls change across generations. The existing classical techniques either provide an undersegmented or oversegmented airway tree, and manual intervention is required for optimal airway tree segmentation. The recent development of deep learning methods provides a fully automatic way of segmenting airway trees; however, these methods usually require high GPU memory usage and are difficult to implement in low computational resource environments. Therefore, in this study, we propose a data-centric deep learning technique with big interpolated data, Interpolation-Split, to boost the segmentation performance of the airway tree. The proposed technique utilises interpolation and image split to improve data usefulness and quality. Then, an ensemble learning strategy is implemented to aggregate the segmented airway segments at different scales. In terms of average segmentation performance (dice similarity coefficient, DSC), our method (A) achieves 90.55%, 89.52%, and 85.80%; (B) outperforms the baseline models by 2.89%, 3.86%, and 3.87% on average; and (C) produces maximum segmentation performance gain by 14.11%, 9.28%, and 12.70% for individual cases when (1) nnU-Net with instant normalisation and leaky ReLU; (2) nnU-Net with batch normalisation and ReLU; and (3) modified dilated U-Net are used respectively. Our proposed method outperformed the state-of-the-art airway segmentation approaches. Furthermore, our proposed technique has low RAM and GPU memory usage, and it is GPU memory-efficient and highly flexible, enabling it to be deployed on any 2D deep learning model.

Published
2024
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3. Abnormal Cellular Populations Shape Thymic Epithelial Tumor Heterogeneity and Anti‐Tumor by Blocking Metabolic Interactions in Organoids

Author

Xuefei Liu, Changchun Wang, Yueyu Huang, Qiaoli Lv, Chang Yu, Jianghua Ying, Lianhui Duan, Yangzhong Guo, Guanyin Huang, Wenhui Shen, Ming Jiang, Weimin Mao, Zhixiang Zuo, and An Zhao

Subjects
histopathology, ionocyte, KRT14+ progenitor cells, metabolic interaction, thymic epithelial tumors, Science
Abstract

Abstract A variety of abnormal epithelial cells and immature and mature immune cells in thymic epithelial tumors (TETs) affect histopathological features, the degree of malignancy, and the response to treatment. Here, gene expression, trajectory inference, and T cell antigen receptor (TCR)‐based lineage tracking are profiled in TETs at single‐cell resolution. An original subpopulation of KRT14+ progenitor cells with a spindle cell phenotype is shown. An abnormal infiltration of immature T cells with a TCR hyper‐rearrangement state is revealed, due to the lack of CCL21+ medullary epithelial cells. For thymic carcinoma, the novel biomarkers of MSLN, CCL20, and SLC1A5 are identified and observed an elevated expression of LAG3 and HAVCR2 in malignant tumorn‐infiltrating mature T cells. These common features based on the single‐cell populations may inform pathological reclassification of TETs. Meanwhile, it is found that macrophages (MACs) attract thymic tumor cells through the LGALS9‐SLC1A5 axis, providing them with glutamine to elicit metabolic reprogramming. This MAC‐based metabolic pattern can promote malignancy progression. Additionally, an interactive immune environment in TETs is identified that correlates with the infiltration of abnormal FOXI1+ CFTR− ionocytes. Collectively, the data broaden the knowledge of TET cellular ecosystems, providing a basis for tackling histopathological diagnosis and related treatment.

Published
2024
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6. Pharmacokinetics and pharmacodynamics of mibavademab (a leptin receptor agonist): Results from a first‐in‐human phase I study

Author

Andrew Gewitz, Jeanne Mendell, Yuhuan Wang, Charles Harris, Benjamin A. Olenchock, Simona Podgrabinska, Wenjun Zheng, An Zhao, Hao Pan, Frédéric Vanhoutte, and John D. Davis

Subjects
Therapeutics. Pharmacology, RM1-950, Public aspects of medicine, RA1-1270
Abstract

Abstract Mibavademab (previously known as REGN4461), a fully human monoclonal antibody, is being investigated for the treatment of conditions associated with leptin deficiency. Here, we report pharmacokinetics (PKs), pharmacodynamics, and immunogenicity from a phase I study in healthy participants (NCT03530514). In part A, lean or overweight healthy participants were randomized to single‐ascending‐dose cohorts of 0.3, 1.0, 3.0, 10, and 30 mg/kg intravenous (i.v.), or 300 and 600 mg subcutaneous doses of mibavademab or placebo. In part B, overweight or obese participants were randomized to receive multiple doses of mibavademab (15 mg/kg i.v. loading dose and 10 mg/kg i.v. at weeks 3, 6, and 9) or placebo, stratified by body mass index and baseline leptin levels: low leptin (

Published
2024
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7. Transient thermal models of induction machines under Inter‐turn short‐circuit fault conditions

Author

An Zhao, Giovanni Zanuso, and Luca Peretti

Subjects
electrical faults, finite element analysis, lumped parameter networks, short‐circuit currents, temperature distribution, thermal analysis, Applications of electric power, TK4001-4102
Abstract

Abstract Induction machines are the working horses in a lot of industries. Inter‐turn short‐circuit (ITSC) fault is one of the most common failure modes taking place in them. It can generate large fault currents that lead to a local temperature rise in the faulty stator slot, which deteriorates the working performance of the machine or even cascades to a complete machine breakdown. This article focuses on developing transient thermal models for an induction machine under ITSC faults. The aim of these thermal models is to understand the thermal behaviour of the induction machine at different ITSC fault propagation stages. The first thermal model is based on finite element method (FEM) simulation that includes a physics‐based model for the thermal contact resistance. The second thermal model is a lumped parameter thermal network that models the stator as inter‐connected slot‐number parts. They are both capable of modelling the characteristic of non‐uniform temperature along the circumferential direction for the induction machine operating under ITSC fault conditions. After area‐weighted averaging the results of the FEM simulation, it is found they are quantitatively consistent, with an average relative error magnitude of 5%, as well.

Published
2023
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8. Challenges and Practices of Knowledge Sharing in E-Learning: A Systematic Literature Review

Author

Gary Yu Zhao, Cindy Zhiling Tu, and Joni Adkins

Abstract

E-learning has become an emerging approach to delivering knowledge and information to students. It presents significant challenges to sharing and understanding learners' knowledge, inquiries, interest, and needs. Educational organizations are expected to apply the appropriate practice to address knowledge-sharing challenges in the e-learning environment. In this study, through a systematic literature review, we intend to identify and synthesize knowledge-sharing challenges and practices as well as classify the most discussed challenges and practices in various contextual settings. The findings classify the knowledge-sharing challenges and practices in e-learning from four perspectives: organization, individual, knowledge and technology. This study aims to build a knowledge base to support future research and effective knowledge-sharing practices in the e-learning environment.

Published
2024

9. FEM and CFD thermal modeling of an axial-flux induction machine with experimental validation

Author

An Zhao, Huanyu Chen, Christian Boßer, and Mats Leksell

Subjects
Thermal modeling, FEM, CFD, Conjugate heat transfer, Temperature measurement, Axial-flux induction machine, Engineering (General). Civil engineering (General), TA1-2040
Abstract

Axial-flux electrical machines are ideal candidates as in-wheel motors for electrical vehicles (EVs). Due to their characteristics of high power density and compact structure, thermal management is vital for them. Lowering the temperature of the stator windings can protect the insulation material from rapid degradation and reduce the extra copper losses by decreasing their electrical resistance. Contrary to the widely reported axial-flux permanent magnet synchronous machines, thermal modeling methods of axial-flux induction machines are rarely seen in previous literature. Hence, the present work aims at investigating their thermal response based on both the finite element method (FEM) and computational fluid dynamics (CFD) techniques. In addition, a test rig is built to validate the computed results of these two thermal models with the experimental measurement. The CFD conjugate heat transfer analysis is found to be more accurate than the FEM thermal analysis in predicting the temperature distribution of different components in the machine and the temperature rise of the airflow, with lower than 5 ∘C average errors deviating from the corresponding measured data at three rotation speeds. Additionally, the CFD simulation is able to capture the backflow occurring near the outlets of the casing that has been found during the experiments.

Published
2024
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10. Biocompatible optical physically unclonable function hydrogel microparticles for on-dose authentication

Author

Mengmeng Zhang, Aparna Raghunath, An Zhao, and Huseyin Burak Eral

Subjects
Anti-counterfeiting, On-dose authentication, Physical unclonable function, Biocompatible, Hydrogel, Colloid, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

On-dose authentication (ODA) enhances security by incorporating customized molecular or micro-tags into each pill, preventing counterfeit products in genuine packages. ODA's security relies on tag non-replication and non-reverse engineering. Combining ODA with graphical Physical Unclonable Functions (PUF) promises maximum security. PUF uses intrinsic micro or nanoscale randomness as a unique ‘fingerprint’. However, current graphical PUFs have limitations like specific illumination requirements and the use of toxic materials, restricting their use in pharmaceuticals.In this study, we propose a novel approach called on-dose PUF. This method involves embedding microspheres randomly within micro biocompatible hydrogel particles. We showcase two distinct types of such on-dose PUFs. The first type utilizes randomly distributed superparamagnetic colloids (SPC) of identical diameters, while the second type utilizes vortexed sunflower oil drops of various diameters. The diameter and coordinates of the microspheres serve as input for generating cryptographic keys. A universal circle identification and binning program is used for extracting this information. One advantage of this approach is that it enables imaging using white light illumination and low-magnification microscopy, as color and signal intensity information are not crucial. This method enables patients to verify their medication by using their mobile phones from home.To assess the performance of the proposed on-dose PUF, we conducted canonical investigations on the single-diameter system. This system can only generate one layer of cryptographic keys, making it potentially more vulnerable than the multiple-diameter system. However, the single-diameter system successfully passed NIST Statistical tests and exhibited sufficient randomness, ideal bit uniformity, Hamming distance, and device uniqueness. Furthermore, we found that the encoding capacity of the single-diameter system was 9.2×1018, providing ample labeling potential.

Published
2024
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11. It Is All 'English' to Me: Can Cross-Culture Overlaps Facilitate L2 Idioms Teaching?

Author

Yiding Zhao

Abstract

Idioms are highly conventionalized expressions that allow users to express beyond literal meanings. Despite the language difference, counterparts of idioms may overlap cross-culturally due to similar origin, social habits, and experiences. It is therefore interesting to probe whether L2 learners may benefit from deliberate instructions built on shared counterparts existing in both L1 and L2, known as cross-language overlaps. Although the phenomenon of cross-language overlap has been reported by lexical and collocation processing research (e.g., Carrol & Conklin, 2017; Hubers et al., 2020), it is yet uncertain whether its impact is influential for L2 idiom learning with the absence of semantic or syntactical similarity. To address this gap, the current study attempted to investigate whether raising English as a foreign language learners' awareness of cross-language overlap may facilitate L2 idiom teaching and how such overlap-enhancement instruction may change over different congruence conditions (congruence-available and incongruence conditions) and time. The study collected L2 idiom learning data from forty-five Chinese English learners, following a pretest-posttest design. While the experimental group (n=26) learned twenty-eight target L2 idioms with an enhancement instruction emphasizing the overlaps, the control group (n=28) learned these idioms with a conventional instruction method. Findings of this study revealed that participants were able to benefit from cross-language overlaps from both conditions, and the enhancement instruction on the semantic overlaps would result in significant learning improvement. It is therefore pedagogically advisable for language teachers to marry up L1-L2 idiom phrases containing similar counterparts and deliberately focus on the semantic congruency.

Published
2024

12. A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study

Author

Yaozhi Lu, Shahab Aslani, An Zhao, Ahmed Shahin, David Barber, Mark Emberton, Daniel C. Alexander, and Joseph Jacob

Subjects
Computed tomography, Lung, Deep learning, Computer vision, Saliency map, Longitudinal data, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

In this study, we present a hybrid CNN-RNN approach to investigate long-term survival of subjects in a lung cancer screening study. Subjects who died of cardiovascular and respiratory causes were identified whereby the CNN model was used to capture imaging features in the CT scans and the RNN model was used to investigate time series and thus global information. To account for heterogeneity in patients' follow-up times, two different variants of LSTM models were evaluated, each incorporating different strategies to address irregularities in follow-up time. The models were trained on subjects who underwent cardiovascular and respiratory deaths and a control cohort matched to participant age, gender, and smoking history. The combined model can achieve an AUC of 0.76 which outperforms humans at cardiovascular mortality prediction. The corresponding F1 and Matthews Correlation Coefficient are 0.63 and 0.42 respectively. The generalisability of the model is further validated on an ‘external’ cohort. The same models were applied to survival analysis with the Cox Proportional Hazard model. It was demonstrated that incorporating the follow-up history can lead to improvement in survival prediction. The Cox neural network can achieve an IPCW C-index of 0.75 on the internal dataset and 0.69 on an external dataset. Delineating subjects at increased risk of cardiorespiratory mortality can alert clinicians to request further more detailed functional or imaging studies to improve the assessment of cardiorespiratory disease burden. Such strategies may uncover unsuspected and under-recognised pathologies thereby potentially reducing patient morbidity.

Published
2023
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13. Flow field simulation and structure optimization of a new type of vermicelli drying room

Author

AN Zhao-yuan and SHI Zhan-qun

Subjects
vermicelli, drying room, flow field simulation, structure optimization, Food processing and manufacture, TP368-456
Abstract

Objective: This study aimed to improve the hot air utilization efficiency of the existing vermicelli drying room. Methods: Using the k—ε turbulence model as the theoretical model for the simulation analysis of the drying room, the core component of the automated production line, the Fluent software is used to simulate the drying room, and the movement characteristics and flow velocity distribution of the air in the drying room were obtained. Results: The structure of the drying room was optimized and drainage pipes were added. The initial plan was 120.83% faster than the original drying box in the direction perpendicular to the vermicelli drying plate. The cross-sectional shape and size of the drainage pipe were further optimized. The average speed at the outlet position of the drainage pipe with cross-section of 200 mm×200 mm was increased by 19.2% compared with the first optimized scheme, and the coefficient of wind speed unevenness has decreased by 8.89%. Conclusion: After optimization, the hot air utilization efficiency of the drying room, and the wind speed uniformity performance at each outlet are improved.

Published
2022
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14. Transforming the Hybrid Cloud for Emerging AI Workloads

Author

Chen, Deming, Youssef, Alaa, Pendse, Ruchi, Schleife, André, Clark, Bryan K., Hamann, Hendrik, He, Jingrui, Laino, Teodoro, Varshney, Lav, Wang, Yuxiong, Sil, Avirup, Jabbarvand, Reyhaneh, Xu, Tianyin, Kindratenko, Volodymyr, Costa, Carlos, Adve, Sarita, Mendis, Charith, Zhang, Minjia, Núñez-Corrales, Santiago, Ganti, Raghu, Srivatsa, Mudhakar, Kim, Nam Sung, Torrellas, Josep, Huang, Jian, Seelam, Seetharami, Nahrstedt, Klara, Abdelzaher, Tarek, Eilam, Tamar, Zhao, Huimin, Manica, Matteo, Iyer, Ravishankar, Hirzel, Martin, Adve, Vikram, Marinov, Darko, Franke, Hubertus, Tong, Hanghang, Ainsworth, Elizabeth, Zhao, Han, Vasisht, Deepak, Do, Minh, Oliveira, Fabio, Pacifici, Giovanni, Puri, Ruchir, and Nagpurkar, Priya

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence, Computer Science - Hardware Architecture, Computer Science - Emerging Technologies, Computer Science - Multiagent Systems
Abstract

This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative, full-stack co-design approaches, emphasizing usability, manageability, affordability, adaptability, efficiency, and scalability. By integrating cutting-edge technologies such as generative and agentic AI, cross-layer automation and optimization, unified control plane, and composable and adaptive system architecture, the proposed framework addresses critical challenges in energy efficiency, performance, and cost-effectiveness. Incorporating quantum computing as it matures will enable quantum-accelerated simulations for materials science, climate modeling, and other high-impact domains. Collaborative efforts between academia and industry are central to this vision, driving advancements in foundation models for material design and climate solutions, scalable multimodal data processing, and enhanced physics-based AI emulators for applications like weather forecasting and carbon sequestration. Research priorities include advancing AI agentic systems, LLM as an Abstraction (LLMaaA), AI model optimization and unified abstractions across heterogeneous infrastructure, end-to-end edge-cloud transformation, efficient programming model, middleware and platform, secure infrastructure, application-adaptive cloud systems, and new quantum-classical collaborative workflows. These ideas and solutions encompass both theoretical and practical research questions, requiring coordinated input and support from the research community. This joint initiative aims to establish hybrid clouds as secure, efficient, and sustainable platforms, fostering breakthroughs in AI-driven applications and scientific discovery across academia, industry, and society., Comment: 70 pages, 27 figures

Published
2024

15. Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM

Author

Yu, Jiawei, Li, Yuang, Qiao, Xiaosong, Zhao, Huan, Zhao, Xiaofeng, Tang, Wei, Zhang, Min, Yang, Hao, and Su, Jinsong

Subjects
Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Text-to-speech (TTS) models have been widely adopted to enhance automatic speech recognition (ASR) systems using text-only corpora, thereby reducing the cost of labeling real speech data. Existing research primarily utilizes additional text data and predefined speech styles supported by TTS models. In this paper, we propose Hard-Synth, a novel ASR data augmentation method that leverages large language models (LLMs) and advanced zero-shot TTS. Our approach employs LLMs to generate diverse in-domain text through rewriting, without relying on additional text data. Rather than using predefined speech styles, we introduce a hard prompt selection method with zero-shot TTS to clone speech styles that the ASR model finds challenging to recognize. Experiments demonstrate that Hard-Synth significantly enhances the Conformer model, achieving relative word error rate (WER) reductions of 6.5\%/4.4\% on LibriSpeech dev/test-other subsets. Additionally, we show that Hard-Synth is data-efficient and capable of reducing bias in ASR.

Published
2024

16. Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning

Author

Zhao, Gang, Zhang, Ximing, Lu, Chenji, Zhao, Hui, Wu, Tianshu, Wang, Pengjie, Xu, Jian, and Zheng, Bo

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

Effective query-item relevance modeling is pivotal for enhancing user experience and safeguarding user satisfaction in e-commerce search systems. Recently, benefiting from the vast inherent knowledge, Large Language Model (LLM) approach demonstrates strong performance and long-tail generalization ability compared with previous neural-based specialized relevance learning methods. Though promising, current LLM-based methods encounter the following inadequacies in practice: First, the massive parameters and computational demands make it difficult to be deployed online. Second, distilling LLM models to online models is a feasible direction, but the LLM relevance modeling is a black box, and its rich intrinsic knowledge is difficult to extract and apply online. To improve the interpretability of LLM and boost the performance of online relevance models via LLM, we propose an Explainable LLM-driven Multi-dimensional Distillation framework for e-commerce relevance learning, which comprises two core components: (1) An Explainable LLM for relevance modeling (ELLM-rele), which decomposes the relevance learning into intermediate steps and models relevance learning as a Chain-of-Thought (CoT) reasoning, thereby enhancing both interpretability and performance of LLM. (2) A Multi-dimensional Knowledge Distillation (MKD) architecture that transfers the knowledge of ELLM-rele to current deployable interaction-based and representation-based student models from both the relevance score distribution and CoT reasoning aspects. Through distilling the probabilistic and CoT reasoning knowledge, MKD improves both the semantic interaction and long-tail generalization abilities of student models. Extensive offline evaluations and online experiments on Taobao search ad scene demonstrate that our proposed framework significantly enhances e-commerce relevance learning performance and user experience., Comment: Submitted to WWW 2025

Published
2024

17. Automated 3D Physical Simulation of Open-world Scene with Gaussian Splatting

Author

Zhao, Haoyu, Wang, Hao, Zhao, Xingyue, Wang, Hongqiu, Wu, Zhiyu, Long, Chengjiang, and Zou, Hua

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent advancements in 3D generation models have opened new possibilities for simulating dynamic 3D object movements and customizing behaviors, yet creating this content remains challenging. Current methods often require manual assignment of precise physical properties for simulations or rely on video generation models to predict them, which is computationally intensive. In this paper, we rethink the usage of multi-modal large language model (MLLM) in physics-based simulation, and present Sim Anything, a physics-based approach that endows static 3D objects with interactive dynamics. We begin with detailed scene reconstruction and object-level 3D open-vocabulary segmentation, progressing to multi-view image in-painting. Inspired by human visual reasoning, we propose MLLM-based Physical Property Perception (MLLM-P3) to predict mean physical properties of objects in a zero-shot manner. Based on the mean values and the object's geometry, the Material Property Distribution Prediction model (MPDP) model then estimates the full distribution, reformulating the problem as probability distribution estimation to reduce computational costs. Finally, we simulate objects in an open-world scene with particles sampled via the Physical-Geometric Adaptive Sampling (PGAS) strategy, efficiently capturing complex deformations and significantly reducing computational costs. Extensive experiments and user studies demonstrate our Sim Anything achieves more realistic motion than state-of-the-art methods within 2 minutes on a single GPU.

Published
2024

18. DESI 2024 VII: Cosmological Constraints from the Full-Shape Modeling of Clustering Measurements

Author

DESI Collaboration, Adame, A. G., Aguilar, J., Ahlen, S., Alam, S., Alexander, D. M., Prieto, C. Allende, Alvarez, M., Alves, O., Anand, A., Andrade, U., Armengaud, E., Avila, S., Aviles, A., Awan, H., Bahr-Kalus, B., Bailey, S., Baltay, C., Bault, A., Behera, J., BenZvi, S., Beutler, F., Bianchi, D., Blake, C., Blum, R., Bonici, M., Brieden, S., Brodzeller, A., Brooks, D., Buckley-Geer, E., Burtin, E., Calderon, R., Canning, R., Rosell, A. Carnero, Cereskaite, R., Cervantes-Cota, J. L., Chabanier, S., Chaussidon, E., Chaves-Montero, J., Chebat, D., Chen, S., Chen, X., Claybaugh, T., Cole, S., Cuceu, A., Davis, T. M., Dawson, K., de la Macorra, A., de Mattia, A., Deiosso, N., Dey, A., Dey, B., Ding, Z., Doel, P., Edelstein, J., Eftekharzadeh, S., Eisenstein, D. J., Elbers, W., Elliott, A., Fagrelius, P., Fanning, K., Ferraro, S., Ereza, J., Findlay, N., Flaugher, B., Font-Ribera, A., Forero-Sánchez, D., Forero-Romero, J. E., Frenk, C. S., Garcia-Quintero, C., Garrison, L. H., Gaztañaga, E., Gil-Marín, H., Gontcho, S. Gontcho A, Gonzalez-Morales, A. X., Gonzalez-Perez, V., Gordon, C., Green, D., Gruen, D., Gsponer, R., Gutierrez, G., Guy, J., Hadzhiyska, B., Hahn, C., Hanif, M. M. S, Herrera-Alcantar, H. K., Honscheid, K., Howlett, C., Huterer, D., Iršič, V., Ishak, M., Joyce, R., Juneau, S., Karaçaylı, N. G., Kehoe, R., Kent, S., Kirkby, D., Kong, H., Koposov, S. E., Kremin, A., Krolewski, A., Lahav, O., Lai, Y., Lan, T. -W., Landriau, M., Lang, D., Lasker, J., Goff, J. M. Le, Guillou, L. Le, Leauthaud, A., Levi, M. E., Li, T. S., Lodha, K., Magneville, C., Manera, M., Margala, D., Martini, P., Matthewson, W., Maus, M., McDonald, P., Medina-Varela, L., Meisner, A., Mena-Fernández, J., Miquel, R., Moon, J., Moore, S., Moustakas, J., Mudur, N., Mueller, E., Muñoz-Gutiérrez, A., Myers, A. D., Nadathur, S., Napolitano, L., Neveux, R., Newman, J. A., Nguyen, N. M., Nie, J., Niz, G., Noriega, H. E., Padmanabhan, N., Paillas, E., Palanque-Delabrouille, N., Pan, J., Penmetsa, S., Percival, W. J., Pieri, M. M., Pinon, M., Poppett, C., Porredon, A., Prada, F., Pérez-Fernández, A., Pérez-Ràfols, I., Rabinowitz, D., Raichoor, A., Ramírez-Pérez, C., Ramirez-Solano, S., Rashkovetskyi, M., Ravoux, C., Rezaie, M., Rich, J., Rocher, A., Rockosi, C., Roe, N. A., Rosado-Marin, A., Ross, A. J., Rossi, G., Ruggeri, R., Ruhlmann-Kleider, V., Samushia, L., Sanchez, E., Saulder, C., Schlafly, E. F., Schlegel, D., Schubnell, M., Seo, H., Shafieloo, A., Sharples, R., Silber, J., Slosar, A., Smith, A., Sprayberry, D., Tan, T., Tarlé, G., Taylor, P., Trusov, S., Vaisakh, R., Valcin, D., Valdes, F., Valogiannis, G., Vargas-Magaña, M., Verde, L., Walther, M., Wang, B., Wang, M. S., Weaver, B. A., Weaverdyck, N., Wechsler, R. H., Weinberg, D. H., White, M., Wilson, M. J., Yi, L., Yu, J., Yu, Y., Yuan, S., Yèche, C., Zaborowski, E. A., Zarrouk, P., Zhang, H., Zhao, C., Zhao, R., Zhou, R., Zhuang, T., and Zou, H.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present cosmological results from the measurement of clustering of galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). We adopt the full-shape (FS) modeling of the power spectrum, including the effects of redshift-space distortions, in an analysis which has been validated in a series of supporting papers. In the flat $\Lambda$CDM cosmological model, DESI (FS+BAO), combined with a baryon density prior from Big Bang Nucleosynthesis and a weak prior on the scalar spectral index, determines matter density to $\Omega_\mathrm{m}=0.2962\pm 0.0095$, and the amplitude of mass fluctuations to $\sigma_8=0.842\pm 0.034$. The addition of the cosmic microwave background (CMB) data tightens these constraints to $\Omega_\mathrm{m}=0.3056\pm 0.0049$ and $\sigma_8=0.8121\pm 0.0053$, while further addition of the the joint clustering and lensing analysis from the Dark Energy Survey Year-3 (DESY3) data leads to a 0.4% determination of the Hubble constant, $H_0 = (68.40\pm 0.27)\,{\rm km\,s^{-1}\,Mpc^{-1}}$. In models with a time-varying dark energy equation of state, combinations of DESI (FS+BAO) with CMB and type Ia supernovae continue to show the preference, previously found in the DESI DR1 BAO analysis, for $w_0>-1$ and $w_a<0$ with similar levels of significance. DESI data, in combination with the CMB, impose the upper limits on the sum of the neutrino masses of $\sum m_\nu < 0.071\,{\rm eV}$ at 95% confidence. DESI data alone measure the modified-gravity parameter that controls the clustering of massive particles, $\mu_0=0.11^{+0.45}_{-0.54}$, while the combination of DESI with the CMB and the clustering and lensing analysis from DESY3 constrains both modified-gravity parameters, giving $\mu_0 = 0.04\pm 0.22$ and $\Sigma_0 = 0.044\pm 0.047$, in agreement with general relativity. [Abridged.], Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/). 55 pages, 10 figures

Published
2024

19. DESI 2024 II: Sample Definitions, Characteristics, and Two-point Clustering Statistics

Author

DESI Collaboration, Adame, A. G., Aguilar, J., Ahlen, S., Alam, S., Alexander, D. M., Alvarez, M., Alves, O., Anand, A., Andrade, U., Armengaud, E., Avila, S., Aviles, A., Awan, H., Bailey, S., Baltay, C., Bault, A., Behera, J., BenZvi, S., Beutler, F., Bianchi, D., Blake, C., Blum, R., Brieden, S., Brodzeller, A., Brooks, D., Brown, Z., Buckley-Geer, E., Burtin, E., Calderon, R., Canning, R., Rosell, A. Carnero, Cereskaite, R., Cervantes-Cota, J. L., Chabanier, S., Chaussidon, E., Chaves-Montero, J., Chen, S., Chen, X., Claybaugh, T., Cole, S., Cuceu, A., Davis, T. M., Dawson, K., de la Macorra, A., de Mattia, A., Deiosso, N., Demina, R., Dey, A., Dey, B., Ding, Z., Doel, P., Edelstein, J., Eftekharzadeh, S., Eisenstein, D. J., Elliott, A., Fagrelius, P., Fanning, K., Ferraro, S., Ereza, J., Findlay, N., Flaugher, B., Font-Ribera, A., Forero-Sánchez, D., Forero-Romero, J. E., Frenk, C. S., Garcia-Quintero, C., Gaztañaga, E., Gil-Marín, H., Gontcho, S. Gontcho A, Gonzalez-Morales, A. X., Gonzalez-Perez, V., Gordon, C., Green, D., Gruen, D., Gsponer, R., Gutierrez, G., Guy, J., Hadzhiyska, B., Hahn, C., Hanif, M. M. S, Herrera-Alcantar, H. K., Honscheid, K., Hou, J., Howlett, C., Huterer, D., Iršič, V., Ishak, M., Juneau, S., Karaçaylı, N. G., Kehoe, R., Kent, S., Kirkby, D., Kitaura, F. -S., Kong, H., Kremin, A., Krolewski, A., Lai, Y., Lan, T. -W., Landriau, M., Lang, D., Lasker, J., Goff, J. M. Le, Guillou, L. Le, Leauthaud, A., Levi, M. E., Li, T. S., Lodha, K., Magneville, C., Manera, M., Margala, D., Martini, P., Maus, M., McDonald, P., Medina-Varela, L., Meisner, A., Mena-Fernández, J., Miquel, R., Moon, J., Moore, S., Moustakas, J., Mudur, N., Mueller, E., Muñoz-Gutiérrez, A., Myers, A. D., Nadathur, S., Napolitano, L., Neveux, R., Newman, J. A., Nguyen, N. M., Nie, J., Niz, G., Noriega, H. E., Padmanabhan, N., Paillas, E., Palanque-Delabrouille, N., Pan, J., Penmetsa, S., Percival, W. J., Pieri, M. M., Pinon, M., Poppett, C., Porredon, A., Prada, F., Pérez-Fernández, A., Pérez-Ràfols, I., Rabinowitz, D., Raichoor, A., Ramírez-Pérez, C., Ramirez-Solano, S., Rashkovetskyi, M., Ravoux, C., Rezaie, M., Rich, J., Rocher, A., Rockosi, C., Roe, N. A., Rosado-Marin, A., Ross, A. J., Rossi, G., Ruggeri, R., Ruhlmann-Kleider, V., Samushia, L., Sanchez, E., Saulder, C., Schlafly, E. F., Schlegel, D., Scholte, D., Schubnell, M., Seo, H., Sharples, R., Silber, J., Slosar, A., Smith, A., Sprayberry, D., Tan, T., Tarlé, G., Trusov, S., Vaisakh, R., Valcin, D., Valdes, F., Vargas-Magaña, M., Verde, L., Walther, M., Wang, B., Wang, M. S., Weaver, B. A., Weaverdyck, N., Wechsler, R. H., Weinberg, D. H., White, M., Wilson, M. J., Yu, J., Yu, Y., Yuan, S., Yèche, C., Zaborowski, E. A., Zarrouk, P., Zhang, H., Zhao, C., Zhao, R., Zhou, R., and Zou, H.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present the samples of galaxies and quasars used for DESI 2024 cosmological analyses, drawn from the DESI Data Release 1 (DR1). We describe the construction of large-scale structure (LSS) catalogs from these samples, which include matched sets of synthetic reference `randoms' and weights that account for variations in the observed density of the samples due to experimental design and varying instrument performance. We detail how we correct for variations in observational completeness, the input `target' densities due to imaging systematics, and the ability to confidently measure redshifts from DESI spectra. We then summarize how remaining uncertainties in the corrections can be translated to systematic uncertainties for particular analyses. We describe the weights added to maximize the signal-to-noise of DESI DR1 2-point clustering measurements. We detail measurement pipelines applied to the LSS catalogs that obtain 2-point clustering measurements in configuration and Fourier space. The resulting 2-point measurements depend on window functions and normalization constraints particular to each sample, and we present the corrections required to match models to the data. We compare the configuration- and Fourier-space 2-point clustering of the data samples to that recovered from simulations of DESI DR1 and find they are, generally, in statistical agreement to within 2\% in the inferred real-space over-density field. The LSS catalogs, 2-point measurements, and their covariance matrices will be released publicly with DESI DR1., Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/)

Published
2024

20. DESI 2024 V: Full-Shape Galaxy Clustering from Galaxies and Quasars

Author

DESI Collaboration, Adame, A. G., Aguilar, J., Ahlen, S., Alam, S., Alexander, D. M., Alvarez, M., Alves, O., Anand, A., Andrade, U., Armengaud, E., Avila, S., Aviles, A., Awan, H., Bailey, S., Baltay, C., Bault, A., Behera, J., BenZvi, S., Beutler, F., Bianchi, D., Blake, C., Blum, R., Brieden, S., Brodzeller, A., Brooks, D., Buckley-Geer, E., Burtin, E., Calderon, R., Canning, R., Rosell, A. Carnero, Cereskaite, R., Cervantes-Cota, J. L., Chabanier, S., Chaussidon, E., Chaves-Montero, J., Chen, S., Chen, X., Claybaugh, T., Cole, S., Cuceu, A., Davis, T. M., Dawson, K., de la Macorra, A., de Mattia, A., Deiosso, N., Dey, A., Dey, B., Ding, Z., Doel, P., Edelstein, J., Eftekharzadeh, S., Eisenstein, D. J., Elliott, A., Fagrelius, P., Fanning, K., Ferraro, S., Ereza, J., Findlay, N., Flaugher, B., Font-Ribera, A., Forero-Sánchez, D., Forero-Romero, J. E., Garcia-Quintero, C., Garrison, L. H., Gaztañaga, E., Gil-Marín, H., Gontcho, S. Gontcho A, Gonzalez-Morales, A. X., Gonzalez-Perez, V., Gordon, C., Green, D., Gruen, D., Gsponer, R., Gutierrez, G., Guy, J., Hadzhiyska, B., Hahn, C., Hanif, M. M. S, Herrera-Alcantar, H. K., Honscheid, K., Howlett, C., Huterer, D., Iršič, V., Ishak, M., Juneau, S., Karaçaylı, N. G., Kehoe, R., Kent, S., Kirkby, D., Kong, H., Koposov, S. E., Kremin, A., Krolewski, A., Lai, Y., Lan, T. -W., Landriau, M., Lang, D., Lasker, J., Goff, J. M. Le, Guillou, L. Le, Leauthaud, A., Levi, M. E., Li, T. S., Lodha, K., Magneville, C., Manera, M., Margala, D., Martini, P., Maus, M., McDonald, P., Medina-Varela, L., Meisner, A., Mena-Fernández, J., Miquel, R., Moon, J., Moore, S., Moustakas, J., Mueller, E., Muñoz-Gutiérrez, A., Myers, A. D., Nadathur, S., Napolitano, L., Neveux, R., Newman, J. A., Nguyen, N. M., Nie, J., Niz, G., Noriega, H. E., Padmanabhan, N., Paillas, E., Palanque-Delabrouille, N., Pan, J., Penmetsa, S., Percival, W. J., Pieri, M. M., Pinon, M., Poppett, C., Porredon, A., Prada, F., Pérez-Fernández, A., Pérez-Ràfols, I., Rabinowitz, D., Raichoor, A., Ramírez-Pérez, C., Ramirez-Solano, S., Rashkovetskyi, M., Ravoux, C., Rezaie, M., Rich, J., Rocher, A., Rockosi, C., Rodríguez-Martínez, F., Roe, N. A., Rosado-Marin, A., Ross, A. J., Rossi, G., Ruggeri, R., Ruhlmann-Kleider, V., Samushia, L., Sanchez, E., Saulder, C., Schlafly, E. F., Schlegel, D., Schubnell, M., Seo, H., Sharples, R., Silber, J., Slosar, A., Smith, A., Sprayberry, D., Tan, T., Tarlé, G., Trusov, S., Vaisakh, R., Valcin, D., Valdes, F., Vargas-Magaña, M., Verde, L., Walther, M., Wang, B., Wang, M. S., Weaver, B. A., Weaverdyck, N., Wechsler, R. H., Weinberg, D. H., White, M., Wilson, M. J., Yu, J., Yu, Y., Yuan, S., Yèche, C., Zaborowski, E. A., Zarrouk, P., Zhang, H., Zhao, C., Zhao, R., Zhou, R., and Zou, H.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present the measurements and cosmological implications of the galaxy two-point clustering using over 4.7 million unique galaxy and quasar redshifts in the range $0.1

Published
2024

21. Evidence for Two Excited $\Omega^{-}$ Hyperons

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, 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., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

Using $e^+e^-$ collision data corresponding to an integrated luminosity of 19 fb$^{-1}$ collected by the BESIII detector at center-of-mass energies ranging from 4.13 to 4.70 GeV, we report the first evidence for a new excited $\Omega^{-}$ hyperon, the $\Omega^*(2109)^{-}$, through the process $e^+ e^- \to \Omega^*(2109)^{-} \bar{\Omega}^{+} +c.c.$ with a significance of 3.7 $\sigma$. The mass and width of $\Omega^*(2109)^{-}$ are measured to be $2108.8 \pm 5.5_{\rm stat} \pm 1.5_{\rm syst} {\rm MeV}/c^{2}$ and $21.6 \pm 17.7_{\rm stat} \pm 9.4_{\rm syst} {\rm MeV}$, respectively. We also present evidence for production of the $\Omega^*(2012)^{-}$ in the process $e^+ e^- \to \Omega^*(2012)^{-} \bar{\Omega}^{+} +c.c.$ with a significance of 3.7 $\sigma$., Comment: 8 pages, 2 figures

Published
2024

22. Tuneable large nonlinear charge transport driven by the quantum metric at room temperatures in TbMn6Sn6

Author

Zhao, Weiyao, Xing, Kaijian, Zhao, Yufei, Chen, Lei, Hong, Min, Yin, Yuefeng, Liu, Yang, Le, Khoa Dang, Gayles, Jacob, Tang, Fang, Fang, Yong, Yan, Binghai, and Karel, Julie

Subjects
Condensed Matter - Materials Science
Abstract

Nonlinear electrodynamics in materials manifests as an electronic response that depends on second- or higher-order powers of the applied electromagnetic field. This response is highly dependent on the underlying crystal symmetries in the material and is typically smaller than the linear responses. Nonlinear responses are therefore usually employed to expose the symmetry breaking, geometric properties of the electronic band structure in materials. Naturally, a material system with a strong nonlinear response is also the key component in nonlinear devices. Here we report the strong room-temperature second-harmonic transport response in a quantum magnet,TbMn6Sn6, which is governed by the quantum metric and can be tuned with applied magnetic fields and temperature. We show that around room temperature, which is close to the spontaneous spin-reorientation transition, the magnetic configurations, and therefore the related symmetry breaking phases, are easily controlled. Our results pave the way from quantum materials to high performance tuneable nonlinear device applications at room temperature., Comment: 12 pages, 3 figures

Published
2024

23. EgoVid-5M: A Large-Scale Video-Action Dataset for Egocentric Video Generation

Author

Wang, Xiaofeng, Zhao, Kang, Liu, Feng, Wang, Jiayu, Zhao, Guosheng, Bao, Xiaoyi, Zhu, Zheng, Zhang, Yingya, and Wang, Xingang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Video generation has emerged as a promising tool for world simulation, leveraging visual data to replicate real-world environments. Within this context, egocentric video generation, which centers on the human perspective, holds significant potential for enhancing applications in virtual reality, augmented reality, and gaming. However, the generation of egocentric videos presents substantial challenges due to the dynamic nature of egocentric viewpoints, the intricate diversity of actions, and the complex variety of scenes encountered. Existing datasets are inadequate for addressing these challenges effectively. To bridge this gap, we present EgoVid-5M, the first high-quality dataset specifically curated for egocentric video generation. EgoVid-5M encompasses 5 million egocentric video clips and is enriched with detailed action annotations, including fine-grained kinematic control and high-level textual descriptions. To ensure the integrity and usability of the dataset, we implement a sophisticated data cleaning pipeline designed to maintain frame consistency, action coherence, and motion smoothness under egocentric conditions. Furthermore, we introduce EgoDreamer, which is capable of generating egocentric videos driven simultaneously by action descriptions and kinematic control signals. The EgoVid-5M dataset, associated action annotations, and all data cleansing metadata will be released for the advancement of research in egocentric video generation., Comment: Project Page: https://egovid.github.io/

Published
2024

24. Orbital Fulde-Ferrell-Larkin-Ovchinnikov state in 2H-NbS2 flakes

Author

Zhao, Xinming, Guo, Guoliang, Yan, Chengyu, Yuan, Noah F. Q., Zhao, Chuanwen, Guan, Huai, Lan, Changshuai, Li, Yihang, Liu, Xin, and Wang, Shun

Subjects
Condensed Matter - Superconductivity, Condensed Matter - Materials Science
Abstract

Symmetry breaking in a layered superconductor with Ising spin-orbit coupling has offered an opportunity to realize unconventional superconductivity. To be more specific, orbital Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state, exhibiting layer-dependent finite-momentum pairing, may emerge in transition metal dichalcogenides materials (TMDC) in the presence of an in-plane magnetic field. Orbital FFLO state can be more robust against the magnetic field than the conventional superconducting state with zero-momentum pairing. This feature renders its potential in field resilient superconducting functionality. Although, orbital FFLO state has been reported in NbSe2 and MoS2, it is not yet clear if orbital FFLO state can be a general feature of TMDC superconductor. Here, we report the observation of orbital FFLO state in 2H-NbS2 flakes and its dependence on the thickness of flake. We conclude that the relatively weak interlayer coupling is instrumental in stabilizing orbital FFLO state at higher temperature with respect to the critical temperature and lower magnetic field with respect to paramagnetic limit in NbS2 in comparison to its NbSe2 counterpart.

Published
2024

25. Study of the light scalar $a_{0}(980)$ through the decay $D^{0} \to a_{0}(980)^-e^{+} \nu_{e}$ with $a_{0}(980)^- \to \eta \pi^-$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

Using 7.93 ${\rm fb^{-1}}$ of $e^+e^-$ collision data collected at a center-of-mass energy of 3.773 ${\rm GeV}$ with the BESIII detector, we present an analysis of the decay $D^{0} \to \eta \pi^- e^+ \nu_{e}$. The branching fraction of the decay $D^{0} \to a_{0}(980)^{-} e^+ \nu_{e}$ with $a_{0}(980)^{-} \to \eta \pi^{-}$ is measured to be $(0.86\pm0.17_{\text{stat}}\pm0.05_{\text{syst}})\times 10^{-4}$. The decay dynamics of this process is studied with a single-pole parameterization of the hadronic form factor and the Flatt\'e formula describing the $a_0(980)$ line shape in the differential decay rate. The product of the form factor $f^{ a_0}_{+}(0)$ and the Cabibbo-Kobayashi-Maskawa matrix element $|V_{cd}|$ is determined for the first time with the result $f^{ a_0}_+(0)|V_{cd}|=0.126\pm0.013_{\rm stat}\pm0.003_{\rm syst}$.

Published
2024

26. MBA-SLAM: Motion Blur Aware Dense Visual SLAM with Radiance Fields Representation

Author

Wang, Peng, Zhao, Lingzhe, Zhang, Yin, Zhao, Shiyu, and Liu, Peidong

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract

Emerging 3D scene representations, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have demonstrated their effectiveness in Simultaneous Localization and Mapping (SLAM) for photo-realistic rendering, particularly when using high-quality video sequences as input. However, existing methods struggle with motion-blurred frames, which are common in real-world scenarios like low-light or long-exposure conditions. This often results in a significant reduction in both camera localization accuracy and map reconstruction quality. To address this challenge, we propose a dense visual SLAM pipeline (i.e. MBA-SLAM) to handle severe motion-blurred inputs. Our approach integrates an efficient motion blur-aware tracker with either neural radiance fields or Gaussian Splatting based mapper. By accurately modeling the physical image formation process of motion-blurred images, our method simultaneously learns 3D scene representation and estimates the cameras' local trajectory during exposure time, enabling proactive compensation for motion blur caused by camera movement. In our experiments, we demonstrate that MBA-SLAM surpasses previous state-of-the-art methods in both camera localization and map reconstruction, showcasing superior performance across a range of datasets, including synthetic and real datasets featuring sharp images as well as those affected by motion blur, highlighting the versatility and robustness of our approach. Code is available at https://github.com/WU-CVGL/MBA-SLAM.

Published
2024

27. MUltiplexed Survey Telescope: Perspectives for Large-Scale Structure Cosmology in the Era of Stage-V Spectroscopic Survey

Author

Zhao, Cheng, Huang, Song, He, Mengfan, Montero-Camacho, Paulo, Liu, Yu, Renard, Pablo, Tang, Yunyi, Verdier, Aurelien, Xu, Wenshuo, Yang, Xiaorui, Yu, Jiaxi, Zhang, Yao, Zhao, Siyi, Zhou, Xingchen, He, Shengyu, Kneib, Jean-Paul, Li, Jiayi, Li, Zhuoyang, Wang, Wen-Ting, Xianyu, Zhong-Zhi, Zhang, Yidian, Gsponer, Rafaela, Li, Xiao-Dong, Rocher, Antoine, Zou, Siwei, Tan, Ting, Huang, Zhiqi, Wang, Zhuoxiao, Li, Pei, Rombach, Maxime, Dong, Chenxing, Forero-Sanchez, Daniel, Shan, Huanyuan, Wang, Tao, Li, Yin, Zhai, Zhongxu, Wang, Yuting, Zhao, Gong-Bo, Shi, Yong, Mao, Shude, Huang, Lei, Guo, Liquan, and Cai, Zheng

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The MUltiplexed Survey Telescope (MUST) is a 6.5-meter telescope under development. Dedicated to highly-multiplexed, wide-field spectroscopic surveys, MUST observes over 20,000 targets simultaneously using 6.2-mm pitch positioning robots within a ~5 deg2 field of view. MUST aims to carry out the first Stage-V spectroscopic survey in the 2030s to map the 3D Universe with over 100 million galaxies and quasars, spanning from the nearby Universe to redshift z~5.5, corresponding to around 1 billion years after the Big Bang. To cover this extensive redshift range, we present an initial conceptual target selection algorithm for different types of galaxies, from local bright galaxies, luminous red galaxies, and emission line galaxies to high-redshift (2 < z < 5.5) Lyman-break galaxies. Using Fisher forecasts, we demonstrate that MUST can address fundamental questions in cosmology, including the nature of dark energy, test of gravity theories, and investigations into primordial physics. This is the first paper in the series of science white papers for MUST, with subsequent developments focusing on additional scientific cases such as galaxy and quasar evolution, Milky Way physics, and dynamic phenomena in the time-domain Universe., Comment: To be submitted to SCPMA

Published
2024

28. AuscultaBase: A Foundational Step Towards AI-Powered Body Sound Diagnostics

Author

Wang, Pingjie, Zhao, Zihan, Zhao, Liudan, He, Miao, Sun, Xin, Zhang, Ya, Sun, Kun, Wang, Yanfeng, and Wang, Yu

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Auscultation of internal body sounds is essential for diagnosing a range of health conditions, yet its effectiveness is often limited by clinicians' expertise and the acoustic constraints of human hearing, restricting its use across various clinical scenarios. To address these challenges, we introduce AuscultaBase, a foundational framework aimed at advancing body sound diagnostics through innovative data integration and contrastive learning techniques. Our contributions include the following: First, we compile AuscultaBase-Corpus, a large-scale, multi-source body sound database encompassing 11 datasets with 40,317 audio recordings and totaling 322.4 hours of heart, lung, and bowel sounds. Second, we develop AuscultaBase-Model, a foundational diagnostic model for body sounds, utilizing contrastive learning on the compiled corpus. Third, we establish AuscultaBase-Bench, a comprehensive benchmark containing 16 sub-tasks, assessing the performance of various open-source acoustic pre-trained models. Evaluation results indicate that our model outperforms all other open-source models in 12 out of 16 tasks, demonstrating the efficacy of our approach in advancing diagnostic capabilities for body sound analysis., Comment: 26 pages

Published
2024

29. A hybrid single quantum dot coupled cavity on a CMOS-compatible SiC photonic chip for Purcell-enhanced deterministic single-photon emission

Author

Zhu, Yifan, Liu, Runze, Yi, Ailun, Wang, Xudong, Qin, Yuanhao, Zhao, Zihao, Zhao, Junyi, Chen, Bowen, Zhang, Xiuqi, Song, Sannian, Huo, Yongheng, Ou, Xin, and Zhang, Jiaxiang

Subjects
Quantum Physics, Physics - Optics
Abstract

The ability to control nonclassical light emission from a single quantum emitter by an integrated cavity may unleash new perspectives for integrated photonic quantum applications. However, coupling a single quantum emitter to cavity within photonic circuitry towards creation of the Purcell-enhanced single-photon emission is elusive due to the complexity of integrating active devices in low-loss photonic circuits. Here we demonstrate a hybrid micro-ring resonator (HMRR) coupled with self-assembled quantum dots (QDs) for cavity-enhanced deterministic single-photon emission. The HMRR cavity supports whispering-gallery modes with quality factors up to 7800. By further introducing a micro-heater, we show that the photon emission of QDs can be locally and dynamically tuned over one free spectral ranges of the HMRR (~4 nm). This allows precise tuning of individual QDs in resonance with the cavity modes, thereby enhancing single-photon emission with a Purcell factor of about 4.9. Our results on the hybrid integrated cavities coupled with two-level quantum emitters emerge as promising devices for chip-based scalable photonic quantum applications.

Published
2024
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30. FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial Documents

Author

Zhao, Yilun, Long, Yitao, Jiang, Yuru, Wang, Chengye, Chen, Weiyuan, Liu, Hongjun, Zhang, Yiming, Tang, Xiangru, Zhao, Chen, and Cohan, Arman

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

We introduce FinDVer, a comprehensive benchmark specifically designed to evaluate the explainable claim verification capabilities of LLMs in the context of understanding and analyzing long, hybrid-content financial documents. FinDVer contains 2,400 expert-annotated examples, divided into three subsets: information extraction, numerical reasoning, and knowledge-intensive reasoning, each addressing common scenarios encountered in real-world financial contexts. We assess a broad spectrum of LLMs under long-context and RAG settings. Our results show that even the current best-performing system, GPT-4o, still lags behind human experts. We further provide in-depth analysis on long-context and RAG setting, Chain-of-Thought reasoning, and model reasoning errors, offering insights to drive future advancements. We believe that FinDVer can serve as a valuable benchmark for evaluating LLMs in claim verification over complex, expert-domain documents., Comment: EMNLP 2024

Published
2024

31. Distributed-Order Fractional Graph Operating Network

Author

Zhao, Kai, Li, Xuhao, Kang, Qiyu, Ji, Feng, Ding, Qinxu, Zhao, Yanan, Liang, Wenfei, and Tay, Wee Peng

Subjects
Computer Science - Machine Learning
Abstract

We introduce the Distributed-order fRActional Graph Operating Network (DRAGON), a novel continuous Graph Neural Network (GNN) framework that incorporates distributed-order fractional calculus. Unlike traditional continuous GNNs that utilize integer-order or single fractional-order differential equations, DRAGON uses a learnable probability distribution over a range of real numbers for the derivative orders. By allowing a flexible and learnable superposition of multiple derivative orders, our framework captures complex graph feature updating dynamics beyond the reach of conventional models. We provide a comprehensive interpretation of our framework's capability to capture intricate dynamics through the lens of a non-Markovian graph random walk with node feature updating driven by an anomalous diffusion process over the graph. Furthermore, to highlight the versatility of the DRAGON framework, we conduct empirical evaluations across a range of graph learning tasks. The results consistently demonstrate superior performance when compared to traditional continuous GNN models. The implementation code is available at \url{https://github.com/zknus/NeurIPS-2024-DRAGON}.

Published
2024

32. CPEG: Leveraging Consistency Policy with Consensus Guidance for Multi-agent Exploration

Author

Fu, Yuqian, Zhu, Yuanheng, Li, Haoran, Zhao, Zijie, Chai, Jiajun, and Zhao, Dongbin

Subjects
Computer Science - Multiagent Systems
Abstract

Efficient exploration is crucial in cooperative multi-agent reinforcement learning (MARL), especially in sparse-reward settings. However, due to the reliance on the unimodal policy, existing methods are prone to falling into the local optima, hindering the effective exploration of better policies. Furthermore, tackling multi-agent tasks in complex environments requires cooperation during exploration, posing substantial challenges for MARL methods. To address these issues, we propose a Consistency Policy with consEnsus Guidance (CPEG), with two primary components: (a) introducing a multimodal policy to enhance exploration capabilities, and (b) sharing the consensus among agents to foster agent cooperation. For component (a), CPEG incorporates a Consistency model as the policy, leveraging its multimodal nature and stochastic characteristics to facilitate exploration. Regarding component (b), CPEG introduces a Consensus Learner to deduce the consensus on the global state from local observations. This consensus then serves as a guidance for the Consistency Policy, promoting cooperation among agents. The proposed method is evaluated in multi-agent particle environments (MPE) and multi-agent MuJoCo (MAMuJoCo), and empirical results indicate that CPEG not only achieves improvements in sparse-reward settings but also matches the performance of baselines in dense-reward environments.

Published
2024

33. Combining strongly lensed and unlensed fast radio bursts: to be a more precise late-universe probe

Author

Zhang, Ji-Guo, Jiang, Yi-Fan, Zhao, Ze-Wei, Qi, Jing-Zhao, Zhang, Jing-Fei, and Zhang, Xin

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology, High Energy Physics - Phenomenology
Abstract

The Macquart relation and time-delay cosmography are now two promising ways to fast radio burst (FRB) cosmology. In this work, we propose a joint method that combines strongly lensed and unlensed FRBs for improving cosmological parameter estimation by using simulated FRB data from the future sensitive coherent all-sky monitor survey, which is expected to detect a large number of FRBs including galaxy-galaxy strongly lensed events. We find that using a detectable sample of 100,000 localized FRBs including $40$ lensed events can simultaneously constrain the Hubble constant and the equation of state of dark energy, with high precision of $\varepsilon(H_0)=0.4\%$ and $\varepsilon(w)=4.5\%$ in the simplest dynamical dark energy model. The joint analysis of unlensed and lensed FRBs significantly improves the constraint on $H_0$, which could be more effective than combining either the unlensed FRBs with future gravitational wave (GW) standard sirens or the lensed FRBs with CMB. Furthermore, combining the full FRB sample with the CMB+BAO+SNe data yields $\sigma(H_0)=0.29~{\rm km~s^{-1}~Mpc^{-1}}$, $\sigma(w_0)=0.046$, and $\sigma(w_a)=0.15$ in the two-parameter dynamical dark energy model, which outperform the results from the CMB+BAO+SNe+GW data. This reinforces the cosmological implications of a multi-wavelength observational strategy in optical and radio bands. We conclude that the future FRB observations will shed light on the nature of dark energy and also the Hubble tension if enough events with long-duration lensing are incorporated., Comment: 18 pages, 7 figures

Published
2024

34. Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent

Author

Sun, Xingwu, Chen, Yanfeng, Huang, Yiqing, Xie, Ruobing, Zhu, Jiaqi, Zhang, Kai, Li, Shuaipeng, Yang, Zhen, Han, Jonny, Shu, Xiaobo, Bu, Jiahao, Chen, Zhongzhi, Huang, Xuemeng, Lian, Fengzong, Yang, Saiyong, Yan, Jianfeng, Zeng, Yuyuan, Ren, Xiaoqin, Yu, Chao, Wu, Lulu, Mao, Yue, Xia, Jun, Yang, Tao, Zheng, Suncong, Wu, Kan, Jiao, Dian, Xue, Jinbao, Zhang, Xipeng, Wu, Decheng, Liu, Kai, Wu, Dengpeng, Xu, Guanghui, Chen, Shaohua, Chen, Shuang, Feng, Xiao, Hong, Yigeng, Zheng, Junqiang, Xu, Chengcheng, Li, Zongwei, Kuang, Xiong, Hu, Jianglu, Chen, Yiqi, Deng, Yuchi, Li, Guiyang, Liu, Ao, Zhang, Chenchen, Hu, Shihui, Zhao, Zilong, Wu, Zifan, Ding, Yao, Wang, Weichao, Liu, Han, Wang, Roberts, Fei, Hao, Yu, Peijie, Zhao, Ze, Cao, Xun, Wang, Hai, Xiang, Fusheng, Huang, Mengyuan, Xiong, Zhiyuan, Hu, Bin, Hou, Xuebin, Jiang, Lei, Ma, Jianqiang, Wu, Jiajia, Deng, Yaping, Shen, Yi, Wang, Qian, Liu, Weijie, Liu, Jie, Chen, Meng, Dong, Liang, Jia, Weiwen, Chen, Hu, Liu, Feifei, Yuan, Rui, Xu, Huilin, Yan, Zhenxiang, Cao, Tengfei, Hu, Zhichao, Feng, Xinhua, Du, Dong, Yu, Tinghao, Tao, Yangyu, Zhang, Feng, Zhu, Jianchen, Xu, Chengzhong, Li, Xirui, Zha, Chong, Ouyang, Wen, Xia, Yinben, Li, Xiang, He, Zekun, Chen, Rongpeng, Song, Jiawei, Chen, Ruibin, Jiang, Fan, Zhao, Chongqing, Wang, Bo, Gong, Hao, Gan, Rong, Hu, Winston, Kang, Zhanhui, Yang, Yong, Liu, Yuhong, Wang, Di, and Jiang, Jie

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

In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens. We conduct a thorough evaluation of Hunyuan-Large's superior performance across various benchmarks including language understanding and generation, logical reasoning, mathematical problem-solving, coding, long-context, and aggregated tasks, where it outperforms LLama3.1-70B and exhibits comparable performance when compared to the significantly larger LLama3.1-405B model. Key practice of Hunyuan-Large include large-scale synthetic data that is orders larger than in previous literature, a mixed expert routing strategy, a key-value cache compression technique, and an expert-specific learning rate strategy. Additionally, we also investigate the scaling laws and learning rate schedule of mixture of experts models, providing valuable insights and guidances for future model development and optimization. The code and checkpoints of Hunyuan-Large are released to facilitate future innovations and applications. Codes: https://github.com/Tencent/Hunyuan-Large Models: https://huggingface.co/tencent/Tencent-Hunyuan-Large, Comment: 17 pages, 4 Figures

Published
2024

35. Formation mechanisms and fluorescence properties of carbon dots in coal burning dust from coal fired power plants

Author

Zhao, Zhexian, Zhang, Weizuo, Zhang, Jin, Li, Yuzhao, Bai, Han, Zhao, Fangming, Jin, Zhongcai, Tang, Ju, Xiao, Yiming, Xu, Wen, and Lü, Yanfei

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

Carbon dots (CDs) shows great application potential with their unique and excellent performances. Coal and its derivatives are rich in aromatic ring structure, which is suitable for preparing CDs in microstructure. Coal burning dust from coal-fired power plants can be utilized as a rich resource to separate and extract CDs. It has been shown in our results that there have two main possible mechanisms for the formation of CDs in coal burning dust. One is the self-assembly of polycyclic aromatic hydrocarbons contained in coal or produced by incomplete combustion of coal. The other mechanism is that the bridge bonds linking different aromatic structures in coal are breaking which would form CDs with different functional groups when the coals are burning at high temperature. Under violet light excitation at 310-340 nm or red light at 610-640 nm, CDs extracted from coal burning dust can emit purple fluorescence around 410 nm. The mechanism of up-conversion fluorescence emission of CDs is due to a two-photon absorption process. The recycling of CDs from coal burning dust from coal-fired power plants are not only good to protect environment but also would be helpful for mass production of CDs.

Published
2024
Full Text
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36. Detection of two TeV gamma-ray outbursts from NGC 1275 by LHAASO

Author

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.

Subjects
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

Published
2024

37. Search for $\Lambda$-$\bar{\Lambda} $ oscillation in $J/\psi\rightarrow\Lambda\bar{\Lambda}$ decay

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

Using $(10087\pm44)\times 10^{6}$ $J/\psi$ decays collected by the BESIII detector at the BEPCII collider, we search for baryon number violation via $\Lambda-\bar{\Lambda}$ oscillation in the decay $J/\psi \to \Lambda \bar{\Lambda}$. No evidence for $\Lambda-\bar\Lambda$ oscillation is observed. The upper limit on the time-integrated probability of $\Lambda-\bar{\Lambda}$ oscillation is estimated to be $1.4\times 10^{-6}$, corresponding to an oscillation parameter less than $2.1\times 10^{-18}~\mathrm{GeV}$ at $90\%$ confidence level., Comment: 8 pages, 2 figures

Published
2024

38. AutoGLM: Autonomous Foundation Agents for GUIs

Author

Liu, Xiao, Qin, Bo, Liang, Dongzhu, Dong, Guang, Lai, Hanyu, Zhang, Hanchen, Zhao, Hanlin, Iong, Iat Long, Sun, Jiadai, Wang, Jiaqi, Gao, Junjie, Shan, Junjun, Liu, Kangning, Zhang, Shudan, Yao, Shuntian, Cheng, Siyi, Yao, Wentao, Zhao, Wenyi, Liu, Xinghan, Liu, Xinyi, Chen, Xinying, Yang, Xinyue, Yang, Yang, Xu, Yifan, Yang, Yu, Wang, Yujia, Xu, Yulin, Qi, Zehan, Dong, Yuxiao, and Tang, Jie

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

We present AutoGLM, a new series in the ChatGLM family, designed to serve as foundation agents for autonomous control of digital devices through Graphical User Interfaces (GUIs). While foundation models excel at acquiring human knowledge, they often struggle with decision-making in dynamic real-world environments, limiting their progress toward artificial general intelligence. This limitation underscores the importance of developing foundation agents capable of learning through autonomous environmental interactions by reinforcing existing models. Focusing on Web Browser and Phone as representative GUI scenarios, we have developed AutoGLM as a practical foundation agent system for real-world GUI interactions. Our approach integrates a comprehensive suite of techniques and infrastructures to create deployable agent systems suitable for user delivery. Through this development, we have derived two key insights: First, the design of an appropriate "intermediate interface" for GUI control is crucial, enabling the separation of planning and grounding behaviors, which require distinct optimization for flexibility and accuracy respectively. Second, we have developed a novel progressive training framework that enables self-evolving online curriculum reinforcement learning for AutoGLM. Our evaluations demonstrate AutoGLM's effectiveness across multiple domains. For web browsing, AutoGLM achieves a 55.2% success rate on VAB-WebArena-Lite (improving to 59.1% with a second attempt) and 96.2% on OpenTable evaluation tasks. In Android device control, AutoGLM attains a 36.2% success rate on AndroidLab (VAB-Mobile) and 89.7% on common tasks in popular Chinese APPs.

Published
2024

39. Einstein Probe discovery of EP240408a: a peculiar X-ray transient with an intermediate timescale

Author

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

Published
2024
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40. KaLDeX: Kalman Filter based Linear Deformable Cross Attention for Retina Vessel Segmentation

Author

Zhao, Zhihao, Faghihroohi, Shahrooz, Zhao, Yinzheng, Yang, Junjie, Zhong, Shipeng, Huang, Kai, Navab, Nassir, Li, Boyang, and Nasseri, M. Ali

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Background and Objective: In the realm of ophthalmic imaging, accurate vascular segmentation is paramount for diagnosing and managing various eye diseases. Contemporary deep learning-based vascular segmentation models rival human accuracy but still face substantial challenges in accurately segmenting minuscule blood vessels in neural network applications. Due to the necessity of multiple downsampling operations in the CNN models, fine details from high-resolution images are inevitably lost. The objective of this study is to design a structure to capture the delicate and small blood vessels. Methods: To address these issues, we propose a novel network (KaLDeX) for vascular segmentation leveraging a Kalman filter based linear deformable cross attention (LDCA) module, integrated within a UNet++ framework. Our approach is based on two key components: Kalman filter (KF) based linear deformable convolution (LD) and cross-attention (CA) modules. The LD module is designed to adaptively adjust the focus on thin vessels that might be overlooked in standard convolution. The CA module improves the global understanding of vascular structures by aggregating the detailed features from the LD module with the high level features from the UNet++ architecture. Finally, we adopt a topological loss function based on persistent homology to constrain the topological continuity of the segmentation. Results: The proposed method is evaluated on retinal fundus image datasets (DRIVE, CHASE_BD1, and STARE) as well as the 3mm and 6mm of the OCTA-500 dataset, achieving an average accuracy (ACC) of 97.25%, 97.77%, 97.85%, 98.89%, and 98.21%, respectively. Conclusions: Empirical evidence shows that our method outperforms the current best models on different vessel segmentation datasets. Our source code is available at: https://github.com/AIEyeSystem/KalDeX.

Published
2024

41. Extrapolating Prospective Glaucoma Fundus Images through Diffusion Model in Irregular Longitudinal Sequences

Author

Zhao, Zhihao, Yang, Junjie, Faghihroohi, Shahrooz, Zhao, Yinzheng, Zapp, Daniel, Huang, Kai, Navab, Nassir, and Nasseri, M. Ali

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The utilization of longitudinal datasets for glaucoma progression prediction offers a compelling approach to support early therapeutic interventions. Predominant methodologies in this domain have primarily focused on the direct prediction of glaucoma stage labels from longitudinal datasets. However, such methods may not adequately encapsulate the nuanced developmental trajectory of the disease. To enhance the diagnostic acumen of medical practitioners, we propose a novel diffusion-based model to predict prospective images by extrapolating from existing longitudinal fundus images of patients. The methodology delineated in this study distinctively leverages sequences of images as inputs. Subsequently, a time-aligned mask is employed to select a specific year for image generation. During the training phase, the time-aligned mask resolves the issue of irregular temporal intervals in longitudinal image sequence sampling. Additionally, we utilize a strategy of randomly masking a frame in the sequence to establish the ground truth. This methodology aids the network in continuously acquiring knowledge regarding the internal relationships among the sequences throughout the learning phase. Moreover, the introduction of textual labels is instrumental in categorizing images generated within the sequence. The empirical findings from the conducted experiments indicate that our proposed model not only effectively generates longitudinal data but also significantly improves the precision of downstream classification tasks., Comment: Accepted at BIBM 2024

Published
2024

42. Collaborative Knowledge Fusion: A Novel Approach for Multi-task Recommender Systems via LLMs

Author

Zhao, Chuang, Su, Xing, He, Ming, Zhao, Hongke, Fan, Jianping, and Li, Xiaomeng

Subjects
Computer Science - Information Retrieval
Abstract

Owing to the impressive general intelligence of large language models (LLMs), there has been a growing trend to integrate them into recommender systems to gain a more profound insight into human interests and intentions. Existing LLMs-based recommender systems primarily leverage item attributes and user interaction histories in textual format, improving the single task like rating prediction or explainable recommendation. Nevertheless, these approaches overlook the crucial contribution of traditional collaborative signals in discerning users' profound intentions and disregard the interrelatedness among tasks. To address these limitations, we introduce a novel framework known as CKF, specifically developed to boost multi-task recommendations via personalized collaborative knowledge fusion into LLMs. Specifically, our method synergizes traditional collaborative filtering models to produce collaborative embeddings, subsequently employing the meta-network to construct personalized mapping bridges tailored for each user. Upon mapped, the embeddings are incorporated into meticulously designed prompt templates and then fed into an advanced LLM to represent user interests. To investigate the intrinsic relationship among diverse recommendation tasks, we develop Multi-Lora, a new parameter-efficient approach for multi-task optimization, adept at distinctly segregating task-shared and task-specific information. This method forges a connection between LLMs and recommendation scenarios, while simultaneously enriching the supervisory signal through mutual knowledge transfer among various tasks. Extensive experiments and in-depth robustness analyses across four common recommendation tasks on four large public data sets substantiate the effectiveness and superiority of our framework.

Published
2024

43. Measurement of the branching fraction of $D^+ \to \tau^+\nu_{\tau}$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Tian, J. Y., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, 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., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

By analyzing $e^{+}e^{-}$ collision data with an integrated luminosity of 7.9~fb$^{-1}$ collected with the BESIII detector at the center-of-mass energy of 3.773~GeV, the branching fraction of $D^+\to\tau^+\nu_{\tau}$ is determined as $\mathcal{B}=(9.9\pm 1.1_\mathrm{stat}\pm 0.5_\mathrm{syst})\times10^{-4}$. Taking the most precise result $\mathcal{B}(D^+\to\mu^+\nu_{\mu})=(3.981\pm 0.079_\mathrm{stat}\pm0.040_\mathrm{syst})\times10^{-4}$, we determine $R_{\tau/\mu} = \Gamma(D^+\to\tau^+\nu_{\tau})/\Gamma(D^+\to\mu^+\nu_{\mu})= 2.49\pm0.31$, achieving a factor of two improvement in precision compared to the previous BESIII result. This measurement is in agreement with the standard model prediction of lepton flavor universality within one standard deviation.

Published
2024

44. Conceptual Design of the Muonium-to-Antimuonium Conversion Experiment (MACE)

Author

Bai, Ai-Yu, Cai, Hanjie, Chen, Chang-Lin, Chen, Siyuan, Chen, Xurong, Chen, Yu, Cheng, Weibin, Dai, Ling-Yun, Fan, Rui-Rui, Gong, Li, Guo, Zihao, He, Yuan, Hou, Zhilong, Huang, Yinyuan, Jia, Huan, Jiang, Hao, Jing, Han-Tao, Kang, Xiaoshen, Li, Hai-Bo, Li, Jincheng, Li, Yang, Liu, Shulin, Lu, Guihao, Miao, Han, Ning, Yunsong, Niu, Jianwei, Peng, Huaxing, Petrov, Alexey A., Qin, Yuanshuai, Sun, Mingchen, Tang, Jian, Tang, Jing-Yu, Tian, Ye, Wang, Rong, Wang, Xiaodong, Wang, Zhichao, Wu, Chen, Xing, Tian-Yu, Xiong, Weizhi, Xu, Yu, Yan, Baojun, Yao, De-Liang, Yu, Tao, Yuan, Ye, Yuan, Yi, Zhang, Yao, Zhang, Yongchao, Zhang, Zhilv, Zhao, Guang, and Zhao, Shihan

Subjects
High Energy Physics - Experiment, High Energy Physics - Phenomenology, Physics - Accelerator Physics, Physics - Instrumentation and Detectors
Abstract

The spontaneous conversion of muonium to antimuonium is one of the interesting charged lepton flavor violation phenomena, offering a sensitive probe of potential new physics and serving as a tool to constrain the parameter space beyond the Standard Model. Utilizing a high-intensity muon beam, a Michel electron magnetic spectrometer and a positron transport solenoid together with a positron detection system, MACE aims to discover or constrain this rare process at the conversion probability beyond the level of $10^{-13}$. This report provides an overview of the theoretical framework and detailed experimental design in the search for the muonium-to-antimuonium conversion., Comment: 115 pages, 59 figures

Published
2024

45. Modelling the covariance matrix for the power spectra before and after the BAO reconstruction

Author

Zhao, Ruiyang, Koyama, Kazuya, Wang, Yuting, and Zhao, Gong-Bo

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The baryon acoustic oscillation (BAO) reconstruction plays a crucial role in cosmological analysis for spectroscopic galaxy surveys because it can make the density field effectively more linear and more Gaussian. The combination of the power spectra before and after the BAO reconstruction helps break degeneracies among parameters, then improve the constraints on cosmological parameters. It is therefore important to estimate the covariance matrix between pre- and post-reconstructed power spectra. In this work, we use perturbation theory to estimate the covariance matrix of the related power spectra multipoles, and check the accuracy of the derived covariance model using a large suite of dark matter halo catalogs at $z=0.5$. We find that the diagonal part of the auto covariance is well described by the Gaussian prediction, while the cross covariance deviates from the Gaussian prediction quickly when $k > 0.1\,h\,\mathrm{Mpc}^{-1}$. Additionally, we find the non-Gaussian effect in the non-diagonal part of the cross covariance is comparable to, or even stronger than the pre-reconstruction covariance. By adding the non-Gaussian contribution, we obtain good agreement between analytical and numerical covariance matrices in the non-diagonal part up to $k \simeq 0.15\,h\,\mathrm{Mpc}^{-1}$. The agreement in the diagonal part is also improved, but still under-predicts the correlation in the cross covariance block., Comment: 24 pages, 10 figures. Comments are welcome

Published
2024

46. 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

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., 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.

Subjects
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.

Published
2024

47. Room temperature spin-layer locking of exciton-polariton nonlinearities

Author

Zhao, Jiaxin, Fieramosca, Antonio, Dini, Kevin, Shang, Qiuyu, Bao, Ruiqi, Luo, Yuan, Shen, Kaijun, Zhao, Yang, Su, Rui, Perez, Jesus Zuniga, Gao, Weibo, Ardizzone, Vincenzo, Sanvitto, Daniele, Xiong, Qihua, and Liew, Timothy C. H.

Subjects
Physics - Optics
Abstract

Recent advancements in transition metal dichalcogenides (TMDs) have unveiled exceptional optical and electronic characteristics, opened up new opportunities, and provided a unique platform for exploring light-matter interactions under the strong coupling regime. The exploitation of exciton-polaritons, with their peculiar hybrid light-matter properties, for the development of spintronic customizable devices that enhance both the information capacity and functionality at ambient temperatures is often suggested as a promising route. However, although TMD polaritons have shown promising potential, the microscopic mechanisms leading to nonlinearities in TMD polaritons are complex and their spin-anisotropy, a crucial requirement for many proposed polaritonic devices, has been missing. Here, we demonstrate the absence of spin-anisotropic interaction in a monolayer WS2 microcavity (at room temperature) and show how spin-dependent interactions can be controlled and spin anisotropy recovered by engineering double WS2 layer structures with varied interlayer spacing. We attribute this phenomenon to a distinctive feature in exciton-polariton physics: layer-dependent polariton-phonon coupling. We use theoretical calculations of the phonon electrostatic potentials finding a drastically different coupling strength for single and double monolayer samples and discuss qualitatively how this explains the observed spin-anisotropic response. This is further consistent with experiments on multi WS2 layer samples and the identification of a critical separation distance, above which an effective single monolayer spin-anisotropic response is recovered, both in experiment and theory. Our work lays the groundwork for the development of spin-optronic polaritonic devices at room temperature.

Published
2024

48. LEIA discovery of the longest-lasting and most energetic stellar X-ray flare ever detected

Author

Mao, Xuan, Liu, He-Yang, Wang, Song, Ling, Zhixing, Yuan, Weimin, Cheng, Huaqing, Pan, Haiwu, Li, Dongyue, Favata, Fabio, Ji, Tuo, Zhang, Jujia, Zhao, Xinlin, Wan, Jing, Cai, Zhiming, Castro-Tirado, Alberto J., Dai, Yanfeng, Deng, Licai, Ding, Xu, Ji, Kaifan, Jin, Chichuan, Lei, Yajuan, Li, Huali, Lin, Jun, Liu, Huaqiu, Liu, Mingjun, Liu, Shuai, Liu, Yuan, Sun, Hui, Sun, Shengli, Sun, Xiaojin, Shi, Jianrong, Wang, Jianguo, Wang, Jingxiu, Wang, Wenxin, Wei, Jianyan, Xin, Liping, Xiong, Dingrong, Zhang, Chen, Zhang, Wenda, Zhang, Yonghe, Zhang, Xiaofeng, Zhao, Donghua, and Zhou, Guiping

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract

LEIA (Lobster Eye Imager for Astronomy) detected a new X-ray transient on November 7, 2022, identified as a superflare event occurring on a nearby RS CVn-type binary HD 251108. The flux increase was also detected in follow-up observations at X-ray, UV and optical wavelengths. The flare lasted for about 40 days in soft X-ray observations, reaching a peak luminosity of ~1.1 * 10^34 erg/s in 0.5-4.0 keV, which is roughly 60 times the quiescent luminosity. Optical brightening was observed for only one night. The X-ray light curve is well described by a double "FRED" (fast rise and exponential decay) model, attributed to the cooling process of a loop arcade structure formed subsequent to the initial large loop with a half-length of ~1.9 times the radius of the host star. Time-resolved X-ray spectra were fitted with a two-temperature apec model, showing significant evolution of plasma temperature, emission measure, and metal abundance over time. The estimated energy released in the LEIA band is ~3 * 10^39 erg, suggesting this is likely the most energetic X-ray stellar flare with the longest duration detected to date., Comment: submitted to ApJL, 22 pages, 9 figures, 7 tables

Published
2024

49. Ground calibration and network of the first CATCH pathfinder

Author

Huang, Yiming, Xiao, Jingyu, Tao, Lian, Zhang, Shuang-Nan, Yin, Qian-Qing, Wang, Yusa, Zhao, Zijian, Zhang, Chen, Zhao, Qingchang, Ma, Xiang, Zhao, Shujie, Zhou, Heng, Wen, Xiangyang, Li, Zhengwei, Xiong, Shaolin, Zhang, Juan, Bu, Qingcui, Cang, Jirong, Cao, Dezhi, Chen, Wen, Ding, Siran, Dai, Yanfeng, Gao, Min, Gao, Yang, He, Huilin, Hou, Shujin, Hou, Dongjie, Hu, Tai, Huang, Guoli, Huang, Yue, Jia, Liping, Jin, Ge, Li, Dalin, Li, Jinsong, Li, Panping, Li, Yajun, Liu, Xiaojing, Ma, Ruican, Men, Lingling, Pan, Xingyu, Qi, Liqiang, Song, Liming, Sun, Xianfei, Tang, Qingwen, Xiong, Liyuan, Xu, Yibo, Yang, Sheng, Yang, Yanji, Yang, Yong, Zhang, Aimei, Zhang, Wei, Zhang, Yifan, Zhang, Yueting, Zhao, Donghua, Zhao, Kang, and Zhu, Yuxuan

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The Chasing All Transients Constellation Hunters (CATCH) space mission is focused on exploring the dynamic universe via X-ray follow-up observations of various transients. The first pathfinder of the CATCH mission, CATCH-1, was launched on June 22, 2024, alongside the Space-based multiband astronomical Variable Objects Monitor (SVOM) mission. CATCH-1 is equipped with narrow-field optimized Micro Pore Optics (MPOs) featuring a large effective area and incorporates four Silicon Drift Detectors (SDDs) in its focal plane. This paper presents the system calibration results conducted before the satellite integration. Utilizing the data on the performance of the mirror and detectors obtained through the system calibration, combined with simulated data, the ground calibration database can be established. Measuring the relative positions of the mirror and detector system, which were adjusted during system calibration, allows for accurate installation of the entire satellite. Furthermore, the paper outlines the operational workflow of the ground network post-satellite launch.

Published
2024

50. Phase sensitivity for an SU(1,1) interferometer via multiphoton subtraction at the output port

Author

Jiang, Tao, Zhao, Zekun, Kang, Qingqian, Zhao, Teng, Zhou, Nanrun, Liu, Cunjin, and Hu, Liyun

Subjects
Quantum Physics
Abstract

In the field of quantum precision measurement, enhancing phase sensitivity is crucial for various applications, including quantum metrology and quantum sensing technologies. We theoretically investigate the improvement in phase sensitivity and quantum Fisher information achieved through multiphoton subtraction operations at the output port of an SU(1,1) interferometer under conditions of photon loss. We use vacuum and coherent states as the inputs and detect the outputs by intensity detection. The results indicate that internal photon losses within the SU(1,1) interferometer have a more significant impact on the phase sensitivity compared to external photon losses. Moreover, increasing the number of photon subtractions m effectively enhances both the phase sensitivity and the quantum Fisher information. Notably, even under conditions of severe photon loss, the multiphoton subtraction operations can enable the phase sensitivity to surpass the standard quantum limit, approaching both the Heisenberg limit and the quantum Cram\'er-Rao bound. This study provides a new theoretical basis for enhancing the phase sensitivity in the SU(1,1) interferometer.

Published
2024

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