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As a Goldstone boson with zero quantum number and zero SM charge, the decays of long-lived $\eta$ ($\eta^{\prime}$) meson provide a unique window to search new physics beyond the standard model and new sources of CP violation, to test the low-energy QCD theory, and to measure the fundamental parameters of light quarks. For such goals in the physics frontiers we discuss a plan of building a super $\eta$ factory at HIAF high-energy terminal or at CiADS after upgrade. The high-intensity proton beam at HIAF provides a great opportunity of producing a huge number of $\eta$ samples of more than $10^{13}$ events per year, with multiple layers of thin targets of light nucleus. The physics goals, the first-version conceptual design of the spectrometer, and the preliminary simulation results are present., Comment: 19 pages, 9 figures

We use a newly recognized gluon distribution in the nucleon, which was predicted by a QCD evolution equation to consistently explain several intriguing phenomena associated with gamma-ray bursts. They are the GeV-TeV spectra of GRB 221009A, the remarkably symmetrical explosion cloud in kilonova AT2017gfo, and the absence of a very high-energy gamma-ray signature in GRB 170817A. We find that these occurrences can be attributed to the gluon condensation within nucleons, i.e., a significant number of soft gluons within nucleons are condensed at a critical momentum, resulting in the emergence of a steep and high peak in the gluon distributions. Through this profound connection between microscopic and macroscopic phenomena, we have not only expanded the applications of the hadronic scenario in cosmic gamma-ray emissions but also presented new evidence for the existence of gluon condensation., Comment: 30 pages, 6 figures, submitted to JCAP

The opioid epidemic, referring to the growing hospitalizations and deaths because of overdose of opioid usage and addiction, has become a severe health problem in the United States. Many strategies have been developed by the federal and local governments and health communities to combat this crisis. Among them, improving our understanding of the epidemic through better health surveillance is one of the top priorities. In addition to direct testing, machine learning approaches may also allow us to detect opioid users by analyzing data from social media because many opioid users may choose not to do the tests but may share their experiences on social media anonymously. In this paper, we take advantage of recent advances in machine learning, collect and analyze user posts from a popular social network Reddit with the goal to identify opioid users. Posts from more than 1,000 users who have posted on three sub-reddits over a period of one month have been collected. In addition to the ones that contain keywords such as opioid, opiate, or heroin, we have also collected posts that contain slang words of opioid such as black or chocolate. We apply an attention-based bidirectional long short memory model to identify opioid users. Experimental results show that the approaches significantly outperform competitive algorithms in terms of F1-score. Furthermore, the model allows us to extract most informative words, such as opiate, opioid, and black, from posts via the attention layer, which provides more insights on how the machine learning algorithm works in distinguishing drug users from non-drug users.

Graph embedding methods such as Graph Neural Networks (GNNs) and Graph Transformers have contributed to the development of graph reasoning algorithms for various tasks on knowledge graphs. However, the lack of interpretability and explainability of graph embedding methods has limited their applicability in scenarios requiring explicit reasoning. In this paper, we introduce the Graph Agent (GA), an intelligent agent methodology of leveraging large language models (LLMs), inductive-deductive reasoning modules, and long-term memory for knowledge graph reasoning tasks. GA integrates aspects of symbolic reasoning and existing graph embedding methods to provide an innovative approach for complex graph reasoning tasks. By converting graph structures into textual data, GA enables LLMs to process, reason, and provide predictions alongside human-interpretable explanations. The effectiveness of the GA was evaluated on node classification and link prediction tasks. Results showed that GA reached state-of-the-art performance, demonstrating accuracy of 90.65%, 95.48%, and 89.32% on Cora, PubMed, and PrimeKG datasets, respectively. Compared to existing GNN and transformer models, GA offered advantages of explicit reasoning ability, free-of-training, easy adaption to various graph reasoning tasks

Understanding the production mechanisms and utilizing them as probes to investigate the structure of exotic states represent some of the most actively studied research areas in particle physics. In this study, we present a theoretical analysis of the charmed meson $T^{-}_{cc}$ in the $\gamma{}p\to{}D^{+}T^{-}_{cc}\Lambda^{+}_{c}$ and $\gamma\gamma\to{}D^{+}T^{-}_{cc}D^{*0}$ reactions, considering $T^{+}_{cc}$ as a $DD^{*}$ molecule. The differential cross-section and total cross-section for the photoproduction of $T^{-}_{cc}$ in the $\gamma{}p\to{}D^+T^{-}_{cc}\Lambda^{+}_{c}$ and $\gamma\gamma\to{}D^{+}T^{-}_{cc}D^{*0}$ reactions are presented for nucleus-nucleus (nucleon-nucleon) ultraperipheral collisions at HL-LHC and RHIC, respectively. Taking into account the integrated luminosity per typical run and the luminosity of photons from the nucleus (nucleon), we observe a significant event count for $T^ {-} _{cc}$ production in p-p ultraperipheral collisions at HL-LHC., Comment: 7 pages,5 figures

The biomedical field relies heavily on concept linking in various areas such as literature mining, graph alignment, information retrieval, question-answering, data, and knowledge integration. Although large language models (LLMs) have made significant strides in many natural language processing tasks, their effectiveness in biomedical concept mapping is yet to be fully explored. This research investigates a method that exploits the in-context learning (ICL) capabilities of large models for biomedical concept linking. The proposed approach adopts a two-stage retrieve-and-rank framework. Initially, biomedical concepts are embedded using language models, and then embedding similarity is utilized to retrieve the top candidates. These candidates' contextual information is subsequently incorporated into the prompt and processed by a large language model to re-rank the concepts. This approach achieved an accuracy of 90.% in BC5CDR disease entity normalization and 94.7% in chemical entity normalization, exhibiting a competitive performance relative to supervised learning methods. Further, it showed a significant improvement, with an over 20-point absolute increase in F1 score on an oncology matching dataset. Extensive qualitative assessments were conducted, and the benefits and potential shortcomings of using large language models within the biomedical domain were discussed. were discussed.