Supporting Non-Linear and Non-Continuous Media Access in Peer-to-Peer Multimedia Systems

Ph.DDOCTOR OF PHILOSOPH

An Analytic Solution of Hydrodynamic Equations with Source Terms in Heavy Ion Collisions

The energy and baryon densities in heavy ion collisions are estimated by analytically solving a 1+1 dimensional hydrodynamical model with source terms. Particularly, a competition between the energy and baryon sources and the expansion of the system is discussed in detail.Comment: LaTeX2e, 7 pages, 4 postscript figures, submitted to Int. J. Mod. Phys.

Plug-and-Play Medical Dialogue System

Medical dialogue systems aim to provide accurate answers to patients, necessitating specific domain knowledge. Recent advancements in Large Language Models (LLMs) have demonstrated their exceptional capabilities in the medical Q&A domain, indicating a rich understanding of common sense. However, LLMs are insufficient for direct diagnosis due to the absence of diagnostic strategies. The conventional approach to address this challenge involves expensive fine-tuning of LLMs. Alternatively, a more appealing solution is the development of a plugin that empowers LLMs to perform medical conversation tasks. Drawing inspiration from in-context learning, we propose PlugMed, a Plug-and-Play Medical Dialogue System that facilitates appropriate dialogue actions by LLMs through two modules: the prompt generation (PG) module and the response ranking (RR) module. The PG module is designed to capture dialogue information from both global and local perspectives. It selects suitable prompts by assessing their similarity to the entire dialogue history and recent utterances grouped by patient symptoms, respectively. Additionally, the RR module incorporates fine-tuned SLMs as response filters and selects appropriate responses generated by LLMs. Moreover, we devise a novel evaluation method based on intent and medical entities matching to assess the efficacy of dialogue strategies in medical conversations more effectively. Experimental evaluations conducted on three unlabeled medical dialogue datasets, including both automatic and manual assessments, demonstrate that our model surpasses the strong fine-tuning baselines.Comment: 9 pages, 3 figures, Possible submission to Emnlp or AAA

CD24 Expression as a Marker for Predicting Clinical Outcome in Human Gliomas

CD24 is overexpressed in glioma cells in vitro and in vivo. However, the correlation of its expression with clinicopathological parameters of gliomas and its prognostic significance in this tumor remain largely unknown. To address this problem, 151 glioma specimens and 10 nonneoplastic brain tissues were collected. Quantitative real-time PCR, immunochemistry assay, and Western blot analysis were carried out to investigate the expression of CD24. As per the results, CD24 was overexpressed in gliomas. Its expression levels in glioma tissues with higher grade (P < 0.001) and lower KPS (P < 0.001) were significantly higher than those with lower grade and higher KPS, respectively. Cox multifactor analysis showed that CD24 (P = 0.02) was an independent prognosis factor for human glioma. Our data provides convincing evidence for the first time that the overexpression of CD24 at gene and protein levels is correlated with advanced clinicopathological parameters and poor prognosis in patients with glioma

A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models

While large language models exhibit remarkable performance in the Question Answering task, they are susceptible to hallucinations. Challenges arise when these models grapple with understanding multi-hop relations in complex questions or lack the necessary knowledge for a comprehensive response. To address this issue, we introduce the "Decompose-and-Query" framework (D&Q). This framework guides the model to think and utilize external knowledge similar to ReAct, while also restricting its thinking to reliable information, effectively mitigating the risk of hallucinations. Experiments confirm the effectiveness of D&Q: On our ChitChatQA dataset, D&Q does not lose to ChatGPT in 67% of cases; on the HotPotQA question-only setting, D&Q achieved an F1 score of 59.6%. Our code is available at https://github.com/alkaidpku/DQ-ToolQA

Electrochemically Inert g-C3N4 Promotes Water Oxidation Catalysis

Electrode surface wettability is critically important for heterogeneous electrochemical reactions taking place in aqueous and nonaqueous media. Herein, electrochemically inert g-C 3 N 4 (GCN) is successfully demonstrated to significantly enhance water oxidation by constructing a superhydrophilic catalyst surface and promoting substantial exposure of active sites. As a proof-of-concept application, superhydrophilic GCN/Ni(OH) 2 (GCNN) hybrids with monodispersed Ni(OH) 2 nanoplates strongly anchored on GCN are synthesized for enhanced water oxidation catalysis. Owing to the superhydrophilicity of functionalized GCN, the surface wettability of GCNN (contact angle 0°) is substantially improved as compared with bare Ni(OH) 2 (contact angle 21°). Besides, GCN nanosheets can effectively suppress Ni(OH) 2 aggregation to help expose more active sites. Benefiting from the well-defined catalyst surface, the optimal GCNN hybrid shows significantly enhanced electrochemical performance over bare Ni(OH) 2 nanosheets, although GCN is electrochemically inert. In addition, similar catalytic performance promotion resulting from wettability improvement induced by incorporation of hydrophilic GCN is also successfully demonstrated on Co(OH) 2 . The present results demonstrate that, in addition to developing new catalysts, building efficient surface chemistry is also vital to achieve extraordinary water oxidation performance