High-resolution power equipment recognition based on improved self-attention

The current trend of automating inspections at substations has sparked a surge in interest in the field of transformer image recognition. However, due to restrictions in the number of parameters in existing models, high-resolution images can't be directly applied, leaving significant room for enhancing recognition accuracy. Addressing this challenge, the paper introduces a novel improvement on deep self-attention networks tailored for this issue. The proposed model comprises four key components: a foundational network, a region proposal network, a module for extracting and segmenting target areas, and a final prediction network. The innovative approach of this paper differentiates itself by decoupling the processes of part localization and recognition, initially using low-resolution images for localization followed by high-resolution images for recognition. Moreover, the deep self-attention network's prediction mechanism uniquely incorporates the semantic context of images, resulting in substantially improved recognition performance. Comparative experiments validate that this method outperforms the two other prevalent target recognition models, offering a groundbreaking perspective for automating electrical equipment inspections

Recombinant mycobacterium tuberculosis fusion protein for diagnosis of mycobacterium tuberculosis infection: a short-term economic evaluation

ObjectivesRecombinant Mycobacterium tuberculosis fusion protein (EC) was anticipated to be used for the scale-up of clinical application for diagnosis of Mycobacterium tuberculosis infection in China, but it lacked a head-to-head economic evaluation based on the Chinese population. This study aimed to estimate the cost-utility and the cost-effectiveness of both EC and tuberculin pure protein derivative (TB-PPD) for diagnosis of Mycobacterium tuberculosis infection in the short term.MethodsFrom a Chinese societal perspective, both cost-utility analysis and cost-effectiveness analysis were performed to evaluate the economics of EC and TB-PPD for a one-year period based on clinical trials and decision tree model, with quality-adjusted life years (QALYs) as the utility-measured primary outcome and diagnostic performance (including the misdiagnosis rate, the omission diagnostic rate, the number of patients correctly classified, and the number of tuberculosis cases avoided) as the effective-measured secondary outcome. One-way and probabilistic sensitivity analyses were performed to validate the robustness of the base-case analysis, and a scenario analysis was conducted to evaluate the difference in the charging method between EC and TB-PPD.ResultsThe base-case analysis showed that, compared with TB-PPD, EC was the dominant strategy with an incremental cost-utility ratio (ICUR) of saving 192,043.60 CNY per QALY gained, and with an incremental cost-effectiveness ratio (ICER) of saving 7,263.53 CNY per misdiagnosis rate reduction. In addition, there was no statistical difference in terms of the omission diagnostic rate, the number of patients correctly classified, and the number of tuberculosis cases avoided, and EC was a similar cost-saving strategy with a lower test cost (98.00 CNY) than that of TB-PPD (136.78 CNY). The sensitivity analysis showed the robustness of cost-utility and cost-effectiveness analysis, and the scenario analysis indicated cost-utility in EC and cost-effectiveness in TB-PPD.ConclusionThis economic evaluation from a societal perspective showed that, compared to TB-PPD, EC was likely to be a cost-utility and cost-effective intervention in the short term in China

ProAgent: Building Proactive Cooperative Agents with Large Language Models

Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems. Current approaches to developing cooperative agents rely primarily on learning-based methods, whose policy generalization depends heavily on the diversity of teammates they interact with during the training phase. Such reliance, however, constrains the agents' capacity for strategic adaptation when cooperating with unfamiliar teammates, which becomes a significant challenge in zero-shot coordination scenarios. To address this challenge, we propose ProAgent, a novel framework that harnesses large language models (LLMs) to create proactive agents capable of dynamically adapting their behavior to enhance cooperation with teammates. ProAgent can analyze the present state, and infer the intentions of teammates from observations. It then updates its beliefs in alignment with the teammates' subsequent actual behaviors. Moreover, ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various of coordination scenarios. Experimental evaluations conducted within the Overcooked-AI environment unveil the remarkable performance superiority of ProAgent, outperforming five methods based on self-play and population-based training when cooperating with AI agents. Furthermore, in partnered with human proxy models, its performance exhibits an average improvement exceeding 10% compared to the current state-of-the-art method. For more information about our project, please visit~\url{https://pku-proagent.github.io}.Comment: v3 is the AAAI'24 camera ready version, which polished abstract and introduction based on the reviewers' comments, and enriched related works. 7 pages of main content, 2 pages of references, 2 figures and 1 tabl

METTL14 regulates chromatin bivalent domains in mouse embryonic stem cells

METTL14 (methyltransferase-like 14) is an RNA-binding protein that partners with METTL3 to mediate N6-methyladenosine (m6A) methylation. Recent studies identified a function for METTL3 in heterochromatin in mouse embryonic stem cells (mESCs), but the molecular function of METTL14 on chromatin in mESCs remains unclear. Here, we show that METTL14 specifically binds and regulates bivalent domains, which are marked by trimethylation of histone H3 lysine 27 (H3K27me3) and lysine 4 (H3K4me3). Knockout of Mettl14 results in decreased H3K27me3 but increased H3K4me3 levels, leading to increased transcription. We find that bivalent domain regulation by METTL14 is independent of METTL3 or m6A modification. METTL14 enhances H3K27me3 and reduces H3K4me3 by interacting with and probably recruiting the H3K27 methyltransferase polycomb repressive complex 2 (PRC2) and H3K4 demethylase KDM5B to chromatin. Our findings identify an METTL3-independent role of METTL14 in maintaining the integrity of bivalent domains in mESCs, thus indicating a mechanism of bivalent domain regulation in mammals

Doctoral Chamber Recital

List of performers and performances

Homogeneous and Heterogeneous Catalysis Impact on Pyrolyzed Cellulose to Produce Bio-Oil

Effectively utilizing catalytic pyrolysis to upgrade bio-oil products prepared from biomass has many potential benefits for the environment. In this paper, cellulose (a major component of plants and a biomass model compound) is pyrolyzed and catalyzed with different catalysts: Ni2Fe3, ZSM-5, and Ni2Fe3/ZSM-5. Two different pyrolysis processes are investigated to compare homogeneous and heterogeneous catalysis influence on the products. The results indicate that the Ni2Fe3 cluster catalyst shows the best activity as a homogeneous catalysis. It can also be recycled repeatedly, increases the yield of bio-oil, and improves the quality of the bio-oil by decreasing the sugar concentration. Furthermore, it also catalyzes the formation of a small amount of hydrocarbon compounds. In the case of Ni2Fe3/ZSM-5 catalyst, it shows a lower yield of bio-oil but also decreases the sugar concentration significantly. Ni2Fe3, not only can it be used as homogeneous catalysis mixed with cellulose but also shows catalytic activity as a supported catalyst on ZSM-5, with higher catalytic activity than ZSM-5. These results indicate that the Ni2Fe3 catalyst has significant activity for potential use in industry to produce high quality bio-oil from biomass