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1,124 results on '"Wang Jiaqiang"'

1. FOCUS-PDCA can effectively optimize the critical value of test items

Author

Xie Chunbao, Zhang Jianbo, Luo Jiangrong, Jian Meiling, Zhao Taiqiang, Wang Jiaqiang, Jiang Linxi, Dai Chao, Wei Yao, Jiang Li, and Shi Yi

Subjects
critical value, optimization, focus-pdca, Biochemistry, QD415-436
Abstract

Background: To optimize the critical value of test items using FOCUS-PDCA (find, organize, clarify, understand, select, plan, do, check and act), and to set the personalized critical value of the test for different departments. Methods: We searched for literature reporting on the critical value and FOCUS-PDCA published over recent 5 years in order to understand the significance and status quo of critical value and FOCUS-PDCA. We also collected and analyzed the critical value data of hospital tests performed in Sichuan province hospitals in 2019, which were later compared to data from 2020 to determine the FOCUSPDCA cycle. Results: The proportion of critical values in the whole hospital decreased from 3.5% before optimization to 2.5% to 3% after optimization. The critical values of ICU, hematology, nephrology, urology, and neonatal departments after optimization significantly decreased compared with those before optimization, while the critical values of cardiac surgery, emergency ICU, cardiology, and neurosurgery ICU showed no significant difference before and after optimization. Contrary, the critical values of the infection department after optimization significantly increased before optimization. Conclusions: FOCUS-PDCA can effectively optimize the critical value of test items, which is beneficial for rational utilization of medical resources.

Published
2022
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2. LOMA: Language-assisted Semantic Occupancy Network via Triplane Mamba

Author

Cui, Yubo, Li, Zhiheng, Wang, Jiaqiang, and Fang, Zheng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Vision-based 3D occupancy prediction has become a popular research task due to its versatility and affordability. Nowadays, conventional methods usually project the image-based vision features to 3D space and learn the geometric information through the attention mechanism, enabling the 3D semantic occupancy prediction. However, these works usually face two main challenges: 1) Limited geometric information. Due to the lack of geometric information in the image itself, it is challenging to directly predict 3D space information, especially in large-scale outdoor scenes. 2) Local restricted interaction. Due to the quadratic complexity of the attention mechanism, they often use modified local attention to fuse features, resulting in a restricted fusion. To address these problems, in this paper, we propose a language-assisted 3D semantic occupancy prediction network, named LOMA. In the proposed vision-language framework, we first introduce a VL-aware Scene Generator (VSG) module to generate the 3D language feature of the scene. By leveraging the vision-language model, this module provides implicit geometric knowledge and explicit semantic information from the language. Furthermore, we present a Tri-plane Fusion Mamba (TFM) block to efficiently fuse the 3D language feature and 3D vision feature. The proposed module not only fuses the two features with global modeling but also avoids too much computation costs. Experiments on the SemanticKITTI and SSCBench-KITTI360 datasets show that our algorithm achieves new state-of-the-art performances in both geometric and semantic completion tasks. Our code will be open soon., Comment: Accepted by AAAI2025

Published
2024

9. The VolcTrans System for WMT22 Multilingual Machine Translation Task

Author

Qian, Xian, Hu, Kai, Wang, Jiaqiang, Liu, Yifeng, Pan, Xingyuan, Cao, Jun, and Wang, Mingxuan

Subjects
Computer Science - Computation and Language
Abstract

This report describes our VolcTrans system for the WMT22 shared task on large-scale multilingual machine translation. We participated in the unconstrained track which allows the use of external resources. Our system is a transformerbased multilingual model trained on data from multiple sources including the public training set from the data track, NLLB data provided by Meta AI, self-collected parallel corpora, and pseudo bitext from back-translation. A series of heuristic rules clean both bilingual and monolingual texts. On the official test set, our system achieves 17.3 BLEU, 21.9 spBLEU, and 41.9 chrF2++ on average over all language pairs. The average inference speed is 11.5 sentences per second using a single Nvidia Tesla V100 GPU. Our code and trained models are available at https://github.com/xian8/wmt22, Comment: WMT 2022, 8 pages

Published
2022

43. Accelerating Recommendation Inference via GPU Streams

Author

Niu, Yuean, Xu, Zhizhen, Xu, Chen, Wang, Jiaqiang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Xin, editor, Sapino, Maria Luisa, editor, Han, Wook-Shin, editor, El Abbadi, Amr, editor, Dobbie, Gill, editor, Feng, Zhiyong, editor, Shao, Yingxiao, editor, and Yin, Hongzhi, editor

Published
2023
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