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Your search keyword '"Zhou, Changjiang"' showing total 263 results
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263 results on '"Zhou, Changjiang"'

2. CorpusBrain++: A Continual Generative Pre-Training Framework for Knowledge-Intensive Language Tasks

Author

Guo, Jiafeng, Zhou, Changjiang, Zhang, Ruqing, Chen, Jiangui, de Rijke, Maarten, Fan, Yixing, and Cheng, Xueqi

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

Knowledge-intensive language tasks (KILTs) typically require retrieving relevant documents from trustworthy corpora, e.g., Wikipedia, to produce specific answers. Very recently, a pre-trained generative retrieval model for KILTs, named CorpusBrain, was proposed and reached new state-of-the-art retrieval performance. However, most existing research on KILTs, including CorpusBrain, has predominantly focused on a static document collection, overlooking the dynamic nature of real-world scenarios, where new documents are continuously being incorporated into the source corpus. To address this gap, it is crucial to explore the capability of retrieval models to effectively handle the dynamic retrieval scenario inherent in KILTs. In this work, we first introduce the continual document learning (CDL) task for KILTs and build a novel benchmark dataset named KILT++ based on the original KILT dataset for evaluation. Then, we conduct a comprehensive study over the use of pre-trained CorpusBrain on KILT++. Unlike the promising results in the stationary scenario, CorpusBrain is prone to catastrophic forgetting in the dynamic scenario, hence hampering the retrieval performance. To alleviate this issue, we propose CorpusBrain++, a continual generative pre-training framework. Empirical results demonstrate the significant effectiveness and remarkable efficiency of CorpusBrain++ in comparison to both traditional and generative IR methods., Comment: Submitted to ACM Transactions on Information Systems

Published
2024

3. End-to-End Entity Detection with Proposer and Regressor

Author

Wen, Xueru, Zhou, Changjiang, Tang, Haotian, Liang, Luguang, Jiang, Yu, and Qi, Hong

Subjects
Computer Science - Computation and Language
Abstract

Named entity recognition is a traditional task in natural language processing. In particular, nested entity recognition receives extensive attention for the widespread existence of the nesting scenario. The latest research migrates the well-established paradigm of set prediction in object detection to cope with entity nesting. However, the manual creation of query vectors, which fail to adapt to the rich semantic information in the context, limits these approaches. An end-to-end entity detection approach with proposer and regressor is presented in this paper to tackle the issues. First, the proposer utilizes the feature pyramid network to generate high-quality entity proposals. Then, the regressor refines the proposals for generating the final prediction. The model adopts encoder-only architecture and thus obtains the advantages of the richness of query semantics, high precision of entity localization, and easiness of model training. Moreover, we introduce the novel spatially modulated attention and progressive refinement for further improvement. Extensive experiments demonstrate that our model achieves advanced performance in flat and nested NER, achieving a new state-of-the-art F1 score of 80.74 on the GENIA dataset and 72.38 on the WeiboNER dataset., Comment: Accepted to the Neural Processing Letters (2023)

Published
2022
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4. Type-supervised sequence labeling based on the heterogeneous star graph for named entity recognition

Author

Wen, Xueru, Zhou, Changjiang, Tang, Haotian, Liang, Luguang, Jiang, Yu, and Qi, Hong

Subjects
Computer Science - Computation and Language
Abstract

Named entity recognition is a fundamental task in natural language processing, identifying the span and category of entities in unstructured texts. The traditional sequence labeling methodology ignores the nested entities, i.e. entities included in other entity mentions. Many approaches attempt to address this scenario, most of which rely on complex structures or have high computation complexity. The representation learning of the heterogeneous star graph containing text nodes and type nodes is investigated in this paper. In addition, we revise the graph attention mechanism into a hybrid form to address its unreasonableness in specific topologies. The model performs the type-supervised sequence labeling after updating nodes in the graph. The annotation scheme is an extension of the single-layer sequence labeling and is able to cope with the vast majority of nested entities. Extensive experiments on public NER datasets reveal the effectiveness of our model in extracting both flat and nested entities. The method achieved state-of-the-art performance on both flat and nested datasets. The significant improvement in accuracy reflects the superiority of the multi-layer labeling strategy.

Published
2022

20. EXPERIMENT AND SIMULATION ON MECHANICAL PROPERTIES OF OSTEOPOROTIC MOUSE BONE (MT)

Author

ZHAO Qing, YIN ZiYang, ZHOU Wen, REN GuanLin, KUANG ChunMei, HU Cong, and ZHOU ChangJiang

Subjects
Mouse tibia, Osteoporosis, Mechanical properties, Finite element simulation, Mechanical engineering and machinery, TJ1-1570, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

The osteoporosis model of mouse tibia was established based on castration, and the effect of osteoporosis on the mechanical properties of tibia was investigated. The two-dimensional sections of mouse tibia were obtained by Micro-CT. The three-dimensional digital model of mouse tibia was obtained by reconstruction technology, and trabecular bone shape and density were analyzed. The effect of osteoporosis on the mechanical properties of tibia was explored by testing and finite element simulation. The results showed that osteoporosis could damage the trabecular structure and decrease the mechanical properties of tibia. Compared with healthy tibia, the maximum load, bending stiffness and elastic modulus of tibia decreased by 20%, 30% and 25%, respectively, and the simulation results were in good agreement with the experiment. This study provides an important basis for the clinical diagnosis and treatment of osteoporosis.

Published
2023
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42. High Frequency Ultrasound Transducer Based on Sm-Doped Pb(Mg1/3Nb2/3)O3-0.28PbTiO3 Ceramic for Intravascular Ultrasound Imaging.

Author

Weiyan, Ding, Chen, Xingfei, Zhang, Yongcheng, Li, Xiaobing, Sun, Fenglong, Yang, Zhaoping, Tang, Xi, Zhou, Changjiang, Wang, Feifei, and Zhao, Xiangyong

Subjects
ULTRASONIC imaging, PIEZOELECTRIC materials, TRANSDUCERS, INTRAVASCULAR ultrasonography, ACOUSTIC imaging
Abstract

Sm-doped Pb(Mg1/3Nb2/3)O3-0.28PbTiO3 (PMN-0.28PT) ceramic has been reported to exhibit very large piezoelectric response (d 33~1300 pC/N) that can be comparable with PMN-0.30PT single crystal. Based on the Sm-doped PMN-0.28PT ceramics, a high frequency ultrasound transducer with the center frequency above 30 MHz has been designed and fabricated for intravascular ultrasound imaging, and the performance of the transducer was investigated via ultrasound pulse-echo tests. Further, for a porcine vessel wall, the 2D and 3D ultrasound images were constructed using signal acquisition and processing from the fabricated high-frequency transducer. The obtained details of the vessel wall by the IVUS transducer indicate that Sm-doped PMN-0.28PT ceramic is a promising candidate for high frequency transducers. [ABSTRACT FROM AUTHOR]

Published
2024
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43. TCD-Guided management in carotid endarterectomy: a retrospective study.

Author

Yang, Na, Wang, Qinghong, Qi, Hongmei, Song, Zhen, Zhou, Changjiang, Zhang, Shengqiang, and Zhang, Bai

Subjects
SURGICAL complications, RECEIVER operating characteristic curves, CEREBRAL ischemia, BLOOD circulation, CAROTID artery, CAROTID endarterectomy
Abstract

Backgroundː: Stroke, primarily resulting from ischemic conditions, is the foremost cause of mortality and long-term impairment and is frequently associated with narrowing of the carotid arteries. Although carotid endarterectomy (CEA) is the treatment of choice, it carries the risk of cerebral ischemia and reduced blood flow. Transcranial Doppler (TCD) ultrasound offers a nonintrusive method to assess cerebral blood circulation during CEA, potentially enhancing surgical outcomes. The objective of this study was to assess the clinical utility and safety of TCD monitoring during CEA and to identify factors influencing postoperative complications. Methods: This retrospective analysis included 158 CEA patients (from January 2021–August 2023) who underwent TCD monitoring and whose data were compared to historical standard care data. The primary outcomes were operation duration and artery occlusion time. Secondary outcomes included carotid shunt usage, seven-day postoperative complications, and six-month carotid artery patency. Logistic regression identified factors linked to adverse reactions, and a predictive model was evaluated with a receiver operating characteristic (ROC) curve. Resultsː: Comparative analysis indicated significant reductions in both the duration of surgery (113.26 ± 7.29 min) and artery occlusion time (21.85 ± 2.92 min) for patients monitored with TCD (P < 0.001) and an increase in carotid shunt implementation (25% as opposed to traditional care). The observed postoperative complications were minor, with a nonsignificant trend that favored the use of TCD-monitored procedures (1% vs. historical rates). Factors such as patient age and plaque echogenicity were found to be predictive of postoperative issues, with plaque echogenicity emerging as a significant predictive factor (OR = 10.70, 95% CI: 2.14–202, P = 0.02) upon multivariate analysis. The predictive model exhibited high precision (AUC = 0.93). Conclusion: This retrospective evaluation suggested that TCD monitoring in the CEA may reduce procedural time and potentially decrease postoperative complications, supporting its use for personalized surgical planning. [ABSTRACT FROM AUTHOR]

Published
2024
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48. A Multilocus Blood-Based Assay Targeting Circulating Tumor DNA Methylation Enables Early Detection and Early Relapse Prediction of Colorectal Cancer

Author

Cai, Guoxiang, Cai, Mingyan, Feng, Zhiqiang, Liu, Rui, Liang, Li, Zhou, Pinghong, Zhu, Boqun, Mo, Shaobo, Wang, Hui, Lan, Xiaoliang, Cai, Sanjun, Xu, Ye, Wang, Renjie, Dai, Weixin, Han, Lingyu, Xiang, Wenqiang, Wang, Beili, Guo, Wei, Zhang, Lijing, Zhou, Changjiang, Luo, Binyang, Li, Yingfei, Nie, Yuqiang, Ma, Chengcheng, and Su, Zhixi

Published
2021
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