Yun Xue
Biographic Data
| ID | 9833879 |
|---|---|
| NAME | Yun Xue |
| GIVEN NAMES | Yun |
| FAMILY NAME | Xue |
| SIGNATURE | XUE Y |
| AFFILIATIONS | South China Normal University |
| ORCID | 0000-0002-4048-5298 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Resistance characteristics of culture-positive tuberculosis from 2015 to 2022
Introduction This study aimed to investigate the prevalence of resistance to first-line anti-tuberculosis (TB) drugs and the molecular mechanisms underlying resistance mutations in patients with culture-positive Mycobacterium tuberculosis complex (MTBC). The findings provide a data basis for developing more precise and regionally tailored anti-TB treatment regimens. Methods From 2015 to 2022, a total of 3,605 strains isolated from 10 designated T…
Enhanced Syntactic and Semantic Graph Convolutional Network With Contrastive Learning for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity of a given specific aspect in the sentence. Recent studies focus on leveraging graph convolutional neural networks to encode both syntactic and semantic information. However, current syntactic parsers, which are not specifically for ABSA, introduce noise to the syntactic information. Besides, ongoing studies ignore the distinctiveness of semantics and syntax. To address…
Modeling Inter-Aspect Relations With Clause and Contrastive Learning for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims to identify the sentiment polarity of the given aspect. Recent studies fail to establish the relation among multiple aspects in one sentence. To address this issue, a clause-level relational graph attention network with contrastive learning (CLRCL) model is proposed. Specifically, the given sentence is segmented into clauses to obtain the relation between t…
End-to-End Visual Grounding Framework for Multimodal NER in Social Media Posts
Multimodal named entity recognition (MNER) for social media aims to detect named entities in user-generated posts with the aid of visual information from attached images. Existing methods use pretrained visual models or visual grounding (VG) toolkits to learn visual information. However, they still suffer from the mismatch issue, where the visual features extracted from visual encoder are inconsistent with actual requirements for cross-modal inte…
Dynamic Graph Construction Framework for Multimodal Named Entity Recognition in Social Media
Multimodal named entity recognition (MNER) aims to detect named entities and identify the entity types based on texts and attached images, which also generates inputs for other comprehensive tasks, such as multimodal machine translation, visual dialog, and multimodal sentiment analysis. Existing studies have limitations in text-image matching and multimodal semantic disparity reduction. For one thing, current methods fail to resolve both overall …
Epidemiological characteristics and risk factors of multidrug-resistant tuberculosis in Luoyang, China
Objective We aimed to examine the prevalence of multidrug-resistant tuberculosis (MDR-TB) in Luoyang, China, identify related risk factors, inform clinical practices, and establish standardized anti-tubercular treatment regimens. Methods We conducted a retrospective analysis of high-resolution melting curve (HRM) data from 17,773 cases (2,748 of which were positive) between June 2019 and May 2022 to assess the prevalence of MDR-TB and to identify…
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Epidemiological characteristics and risk factors of multidrug-resistant tuberculosis in Luoyang, China
Objective We aimed to examine the prevalence of multidrug-resistant tuberculosis (MDR-TB) in Luoyang, China, identify related risk factors, inform clinical practices, and establish standardized anti-tubercular treatment regimens. Methods We conducted a retrospective analysis of high-resolution melting curve (HRM) data from 17,773 cases (2,748 of which were positive) between June 2019 and May 2022 to assess the prevalence of MDR-TB and to identify…
Enhanced Syntactic and Semantic Graph Convolutional Network With Contrastive Learning for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity of a given specific aspect in the sentence. Recent studies focus on leveraging graph convolutional neural networks to encode both syntactic and semantic information. However, current syntactic parsers, which are not specifically for ABSA, introduce noise to the syntactic information. Besides, ongoing studies ignore the distinctiveness of semantics and syntax. To address…
Modeling Inter-Aspect Relations With Clause and Contrastive Learning for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims to identify the sentiment polarity of the given aspect. Recent studies fail to establish the relation among multiple aspects in one sentence. To address this issue, a clause-level relational graph attention network with contrastive learning (CLRCL) model is proposed. Specifically, the given sentence is segmented into clauses to obtain the relation between t…
End-to-End Visual Grounding Framework for Multimodal NER in Social Media Posts
Multimodal named entity recognition (MNER) for social media aims to detect named entities in user-generated posts with the aid of visual information from attached images. Existing methods use pretrained visual models or visual grounding (VG) toolkits to learn visual information. However, they still suffer from the mismatch issue, where the visual features extracted from visual encoder are inconsistent with actual requirements for cross-modal inte…
Dynamic Graph Construction Framework for Multimodal Named Entity Recognition in Social Media
Multimodal named entity recognition (MNER) aims to detect named entities and identify the entity types based on texts and attached images, which also generates inputs for other comprehensive tasks, such as multimodal machine translation, visual dialog, and multimodal sentiment analysis. Existing studies have limitations in text-image matching and multimodal semantic disparity reduction. For one thing, current methods fail to resolve both overall …
Resistance characteristics of culture-positive tuberculosis from 2015 to 2022
Introduction This study aimed to investigate the prevalence of resistance to first-line anti-tuberculosis (TB) drugs and the molecular mechanisms underlying resistance mutations in patients with culture-positive Mycobacterium tuberculosis complex (MTBC). The findings provide a data basis for developing more precise and regionally tailored anti-TB treatment regimens. Methods From 2015 to 2022, a total of 3,605 strains isolated from 10 designated T…
Computer Science (4 works) · Artificial Intelligence (3 works) · Graph (3 works) · Natural language processing (3 works) · Theoretical Computer Science (3 works) · Advanced Text Analysis Techniques (2 works) · Biology (2 works) · Diagnosis and treatment of tuberculosis (2 works) · Drug resistance (2 works) · Internal Medicine (2 works)