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  1. AU="Wu, Xiaobao"
  2. AU="Cauchon, Michel"
  3. AU="Pei-Chi Chou"
  4. AU="Treeck, Till van"
  5. AU="Hung, Jennifer K W"
  6. AU="Song, Xiao-yu R"
  7. AU="Abounader, Roger"
  8. AU="van Sterkenburg, M A J A"
  9. AU="Sayed Masoud Hosseini"
  10. AU="Pathania, Deepak"
  11. AU=Xu Yuzhong AU=Xu Yuzhong
  12. AU="Mehmood, Huzaifa"
  13. AU="Etcheverry, Amandine"
  14. AU="Sein, Andrea M"
  15. AU="Vo, Thi-Phi-Giao"
  16. AU="Allan, Alasdair"
  17. AU="Thurlow, Lance R"
  18. AU="Lum, Krystal"
  19. AU="Wilms, Miriam"
  20. AU="Russell, Michael H"
  21. AU="Bedoya-Arias, Hugo A"
  22. AU="Hijri, Mohamed"
  23. AU="Priscilla Gates"
  24. AU="Reiber, Matthew"
  25. AU="Bauer-Rowe, Khristian E"
  26. AU="Tanner, Martin E"
  27. AU="Creech, Gardner S"
  28. AU="José P. Oliveira-Filho"
  29. AU="Munt, Jennifer E"
  30. AU="Whiley, Phillip J"
  31. AU="V.Sudhir, "
  32. AU="Chatow, Lior"
  33. AU=Xue Zhe
  34. AU="Peter D. Yurchenco"
  35. AU="Várbíró, Gábor"
  36. AU="Sheleg, Dmitriy"
  37. AU="Panzirer, David"

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  1. Buch ; Online: A Survey on Neural Topic Models

    Wu, Xiaobao / Nguyen, Thong / Luu, Anh Tuan

    Methods, Applications, and Challenges

    2024  

    Abstract: Topic models have been prevalent for decades to discover latent topics and infer topic proportions of documents in an unsupervised fashion. They have been widely used in various applications like text analysis and context recommendation. Recently, the ... ...

    Abstract Topic models have been prevalent for decades to discover latent topics and infer topic proportions of documents in an unsupervised fashion. They have been widely used in various applications like text analysis and context recommendation. Recently, the rise of neural networks has facilitated the emergence of a new research field -- Neural Topic Models (NTMs). Different from conventional topic models, NTMs directly optimize parameters without requiring model-specific derivations. This endows NTMs with better scalability and flexibility, resulting in significant research attention and plentiful new methods and applications. In this paper, we present a comprehensive survey on neural topic models concerning methods, applications, and challenges. Specifically, we systematically organize current NTM methods according to their network structures and introduce the NTMs for various scenarios like short texts and cross-lingual documents. We also discuss a wide range of popular applications built on NTMs. Finally, we highlight the challenges confronted by NTMs to inspire future research.

    Comment: Accepted to Artifcial Intelligence Review. See https://doi.org/10.1007/s10462-023-10661-7 and a paper list at https://github.com/BobXWu/Paper-Neural-Topic-Models
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Information Retrieval
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2024-01-27
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  2. Artikel: Analysis of the Efficacy of Multidrug Combination Chemotherapy Regimens for Osteosarcoma and the Management of Chemotherapeutic Reactions.

    Tian, Dawei / Feng, Kun / Wu, Xiaobao / Gao, Chao / Hu, Lixin

    Publikation ZURÜCKGEZOGEN

    Evidence-based complementary and alternative medicine : eCAM

    2022  Band 2022, Seite(n) 6510429

    Abstract: Purpose: To analyse the efficacy of high-dose methotrexate + adriamycin + cisplatin (HD-MTX + ADR + PDD, MAP) regimens applied to osteosarcoma and the pretreatment and resolution of chemotherapeutic reactions.: Methods: The clinical data of 21 ... ...

    Abstract Purpose: To analyse the efficacy of high-dose methotrexate + adriamycin + cisplatin (HD-MTX + ADR + PDD, MAP) regimens applied to osteosarcoma and the pretreatment and resolution of chemotherapeutic reactions.
    Methods: The clinical data of 21 patients with osteosarcoma in our hospital from January 2015 to January 2018 were retrospectively analysed. All patients were treated with the MAP protocol, 21 days for 1 cycle, and treated with artificial joint replacement or amputation after 3∼4 cycles of treatment. The tumour tissue necrosis rate, limb preservation success rate after treatment, and chemotherapy response during chemotherapy were counted and analysed for all patients. A local recurrence rate, a distant metastasis rate, and an overall survival rate were recorded during the 3-year follow-up period.
    Results: After treatment, the percentage of tumour tissue necrosis ≥90% was 85.71% (18/21) and the percentage of successful limb preservation was 57.14% (12/21) in 21 patients with osteosarcoma. During chemotherapy, all 21 patients with osteosarcoma experienced various degrees of chemotherapy reactions, mainly bone marrow suppression of 100% (21/21), gastrointestinal reactions of 100% (21/21), liver function impairment of 66.67% (14/21), and cardiotoxicity of 52.38% (11/21), all of which improved and completed treatment after treatment. During the 3-year follow-up period, the 21 patients with osteosarcoma had a local recurrence rate of 9.52% (2/21), a distant metastasis rate of 28.57% (6/21), and an overall survival rate of 80.95% (17/21).
    Conclusion: With stringent protection and relief measures, patients with osteosarcoma treated with the MAP regimen have promising near-term outcomes, with high survival rates over 3 years and tolerable chemotherapy responses. The clinical trial is registered under L2015093.
    Sprache Englisch
    Erscheinungsdatum 2022-08-25
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Retracted Publication
    ZDB-ID 2171158-6
    ISSN 1741-4288 ; 1741-427X
    ISSN (online) 1741-4288
    ISSN 1741-427X
    DOI 10.1155/2022/6510429
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 5995: Hefte anzeigen Standort:
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  3. Buch ; Online: Effective Neural Topic Modeling with Embedding Clustering Regularization

    Wu, Xiaobao / Dong, Xinshuai / Nguyen, Thong / Luu, Anh Tuan

    2023  

    Abstract: Topic models have been prevalent for decades with various applications. However, existing topic models commonly suffer from the notorious topic collapsing: discovered topics semantically collapse towards each other, leading to highly repetitive topics, ... ...

    Abstract Topic models have been prevalent for decades with various applications. However, existing topic models commonly suffer from the notorious topic collapsing: discovered topics semantically collapse towards each other, leading to highly repetitive topics, insufficient topic discovery, and damaged model interpretability. In this paper, we propose a new neural topic model, Embedding Clustering Regularization Topic Model (ECRTM). Besides the existing reconstruction error, we propose a novel Embedding Clustering Regularization (ECR), which forces each topic embedding to be the center of a separately aggregated word embedding cluster in the semantic space. This enables each produced topic to contain distinct word semantics, which alleviates topic collapsing. Regularized by ECR, our ECRTM generates diverse and coherent topics together with high-quality topic distributions of documents. Extensive experiments on benchmark datasets demonstrate that ECRTM effectively addresses the topic collapsing issue and consistently surpasses state-of-the-art baselines in terms of topic quality, topic distributions of documents, and downstream classification tasks.

    Comment: Accepted to ICML 2023 conference
    Schlagwörter Computer Science - Computation and Language
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-06-07
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  4. Artikel: Effects of Methyl Jasmonate Fumigation on the Growth and Detoxification Ability of

    Chen, Lina / Song, Jia / Wang, Jun / Ye, Mao / Deng, Qianqian / Wu, Xiaobao / Wu, Xiaoyi / Ren, Bing

    Insects

    2023  Band 14, Heft 2

    Abstract: Methyl jasmonate (MeJA) is a volatile substance derived from jasmonic acid (JA), and it responds to interbiotic and abiotic stresses by participating in interplant communication. Despite its function in interplant communication, the specific role of MeJA ...

    Abstract Methyl jasmonate (MeJA) is a volatile substance derived from jasmonic acid (JA), and it responds to interbiotic and abiotic stresses by participating in interplant communication. Despite its function in interplant communication, the specific role of MeJA in insect defense responses is poorly understood. In this study, we found that carboxylesterase (CarE) activities, glutathione-S-transferase (GSTs) activities, and cytochrome mono-oxygenases (P450s) content increased more after the feeding of diets containing xanthotoxin, while larvae exposed to MeJA fumigation also showed higher enzyme activity in a dose-dependent manner: lower and medium concentrations of MeJA induced higher detoxification enzyme activities than higher concentrations of MeJA. Moreover, MeJA improved the growth of larvae fed on the control diet without toxins and diets with lower concentrations of xanthotoxin (0.05%); however, MeJA could not protect the larvae against higher concentrations of xanthotoxin (0.1%, 0.2%). In summary, we demonstrated that MeJA is effective at inducing
    Sprache Englisch
    Erscheinungsdatum 2023-01-31
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2662247-6
    ISSN 2075-4450
    ISSN 2075-4450
    DOI 10.3390/insects14020145
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Buch ; Online: On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling

    Wu, Xiaobao / Pan, Fengjun / Nguyen, Thong / Feng, Yichao / Liu, Chaoqun / Nguyen, Cong-Duy / Luu, Anh Tuan

    2024  

    Abstract: Hierarchical topic modeling aims to discover latent topics from a corpus and organize them into a hierarchy to understand documents with desirable semantic granularity. However, existing work struggles with producing topic hierarchies of low affinity, ... ...

    Abstract Hierarchical topic modeling aims to discover latent topics from a corpus and organize them into a hierarchy to understand documents with desirable semantic granularity. However, existing work struggles with producing topic hierarchies of low affinity, rationality, and diversity, which hampers document understanding. To overcome these challenges, we in this paper propose Transport Plan and Context-aware Hierarchical Topic Model (TraCo). Instead of early simple topic dependencies, we propose a transport plan dependency method. It constrains dependencies to ensure their sparsity and balance, and also regularizes topic hierarchy building with them. This improves affinity and diversity of hierarchies. We further propose a context-aware disentangled decoder. Rather than previously entangled decoding, it distributes different semantic granularity to topics at different levels by disentangled decoding. This facilitates the rationality of hierarchies. Experiments on benchmark datasets demonstrate that our method surpasses state-of-the-art baselines, effectively improving the affinity, rationality, and diversity of hierarchical topic modeling with better performance on downstream tasks.

    Comment: Accepted to AAAI2024 conference. Our code is available at https://github.com/bobxwu/TraCo
    Schlagwörter Computer Science - Computation and Language
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2024-01-25
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    Dokumenttyp Buch ; Online
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  6. Buch ; Online: InfoCTM

    Wu, Xiaobao / Dong, Xinshuai / Nguyen, Thong / Liu, Chaoqun / Pan, Liangming / Luu, Anh Tuan

    A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling

    2023  

    Abstract: Cross-lingual topic models have been prevalent for cross-lingual text analysis by revealing aligned latent topics. However, most existing methods suffer from producing repetitive topics that hinder further analysis and performance decline caused by low- ... ...

    Abstract Cross-lingual topic models have been prevalent for cross-lingual text analysis by revealing aligned latent topics. However, most existing methods suffer from producing repetitive topics that hinder further analysis and performance decline caused by low-coverage dictionaries. In this paper, we propose the Cross-lingual Topic Modeling with Mutual Information (InfoCTM). Instead of the direct alignment in previous work, we propose a topic alignment with mutual information method. This works as a regularization to properly align topics and prevent degenerate topic representations of words, which mitigates the repetitive topic issue. To address the low-coverage dictionary issue, we further propose a cross-lingual vocabulary linking method that finds more linked cross-lingual words for topic alignment beyond the translations of a given dictionary. Extensive experiments on English, Chinese, and Japanese datasets demonstrate that our method outperforms state-of-the-art baselines, producing more coherent, diverse, and well-aligned topics and showing better transferability for cross-lingual classification tasks.

    Comment: Accepted to AAAI2023 conference
    Schlagwörter Computer Science - Computation and Language
    Erscheinungsdatum 2023-04-07
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  7. Buch ; Online: DemaFormer

    Nguyen, Thong / Wu, Xiaobao / Dong, Xinshuai / Nguyen, Cong-Duy / Ng, See-Kiong / Tuan, Luu Anh

    Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding

    2023  

    Abstract: Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query. Recent advances employ the attention mechanism to learn the relations between video moments and the text query. However, naive attention ...

    Abstract Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query. Recent advances employ the attention mechanism to learn the relations between video moments and the text query. However, naive attention might not be able to appropriately capture such relations, resulting in ineffective distributions where target video moments are difficult to separate from the remaining ones. To resolve the issue, we propose an energy-based model framework to explicitly learn moment-query distributions. Moreover, we propose DemaFormer, a novel Transformer-based architecture that utilizes exponential moving average with a learnable damping factor to effectively encode moment-query inputs. Comprehensive experiments on four public temporal language grounding datasets showcase the superiority of our methods over the state-of-the-art baselines.

    Comment: Accepted at EMNLP 2023 (Findings)
    Schlagwörter Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Computation and Language
    Thema/Rubrik (Code) 004
    Erscheinungsdatum 2023-12-05
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  8. Artikel ; Online: Damage of brown planthopper (BPH)

    Deng, Qian-Qian / Ye, Mao / Wu, Xiao-Bao / Song, Jia / Wang, Jun / Chen, Li-Na / Zhu, Zhong-Yan / Xie, Jing

    Plant signaling & behavior

    2022  Band 17, Heft 1, Seite(n) 2096790

    Abstract: Herbivore-induced defense responses are often specific, whereas plants could induce distinct defense responses corresponding to infestation by different herbivorous insects. Brown plant hopper (BPH) ...

    Abstract Herbivore-induced defense responses are often specific, whereas plants could induce distinct defense responses corresponding to infestation by different herbivorous insects. Brown plant hopper (BPH)
    Mesh-Begriff(e) Animals ; Gene Expression Regulation, Plant ; Hemiptera/physiology ; Moths ; Oryza/genetics ; Salicylic Acid
    Chemische Substanzen Salicylic Acid (O414PZ4LPZ)
    Sprache Englisch
    Erscheinungsdatum 2022-07-27
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1559-2324
    ISSN (online) 1559-2324
    DOI 10.1080/15592324.2022.2096790
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Artikel ; Online: Damage of brown planthopper (BPH) Nilaparvata lugens and rice leaf folder (LF) Cnaphalocrocis medinalis in parent plants lead to distinct resistance in ratoon rice

    Deng, Qian-Qian / Ye, Mao / Wu, Xiao-Bao / Song, Jia / Wang, Jun / Chen, Li-Na / Zhu, Zhong-Yan / Xie, Jing

    Plant Signaling & Behavior. 2022 Dec. 31, v. 17, no. 1 p.2096790-

    2022  

    Abstract: Herbivore-induced defense responses are often specific, whereas plants could induce distinct defense responses corresponding to infestation by different herbivorous insects. Brown plant hopper (BPH) Nilaparvata lugens, a phloem-feeding insect, and rice ... ...

    Abstract Herbivore-induced defense responses are often specific, whereas plants could induce distinct defense responses corresponding to infestation by different herbivorous insects. Brown plant hopper (BPH) Nilaparvata lugens, a phloem-feeding insect, and rice leaf folder (LF) Cnaphalocrocis medinalis, a chewing insect, are both specialist herbivores on rice. To characterize the distinct resistance primed by prior damage to these two specialist herbivores, we challenged rice plants with two herbivores during vegetative growth of parent plants and assessed plant resistance in subsequent ratoons. Here, we show that LF and BPH induce different suites of defense responses in parent rice plants, LF induced higher level of JA accumulation and OsAOS, OsCOI1 transcripts, while BPH induced higher accumulation of SA and OsPAL1 transcripts. Moreover, an apparent loss of LF resistance was observed in OsAOS, OsCOI1 RNAi lines. Ratoon plants generated from parents receiving prior LF infestation exhibited higher jasmonic acid (JA) levels and elevated levels of transcripts of defense-related genes associated with JA signaling, while ratoon generated from parents receiving prior BPH infestation exhibited higher salicylic acid (SA) levels and elevated levels of transcripts of defense-related genes associated with SA signaling. Moreover, previous LF infestation obviously elevated ratoons resistance to LF, while previous infestation by BPH led to enhanced resistance in ratoons to BPH. Pre-priming of ratoons defense to LF was significantly reduced in OsAOS and OsCOI1 RNAi plant, but silencing OsAOS and OsCOI1 did not attenuate ratoons resistance to BPH. These results suggest that infestation of two specialist herbivores with different feeding styles in parent crop led to distinct defense responses in subsequent rations, and the acquired resistance to LF in ratoons is associated with priming of jasmonic acid-dependent defense responses.
    Schlagwörter Cnaphalocrocis medinalis ; Nilaparvata lugens ; behavior ; herbivores ; insects ; jasmonic acid ; rice ; salicylic acid ; shoots ; vegetative growth ; Brown planthopper and leaffolder ; specific anti-herbivore resistance ; primed defense ; signaling transduction pathways ; ratoon rice ; rice (Oryza sativa)
    Sprache Englisch
    Erscheinungsverlauf 2022-1231
    Erscheinungsort Taylor & Francis
    Dokumenttyp Artikel ; Online
    ISSN 1559-2324
    DOI 10.1080/15592324.2022.2096790
    Datenquelle NAL Katalog (AGRICOLA)

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  10. Buch ; Online: Vision-and-Language Pretraining

    Nguyen, Thong / Nguyen, Cong-Duy / Wu, Xiaobao / Ng, See-Kiong / Luu, Anh Tuan

    2022  

    Abstract: With the burgeoning amount of data of image-text pairs and diversity of Vision-and-Language (V\&L) tasks, scholars have introduced an abundance of deep learning models in this research domain. Furthermore, in recent years, transfer learning has also ... ...

    Abstract With the burgeoning amount of data of image-text pairs and diversity of Vision-and-Language (V\&L) tasks, scholars have introduced an abundance of deep learning models in this research domain. Furthermore, in recent years, transfer learning has also shown tremendous success in Computer Vision for tasks such as Image Classification, Object Detection, etc., and in Natural Language Processing for Question Answering, Machine Translation, etc. Inheriting the spirit of Transfer Learning, research works in V\&L have devised multiple pretraining techniques on large-scale datasets in order to enhance the performance of downstream tasks. The aim of this article is to provide a comprehensive revision of contemporary V\&L pretraining models. In particular, we categorize and delineate pretraining approaches, along with the summary of state-of-the-art vision-and-language pretrained models. Moreover, a list of training datasets and downstream tasks is supplied to further polish the perspective into V\&L pretraining. Lastly, we decided to take a further step to discuss numerous directions for future research.

    Comment: 46 pages, 2 figures
    Schlagwörter Computer Science - Computation and Language
    Thema/Rubrik (Code) 004
    Erscheinungsdatum 2022-07-04
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