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学者姓名：廖祥文
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Abstract ：
Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.
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| GB/T 7714 | Guo, Lei , Xie, Chengyi , Miao, Rui et al. DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging [J]. | ANALYTICAL CHEMISTRY , 2024 , 96 (9) : 3829-3836 . |
| MLA | Guo, Lei et al. "DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging" . | ANALYTICAL CHEMISTRY 96 . 9 (2024) : 3829-3836 . |
| APA | Guo, Lei , Xie, Chengyi , Miao, Rui , Xu, Jingjing , Xu, Xiangnan , Fang, Jiacheng et al. DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging . | ANALYTICAL CHEMISTRY , 2024 , 96 (9) , 3829-3836 . |
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Abstract ：
Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.
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| GB/T 7714 | Guo, Lei , Xie, Chengyi , Miao, Rui et al. DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging [J]. | ANALYTICAL CHEMISTRY , 2024 , 96 (9) : 3829-3836 . |
| MLA | Guo, Lei et al. "DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging" . | ANALYTICAL CHEMISTRY 96 . 9 (2024) : 3829-3836 . |
| APA | Guo, Lei , Xie, Chengyi , Miao, Rui , Xu, Jingjing , Xu, Xiangnan , Fang, Jiacheng et al. DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging . | ANALYTICAL CHEMISTRY , 2024 , 96 (9) , 3829-3836 . |
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This study presents a novel approach in the application of deep learning for the classification of esophageal squamous cell carcinoma (Escc) using whole-slide images (WSIs). Our methodology uniquely combines Convolutional Neural Network (CNN) with Transformer, leveraging the strengths of both architectures to enhance the accuracy and efficiency of cancer detection and classification in histopathological images. In this research, we first preprocess a substantial dataset of WSI samples, annotated by expert pathologists, to train and validate our model. The CNN component effectively extracts detailed local features from the high-resolution images, while the Transformer, known for its capability in handling sequential data, adeptly manages the global context, addressing the challenges posed by the complex and heterogeneous nature of WSIs. The accuracy, F1 score, recall, and precision of our proposed model on the dataset provided by Fujian Cancer Hospital are 94.71%, 94.32%, 94.68%, and 94.08%, respectively, which are significantly better than other models. This study not only assists pathologists in analyzing esophageal squamous carcinoma WSIs but also paves the way for further research into the combined application of CNN and Transformer in the diagnosis of other types of cancer. © 2024 IEEE.
Keyword ：
CNN CNN esophageal squamous cell carcinoma (Escc) esophageal squamous cell carcinoma (Escc) Transformer Transformer WSIs WSIs
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| GB/T 7714 | Kang, Z. , Zhang, H. , Chen, M. et al. EsccNet: A Hybrid CNN and Transformers Model for the Classification of Whole Slide Images of Esophageal Squamous Cell Carcinoma [未知]. |
| MLA | Kang, Z. et al. "EsccNet: A Hybrid CNN and Transformers Model for the Classification of Whole Slide Images of Esophageal Squamous Cell Carcinoma" [未知]. |
| APA | Kang, Z. , Zhang, H. , Chen, M. , Liao, X. . EsccNet: A Hybrid CNN and Transformers Model for the Classification of Whole Slide Images of Esophageal Squamous Cell Carcinoma [未知]. |
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Empathetic response generation endeavors to empower dialogue systems to perceive speakers' emotions and generate empathetic responses accordingly. Psychological research demonstrates that emotion, as an essential factor in empathy, encompasses trait emotions, which are static and context-independent, and state emotions, which are dynamic and context-dependent. However, previous studies treat them in isolation, leading to insufficient emotional perception of the context, and subsequently, less effective empathetic expression. To address this problem, we propose Combining Trait and State emotions for Empathetic Response Model (CTSM). Specifically, to sufficiently perceive emotions in dialogue, we first construct and encode trait and state emotion embeddings, and then we further enhance emotional perception capability through an emotion guidance module that guides emotion representation. In addition, we propose a cross-contrastive learning decoder to enhance the model's empathetic expression capability by aligning trait and state emotions between generated responses and contexts. Both automatic and manual evaluation results demonstrate that CTSM outperforms state-of-the-art baselines and can generate more empathetic responses. Our code is available at https://github.com/wangyufeng-empty/CTSM. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.
Keyword ：
Behavioral research Behavioral research Emotion Recognition Emotion Recognition Learning systems Learning systems Speech processing Speech processing Speech recognition Speech recognition
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| GB/T 7714 | Wang, Yufeng , Chen, Chao , Yang, Zhou et al. CTSM: Combining Trait and State Emotions for Empathetic Response Model [C] . 2024 : 4214-4225 . |
| MLA | Wang, Yufeng et al. "CTSM: Combining Trait and State Emotions for Empathetic Response Model" . (2024) : 4214-4225 . |
| APA | Wang, Yufeng , Chen, Chao , Yang, Zhou , Wang, Shuhui , Liao, Xiangwen . CTSM: Combining Trait and State Emotions for Empathetic Response Model . (2024) : 4214-4225 . |
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The advancement of generative artificial intelligence technology has significantly contributed to the progress in various fields. However, this technological development has also inadvertently facilitated the creation and widespread dissemination of misinformation. Prior research has concentrated on addressing grammatical issues, inflammatory content, and other pertinent features by employing deep learning models to characterize and model deceptive elements within fake news content. These approaches not only are lack of the capability to assess the content itself, but also fall short in elucidating the reasons behind the model's classification. Based on the above problems, we propose a fine-grained fake news detection method with implicit semantic enhancement. This method fully utilizes the summarization and reasoning capabilities of the existing generative large language model. The method employs inference based on major events, fine-grained minor events, and implicit information to systematically evaluate the authenticity of news content. This method strategically leverages the full potential of the model by decomposing tasks, thereby not only optimizing its proficiency but also significantly enhancing its prowess in capturing instances of fake news. Simultaneously, it is designed to be interpretable, providing a solid foundation for detection. With its inherent ability, this method not only ensures reliable identification but also holds vast potential for diverse applications. © 2024 Science Press. All rights reserved.
Keyword ：
Computational linguistics Computational linguistics Deep learning Deep learning Fake detection Fake detection Semantics Semantics Social networking (online) Social networking (online)
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| GB/T 7714 | Jing, Ke , Zheyong, Xie , Tong, Xu et al. An Implicit Semantic Enhanced Fine-Grained Fake News Detection Method Based on Large Language Models [J]. | Computer Research and Development , 2024 , 61 (5) : 1250-1260 . |
| MLA | Jing, Ke et al. "An Implicit Semantic Enhanced Fine-Grained Fake News Detection Method Based on Large Language Models" . | Computer Research and Development 61 . 5 (2024) : 1250-1260 . |
| APA | Jing, Ke , Zheyong, Xie , Tong, Xu , Yuhao, Chen , Xiangwen, Liao , Enhong, Chen . An Implicit Semantic Enhanced Fine-Grained Fake News Detection Method Based on Large Language Models . | Computer Research and Development , 2024 , 61 (5) , 1250-1260 . |
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Liver cancer manifests as a profoundly heterogeneous malignancy, posing significant challenges in terms of both therapeutic intervention and prognostic evaluation. Given that the liver is the largest metabolic organ, a prognostic risk model grounded in single-cell transcriptome analysis and a metabolic perspective can facilitate precise prevention and treatment strategies for liver cancer. Hence, we identified 11 cell types in a scRNA-seq profile comprising 105,829 cells and found that the metabolic activity of malignant cells increased significantly. Subsequently, a prognostic risk model incorporating tumor heterogeneity, cell interactions, tumor cell metabolism, and differentially expressed genes was established based on eight genes; this model can accurately distinguish the survival outcomes of liver cancer patients and predict the response to immunotherapy. Analyzing the immune status and drug sensitivity of the high- and low-risk groups identified by the model revealed that the high-risk group had more active immune cell status and greater expression of immune checkpoints, indicating potential risks associated with liver cancer-targeted drugs. In summary, this study provides direct evidence for the stratification and precise treatment of liver cancer patients, and is an important step in establishing reliable predictors of treatment efficacy in liver cancer patients. © 2024 by the authors.
Keyword ：
liver cancer liver cancer metabolic reprogramming metabolic reprogramming prognostic risk model prognostic risk model single-cell RNA-seq single-cell RNA-seq
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| GB/T 7714 | Xiong, Z. , Li, L. , Wang, G. et al. Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model [J]. | Genes , 2024 , 15 (6) . |
| MLA | Xiong, Z. et al. "Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model" . | Genes 15 . 6 (2024) . |
| APA | Xiong, Z. , Li, L. , Wang, G. , Guo, L. , Luo, S. , Liao, X. et al. Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model . | Genes , 2024 , 15 (6) . |
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Abstract ：
Empathetic response generation endeavors to empower dialogue systems to perceive speakers' emotions and generate empathetic responses accordingly. Psychological research demonstrates that emotion, as an essential factor in empathy, encompasses trait emotions, which are static and context-independent, and state emotions, which are dynamic and context-dependent. However, previous studies treat them in isolation, leading to insufficient emotional perception of the context, and subsequently, less effective empathetic expression. To address this problem, we propose Combining Trait and State emotions for Empathetic Response Model (CTSM). Specifically, to sufficiently perceive emotions in dialogue, we first construct and encode trait and state emotion embeddings, and then we further enhance emotional perception capability through an emotion guidance module that guides emotion representation. In addition, we propose a cross-contrastive learning decoder to enhance the model's empathetic expression capability by aligning trait and state emotions between generated responses and contexts. Both automatic and manual evaluation results demonstrate that CTSM outperforms state-of-the-art baselines and can generate more empathetic responses. Our code is available at https://github.com/wangyufeng-empty/CTSM. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.
Keyword ：
contrastive learning contrastive learning dialogue system dialogue system emotion recognition emotion recognition empathetic response generation empathetic response generation
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| GB/T 7714 | Wang, Y. , Chen, C. , Yang, Z. et al. CTSM: Combining Trait and State Emotions for Empathetic Response Model [未知]. |
| MLA | Wang, Y. et al. "CTSM: Combining Trait and State Emotions for Empathetic Response Model" [未知]. |
| APA | Wang, Y. , Chen, C. , Yang, Z. , Wang, S. , Liao, X. . CTSM: Combining Trait and State Emotions for Empathetic Response Model [未知]. |
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Abstract ：
Empathetic response generation aims to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses. Psychological theories posit that comprehending emotional and cognitive states necessitates iteratively capturing and understanding associated words across dialogue utterances. However, existing approaches regard dialogue utterances as either a long sequence or independent utterances for comprehension, which are prone to overlook the associated words between them. To address this issue, we propose an Iterative Associative Memory Model (IAMM)(1) for empathetic response generation. Specifically, we employ a novel second-order interaction attention mechanism to iteratively capture vital associated words between dialogue utterances and situations, dialogue history, and a memory module (for storing associated words), thereby accurately and nuancedly comprehending the utterances. We conduct experiments on the Empathetic-Dialogue dataset. Both automatic and human evaluations validate the efficacy of the model. Variant experiments on LLMs also demonstrate that attending to associated words improves empathetic comprehension and expression.
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| GB/T 7714 | Yang, Zhou , Ren, Zhaochun , Wang, Yufeng et al. An Iterative Associative Memory Model for Empathetic Response Generation [J]. | PROCEEDINGS OF THE 62ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1: LONG PAPERS , 2024 : 3081-3092 . |
| MLA | Yang, Zhou et al. "An Iterative Associative Memory Model for Empathetic Response Generation" . | PROCEEDINGS OF THE 62ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1: LONG PAPERS (2024) : 3081-3092 . |
| APA | Yang, Zhou , Ren, Zhaochun , Wang, Yufeng , Sun, Haizhou , Chen, Chao , Zhu, Xiaofei et al. An Iterative Associative Memory Model for Empathetic Response Generation . | PROCEEDINGS OF THE 62ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1: LONG PAPERS , 2024 , 3081-3092 . |
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多模态生成式摘要往往采用序列到序列(Seq2Seq)框架,目标函数在字符级别优化模型,根据局部最优解生成单词,忽略了摘要样本全局语义信息,使得摘要与多模态信息产生语义偏差,容易造成事实性错误.针对上述问题,提出一种基于语义相关性分析的多模态摘要模型.首先,在Seq2Seq框架基础上对多模态摘要进行训练,生成语义多样性的候选摘要;其次,构建基于语义相关性分析的摘要评估器,从全局的角度学习候选摘要之间的语义差异性和真实评价指标ROUGE(Recall-Oriented Understudy for Gisting Evaluation)的排序模式,从而在摘要样本层面优化模型;最后,不依赖参考摘要,利用摘要评估器对候选摘要进行评价,使得选出的摘要与源文本在语义空间中尽可能相似.实验结果表明,在公开数据集MMSS上,相较于MPMSE(Multimodal Pointer-generator via Multimodal Selective Encoding)模型,所提模型在ROUGE-1、ROUGE-2、ROUGE-L评价指标上分别提升了3.17、1.21和2.24个百分点.
Keyword ：
事实性错误 事实性错误 多模态 多模态 序列到序列 序列到序列 生成式摘要 生成式摘要 语义相关性 语义相关性
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| GB/T 7714 | 林于翔 , 吴运兵 , 阴爱英 et al. 基于语义相关性分析的多模态摘要模型 [J]. | 计算机应用 , 2024 , 44 (1) : 65-72 . |
| MLA | 林于翔 et al. "基于语义相关性分析的多模态摘要模型" . | 计算机应用 44 . 1 (2024) : 65-72 . |
| APA | 林于翔 , 吴运兵 , 阴爱英 , 廖祥文 . 基于语义相关性分析的多模态摘要模型 . | 计算机应用 , 2024 , 44 (1) , 65-72 . |
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Abstract ：
Empathetic response generation endeavours to perceive the interlocutor's emotional and cognitive states in the dialogue and express proper responses. Previous studies detect the interlocutor's states by understanding the immediate context of the dialogue. However, these methods are at an elementary/intermediate level of empathetic understanding due to the neglect of the broader context (i.e., the situation) and its associations with the dialogue, leading to inaccurate comprehension of the interlocutor's states. In this paper, we utilize the EMPATHETIC-DIALOGUES dataset consisting of 25k dialogues, and on this basis, we propose a Situation-Dialogue Association Model (SDAM). SDAM focuses on the broader context, i.e., the situation, and enhances the understanding of empathy from explicit and implicit associations. Regarding explicit associations, we propose a bidirectional filtering encoder. It selects relevant keywords between the situation and dialogue, learning their direct lexical relevance. For implicit associations, we use a knowledge-based hypergraph network grounded to learn convoluted connections between the situation and the dialogue. Moreover, we also introduce a simple finetuning approach that combines SDAM with large language models to further strengthen the empathetic understanding capability. Compared to the baseline, SDAM demonstrates superior empathetic ability. In terms of emotion accuracy, fluency, and response diversity (Distinct1/Distinct-2), SDAM achieves improvements of 12.25 (a 30.47% increase), 0.3 (a 0.85% increase), and 0.86/1.23 (116.22% and 30.67% increases), respectively. Additionally, our variant model based on large language models exhibits better emotion recognition capability without compromising response quality, specifically achieving an improvement of 0.23 (a 0.37% increase) in emotion accuracy.
Keyword ：
Emotional detection Emotional detection Empathetic response generation Empathetic response generation Natural language processing Natural language processing Text generation Text generation
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| GB/T 7714 | Yang, Zhou , Ren, Zhaochun , Wang, Yufeng et al. Situation-aware empathetic response generation [J]. | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (6) . |
| MLA | Yang, Zhou et al. "Situation-aware empathetic response generation" . | INFORMATION PROCESSING & MANAGEMENT 61 . 6 (2024) . |
| APA | Yang, Zhou , Ren, Zhaochun , Wang, Yufeng , Sun, Haizhou , Zhu, Xiaofei , Liao, Xiangwen . Situation-aware empathetic response generation . | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (6) . |
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