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International Joint Conference on Neural Networks (IJCNN)

作者： Qingao Wang Xingang Wang Jinxiao Yang Yurun Bi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Multimodal sentiment analysis(MSA) aims to synthesize multiple pieces of information, including textual, visual, and auditory information, to more accurately infer sentiment polarity. Due to the existence of complex and diverse affective causal relationships between different modalities, they interact with each other and constitute a multidimensional expression of human emotions. In the face of unbalanced data, i.e., when there are significant differences in the number of samples between different categories, the attention mechanism of the model tends to focus on spurious correlations in the training data, seriously impairing the model’s generalization ability and leading to bias, unbalanced prediction, and false correlation identification problems. To address the above problems, this study proposes a new multimodal fusion method based on the "within-sample sampling" and "cross-sample sampling" front door adjustment methods in causal inference strategies: the multi-head causal attention fusion method (MCAF). This method aims to capture potential causal relationships between different modalities through different attention heads, helping the model focus more on key causal features and avoid paying too much attention to false correlations in the training data. This helps improve the generalization performance of the model, make the decision-making process of the model more interpretable, and enhance the accuracy and robustness of the model for multimodal sentiment analysis tasks, making it more suitable for practical application scenarios.

关键词： Analytical models Sentiment analysis Visualization Correlation Neural networks Training data Predictive models

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HGTA: News Recommendation Based on Hierarchical Granular Semantic Embeddings and Threshold Attention

HGTA: News Recommendation Based on Hierarchical Granular Sem...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yiwen Li Zhenyu Yang Zhijun Li Xiaoyang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Personalized news recommendation helps users find interesting content among much news information. Most existing news recommendation techniques densely interact a user’s historical clicked news with candidate news to capture their reading interest. However, during this process, even if certain historical clicked news is weakly correlated with the candidate news, the model still interacts with it, generating more noise information when constructing user interests. Simultaneously, the news representation learning process using neural networks is a fine-grained to coarse-grained learning process; i.e., as the network deepens, the fine-grained original news information is easily lost. Therefore, to solve the above problems, this paper proposes a news recommendation model based on hierarchical granular semantic embeddings and threshold attention (HGTA). In HGTA, we propose a threshold attention module to filter unimportant historical clicked news by setting an attention score threshold, i.e., interacting only with historical clicked news that is strongly correlated with the candidate news when capturing user interests. Thus, the model attention is focused, and the noisy interaction information is reduced. Furthermore, when training the neural network, to prevent the loss of fine-grained information, we design a hierarchical granular information extraction module to capture both coarse- and fine-grained news features to better enrich the news semantics. Experiments conducted on the large real-world Microsoft News dataset (MIND) show that our model achieves highly competitive results in terms of popular metrics. Our code is published at the following URL: https://***/liyiweneven/HGTA.

关键词： Uniform resource locators Training Representation learning Federated learning Semantics Neural networks Noise

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Predicting Response to Patients with Gastric Cancer Via a Dynamic-Aware Model with Longitudinal Liquid Biopsy data

SSRN

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SSRN 2024年

作者： Chen, Zifan Zhao, Jie Li, Yanyan Li, Yilin Liu, Huimin Feng, Xujiao Nan, Xinyu Dong, Bin Shen, Lin Chen, Yang Zhang, Li Center for Data Science Peking University Beijing China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Peking University Cancer Hospital and Institute Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Guangzhou Medical University Guangzhou China Peking University Beijing China Center for Machine Learning Research Peking University Beijing China Peking University Changsha Institute for Computing and Digital Economy Changsha China

Gastric cancer (GC) presents challenges in predicting treatment responses due to its patient-specific heterogeneity. Recently, liquid biopsies have become recognized as a valuable data modality, offering essential cellular and molecular insights and enabling the capture of time-sensitive information. This study aimed to harness artificial intelligence (AI) technology to analyze longitudinal liquid biopsy data. We collected a dataset from longitudinal liquid biopsies of 91 patients at Peking Cancer Hospital, spanning from July 2019 to April 2022, which included 1,895 tumor-related cellular images and 1,698 tumor marker indices. Subsequently, we introduced the Dynamic-Aware Model (DAM) to predict GC treatment responses. DAM incorporates the dynamic data through AI-engineered components, enabling an in-depth longitudinal analysis. Utilizing three-fold cross-validation, DAM exhibited superior performance over traditional cell-counting-based methods, achieving an AUC of 0.807 in predicting GC treatment responses. In the test set, DAM maintained stable efficacy with an AUC of 0.802. Besides, DAM showed the capability to accurately predict treatment responses from early treatment data. Moreover, DAM's visual analysis of attention mechanisms identified six dynamic focus-area-related visual features and their strong association with treatment response. These findings represent a pioneering effort in applying AI technology for interpreting longitudinal liquid biopsy data and employ visual analytics in GC, offering a promising avenue toward precise response prediction and tailored treatment strategies for patients with ***: This work was supported by the National Natural Science Foundation of China (81801778 to Li Zhang., 82203881 to Yang Chen, U22A20327 to Lin Shen, 12090022 to Bin Dong), Beijing Natural Science Foundation (7222021 to Yang Chen), Beijing Hospitals Authority Youth Programme (QML20231115 to Yang Chen), Clinical Medicine Plus X-Young Scholars Project of P

关键词： Diseases

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JTEA: Implementing Semi-Supervised Entity Alignment Using Joint Teaching Strategies

JTEA: Implementing Semi-Supervised Entity Alignment Using Jo...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Kai Lu Jing Zhao Lichao Ding Zenghao Hao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Shandong Engineering Research Center of Big Data Applied Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Entity alignment(EA) aims to identify equivalent entities in different knowledge graphs or data sources and establish correspondences between them. In recent years, embedding-based methods have made significant progress in the field of entity alignment, yet the shortage of adequate training data (i.e., seed entity pairs) continues to be a primary challenge. Traditional semi-supervised methods typically address this issue by generating potential mappings (i.e., pseudo-mappings) for unlabeled entities. However, these methods often suffer from the influence of erroneous potential mappings or overlook the uncertainty of potential mappings. Therefore, we propose a novel semi-supervised entity alignment method called JTEA. This method combines seed entity pairs with potential mappings through end-to-end joint training, providing strong guidance for model training. In constructing potential mappings, we propose a bidirectional matching strategy (BMS), which generates more detailed potential mappings by jointly adjusting decisions in different directions and uses a joint matching confidence score to characterize its uncertainty. Furthermore, we also introduce a method of matching diversity alignment selection, aiming to screen out more reliable entity alignments to further improve the performance and stability of the model. We conducted a large number of experiments on the benchmark dataset to fully validate the effectiveness of the JTEA method.

关键词： Training Uncertainty Soft sensors Noise Training data Knowledge graphs data models Stability analysis Reliability Thermal stability

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Application of Diffusion Artificial Hummingbird Algorithm for Multimodal Transportation Logistics Distribution Routing Problem

Application of Diffusion Artificial Hummingbird Algorithm fo...

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Chinese Automation Congress (CAC)

作者： Fan Yang Lei Yue Xu Zhang YTO Express Co. Ltd. Shanghai China Post Industry Technology Research and Development Center of State Posts Bureau Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Anhui Yougu Express Intelligent Technology Co. Ltd Wuhu China

With the rapid development of e-commerce and the logistics industry, logistics transportation networks based on different modes of transport have gradually become increasingly complex. Currently, the multimodal transportation logistics distribution routing problem, which is considered a complex vehicle routing problem, has gained significant attention. Therefore, the selection of appropriate routes plays a crucial role in enhancing the economic efficiency and rationality of logistics planning. In order to obtain optimal routes, this paper proposes an improved diffusion artificial hummingbird algorithm based on the hummingbird swarm diffusion flight strategy. This algorithm improves the population initialization method to cover the solution space as comprehensively as possible. In addition, a migration diffusion strategy is proposed, by spreading the worst solution to the outer area of the population to find the optimal solution, thereby improving the global search capability of the algorithm. In order to verify the convergence performance of the algorithm, this paper conducts comparative tests with multiple algorithms, effectively verifying the high accuracy and fast convergence performance of the DAHA algorithm. Applying it to the multimodal logistics distribution path problem can effectively avoid excessively slow convergence speed and The disadvantage of low convergence accuracy. Finally, the feasibility and effectiveness of the algorithm are verified through experiments.

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Single-cell technologies: current and near future

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Science China(Life Sciences) 2025年第1期68卷 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi KeyLaboratory of Spine and Spinal CordInjury Repairand Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchoolofLifeSciencesandTechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) School of StatisticsEast China Normal UniversityShanghai 200062China

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity,tissue organization,and overall organism function(Sun et al.,2024).In 2009,single-cell RNA sequencing(scRNA-seq)was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology,single-cell approaches have expanded to encompass other omics,such as genomics,epigenomics,proteomics,and ***,multimodal technologies including spatially resolved transcriptomics and clustered regularly interspaced short palindromic repeats(CRISPR)screening at the single-cell level further advanced our ability to comprehensively understand cellular ***-cell methods provide deeper insights into disease mechanisms across various conditions,including cancer,developmental disorders,and aging-associated *** this issue,we focus on topics related to single-cell methods,covering the following four aspects:(i)single-cell multi-omic sequencing technology;(ii)single-cell spatial technology;(iii)singlecell CRISPR screening technology;(iv)applications of single-cell technology.

关键词： expanded enable insight

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Biogas Fermentation Monitoring research Based on Borderline-SMOTE Algorithm and Stacking Ensemble Learning

Biogas Fermentation Monitoring Research Based on Borderline-...

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Information and Communication Technology (ICTech), International Conference of

作者： Pengfei Xiu Lijun Fu Yukun Sun Hongliang Wang Xuerao Li Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Big Data Technology and Cognitive Intelligence Laboratory University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

Anaerobic fermentation is the most important link to gas production, this paper solves the current problem with the help of the current complete machine learning technology, combining perception and intelligent processing, intelligent processing of information, and derive a decision-making basis for its control or management [1]. Aiming at the current problems of the anaerobic fermentation process in biogas engineering, the system design was carried out, and the anaerobic fermentation process of biogas and its main influencing factors were investigated, and the monitoring parameters of the system were selected to be temperature, PH value, liquid level and redox potential [2]. Previous studies are mainly based on a single prediction model, which leads to poor generalization ability of the model. In this study, unbalanced samples are handled by Borderline-SMOTE algorithm. Based on the Stacking integrated learning approach, a common single prediction model is used to construct a combined prediction model. The accuracy, precision, recall, F1 score and area under the ROC curve are used to evaluate the advantages and disadvantages of the combined prediction model over the single prediction model.

关键词： Accuracy Biological system modeling Stacking Process control Production Predictive models Prediction algorithms Biogas Fermentation Monitoring

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Causal fMRI-Mamba: Causal State Space Model for Neural Decoding and Brain Task States Recognition

Causal fMRI-Mamba: Causal State Space Model for Neural Decod...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Weihao Deng Fei Han Qinghua Ling Qing Liu Henry Han School of Computer Science and Communication Engineering JiangSu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China School of Computer Jiangsu University of Science and Technology China School of Electronic and Information Engineering West Anhui University China School of Engineering and Computer Science Baylor University USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep learning advances neural decoding in functional magnetic resonance imaging (fMRI) tasks with convolution and attention-based methods. However, these methods struggle with capturing global spatiotemporal information due to high dimensionality, noise and inter-individual difference of fMRI, which also increase computational complexity and prior bias. To this end, a novel causal state space model, Causal fMRI-Mamba, is proposed for neural decoding and task state mapping. It effectively captures global spatiotemporal information via eliminating local redundancies and capturing long-distance dependencies. Meanwhile, a causal representation framework is designed to extract invariant high-order features and disentangle related causal features, enhancing model performance. Furthermore, a dense connection module is expanded to prevent significant causal information loss in hidden states of inter layers. On the HCP brain task state classification task, Causal fMRI-Mamba achieves better performance and generalization than comparison methods.

关键词： Deep learning Perturbation methods Redundancy Noise Functional magnetic resonance imaging Brain modeling Feature extraction Spatiotemporal phenomena Decoding Speech processing

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Opinion-Tree-guided Contrastive Learning for Aspect Sentiment Quadruple Prediction

Opinion-Tree-guided Contrastive Learning for Aspect Sentimen...

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International Conference on Computer Supported Cooperative Work in Design

作者： Zhijun Li Zhenyu Yang Yiwen Li Xiaoyang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In recent years, aspect sentiment quadruple prediction (ASQP) has become a popular task in the field of aspect-based sentiment analysis. When modeling comment statements, it is often necessary to consider structural information between sentiment labels. Existing approaches categorize these labels by encoding text and dependency tree structures separately and then incorporating their representations, but this approach fails to effectively capture the structural information between sentiment labels. To address this problem, we propose a method for ASQP called Opinion-Tree-guided Contrastive Learning (OTCL), which embeds structural information directly into the text instead of modeling it separately. Specifically, OTCL utilizes the opinion tree structure to guide the construction of positive samples of the input text during training. By associating the input text with its positive samples, the text encoder can learn to generate textual representations associated with the opinion tree structure. We validate the effectiveness of our method on two commonly used datasets.

关键词： Training Sentiment analysis Federated learning Contrastive learning Predictive models Encoding Task analysis

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Deep Multi-kernel Clustering Network

Deep Multi-kernel Clustering Network

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IEEE International Conference on data Mining (ICDM)

作者： Lina Ren Ruizhang Huang Shengwei Ma Yongbin Qin Yanping Chen Chuan Lin State Key Laboratory of Public Big Data Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang China Department of Information Engineering Guizhou Light Industry Technical College Guiyang China

In this paper, a deep multi-kernel clustering network, named DMKCN, is proposed to learn a high-quality and structurally separable kernel representation for the clustering task. Specifically, a multi-kernel learner is proposed to choose a suitable kernel function by learning a suitable combination of kernel functions automatically. A kernel-aid encoder module, consisting of a series of multi-kernel learners, is proposed to learn the structurally separable kernel representation. Besides, a dual self-supervised mechanism, consisting of a kernel self-supervised strategy and a representation self-supervised strategy, is designed to uniformly optimize the kernel representation learning and structural partition. The kernel self-supervised strategy is developed to supervise the multi-kernel learners with the consideration of an objective of clustering task, the representation self-supervised strategy is developed to guide the optimization of kernel representation learning by reconstructing the raw data. Extensive experiments on six real-world datasets demonstrate the outstanding performance of our proposed DMKCN.

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