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  1. Artikel ; Online: Multi-task Recommendation Model of Dual Perception Gated Interaction

    LIN Jian, WU Yun, CHEN Yukang

    Jisuanji kexue yu tansuo, Vol 17, Iss 6, Pp 1417-

    2023  Band 1426

    Abstract: Aiming at the problem of negative migration in multi-task recommendation, the multi-task recommen-dation model of dual perception gated interaction (DPGI-MTRM) is proposed. Firstly, in the multi-task sharing network and the proprietary network, the dual- ... ...

    Abstract Aiming at the problem of negative migration in multi-task recommendation, the multi-task recommen-dation model of dual perception gated interaction (DPGI-MTRM) is proposed. Firstly, in the multi-task sharing network and the proprietary network, the dual-sensing feature extraction module (called the dual-sensing expert layer) is innovatively designed. Its function is to obtain the element-level and vector-level dual-sensing feature representation for the input features. Secondly, a task interaction layer is proposed on the basis of the gated network, which interactively calculates the features output by the gated network to extract high-level semantic relevance between tasks, and at the same time uses the residual method plus the original input gated feature vector to reduce possible noise interference caused by task interaction. Finally, by stacking a dual perception expert layer and a gated interaction layer, and then connecting the neural network output layer of a specific task, a multi-task recommendation model of dual perception gated interaction is obtained. In addition, the multi-objective optimization method of gradient normalization is used during model training, so that the model can better converge. Experiments are conducted on the Census-income, Synthetic Data and Ali-CCP datasets, and the AUC (area under curve) and MSE (mean square error) indicators are used for evaluation. Experimental results show that the proposed model performs better than other benchmark models and achieves more advanced performance.
    Schlagwörter multi-task ; dual perception expert layer ; gated interaction layer ; recommendation model ; Electronic computers. Computer science ; QA75.5-76.95
    Thema/Rubrik (Code) 006
    Sprache Chinesisch
    Erscheinungsdatum 2023-06-01T00:00:00Z
    Verlag Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: Multi-modal recommendation algorithm fusing visual and textual features.

    Hu, Xuefeng / Yu, Wenting / Wu, Yun / Chen, Yukang

    PloS one

    2023  Band 18, Heft 6, Seite(n) e0287927

    Abstract: In recommender systems, the lack of interaction data between users and items tends to lead to the problem of data sparsity and cold starts. Recently, the interest modeling frameworks incorporating multi-modal features are widely used in recommendation ... ...

    Abstract In recommender systems, the lack of interaction data between users and items tends to lead to the problem of data sparsity and cold starts. Recently, the interest modeling frameworks incorporating multi-modal features are widely used in recommendation algorithms. These algorithms use image features and text features to extend the available information, which alleviate the data sparsity problem effectively, but they also have some limitations. On the one hand, multi-modal features of user interaction sequences are not considered in the interest modeling process. On the other hand, the aggregation of multi-modal features often employs simple aggregators, such as sums and concatenation, which do not distinguish the importance of different feature interactions. In this paper, to tackle this, we propose the FVTF (Fusing Visual and Textual Features) algorithm. First, we design a user history visual preference extraction module based on the Query-Key-Value attention to model users' historical interests by using of visual features. Second, we design a feature fusion and interaction module based on the multi-head bit-wise attention to adaptively mine important feature combinations and update the higher-order attention fusion representation of features. We conduct experiments on the Movielens-1M dataset, and the experiments show that FVTF achieved the best performance compared with the benchmark recommendation algorithms.
    Sprache Englisch
    Erscheinungsdatum 2023-06-29
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0287927
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel: The bibliometric and altmetric analysis of chronic traumatic encephalopathy research: how great is the impact?

    Guan, Lulu / Tan, Jingwang / Qi, Bote / Chen, Yukang / Tong, Enyu / Pan, Jingcheng / Zou, Yu

    Frontiers in neurology

    2024  Band 15, Seite(n) 1294125

    Abstract: Background: The study of chronic traumatic encephalopathy (CTE) has received great attention from academia and the general public. This study aims to analyze the research productivity on CTE and investigate the most discussed articles in academia and ... ...

    Abstract Background: The study of chronic traumatic encephalopathy (CTE) has received great attention from academia and the general public. This study aims to analyze the research productivity on CTE and investigate the most discussed articles in academia and the general public by conducting bibliometric and altmetric analyses.
    Methods: Data of articles were obtained from the Web of Science Core Databases and Altmetric Explore. VOSviewer and CiteSpace software were used to analyze and visualize the articles. The correlation between Altmetric attention scores (AAS) and citation counts were assessed by Spearman correlation coefficient.
    Results: 788 publications of CTE were eventually gathered and analyzed, and 100 articles with highest citation counts (Top-cited) and 100 articles with highest AASs (Top-AAS) were then identified. The keywords density map showed both the general public and the scientists were particularly interested in the risk factors and pathology of CTE, and scientists were interested in the causes and characteristics of neurodegenerative diseases while the public became increasingly concerned about the detection and prevention of CTE. By examining the shared characteristics of the 44 articles (High-High articles) that overlapped between Top-cited and Top-AAS articles, we identified certain traits that may potentially contribute to their high citation rates and high AASs. Besides, significant positive correlations with varied strength between AAS and citation were observed in the 788 articles, Top-cited, Top-AAS and High-High datasets.
    Conclusion: This study is the first to link bibliometric and altmetric analyses for CTE publications, which may provide deeper understanding of the attention of the scientists and the general public pay to the study of CTE, and offer some guidance and inspiration for future CTE in the selection of research topics and directions.
    Sprache Englisch
    Erscheinungsdatum 2024-02-08
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2564214-5
    ISSN 1664-2295
    ISSN 1664-2295
    DOI 10.3389/fneur.2024.1294125
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Buch ; Online: IST-Net

    Liu, Jianhui / Chen, Yukang / Ye, Xiaoqing / Qi, Xiaojuan

    Prior-free Category-level Pose Estimation with Implicit Space Transformation

    2023  

    Abstract: Category-level 6D pose estimation aims to predict the poses and sizes of unseen objects from a specific category. Thanks to prior deformation, which explicitly adapts a category-specific 3D prior (i.e., a 3D template) to a given object instance, prior- ... ...

    Abstract Category-level 6D pose estimation aims to predict the poses and sizes of unseen objects from a specific category. Thanks to prior deformation, which explicitly adapts a category-specific 3D prior (i.e., a 3D template) to a given object instance, prior-based methods attained great success and have become a major research stream. However, obtaining category-specific priors requires collecting a large amount of 3D models, which is labor-consuming and often not accessible in practice. This motivates us to investigate whether priors are necessary to make prior-based methods effective. Our empirical study shows that the 3D prior itself is not the credit to the high performance. The keypoint actually is the explicit deformation process, which aligns camera and world coordinates supervised by world-space 3D models (also called canonical space). Inspired by these observations, we introduce a simple prior-free implicit space transformation network, namely IST-Net, to transform camera-space features to world-space counterparts and build correspondence between them in an implicit manner without relying on 3D priors. Besides, we design camera- and world-space enhancers to enrich the features with pose-sensitive information and geometrical constraints, respectively. Albeit simple, IST-Net achieves state-of-the-art performance based-on prior-free design, with top inference speed on the REAL275 benchmark. Our code and models are available at https://github.com/CVMI-Lab/IST-Net.

    Comment: Accepted by ICCV2023
    Schlagwörter Computer Science - Computer Vision and Pattern Recognition
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-03-23
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  5. Artikel ; Online: A recombinase polymerase amplification and Pyrococcus furiosus Argonaute combined method for ultra‐sensitive detection of white spot syndrome virus in shrimp

    Wang, Yu / Chen, Yukang / Tang, Yixin / Wang, Yue / Gao, Song / Yang, Lihong / Wang, Pei

    Journal of Fish Diseases. 2023 Dec., v. 46, no. 12 p.1357-1365

    2023  

    Abstract: White spot disease (WSD) in shrimp is an acute infectious disease caused by white spot syndrome virus (WSSV). WSD has seriously threatened the security of shrimp farming, causing huge economic losses worldwide. As there is currently no effective ... ...

    Abstract White spot disease (WSD) in shrimp is an acute infectious disease caused by white spot syndrome virus (WSSV). WSD has seriously threatened the security of shrimp farming, causing huge economic losses worldwide. As there is currently no effective treatment for WSD, developing early detection technologies for WSSV is of great significance for the prevention. In this study, we have established a detection method for WSSV using a combination of recombinase polymerase amplification (RPA) and Pyrococcus furiosus Argonaute (PfAgo). We have achieved a detection sensitivity of single copy per reaction, which is more sensitive than the previously reported detection methods. Additionally, we have demonstrated high specificity. The entire detection process can be completed within 75 min without the need for precise thermal cyclers, making it suitable for on‐site testing. The fluorescence signal generated by the reaction can be quantified using a portable fluorescence detector or observed with the naked eye under a blue light background. This study provides an ultrasensitive on‐site detection method for WSSV in shrimp aquaculture and expands the application of PfAgo in the field of aquatic disease diagnosis.
    Schlagwörter Pyrococcus furiosus ; White spot syndrome virus ; blue light ; detection limit ; disease diagnosis ; fluorescence ; infectious diseases ; recombinase polymerase amplification ; shrimp ; shrimp culture
    Sprache Englisch
    Erscheinungsverlauf 2023-12
    Umfang p. 1357-1365.
    Erscheinungsort John Wiley & Sons, Ltd
    Dokumenttyp Artikel ; Online
    Anmerkung JOURNAL ARTICLE
    ZDB-ID 432109-1
    ISSN 1365-2761 ; 0140-7775
    ISSN (online) 1365-2761
    ISSN 0140-7775
    DOI 10.1111/jfd.13853
    Datenquelle NAL Katalog (AGRICOLA)

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  6. Artikel ; Online: A multi-task learning model with reinforcement optimization for ASD comorbidity discrimination.

    Dong, Heyou / Chen, Dan / Chen, Yukang / Tang, Yunbo / Yin, Dingze / Li, Xiaoli

    Computer methods and programs in biomedicine

    2023  Band 243, Seite(n) 107865

    Abstract: How to discriminate the comorbidities in autism spectrum disorder (ASD) population has long been an intriguing and challenging issue in neuroscience and neurology practices. Taking attention deficit hyperactivity disorder (ADHD) for example, ... ...

    Abstract How to discriminate the comorbidities in autism spectrum disorder (ASD) population has long been an intriguing and challenging issue in neuroscience and neurology practices. Taking attention deficit hyperactivity disorder (ADHD) for example, electroencephalogram (EEG) analysis has alleviated the problem caused by the task of evaluation of similar behaviors of subjects with ASD, ADHD and ASD+ADHD, which requires a very high expertise to reach any concrete conclusions. However, the performance of ASD comorbidity discrimination is still limited by two major difficulties 1) crucial EEG features regarding ASD and ASD+ADHD largely overlap, and 2) reliable data for model training are routinely insufficient. This study proposes a multi-task learning method with "reinforcement optimization" (namely RO-MLT) working in a two-fold manner: 1)Modeling for Discrimination: a multi-task CNN model maintains the target discrimination task (ASD vs. ASD+ADHD) with the aid of the auxiliary task (ASD vs. Typically Developed (TD)), which is designed to mitigate the aforementioned difficulties on model training; and 2) Reinforcement Optimization: a reinforcement learning algorithm enhances the model's feature extraction and fusion capabilities by optimizing its shared structure. Experimental results based on resting-state EEG that collected from 150 ASD, ASD+ADHD or TD children with the RO-MLT method against the state-of-the-art counterparts indicate that RO-MLT is far superior in terms of all performance indicators (e.g., accuracy). Ablation experiments also show that introduction of multi-task learning and reinforcement optimization can achieve a performance boost-up by 11.07%, a gain even higher than the sums of introduction of two individual techniques to the model design.
    Mesh-Begriff(e) Child ; Humans ; Autism Spectrum Disorder ; Electroencephalography ; Comorbidity ; Attention Deficit Disorder with Hyperactivity
    Sprache Englisch
    Erscheinungsdatum 2023-10-20
    Erscheinungsland Ireland
    Dokumenttyp Journal Article
    ZDB-ID 632564-6
    ISSN 1872-7565 ; 0169-2607
    ISSN (online) 1872-7565
    ISSN 0169-2607
    DOI 10.1016/j.cmpb.2023.107865
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.B 521: Hefte anzeigen Standort:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
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  7. Artikel ; Online: A recombinase polymerase amplification and Pyrococcus furiosus Argonaute combined method for ultra-sensitive detection of white spot syndrome virus in shrimp.

    Wang, Yu / Chen, Yukang / Tang, Yixin / Wang, Yue / Gao, Song / Yang, Lihong / Wang, Pei

    Journal of fish diseases

    2023  Band 46, Heft 12, Seite(n) 1357–1365

    Abstract: White spot disease (WSD) in shrimp is an acute infectious disease caused by white spot syndrome virus (WSSV). WSD has seriously threatened the security of shrimp farming, causing huge economic losses worldwide. As there is currently no effective ... ...

    Abstract White spot disease (WSD) in shrimp is an acute infectious disease caused by white spot syndrome virus (WSSV). WSD has seriously threatened the security of shrimp farming, causing huge economic losses worldwide. As there is currently no effective treatment for WSD, developing early detection technologies for WSSV is of great significance for the prevention. In this study, we have established a detection method for WSSV using a combination of recombinase polymerase amplification (RPA) and Pyrococcus furiosus Argonaute (PfAgo). We have achieved a detection sensitivity of single copy per reaction, which is more sensitive than the previously reported detection methods. Additionally, we have demonstrated high specificity. The entire detection process can be completed within 75 min without the need for precise thermal cyclers, making it suitable for on-site testing. The fluorescence signal generated by the reaction can be quantified using a portable fluorescence detector or observed with the naked eye under a blue light background. This study provides an ultrasensitive on-site detection method for WSSV in shrimp aquaculture and expands the application of PfAgo in the field of aquatic disease diagnosis.
    Mesh-Begriff(e) Animals ; Recombinases ; White spot syndrome virus 1/genetics ; Pyrococcus furiosus/genetics ; Penaeidae ; Fish Diseases ; Aquaculture/methods
    Chemische Substanzen Recombinases
    Sprache Englisch
    Erscheinungsdatum 2023-08-27
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 432109-1
    ISSN 1365-2761 ; 0140-7775
    ISSN (online) 1365-2761
    ISSN 0140-7775
    DOI 10.1111/jfd.13853
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Artikel ; Online: Highly sensitive detection of a long-COVID-related SNP in LZTFL1 allele with

    Tang, Yixin / Xu, Miao / Luo, Bo / Wang, Yue / Chen, Yukang / Yu, Guangxi / Yang, Guang / Gao, Song / Wang, Pei

    Acta biochimica et biophysica Sinica

    2024  

    Sprache Englisch
    Erscheinungsdatum 2024-05-27
    Erscheinungsland China
    Dokumenttyp Journal Article
    ZDB-ID 2175256-4
    ISSN 1745-7270 ; 0582-9879 ; 1672-9145
    ISSN (online) 1745-7270
    ISSN 0582-9879 ; 1672-9145
    DOI 10.3724/abbs.2024071
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Buch ; Online: Denoising Diffusion Step-aware Models

    Yang, Shuai / Chen, Yukang / Wang, Luozhou / Liu, Shu / Chen, Yingcong

    2023  

    Abstract: Denoising Diffusion Probabilistic Models (DDPMs) have garnered popularity for data generation across various domains. However, a significant bottleneck is the necessity for whole-network computation during every step of the generative process, leading to ...

    Abstract Denoising Diffusion Probabilistic Models (DDPMs) have garnered popularity for data generation across various domains. However, a significant bottleneck is the necessity for whole-network computation during every step of the generative process, leading to high computational overheads. This paper presents a novel framework, Denoising Diffusion Step-aware Models (DDSM), to address this challenge. Unlike conventional approaches, DDSM employs a spectrum of neural networks whose sizes are adapted according to the importance of each generative step, as determined through evolutionary search. This step-wise network variation effectively circumvents redundant computational efforts, particularly in less critical steps, thereby enhancing the efficiency of the diffusion model. Furthermore, the step-aware design can be seamlessly integrated with other efficiency-geared diffusion models such as DDIMs and latent diffusion, thus broadening the scope of computational savings. Empirical evaluations demonstrate that DDSM achieves computational savings of 49% for CIFAR-10, 61% for CelebA-HQ, 59% for LSUN-bedroom, 71% for AFHQ, and 76% for ImageNet, all without compromising the generation quality.
    Schlagwörter Computer Science - Computer Vision and Pattern Recognition
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-10-05
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  10. Artikel ; Online: Ultrasensitive one-pot detection of monkeypox virus with RPA and CRISPR in a sucrose-aided multiphase aqueous system.

    Wang, Yue / Tang, Yixin / Chen, Yukang / Yu, Guangxi / Zhang, Xue / Yang, Lihong / Zhao, Chenjie / Wang, Pei / Gao, Song

    Microbiology spectrum

    2023  Band 12, Heft 1, Seite(n) e0226723

    Abstract: Importance: The monkeypox virus was declared as a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO) and continues to cause infection cases worldwide. Given the risk of virus evolution, it is essential to ... ...

    Abstract Importance: The monkeypox virus was declared as a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO) and continues to cause infection cases worldwide. Given the risk of virus evolution, it is essential to identify monkeypox virus infection in a timely manner to prevent outbreaks. This study establishes a novel one-pot recombinase polymerase amplification-Clustered Regularly Interspaced Short Palindromic Repeats (RPA-CRISPR) assay for monkeypox virus with an ultra-high sensitivity. The assay shows good specificity, accuracy, and the rapidness and convenience important for point-of-care testing. It provides an effective tool for the early diagnosis of monkeypox, which is useful for the prevention of an epidemic.
    Mesh-Begriff(e) Humans ; Monkeypox virus/genetics ; Mpox (monkeypox) ; Clustered Regularly Interspaced Short Palindromic Repeats ; Disease Outbreaks ; Hydrolases ; Sucrose ; Recombinases ; CRISPR-Cas Systems
    Chemische Substanzen Hydrolases (EC 3.-) ; Sucrose (57-50-1) ; Recombinases
    Sprache Englisch
    Erscheinungsdatum 2023-12-11
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2807133-5
    ISSN 2165-0497 ; 2165-0497
    ISSN (online) 2165-0497
    ISSN 2165-0497
    DOI 10.1128/spectrum.02267-23
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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