Author(s): Qiu W; Yang G; Cao L; Niu S; Li Y; Fang D; Dong Z; Magnuson JT; Schlenk D; Leung KMY; Zheng Y; Zeng Z; Feng L; Zhang X; Zhang Y; Fan W; Huang T; Ma J; Wu M; Tao S; Zheng C;

Global food trade expansion has enriched diets worldwide but also heightened concerns about contaminant spread. Per- and polyfluoroalkyl substances (PFAS) can persist in the environment for decades, yet their risks through food trade remain unclear. The global median estimated daily intake (EDI) ...

Article GUID: 41411415

Author(s): Li Z; Luo Z; Bouguila N; Su W; Fan W;

Multi-view clustering has gained significant attention due to its ability to integrate data from diverse perspectives, frequently outperforming single-view approaches. However, existing methods often assume a Gaussian distribution within the latent embedding space, which can degrade performance when handling high-dimensional data or data with complex, non ...

Article GUID: 40664160

Author(s): Guo J; Fan W; Amayri M; Bouguila N;

This article proposes a novel deep clustering model based on the variational autoencoder (VAE), named GamMM-VAE, which can learn latent representations of training data for clustering in an unsupervised manner. Most existing VAE-based deep clustering methods use the Gaussian mixture model (GMM) as a prior on the latent space. We employ a more flexible asy ...

Article GUID: 39662201

Author(s): Luo Z; Amayri M; Fan W; Bouguila N;

Cross-collection topic models extend previous single-collection topic models, such as Latent Dirichlet Allocation (LDA), to multiple collections. The purpose of cross-collection topic modeling is to model document-topic representations and reveal similarities between each topic and differences among groups. However, the restriction of Dirichlet prior and ...

Article GUID: 36685642