Monobutyl phthalate induces the expression change of G-Protein-Coupled Receptor 30 in rat testicular Sertoli cells

The aim of the study was to explore whether G-Protein-Coupled Receptor 30 (GPR30) was expressed in rat testicular Sertoli cells and to assess the impact of monobutyl phthalate (MBP) on the expression of GPR30 in Sertoli cells. By using RT-PCR, Western-Blot and immunofluorescent microscopy, the expression of GPR30 in rat Sertoli cells was found at both gene and protein level. Cultures of Sertoli cells were exposed to MBP (10– –1000 mM) or a vehicle. The results indicated that the expression of GPR30 increased at gene and protein levels in Sertoli cells following administration of MBP even at a relatively low concentration. We suggest that changes of GPR30 expression may play an important role in the effects of the xenoestrogen MBP on Sertoli cell function. (Folia Histochemica et Cytobiologica 2013, Vol. 51, No. 1, 18–24

Fine-grained Private Knowledge Distillation

Knowledge distillation has emerged as a scalable and effective way for privacy-preserving machine learning. One remaining drawback is that it consumes privacy in a model-level (i.e., client-level) manner, every distillation query incurs privacy loss of one client's all records. In order to attain fine-grained privacy accountant and improve utility, this work proposes a model-free reverse $k$-NN labeling method towards record-level private knowledge distillation, where each record is employed for labeling at most $k$ queries. Theoretically, we provide bounds of labeling error rate under the centralized/local/shuffle model of differential privacy (w.r.t. the number of records per query, privacy budgets). Experimentally, we demonstrate that it achieves new state-of-the-art accuracy with one order of magnitude lower of privacy loss. Specifically, on the CIFAR-$10$ dataset, it reaches $82.1\%$ test accuracy with centralized privacy budget $1.0$; on the MNIST/SVHN dataset, it reaches $99.1\%$/$95.6\%$ accuracy respectively with budget $0.1$. It is the first time deep learning with differential privacy achieve comparable accuracy with reasonable data privacy protection (i.e., $\exp(\epsilon)\leq 1.5$). Our code is available at https://github.com/liyuntong9/rknn

SIAD: Self-supervised Image Anomaly Detection System

Recent trends in AIGC effectively boosted the application of visual inspection. However, most of the available systems work in a human-in-the-loop manner and can not provide long-term support to the online application. To make a step forward, this paper outlines an automatic annotation system called SsaA, working in a self-supervised learning manner, for continuously making the online visual inspection in the manufacturing automation scenarios. Benefit from the self-supervised learning, SsaA is effective to establish a visual inspection application for the whole life-cycle of manufacturing. In the early stage, with only the anomaly-free data, the unsupervised algorithms are adopted to process the pretext task and generate coarse labels for the following data. Then supervised algorithms are trained for the downstream task. With user-friendly web-based interfaces, SsaA is very convenient to integrate and deploy both of the unsupervised and supervised algorithms. So far, the SsaA system has been adopted for some real-life industrial applications.Comment: 4 pages, 3 figures, ICCV 2023 Demo Trac

HHMF: hidden hierarchical matrix factorization for recommender systems

Abstract(#br)Matrix factorization (MF) is one of the most powerful techniques used in recommender systems. MF models the (user, item) interactions behind historical explicit or implicit ratings. Standard MF does not capture the hierarchical structural correlations, such as publisher and advertiser in advertisement recommender systems, or the taxonomy (e.g., tracks, albums, artists, genres) in music recommender systems. There are a few hierarchical MF approaches, but they require the hierarchical structures to be known beforehand. In this paper, we propose a Hidden Hierarchical Matrix Factorization (HHMF) technique, which learns the hidden hierarchical structure from the user-item rating records. HHMF does not require the prior knowledge of hierarchical structure; hence, as opposed to..