The Diagonally Dominant Degree and Disc Separation for the Schur Complement of Ostrowski Matrix

By applying the properties of Schur complement and some inequality techniques, some new estimates of diagonally and doubly diagonally dominant degree of the Schur complement of Ostrowski matrix are obtained, which improve the main results of Liu and Zhang (2005) and Liu et al. (2012). As an application, we present new inclusion regions for eigenvalues of the Schur complement of Ostrowski matrix. In addition, a new upper bound for the infinity norm on the inverse of the Schur complement of Ostrowski matrix is given. Finally, we give numerical examples to illustrate the theory results

Weakly-Supervised Action Localization by Hierarchically-structured Latent Attention Modeling

Weakly-supervised action localization aims to recognize and localize action instancese in untrimmed videos with only video-level labels. Most existing models rely on multiple instance learning(MIL), where the predictions of unlabeled instances are supervised by classifying labeled bags. The MIL-based methods are relatively well studied with cogent performance achieved on classification but not on localization. Generally, they locate temporal regions by the video-level classification but overlook the temporal variations of feature semantics. To address this problem, we propose a novel attention-based hierarchically-structured latent model to learn the temporal variations of feature semantics. Specifically, our model entails two components, the first is an unsupervised change-points detection module that detects change-points by learning the latent representations of video features in a temporal hierarchy based on their rates of change, and the second is an attention-based classification model that selects the change-points of the foreground as the boundaries. To evaluate the effectiveness of our model, we conduct extensive experiments on two benchmark datasets, THUMOS-14 and ActivityNet-v1.3. The experiments show that our method outperforms current state-of-the-art methods, and even achieves comparable performance with fully-supervised methods.Comment: Accepted to ICCV 2023. arXiv admin note: text overlap with arXiv:2203.15187, arXiv:2003.12424, arXiv:2104.02967 by other author

Quantitative Polymerase Chain Reaction Coupled With Sodium Dodecyl Sulfate and Propidium Monoazide for Detection of Viable Streptococcus agalactiae in Milk

Streptococcus agalactiae is an important pathogen causing bovine mastitis. The aim of this study was to develop a simple and specific method for direct detection of S. agalactiae from milk products. Propidium monoazide (PMA) and sodium dodecyl sulfate (SDS) were utilized to eliminate the interference of dead and injured cells in qPCR. Lysozyme (LYZ) was adopted to increase the extraction efficiency of target bacteria DNA in milk matrix. The specific primers were designed based on cfb gene of S. agalactiae for qPCR. The inclusivity and exclusivity of the assay were evaluated using 30 strains. The method was further determined by the detection of S. agalactiae in spiked milk. Results showed significant differences between the SDS–PMA–qPCR, PMA–qPCR and qPCR when a final concentration of 10 mg/ml (R2 = 0.9996, E = 95%) of LYZ was added in DNA extraction. Viable S. agalactiae was effectively detected when SDS and PMA concentrations were 20 μg/ml and 10 μM, respectively, and it was specific and more sensitive than qPCR and PMA–qPCR. Moreover, the SDS–PMA–qPCR assay coupled with LYZ was used to detect viable S. agalactiae in spiked milk, with a limit of detection of 3 × 103 cfu/ml. Therefore, the SDS–PMA–qPCR assay had excellent sensitivity and specificity for detection of viable S. agalactiae in milk