Classifier-head Informed Feature Masking and Prototype-based Logit Smoothing for Out-of-Distribution Detection

Out-of-distribution (OOD) detection is essential when deploying neural networks in the real world. One main challenge is that neural networks often make overconfident predictions on OOD data. In this study, we propose an effective post-hoc OOD detection method based on a new feature masking strategy and a novel logit smoothing strategy. Feature masking determines the important features at the penultimate layer for each in-distribution (ID) class based on the weights of the ID class in the classifier head and masks the rest features. Logit smoothing computes the cosine similarity between the feature vector of the test sample and the prototype of the predicted ID class at the penultimate layer and uses the similarity as an adaptive temperature factor on the logit to alleviate the network's overconfidence prediction for OOD data. With these strategies, we can reduce feature activation of OOD data and enlarge the gap in OOD score between ID and OOD data. Extensive experiments on multiple standard OOD detection benchmarks demonstrate the effectiveness of our method and its compatibility with existing methods, with new state-of-the-art performance achieved from our method. The source code will be released publicly.Comment: 10 pages, 7 figure

Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification

Although great progress in supervised person re-identification (Re-ID) has been made recently, due to the viewpoint variation of a person, Re-ID remains a massive visual challenge. Most existing viewpoint-based person Re-ID methods project images from each viewpoint into separated and unrelated sub-feature spaces. They only model the identity-level distribution inside an individual viewpoint but ignore the underlying relationship between different viewpoints. To address this problem, we propose a novel approach, called \textit{Viewpoint-Aware Loss with Angular Regularization }(\textbf{VA-reID}). Instead of one subspace for each viewpoint, our method projects the feature from different viewpoints into a unified hypersphere and effectively models the feature distribution on both the identity-level and the viewpoint-level. In addition, rather than modeling different viewpoints as hard labels used for conventional viewpoint classification, we introduce viewpoint-aware adaptive label smoothing regularization (VALSR) that assigns the adaptive soft label to feature representation. VALSR can effectively solve the ambiguity of the viewpoint cluster label assignment. Extensive experiments on the Market1501 and DukeMTMC-reID datasets demonstrated that our method outperforms the state-of-the-art supervised Re-ID methods

Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect

Recently, the research interest of person re-identification (ReID) has gradually turned to video-based methods, which acquire a person representation by aggregating frame features of an entire video. However, existing video-based ReID methods do not consider the semantic difference brought by the outputs of different network stages, which potentially compromises the information richness of the person features. Furthermore, traditional methods ignore important relationship among frames, which causes information redundancy in fusion along the time axis. To address these issues, we propose a novel general temporal fusion framework to aggregate frame features on both semantic aspect and time aspect. As for the semantic aspect, a multi-stage fusion network is explored to fuse richer frame features at multiple semantic levels, which can effectively reduce the information loss caused by the traditional single-stage fusion. While, for the time axis, the existing intra-frame attention method is improved by adding a novel inter-frame attention module, which effectively reduces the information redundancy in temporal fusion by taking the relationship among frames into consideration. The experimental results show that our approach can effectively improve the video-based re-identification accuracy, achieving the state-of-the-art performance

Effect of Functional Oligosaccharides and Ordinary Dietary Fiber on Intestinal Microbiota Diversity

Functional oligosaccharides, known as prebiotics, and ordinary dietary fiber have important roles in modulating the structure of intestinal microbiota. To investigate their effects on the intestinal microecosystem, three kinds of diets containing different prebiotics were used to feed mice for 3 weeks, as follows: GI (galacto-oligosaccharides and inulin), PF (polydextrose and insoluble dietary fiber from bran), and a GI/PF mixture (GI and PF, 1:1), 16S rRNA gene sequencing and metabolic analysis of mice feces were then conducted. Compared to the control group, the different prebiotics diets had varying effects on the structure and diversity of intestinal microbiota. GI and PF supplementation led to significant changes in intestinal microbiota, including an increase of Bacteroides and a decrease of Alloprevotella in the GI-fed, but those changes were opposite in PF fed group. Intriguing, in the GI/PF mixture-fed group, intestinal microbiota had the similar structure as the control groups, and flora diversity was upregulated. Fecal metabolic profiling showed that the diversity of intestinal microbiota was helpful in maintaining the stability of fecal metabolites. Our results showed that a single type of oligosaccharides or dietary fiber caused the reduction of bacteria species, and selectively promoted the growth of Bacteroides or Alloprevotella bacteria, resulting in an increase in diamine oxidase (DAO) and/or trimethylamine N-oxide (TMAO) values which was detrimental to health. However, the flora diversity was improved and the DAO values was significantly decreased when the addition of nutritionally balanced GI/PF mixture. Thus, we suggested that maintaining microbiota diversity and the abundance of dominant bacteria in the intestine is extremely important for the health, and that the addition of a combination of oligosaccharides and dietary fiber helps maintain the health of the intestinal microecosystem

Characterizing the Biology of Lytic Bacteriophage vB_EaeM_φEap-3 Infecting Multidrug-Resistant Enterobacter aerogenes

Carbapenem-resistant Enterobacter aerogenes strains are a major clinical problem because of the lack of effective alternative antibiotics. However, viruses that lyze bacteria, called bacteriophages, have potential therapeutic applications in the control of antibiotic-resistant bacteria. In the present study, a lytic bacteriophage specific for E. aerogenes isolates, designated vB_EaeM_φEap-3, was characterized. Based on transmission electron microscopy analysis, phage vB_EaeM_φEap-3 was classified as a member of the family Myoviridae (order, Caudovirales). Host range determination revealed that vB_EaeM_φEap-3 lyzed 18 of the 28 E. aerogenes strains tested, while a one-step growth curve showed a short latent period and a moderate burst size. The stability of vB_EaeM_φEap-3 at various temperatures and pH levels was also examined. Genomic sequencing and bioinformatics analysis revealed that vB_EaeM_φEap-3 has a 175,814-bp double-stranded DNA genome that does not contain any genes considered undesirable for the development of therapeutics (e.g., antibiotic resistance genes, toxin-encoding genes, integrase). The phage genome contained 278 putative protein-coding genes and one tRNA gene, tRNA-Met (AUG). Phylogenetic analysis based on large terminase subunit and major capsid protein sequences suggested that vB_EaeM_φEap-3 belongs to novel genus “Kp15 virus” within the T4-like virus subfamily. Based on host range, genomic, and physiological parameters, we propose that phage vB_EaeM_φEap-3 is a suitable candidate for phage therapy applications

Visual and Fluorescent Detection of Tyrosinase Activity by Using a Dual-Emission Ratiometric Fluorescence Probe

In this work, we designed a dual-emission ratiometric fluorescence probe by hybridizing two differently colored quantum dots (QDs), which possess a built-in correction that eliminates the environmental effects and increases sensor accuracy. Red emissive QDs were embedded in the silica nanoparticle as reference while the green emissive QDs were covalently linked to the silica nanoparticle surface to form ratiometric fluorescence probes (RF-QDs). Dopamine (DA) was then conjugated to the surface of RF-QDs via covalent bonding. The ratiometric fluorescence probe functionalized with dopamine (DA) was highly reactive toward tyrosinase (TYR), which can catalyze the oxidization of DA to dopamine quinine and therefore quenched the fluorescence of the green QDs on the surface of ratiometric fluorescence probe. With the addition of different amounts of TYR, the ratiometric fluorescence intensity of the probe continually varied, leading to color changes from yellow-green to red. So the ratiometric fluorescence probe could be utilized for sensitive and selective detection of TYR activity. There was a good linear relationship between the ratiometric fluorescence intensity and TYR concentration in the range of 0.05–5.0 μg mL^–1, with the detection limit of 0.02 μg mL^–1. Significantly, the ratiometric fluorescence probe has been used to fabricate paper-based test strips for visual detection of TYR activity, which validates the potential on-site application