Facial Action Unit Detection Using Attention and Relation Learning

Attention mechanism has recently attracted increasing attentions in the field of facial action unit (AU) detection. By finding the region of interest of each AU with the attention mechanism, AU-related local features can be captured. Most of the existing attention based AU detection works use prior knowledge to predefine fixed attentions or refine the predefined attentions within a small range, which limits their capacity to model various AUs. In this paper, we propose an end-to-end deep learning based attention and relation learning framework for AU detection with only AU labels, which has not been explored before. In particular, multi-scale features shared by each AU are learned firstly, and then both channel-wise and spatial attentions are adaptively learned to select and extract AU-related local features. Moreover, pixel-level relations for AUs are further captured to refine spatial attentions so as to extract more relevant local features. Without changing the network architecture, our framework can be easily extended for AU intensity estimation. Extensive experiments show that our framework (i) soundly outperforms the state-of-the-art methods for both AU detection and AU intensity estimation on the challenging BP4D, DISFA, FERA 2015 and BP4D+ benchmarks, (ii) can adaptively capture the correlated regions of each AU, and (iii) also works well under severe occlusions and large poses.Comment: This paper is accepted by IEEE Transactions on Affective Computin

Numerical Simulation of the Early Age Three-Dimensional Microstructure Development of Cement Pastes

The formation process of microstructure in cement pastes was first simulated with a numerical model CEMHYD3D. The influence of water-to-cement ratio and particles size on heat release and degree of hydration were then investigated. In addition, the mass of CH and C–S–H phases simulated have been verified by measuring values by thermogravimetry–differential scanning calorimetry (TG-DSC) and X-ray diffraction (XRD)-Rietveld analysis. The evolutions of solids phase percolation and capillary pores de-percolation were also identified by means of a three-dimensional (3D) numerical model. The results show that the simulated values are in good agreement with the available experimental data, indicating a good reliability of the CEMHYD3D model

The Association between Breakfast Skipping and Body Weight, Nutrient Intake, and Metabolic Measures among Participants with Metabolic Syndrome

The effect of skipping breakfast on health, especially in adults, remains a controversial topic. A secondary data analysis was conducted to examine associations between breakfast eating patterns and weight loss, nutrient intake, and metabolic parameters among participants with metabolic syndrome (MetS) (n = 240). Three randomly selected 24-h dietary recalls were collected from each participant at baseline and at the one-year visit. Skipped breakfast was seen in 32.9% at baseline and in 17.4% at the one-year visit, respectively. At baseline, after adjustment for demographics and physical activity, participants who ate breakfast had a higher thiamin, niacin, and folate intake than did breakfast skippers (p \u3c 0.05); other selected parameters including body weight, dietary quality scores, nutrient intake, and metabolic parameters showed no significant differences between the two groups (p ≥ 0.05). From baseline to one year, after adjustment for covariates, mean fat intake increased by 2.7% (95% confidence intervals (CI): −1.0, 6.5%) of total energy in breakfast skippers in comparison to the 1.2% decrease observed in breakfast eaters (95% CI: −3.4, 1.1%) (p = 0.02). Mean changes in other selected parameters showed no significant differences between breakfast skippers and eaters (p \u3e 0.05). This study did not support the hypothesis that skipping breakfast has impact on body weight, nutrient intakes, and selected metabolic measures in participants with MetS

Interactive Spatiotemporal Token Attention Network for Skeleton-based General Interactive Action Recognition

Recognizing interactive action plays an important role in human-robot interaction and collaboration. Previous methods use late fusion and co-attention mechanism to capture interactive relations, which have limited learning capability or inefficiency to adapt to more interacting entities. With assumption that priors of each entity are already known, they also lack evaluations on a more general setting addressing the diversity of subjects. To address these problems, we propose an Interactive Spatiotemporal Token Attention Network (ISTA-Net), which simultaneously model spatial, temporal, and interactive relations. Specifically, our network contains a tokenizer to partition Interactive Spatiotemporal Tokens (ISTs), which is a unified way to represent motions of multiple diverse entities. By extending the entity dimension, ISTs provide better interactive representations. To jointly learn along three dimensions in ISTs, multi-head self-attention blocks integrated with 3D convolutions are designed to capture inter-token correlations. When modeling correlations, a strict entity ordering is usually irrelevant for recognizing interactive actions. To this end, Entity Rearrangement is proposed to eliminate the orderliness in ISTs for interchangeable entities. Extensive experiments on four datasets verify the effectiveness of ISTA-Net by outperforming state-of-the-art methods. Our code is publicly available at https://github.com/Necolizer/ISTA-NetComment: IROS 2023 Camera-ready version. Project website: https://necolizer.github.io/ISTA-Net

Progress in Infertility Control Technology of Fish

Infertility control of fish has been a significant research problem concerning many aquatic breeders. It is necessary to develop infertility control technology for fish to solve the ecological safety problems of existing transgenic fish with qualified characteristics. We reviewed here the implementation of intensely studied available fish infertility control technologies (e.g., triploid technology and antisense RNA technology), kid/kis system, Ntr/Met system, and Gal4/UAS system. Moreover, prospects in infertility control and technological development of fish are disclosed by combining relevant and associated studies

Research Progress on the Fish Complement C3 Gene

Complement (C) is a serum protein with immunity-related functions and enzymatic activities. It can induce inflammatory responses and antibody formation following activation by substances such as pathogens. Moreover, it is a crucial molecule in the congenital immune defense system. Complement manifests earlier than immunoglobulin in the evolutionary process of fish's immune system. Therefore, the complement system of fish is vital for congenital and acquired immunities. A complement system is a polymolecule system comprising more than 30 soluble proteins, membrane-bound proteins, and complement receptors. It can be activated through three pathways for immunoregulation, the clearance of the immune complex, etc. The liver primarily produces the fish complement C3, which is a significant component of the complement system of fishes. C3 is the hub of three activation pathways. Research works concerning the structure, generation process, expression, and functions of C3 in fishes are reviewed in this study