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

Towards Optimal Discrete Online Hashing with Balanced Similarity

When facing large-scale image datasets, online hashing serves as a promising solution for online retrieval and prediction tasks. It encodes the online streaming data into compact binary codes, and simultaneously updates the hash functions to renew codes of the existing dataset. To this end, the existing methods update hash functions solely based on the new data batch, without investigating the correlation between such new data and the existing dataset. In addition, existing works update the hash functions using a relaxation process in its corresponding approximated continuous space. And it remains as an open problem to directly apply discrete optimizations in online hashing. In this paper, we propose a novel supervised online hashing method, termed Balanced Similarity for Online Discrete Hashing (BSODH), to solve the above problems in a unified framework. BSODH employs a well-designed hashing algorithm to preserve the similarity between the streaming data and the existing dataset via an asymmetric graph regularization. We further identify the "data-imbalance" problem brought by the constructed asymmetric graph, which restricts the application of discrete optimization in our problem. Therefore, a novel balanced similarity is further proposed, which uses two equilibrium factors to balance the similar and dissimilar weights and eventually enables the usage of discrete optimizations. Extensive experiments conducted on three widely-used benchmarks demonstrate the advantages of the proposed method over the state-of-the-art methods.Comment: 8 pages, 11 figures, conferenc

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

A Note on the Exponential G

We get the exponential G-martingale theorem with the Kazamaki condition and tell a distinct difference between the Kazamaki’s and Novikov’s criteria with an example

Early embryonic development of green crucian carp <em>Carassius auratus</em> indigentiaus subsp. nov.

The early embryonic development of the green crucian carp (Carassius auratus indigentiaus subsp. nov.) was observed to study its timing and characteristics. The fertilized eggs are round, slightly yellow, and viscous demersal. The egg diameter after water swelling was 1.47 ± 0.04 mm. Embryonic development can be divided into eight stages according to its major characteristics: blastoderm formation, mitotic, blastula, gastrula, neurula, blastopore closure, organogenetic and hatching stages. Under a water temperature of 24 ± 1 °C, salinity of 35 ± 1, and pH of 7.4 ± 0.5, the blastoderm began to form 35 min after fertilization. It entered the mitotic stage at 55 min, blastula stage at 220 min, gastrula stage at 460 min, neurula stage at 675 min, blastopore closure stage at 700 min, organogenetic stage at 900 min and hatching stage at 3390 min. The total length of newly hatched larvae was 4.07 ± 0.35 mm. Regression models of growth characteristics were obtained. The full-length growth rate was fastest from 15 to 26 days, with an average of 0.396 mm/day. Compared with other cyprinid fishes, green crucian carp exhibited some distinct characteristics in certain stages of embryonic development. The eye primordium developed before the sarcomere, and the heart rate was relatively high before the member stage. Yolk fluctuation was observed during the multi-cell phase of embryonic development. The sarcomere formed after the eye primordium. The heart rate in the hatching phase was 136 beats/min. This study provides a reference for embryonic development in green crucian carp, which will assist its large-scale cultivation

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