Affective Behaviour Analysis Using Pretrained Model with Facial Priori

Affective behaviour analysis has aroused researchers' attention due to its broad applications. However, it is labor exhaustive to obtain accurate annotations for massive face images. Thus, we propose to utilize the prior facial information via Masked Auto-Encoder (MAE) pretrained on unlabeled face images. Furthermore, we combine MAE pretrained Vision Transformer (ViT) and AffectNet pretrained CNN to perform multi-task emotion recognition. We notice that expression and action unit (AU) scores are pure and intact features for valence-arousal (VA) regression. As a result, we utilize AffectNet pretrained CNN to extract expression scores concatenating with expression and AU scores from ViT to obtain the final VA features. Moreover, we also propose a co-training framework with two parallel MAE pretrained ViT for expression recognition tasks. In order to make the two views independent, we random mask most patches during the training process. Then, JS divergence is performed to make the predictions of the two views as consistent as possible. The results on ABAW4 show that our methods are effective

Rotavirus Antigenemia in Children Is Associated with Viremia

BACKGROUND: Antigenemia is commonly detected in rotavirus-infected children. Although rotavirus RNA has been detected in serum, definitive proof of rotavirus viremia has not been shown. We aimed to analyze a defined patient population to determine if infectious virus could be detected in sera from children with rotavirus antigenemia. METHODS AND FINDINGS: Serum samples obtained upon hospitalization from children with gastroenteritis (57 stool rotavirus-positive and 41 rotavirus-negative), children with diagnosed bronchiolitis of known (n = 58) or unknown (n = 17) viral etiology, children with noninfectious, nonchronic conditions (n = 17), and healthy adults (n = 28) were tested for rotavirus antigen by enzyme immunoassay (EIA). Results of serum antigen testing were assessed for association with clinical and immunological attributes of the children. Rotavirus antigenemia was detected in 90% (51/57) of children with rotavirus-positive stools, in 89% (8/9) of children without diarrhea but with rotavirus-positive stools, in 12% (2/17) of children with bronchiolitis of unknown etiology without gastroenteritis, and in 12% (5/41) of children with gastroenteritis but with rotavirus-negative stools. Antigenemia was not detected in sera from children with noninfectious nonchronic conditions, children with bronchiolitis of known etiology and no gastroenteritis, or healthy adults. Neither age nor timing of serum collection within eight days after onset of gastroenteritis significantly affected levels of antigenemia, and there was no correlation between antigenemia and viral genotype. However, there was a negative correlation between serum rotavirus antigen and acute rotavirus-specific serum IgA (r = −0.44, p = 0.025) and IgG (r = −0.40, p = 0.01) titers. We examined 11 antigen-positive and nine antigen-negative sera for infectious virus after three blind serial passages in HT-29 cells using immunofluorescence staining for rotavirus structural and nonstructural proteins. Infectious virus was detected in 11/11 (100%) sera from serum antigen-positive children and in two out of nine (22%) sera samples from antigen-negative children (p = 0.002). CONCLUSIONS: Most children infected with rotavirus are viremic. The presence of viremia is directly related to the detection of antigenemia and is independent of the presence of diarrhea. Antigenemia load is inversely related to the titer of antirotavirus antibody in the serum. The finding of infectious rotavirus in the blood suggests extraintestinal involvement in rotavirus pathogenesis; however, the impact of rotavirus viremia on clinical manifestations of infection is unknown

Multi-advisor sequential decision-making without ground truth

Decision-making from potentially unreliable advice is an important problem in many settings, such as lending, investment, ensemble machine learning, and crowd-sourcing. In such settings, advice can often be elicited from multiple advisers and aggregated to make a more reliable decision, especially when the decisions have important consequences. In addition, often, similar decisions are made over time using the same set of advisers. Therefore, the reliability or trustworthiness of advisers can be utilized to improve decision accuracy and learned and updated over time. However, this is challenging especially when there is no access to the ground truth, i.e., when there is no information about the true or ideal decision, even after the fact, or this information is only available after a considerable delay (e.g., in the case of a loan default). While there is extensive work in decision-making from multiple advisers, existing work focuses on single-shot static decision-making, and does not account for the sequential nature of decisions. To address this gap, this thesis addresses settings where multiple decisions are made sequentially over time, without access to the ground truth, and where we have no prior information about advisors' trustworthiness. We refer to this as the multi-advisor sequential decision-making problem. To address this problem, first, we propose the Multi-Advisor Binary Sequential Decision-Making method (MABSDM). In this setting, a decision-maker needs to make decisions on a sequence of problems, which includes the essential factors for making decisions. For each problem, a set of advisors provides advice between binary options and the decision-maker needs to aggregate their advice to make a decision. To be specific, MABSDM (1) models the advisors' trustworthiness sequentially without prior information, (2) makes optimal decisions from the advice and trustworthiness of multiple imperfect advisors without ground truth. In addition, our results show that MABSDM has higher decision accuracy than benchmarks using state-of-the-art models including Bayesian aggregation, weighted voting, and Beta distribution trustworthiness model. Moreover, MABSDM outperforms benchmarks in terms of modelling the trustworthiness of advisors in most results. Second, we then apply MABSDM to an interactive reinforcement learning setting whereby proposing a method named Multi-Advisor Interactive Reinforcement Learning system (MAIRL). In more detail, interactive reinforcement learning is an effective way to accelerate agent learning by feedback from human advisors to agents. However, if the human advisor is not always reliable, it often hinders the agent's training. To address this problem, we introduce multiple advisors to turn this problem into a multi-advisor binary sequential decision-making problem. Specifically, in MAIRL, we use MABSDM to aggregate the binary feedback from multiple imperfect advisors into a reliable reward for agent training in a reward-sparse environment. In addition, the review model in MAIRL can correct the unreliable reward from advisors. In particular, our experiments for evaluating feedback forms show that the binary feedback outperforms other feedback forms including ranking feedback, scaling feedback, and state value feedback. Finally, we conduct grid-world experiments to show that the policy trained by the MAIRL with the review model is closer to the optimal policy than that without a review model. Third, we propose a utility maximization method based on MABSDM, namely Multi-Advisor Dynamic Decision-Making (MADDM). In more detail, in practice, making a correct decision often has great rewards while a failed decision has a significant cost, and gathering advice from a set of advisors has a cost. We take into account balancing the value of decisions and the cost associated with querying advisors in the multi-advisor binary sequential decision-making problem. Therefore, the challenge is finding an advisor selection strategy that retrieves reliable advice and maximizes the overall utility, which is the expected return of the decision-making. To address this challenge, MADDM considers selecting advisors by balancing the advisors' costs, advisors' trustworthiness, and the value of the problem and then using MABSDM to make the optimal decision. Moreover, we evaluate our algorithm through several numerical experiments. The results show that our approach outperforms two other methods that combine state-of-the-art models. Finally, we extend MABSDM to a general method, namely Multi-Advisor Sequential Decision-Making (MASDM), which can make decisions among multiple options, not just binary options. In addition, we evaluate MASDM through extensive experiments in simulated environments. Moreover, we apply our method to ensemble machine learning using the experiments by the MNIST database. The results show that MASDM has better decision accuracy and the ability to trustworthiness assessment than the five benchmarks that use state-of-the-art methods, achieving a maximum improvement of 22% in accuracy compared to Bayesian aggregation methods

MTIRL: Multi-trainer interactive reinforcement learning system

Interactive reinforcement learning can effectively facilitate the agent training via human feedback. However, such methods often require the human teacher to know what is the correct action that the agent should take. In other words, if the human teacher is not always reliable, then it will not be consistently able to guide the agent through its training. In this paper, we propose a more effective interactive reinforcement learning system by introducing multiple trainers, namely Multi-Trainer Interactive Reinforcement Learning (MTIRL), which could aggregate the binary feedback from multiple non-perfect trainers into a more reliable reward for an agent training in a reward-sparse environment. In particular, our trainer feedback aggregation experiments show that our aggregation method has the best accuracy when compared with the majority voting, the weighted voting, and the Bayesian method. Finally, we conduct a grid-world experiment to show that the policy trained by the MTIRL with the review model is closer to the optimal policy than that without a review model

MADDM: multi-advisor dynamic binary decision-making by maximizing the utility

Being able to infer ground truth from the responses of multiple imperfect advisors is a problem of crucial importance in many decision-making applications, such as lending, trading, investment, and crowd-sourcing. In practice, however, gathering answers from a set of advisors has a cost. Therefore, finding an advisor selection strategy that retrieves a reliable answer and maximizes the overall utility is a challenging problem. To address this problem, we propose a novel strategy for optimally selecting a set of advisers in a sequential binary decision-making setting, where multiple decisions need to be made over time. Crucially, we assume no access to ground truth and no prior knowledge about the reliability of advisers. Specifically, our approach considers how to simultaneously (1) select advisors by balancing the advisors' costs and the value of making correct decisions, (2) learn the trustworthiness of advisers dynamically without prior information by asking multiple advisers, and (3) make optimal decisions without access to the ground truth, improving this over time. We evaluate our algorithm through several numerical experiments. The results show that our approach outperforms two other methods that combine state-of-the-art models

Plantamajoside modulates immune dysregulation and hepatic lipid metabolism in rats with nonalcoholic fatty liver disease via AMPK/Nrf2 elevation

Abstract Nonalcoholic fatty liver disease (NAFLD) is a hepatic metabolic syndrome with a rapidly increasing prevalence globally. Plantamajoside (PMS), a phenylethanoid glycoside component extracted from Plantago asiatica, has various biological properties. However, its effect on NAFLD remains unknown. The study aimed to explore the effect and mechanism of PMS on NAFLD in the high‐fat diet (HFD)‐feeding rats. PMS induced a decrease in body and liver weight, and the amelioration in the blood lipid parameters and pathological symptoms in HFD‐feeding rats. The increase in the serum concentrations and the relative protein expressions of proinflammatory factors was decreased by the PMS treatment in HFD‐induced NAFLD rats. Additionally, PMS reduced the excessive lipid vacuoles, and modified the relative expressions of proteins involved in the fatty acid synthesis and uptake in HFD‐feeding rats. Mechanically, the downregulation of AMPK/Nrf2 pathway in HFD‐feeding rats was restored by the PMS treatment. Inhibition of AMPK pathway reversed the PMS‐induced the increase in the level of inflammatory factors, pathological symptoms, excessive lipid vacuoles, and the relative expression of proteins involved in the fatty acid synthesis and uptake. Collectively, PMS ameliorated immune dysregulation and abnormal hepatic lipid metabolism by activating AMPK/Nrf2 pathway in rats with NAFLD