Self-supervised Facial Action Unit Detection with Region and Relation Learning

Facial action unit (AU) detection is a challenging task due to the scarcity of manual annotations. Recent works on AU detection with self-supervised learning have emerged to address this problem, aiming to learn meaningful AU representations from numerous unlabeled data. However, most existing AU detection works with self-supervised learning utilize global facial features only, while AU-related properties such as locality and relevance are not fully explored. In this paper, we propose a novel self-supervised framework for AU detection with the region and relation learning. In particular, AU related attention map is utilized to guide the model to focus more on AU-specific regions to enhance the integrity of AU local features. Meanwhile, an improved Optimal Transport (OT) algorithm is introduced to exploit the correlation characteristics among AUs. In addition, Swin Transformer is exploited to model the long-distance dependencies within each AU region during feature learning. The evaluation results on BP4D and DISFA demonstrate that our proposed method is comparable or even superior to the state-of-the-art self-supervised learning methods and supervised AU detection methods.Comment: Accepted by ICASSP 202

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

An sTGC Prototype Readout System for ATLAS New-Small-Wheel Upgrade

This paper presents a readout system designed for testing the prototype of Small-Strip Thin Gap Chamber (sTGC), which is one of the main detector technologies used for ATLAS New-Small-Wheel Upgrade. This readout system aims at testing one full-size sTGC quadruplet with cosmic muon triggers

Meta-analysis of the relationship between interleukin-6 levels and the prognosis and severity of acute coronary syndrome

This study aimed to explore the relationship between plasma interleukin 6 (IL-6) levels, adverse cardiovascular events, and the severity of acute coronary syndrome (ACS). A literature review was performed of studies regarding IL-6 and ACS extracted from databases including EMBASE, Cqvip, MEDLINE, Web of Knowledge, PubMed, Cochrane Library, China National Knowledge Infrastructure, and Wanfang data. The Newcastle-Ottawa scale (NOS) was used to evaluate the quality of the literature. The literature was screened, its quality was evaluated, and relevant data were extracted for performing meta-analysis using RevMan software (version 5.3). A total of 524 studies were included in the initial survey. After several rounds of screening and analysis, six studies met the inclusion criteria and underwent meta-analysis using a fixed-effect model. Patients were divided into non-severe and severe groups based on the concentration of high-sensitivity C-reactive protein. Meta-analysis of the relationship between IL-6 and the severity of ACS showed that the plasma IL-6 level of patients in the severe group was significantly higher than that of patients in the non-severe group (p<0.00001). Additionally, patients with experience of major adverse cardiovascular events had significantly higher plasma IL-6 levels than did patients without experience of such events (p<0.00001). In summary, patients with ACS and high IL-6 levels tended to be in a critical condition, with a higher risk of adverse cardiovascular events and worse prognosis. Thus, IL-6 levels could indicate whether patients with ACS may have adverse cardiovascular events and determine the severity of ACS

Conditional Adversarial Synthesis of 3D Facial Action Units

Employing deep learning-based approaches for fine-grained facial expression analysis, such as those involving the estimation of Action Unit (AU) intensities, is difficult due to the lack of a large-scale dataset of real faces with sufficiently diverse AU labels for training. In this paper, we consider how AU-level facial image synthesis can be used to substantially augment such a dataset. We propose an AU synthesis framework that combines the well-known 3D Morphable Model (3DMM), which intrinsically disentangles expression parameters from other face attributes, with models that adversarially generate 3DMM expression parameters conditioned on given target AU labels, in contrast to the more conventional approach of generating facial images directly. In this way, we are able to synthesize new combinations of expression parameters and facial images from desired AU labels. Extensive quantitative and qualitative results on the benchmark DISFA dataset demonstrate the effectiveness of our method on 3DMM facial expression parameter synthesis and data augmentation for deep learning-based AU intensity estimation

Protective effects of Naringin in a rat model of spinal cord ischemia–reperfusion injury

Purpose: To evaluate the activity of naringin (NAR) in a rat model of spinal cord ischemic injury (SCII).Methods: Forty female rats were randomized into four groups: saline without  occlusion (control; group I), SCII (group II), 50 mg/kg NAR (group III), or 100 mg/kg NAR (group IV) for 7 days prior to SCI insult (pre-treatment). Neurological and locomotor functions, antioxidant activity, edema and inflammatory markers were determined.Results: Pre-treatment with NAR considerably lowered the incidence of spinal edema, lipid peroxidation products, and inflammatory markers (TNF-α, NF-p65, IL-1β, and IL-6). It also successfully reverted the antioxidative activity to near-normal levels and improved locomotor function by protecting spinal tissue from oxidative damage and inflammatory insults. NAR administration effectively downregulated the protein expression of TNF-α and NF-κB p65 subunit in spinal tissue, thus confirming its antiinflammatory activity.Conclusion: The results suggests that NAR exhibits neuroprotective effects by inhibiting free radical generation and downregulating inflammatory markers in an SCI rat model.Keywords: Naringin, Spinal cord injury, Locomotor function, Edema, Oxidative  stress, Inflammatio