Learning Meta Model for Zero- and Few-shot Face Anti-spoofing

Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks, which need large scale training data to cover as many attacks as possible. However, the trained model is easy to overfit several common attacks and is still vulnerable to unseen attacks. To overcome this challenge, the detector should: 1) learn discriminative features that can generalize to unseen spoofing types from predefined presentation attacks; 2) quickly adapt to new spoofing types by learning from both the predefined attacks and a few examples of the new spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot learning problem. In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task of detecting unseen spoofing types by learning from predefined living and spoofing faces and a few examples of new attacks. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. Experiments show its superior performances on the presented benchmarks to existing methods in existing zero-shot FAS protocols.Comment: Accepted by AAAI202

Deep Learning for Face Anti-Spoofing: A Survey

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, traditional FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community. In this paper, to stimulate future research, we present the first comprehensive review of recent advances in deep learning based FAS. It covers several novel and insightful components: 1) besides supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also investigate recent methods with pixel-wise supervision (e.g., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial RGB camera, we summarize the deep learning applications under multi-modal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensors. We conclude this survey by emphasizing current open issues and highlighting potential prospects.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI

Community trauma exposure and post-traumatic stress disorder in Chinese children and adolescents

BackgroundAn increasing number of studies have shown the association between traumatic events occurring in childhood and adolescence and post-traumatic stress disorder (PTSD). A gap remains in the literature on the epidemiology and influencing factors of traumatic events and post-traumatic stress disorder in communities in northern China. This study aimed to determine the prevalence of traumatic events and PTSD in communities in northern China, to explore the types of stressful traumatic events and the impact of these traumatic events on children and adolescents, and to investigate the effect of sociodemographic factors on PTSD.MethodsA cross-sectional survey study was conducted among 6,027 students (7–17 years old) from 6 cities in Liaoning Province, China. The sample consisted of 2,853 males (47.34%) and 3,174 females (52.66%). The Essen Trauma-Inventory for Children and Adolescents (ETI-CA) Scale was used. The ETI-CA has 5 sections, which include type of traumatic events, worst traumatic event, post-traumatic symptoms, onset, duration, burden of PTSD, and present difficulties in different areas of life. PTSD symptoms were assessed with 23 items in Part 3 of the ETI-CA.ResultsWe found that 2,068 (34.3%) of 6,027 participants experienced trauma events and 686 (33.2%) of 2,068 reported PTSD. Among trauma-exposed youth (2,068), the sudden death of close relatives (33.9%), serious accidents (20.9%), and parental divorce (15.5%) were reported as the worst traumatic events. Studies have shown that after exposure to stressful life events, more than 30% of people feel nervous or upset (39.8%), scared (33.4%), helpless (32.6%), and about 10% have headaches (15.5%), rapid heartbeat (13.3%), and dizziness (11.8%). Multivariable logistic regression analyses showed that students in middle school [OR = 1.29 (1.016, 1.637)], not a student leader [OR = 0.738 (0.618, 0.881)], and their parents in single marital status significantly predicted higher PTSD prevalence the remarried [OR = 0.474 (0.252, 0.893)], married [OR = 0.42 (0.227, 0.778)].ConclusionThe present study suggests the government to train psychological counselors in schools and communities to provide emotional and psychological support, as well as the school leaders and parents to elevate adolescents' psychological suzhi. Particularly, counseling and professional support should be given to those students whose parents are single

The mediating effect of resilience and COVID-19 anxiety on the relationship between social support and insomnia among healthcare workers: a cross-sectional study

BackgroundInsomnia in healthcare workers has become a topic of concern in the health system. The high infectivity and longevity of the COVID-19 pandemic have resulted in great pressure and a high incidence of insomnia among healthcare workers. Insomnia among healthcare workers has a negative impact on high-quality healthcare services in addition to their health. Thus, it's necessary to explore insomnia's underlying mechanisms.ObjectThe present research's aims were threefold: explored the association between social support, resilience, COVID-19 anxiety, and insomnia among healthcare workers during the pandemic, elucidated the underlying mechanism of insomnia, and offered recommendations for improving the health of these workers.Materials and methodsA cross-sectional design was adopted. From May 20 to 30, 2022, 1038 healthcare workers were selected to fill out the Oslo 3-item Social Support Scale, the eight-item Athens Insomnia Scale, the Coronavirus Anxiety Scale, and the Brief Resilience Scale. Descriptive statistics and correlations were analyzed by SPSS 25.0. Mediation analysis was conducted by Mplus 8.3 using 5000 bootstrap samples.ResultsOf the participating 1038 healthcare workers, the prevalence of insomnia was 41.62% (432/1038). Significant associations were found involving insomnia, resilience, COVID-19 anxiety, and social support. Insomnia was directly affected by social support. Moreover, three indirect pathways explain how social support affected insomnia: resilience's mediating role, COVID-19 anxiety's mediating role, and the chain-mediation role of resilience and COVID-19 anxiety.ConclusionThe results validated our hypotheses and supported the opinion of Spielman et al. ‘s three-factor model of insomnia. Social support of healthcare workers has an indirect impact on insomnia in addition to its direct one via independent and chain-mediation effects of resilience and COVID-19 anxiety

Predicting depressive symptom by cardiometabolic indicators in mid-aged and older adults in China: a population-based cross-sectional study

ObjectiveDepressive symptom is a serious mental illness often accompanied by physical and emotional problems. The prevalence of depressive symptom in older adults has become an increasingly important public health priority. Our study used cardiometabolic indicators to predict depressive symptom in middle-aged and older adults in China.MethodsThe data came from the China Health and Retirement Longitudinal Study 2011 (CHARLS2011), which was a cross-sectional study. The analytic sample included 8,942 participants aged 45 years or above. The study evaluated the relationship between cardiometabolic indicators and depression by measuring 13 indicators, including body mass index (BMI), waist circumference, waist-height ratio (WHtR), conicity index, visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), a body shape index (ABSI), body roundness index (BRI), triglyceride glucose index (TyG-index) and its correlation index (TyG-BMI, TyG-waist circumference, TyG-WHtR). Binary logistic regression analysis was used to examine the association between thirteen cardiometabolic indicators and depressive symptom. In addition, the receiver operating characteristic (ROC) curve analysis and area under curve (AUC) were used to evaluate the predictive anthropometric index and to determine the optimum cut-off value.ResultsThe study included 8,942 participants, of whom 4,146 (46.37%) and 4,796 (53.63%) were male and female. The prevalence of depressive symptom in mid-aged and older adults in China was 41.12% in males and 55.05% in females. The results revealed that BMI [AUC = 0.440, 95%CI: 0.422–0.457], waist circumference [AUC = 0.443, 95%CI: 0.425–0.460], WHtR [AUC = 0.459, 95%CI: 0.441–0.476], LAP [AUC = 0.455, 95%CI: 0.437–0.472], BRI [AUC = 0.459, 95%CI: 0.441–0.476], CVAI [AUC = 0.449, 95%CI: 0.432–0.467], TyG-BMI [AUC = 0.447, 95%CI: 0.429–0.465], and TyG-waist circumference [AUC =0.452, 95%CI: 0.434–0.470] were weak predictors of depressive symptom (p < 0.05) in males. In females, BMI [AUC = 0.470, 95%CI: 0.453–0.486], LAP [AUC = 0.484, 95%CI: 0.467–0.500], TyG-BMI [AUC = 0.470, 95%CI: 0.454–0.487], and TyG-waist circumference [AUC =0.481, 95%CI: 0.465–0.498] were weak predictors of depressive symptom (p < 0.05). On the other side, VAI, ABSI, conicity index and TyG index could not predict depressive symptom in middle-aged and older adults.ConclusionMost cardiometabolic indicators have important value in predicting depressive symptom. Our results can provide measures for the early identification of depressive symptom in middle-aged and older adults in China to reduce the prevalence of depressive symptom and improve health

Comparison of metabolic syndrome prevalence and characteristics using five different definitions in China: a population-based retrospective study

BackgroundMetabolic syndrome (MetS) is on the rise in developing countries and is characterized by a series of indications of metabolic disturbance. However, the prevalence of MetS varies under different definitions. The study aimed to compare five definitions of MetS in the China adult population, to explore their prevalence, characteristics and agreement.MethodsThe data for the retrospective study came from the China Health and Retirement Longitudinal Study (CHARLS), consisting of 9,588 participants (≥45). MetS definitions from International Diabetes Federation (IDF) (2006), National Cholesterol Education Program Adult Treatment Panel III (ATPIII) (2005), National Cholesterol Education Program Adult Treatment Panel III (ATPIII) (2001), Chinese Diabetes society (CDS) (2004) and the World Health Organization (WHO) (1999). We used binary and multivariable logistic analysis to explore factors connected with MetS.ResultsThe five definitions of MetS led to different prevalence of MetS:34.52% by IDF (2006), 38.63% by ATP (2005), 25.94% by ATP (2001), 26.31% by CDS (2004), 21.57% by WHO (1999). According to the definition of IDF (2006) (22.32% vs. 45.06%), ATPIII (2005) definition (27.99% vs. 47.82%), ATPIII (2001) definition (15.37% vs. 35.07%), CDS (2004) definition (19.96% vs. 31.80%), and WHO (1999) definition (17.44% vs. 25.14%), the prevalence of MetS in men was low but in women was high. The agreement between the five definitions for men was good except for the IDF (2006) definition and ATPIII (2001) definition (kappa = 0.51), with kappa values from 0.64 to 0.85. For women, the agreement between the five definitions was good ranging from 0.67 to 0.95, however, except for the definition of CDS (2004) and the definition of IDF (2006) (kappa = 0.44), the definition of WHO (1999) and the definition of IDF (2006) (kappa = 0.55), and the definition of WHO (1999) and the definition of ATPIII (2005) (kappa = 0.54). Binary logistic analysis indicated that although the impact and relevance varied by sex and definition, age, education, marital status, current residence, current smoking, alcohol using, taking activities and number of chronic diseases were factors connected to MetS.Conclusionthe prevalence and characteristics of the five definitions of MetS are different in the Chinese population. Therefore, it is vital to use the same definition for a country to diagnose MetS. On the other side, a lower prevalence in men than in women and the consistency of five MetS definitions are good in men but relatively poor in women

Thalamic Activation Modulates the Responses of Neurons in Rat Primary Auditory Cortex: An In Vivo Intracellular Recording Study

Auditory cortical plasticity can be induced through various approaches. The medial geniculate body (MGB) of the auditory thalamus gates the ascending auditory inputs to the cortex. The thalamocortical system has been proposed to play a critical role in the responses of the auditory cortex (AC). In the present study, we investigated the cellular mechanism of the cortical activity, adopting an in vivo intracellular recording technique, recording from the primary auditory cortex (AI) while presenting an acoustic stimulus to the rat and electrically stimulating its MGB. We found that low-frequency stimuli enhanced the amplitudes of sound-evoked excitatory postsynaptic potentials (EPSPs) in AI neurons, whereas high-frequency stimuli depressed these auditory responses. The degree of this modulation depended on the intensities of the train stimuli as well as the intervals between the electrical stimulations and their paired sound stimulations. These findings may have implications regarding the basic mechanisms of MGB activation of auditory cortical plasticity and cortical signal processing