Semi-Supervised Domain Adaptation with Source Label Adaptation

Semi-Supervised Domain Adaptation (SSDA) involves learning to classify unseen target data with a few labeled and lots of unlabeled target data, along with many labeled source data from a related domain. Current SSDA approaches usually aim at aligning the target data to the labeled source data with feature space mapping and pseudo-label assignments. Nevertheless, such a source-oriented model can sometimes align the target data to source data of the wrong classes, degrading the classification performance. This paper presents a novel source-adaptive paradigm that adapts the source data to match the target data. Our key idea is to view the source data as a noisily-labeled version of the ideal target data. Then, we propose an SSDA model that cleans up the label noise dynamically with the help of a robust cleaner component designed from the target perspective. Since the paradigm is very different from the core ideas behind existing SSDA approaches, our proposed model can be easily coupled with them to improve their performance. Empirical results on two state-of-the-art SSDA approaches demonstrate that the proposed model effectively cleans up the noise within the source labels and exhibits superior performance over those approaches across benchmark datasets. Our code is available at https://github.com/chu0802/SLA .Comment: Accepted by CVPR 202

Causal Mediation Analysis for Difference-in-Difference Design and Panel Data

Advantages of panel data, i.e., difference in difference (DID) design data, are a large sample size and easy availability. Therefore, panel data are widely used in epidemiology and in all social science fields. The literatures on causal inferences of panel data setting or DID design are growing, but no theory or mediation analysis method has been proposed for such settings. In this study, we propose a methodology for conducting causal mediation analysis in DID design and panel data setting. We provide formal counterfactual definitions for controlled direct effect and natural direct and indirect effect in panel data setting and DID design, including the identification and required assumptions. We also demonstrate that, under the assumptions of linearity and additivity, controlled direct effects can be estimated by contrasting marginal and conditional DID estimators whereas natural indirect effects can be estimated by calculating the product of the exposure-mediator DID estimator and the mediator-outcome DID estimator. A panel regression-based approach is also proposed. The proposed method is then used to investigate mechanisms of the effects of the Covid 19 pandemic on the mental health status of the population. The results revealed that mobility restrictions mediated approximately 45 % of the causal effect of Covid 19 on mental health status

THE FORMATION OF FACEBOOK STICKINESS: THE PERSPECTIVES OF MEDIA RICHNESS THEORY, USE & GRATIFICATION THEORY AND INTIMACY

With the advent of web 2.0, social networking sites (SNSs) have mushroomed. Gaining competitive advantage by retaining users in the SNS is an important issue for operators. By conceptualizing stickiness as the state of individuals’ prolong stay on the SNS, the aim of this study is to explore the process of formatting SNS stickiness in the context of Facebook from the perspectives of media richness theory, uses & gratifications (U & G) theory, and intimacy. Data was collected from the northern Taiwan University. A total of 187 questionnaires were selected for the data analysis. The results support the following conclusions: 1) the media richness provided by the Facebook website directly influences users’ gratifications, including interpersonal utility and social utility; 2) the intimacy is an important mediating variable involving in the process of formatting Facebook stickiness; and 3) Facebook stickiness is indirectly influenced by gratifications, interpersonal utility and social utility, which exerts its effect through intimacy. By integrating the theoretical perspectives of media richness theory, U & G theory with intimacy into the process model of formatting Facebook stickiness, this study provides both academics and practitioners with insight into how Facebook stickiness form and enable SNS manager to retain their users

Causal Mediation Analysis with Multiple Time-Varying Mediators

In longitudinal studies with time-varying exposures and mediators, the mediational g-formula is an important method for the assessment of direct and indirect effects. However, current methodologies based on the mediational g-formula can deal with only one mediator. This limitation makes these methodologies inapplicable to many scenarios. Hence, we develop a novel methodology by extending the mediational g-formula to cover cases with multiple time-varying mediators. We formulate two variants of our approach that are each suited to a distinct set of assumptions and effect definitions and present nonparametric identification results of each variant. We further show how complex causal mechanisms (whose complexity derives from the presence of multiple time-varying mediators) can be untangled. A parametric method along with a user-friendly algorithm was implemented in R software. We illustrate our method by investigating the complex causal mechanism underlying the progression of chronic obstructive pulmonary disease. We found that the effects of lung function impairment mediated by dyspnea symptoms and mediated by physical activity accounted for 13.7% and 10.8% of the total effect, respectively. Our analyses thus illustrate the power of this approach, providing evidence for the mediating role of dyspnea and physical activity on the causal pathway from lung function impairment to health status

Domain-Generalized Face Anti-Spoofing with Unknown Attacks

Although face anti-spoofing (FAS) methods have achieved remarkable performance on specific domains or attack types, few studies have focused on the simultaneous presence of domain changes and unknown attacks, which is closer to real application scenarios. To handle domain-generalized unknown attacks, we introduce a new method, DGUA-FAS, which consists of a Transformer-based feature extractor and a synthetic unknown attack sample generator (SUASG). The SUASG network simulates unknown attack samples to assist the training of the feature extractor. Experimental results show that our method achieves superior performance on domain generalization FAS with known or unknown attacks.Comment: IEEE International Conference on Image Processing (ICIP 2023