Choices for part-time jobs and the impacts on the wage differentials. A comparative study for Great Britain and the Netherlands

This paper uses the European Household Panel (the ECHP) to analyze individuals' choices on part-time jobs and their impacts on the wage differentials. Our study is a comparative study between Great Britain and the Netherlands. In contrast to most of the previous researches on part-time employment, we make a distinction between short part-time and long part-time jobs. The results show that overall women were more likely to take part-time jobs in both countries, but the effect was much stronger in the Netherlands than it was in Great Britain. We find that there was no substantial wage gap between long part-time and full-time jobs in the Netherlands, working long part-time were more likely to be treated as full-time jobs, which may suggest the presence of 'retention part-time jobs' described by Tilly (1996). On the other hand, the results show that part-time workers in Great Britain suffered relatively larger wage penalties, yet, working short part-time was not significantly different from working long part-time because they both received lower wage rates compared to full-time jobs.short part-time job ; long part-time job and wage differential

Gait recognition under carrying condition : a static dynamic fusion method

When an individual carries an object, such as a briefcase, conventional gait recognition algorithms based on average silhouette/Gait Energy Image (GEI) do not always perform well as the object carried may have the potential of being mistakenly regarded as a part of the human body. To solve such a problem, in this paper, instead of directly applying GEI to represent the gait information, we propose a novel dynamic feature template for classification. Based on this extracted dynamic information and some static feature templates (i.e., head part and trunk part), we cast gait recognition on the large USF (University of South Florida) database by adopting a static/dynamic fusion strategy. For the experiments involving carrying condition covariate, significant improvements are achieved when compared with other classic algorithms

Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization

In this paper, we study multi-block min-max bilevel optimization problems, where the upper level is non-convex strongly-concave minimax objective and the lower level is a strongly convex objective, and there are multiple blocks of dual variables and lower level problems. Due to the intertwined multi-block min-max bilevel structure, the computational cost at each iteration could be prohibitively high, especially with a large number of blocks. To tackle this challenge, we present a single-loop randomized stochastic algorithm, which requires updates for only a constant number of blocks at each iteration. Under some mild assumptions on the problem, we establish its sample complexity of $O(1/\epsilon^4)$ for finding an $\epsilon$-stationary point. This matches the optimal complexity for solving stochastic nonconvex optimization under a general unbiased stochastic oracle model. Moreover, we provide two applications of the proposed method in multi-task deep AUC (area under ROC curve) maximization and multi-task deep partial AUC maximization. Experimental results validate our theory and demonstrate the effectiveness of our method on problems with hundreds of tasks

Can the 12-item General Health Questionnaire be used to measure positive mental health?

Background. Well-being is an important determinant of health and social outcomes. Measures of positive mental health states are needed for population-based research. The 12-item General Health Questionnaire (GHQ-12) has been widely used in many settings and languages, and includes positively and negatively worded items. Our aim was to test the hypothesis that the GHQ-12 assesses both positive and negative mental health and that these domains are independent of one another. Method. Exploratory (EFA) and confirmatory (CFA) factor analyses were conducted using data from the British Household Panel Survey (BHPS) and the Health Survey for England (HSE). Regression models were used to assess whether associations with individual and household characteristics varied across positive and negative mental health dimensions. We also explored higher-level variance in these measures, between electoral wards. Results. We found a consistent, replicable factor structure in both datasets. EFA results indicated a two-factor solution, and CFA demonstrated that this was superior to a one-factor model. These factors correspond to ‘symptoms of mental disorder’ and ‘positive mental health’. Further analyses demonstrated independence of these factors in associations with age, gender, employment status, poor housing and household composition. Statistically significant ward-level variance was found for symptoms of mental disorder but not positive mental health. Conclusions. The GHQ-12 measures both positive and negative aspects of mental health, and although correlated, these dimensions have some independence. The GHQ-12 could be used to measure positive mental health in population-based research

Literal Translation or Free Translation? ---- A Study of the Translation of “你” and “您” in Teahouse

This study compares the Chinese pronoun “你” and “您”, and discovers appropriate translation strategies for them. The translations of “你” and “您” in the famous Chinese drama Teahouse are analyzed and explanations are sought for why certain translation strategies are used in specific contexts of the usage of “你” and “您”. According to the data, this study has found that in Teahouse, a majority of the total instances of “你” are translated literally, while only a small amount of them are translated freely. It has also been found that the majority of all instances of “您” possessing different connotations are translated literally, with free translation applied to the rest. The study‟s findings are not only theoretically helpful, but also can be extended to the practical translation work. The study concludes that in translation, much more attention should be paid to the various connotations of “您”, contexts and background information, along with the hierarchy of relations between different characters in the drama

Hyperbolic Face Anti-Spoofing

Learning generalized face anti-spoofing (FAS) models against presentation attacks is essential for the security of face recognition systems. Previous FAS methods usually encourage models to extract discriminative features, of which the distances within the same class (bonafide or attack) are pushed close while those between bonafide and attack are pulled away. However, these methods are designed based on Euclidean distance, which lacks generalization ability for unseen attack detection due to poor hierarchy embedding ability. According to the evidence that different spoofing attacks are intrinsically hierarchical, we propose to learn richer hierarchical and discriminative spoofing cues in hyperbolic space. Specifically, for unimodal FAS learning, the feature embeddings are projected into the Poincar\'e ball, and then the hyperbolic binary logistic regression layer is cascaded for classification. To further improve generalization, we conduct hyperbolic contrastive learning for the bonafide only while relaxing the constraints on diverse spoofing attacks. To alleviate the vanishing gradient problem in hyperbolic space, a new feature clipping method is proposed to enhance the training stability of hyperbolic models. Besides, we further design a multimodal FAS framework with Euclidean multimodal feature decomposition and hyperbolic multimodal feature fusion & classification. Extensive experiments on three benchmark datasets (i.e., WMCA, PADISI-Face, and SiW-M) with diverse attack types demonstrate that the proposed method can bring significant improvement compared to the Euclidean baselines on unseen attack detection. In addition, the proposed framework is also generalized well on four benchmark datasets (i.e., MSU-MFSD, IDIAP REPLAY-ATTACK, CASIA-FASD, and OULU-NPU) with a limited number of attack types

Deep Learning Techniques for Video Instance Segmentation: A Survey

Video instance segmentation, also known as multi-object tracking and segmentation, is an emerging computer vision research area introduced in 2019, aiming at detecting, segmenting, and tracking instances in videos simultaneously. By tackling the video instance segmentation tasks through effective analysis and utilization of visual information in videos, a range of computer vision-enabled applications (e.g., human action recognition, medical image processing, autonomous vehicle navigation, surveillance, etc) can be implemented. As deep-learning techniques take a dominant role in various computer vision areas, a plethora of deep-learning-based video instance segmentation schemes have been proposed. This survey offers a multifaceted view of deep-learning schemes for video instance segmentation, covering various architectural paradigms, along with comparisons of functional performance, model complexity, and computational overheads. In addition to the common architectural designs, auxiliary techniques for improving the performance of deep-learning models for video instance segmentation are compiled and discussed. Finally, we discuss a range of major challenges and directions for further investigations to help advance this promising research field

On the solvability of the indefinite Hamburger moment problem

In this paper, we present a new approach for the solvability of the indefinite Hamburger moment problem in the class of generalized Nevanlinna functions with a given negative index, which is more algebraic and completely different from the existing method [8] based on the step-by-step Schur algorithm. As a by-product of this approach, we simultaneously obtain a concrete rational solution of such an indefinite Hamburger moment problem when the solvability conditions are met. The basic strategy focuses on the structural characteristics of the Hankel matrix and the relation among the Hankel, Loewner, Bezout and some other structured matrices

S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens

Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face recognition system by presenting spoofed faces. State-of-the-art FAS techniques predominantly rely on deep learning models but their cross-domain generalization capabilities are often hindered by the domain shift problem, which arises due to different distributions between training and testing data. In this study, we develop a generalized FAS method under the Efficient Parameter Transfer Learning (EPTL) paradigm, where we adapt the pre-trained Vision Transformer models for the FAS task. During training, the adapter modules are inserted into the pre-trained ViT model, and the adapters are updated while other pre-trained parameters remain fixed. We find the limitations of previous vanilla adapters in that they are based on linear layers, which lack a spoofing-aware inductive bias and thus restrict the cross-domain generalization. To address this limitation and achieve cross-domain generalized FAS, we propose a novel Statistical Adapter (S-Adapter) that gathers local discriminative and statistical information from localized token histograms. To further improve the generalization of the statistical tokens, we propose a novel Token Style Regularization (TSR), which aims to reduce domain style variance by regularizing Gram matrices extracted from tokens across different domains. Our experimental results demonstrate that our proposed S-Adapter and TSR provide significant benefits in both zero-shot and few-shot cross-domain testing, outperforming state-of-the-art methods on several benchmark tests. We will release the source code upon acceptance