Relative Earnings of Husbands and Wives in Urban China

This paper studies the relative contribution of husbands and wives to the family income in the process of economic transition by using the Chinese Urban Household Survey data from 1988 to 1999. We find that, contrary to the experience of western countries, the share of wives¡¦ labor earnings in urban China tends to decline slightly over time and the share of husbands¡¦ labor earnings is stable. This implies that the role of urban Chinese husbands as the main financial supporters of their families becomes relatively more important during economic transition. We argue that this trend may have reflected the restoration of the functions of household production and labor market in the process of economic transition. This restoration allows households to allocate time, effort and human capital investment for each household member and for each household and market activity in a more efficient way. Our further empirical analysis suggests that at least two factors have accounted for the strengthening of the relative importance of husbands in contributing to family income in urban China: 1) the enlargement of the positive effect of children on husbands and the opposite effect for wives; and 2) the shrinkage of the positive income effect on the leisure of husbands.

Sparse Spatial Transformers for Few-Shot Learning

Learning from limited data is a challenging task since the scarcity of data leads to a poor generalization of the trained model. The classical global pooled representation is likely to lose useful local information. Recently, many few shot learning methods address this challenge by using deep descriptors and learning a pixel-level metric. However, using deep descriptors as feature representations may lose the contextual information of the image. And most of these methods deal with each class in the support set independently, which cannot sufficiently utilize discriminative information and task-specific embeddings. In this paper, we propose a novel Transformer based neural network architecture called Sparse Spatial Transformers (SSFormers), which can find task-relevant features and suppress task-irrelevant features. Specifically, we first divide each input image into several image patches of different sizes to obtain dense local features. These features retain contextual information while expressing local information. Then, a sparse spatial transformer layer is proposed to find spatial correspondence between the query image and the entire support set to select task-relevant image patches and suppress task-irrelevant image patches. Finally, we propose to use an image patch matching module for calculating the distance between dense local representations, thus to determine which category the query image belongs to in the support set. Extensive experiments on popular few-shot learning benchmarks show that our method achieves the state-of-the-art performance

Shaping Visual Representations with Attributes for Few-Shot Recognition

Few-shot recognition aims to recognize novel categories under low-data regimes. Some recent few-shot recognition methods introduce auxiliary semantic modality, i.e., category attribute information, into representation learning, which enhances the feature discrimination and improves the recognition performance. Most of these existing methods only consider the attribute information of support set while ignoring the query set, resulting in a potential loss of performance. In this letter, we propose a novel attribute-shaped learning (ASL) framework, which can jointly perform query attributes generation and discriminative visual representation learning for few-shot recognition. Specifically, a visual-attribute predictor (VAP) is constructed to predict the attributes of queries. By leveraging the attributes information, an attribute-visual attention module (AVAM) is designed, which can adaptively utilize attributes and visual representations to learn more discriminative features. Under the guidance of attribute modality, our method can learn enhanced semantic-aware representation for classification. Experiments demonstrate that our method can achieve competitive results on CUB and SUN benchmarks. Our source code is available at: \url{https://github.com/chenhaoxing/ASL}.Comment: accepted by IEEE Signal Process. Let

The Design of Compass/BeiDou Navigation Satellite Terminal for Migrant Bird Research

A terminal of Compass Navigation Satellite System (CNSS), which can not only support BeiDou-1 and BeiDou-2 but also support Global Positioning System (GPS), is designed to research the activities of the migrant birds, with our novel design of a multiband antenna. By a high-density integration, this terminal is designed with a compact size and light weight. When the terminal is assembled to a whooper swan, its flying trace is recorded by the CNSS, which is in agreement with that of GPS. The flying route map based on the CNSS is useful to check the situation and habit of the migrant bird, which is important for animal protection and bird flu outbreak prediction

Compreendendo mudanças no comportamento de risco moral médico em resposta a reforma governamental de cuidados à saúde

Doctor moral hazard has a significant effect on the doctor-patient relationship, increases the cost of healthcare, and introduces medical risks. It is a global concern. Doctor moral hazard behaviour is evolving in response to China’s healthcare reform program which was inaugurated in 2009.A scientific understanding of doctor behaviour would facilitate the prevention and control of doctor moral hazard behaviour. This study used the principles and methodology of Glaser and Strauss’s grounded theory. Theoretical and snowball samplings were used to identify 60 subjects. Semi-structured in-depth interviews were conducted with each subject. Themes were identified through substantial (open) coding and theoretical coding. Six types of doctor moral hazard behaviour were extracted from the data. A behavioural model was described and diagrammed to provide a conceptual framework of current doctor moral hazard behaviour. The conceptual model of doctor moral hazard behaviour can be used in several ways to correct or prevent undesirable actions. Rules governing hospital procedures can be strengthened and enforced by supervision and punishment; the asymmetry of information between doctor and patient can be reduced; patient participation in treatment decisions can be increased; the effectiveness of medical ethics education can be improved.Para un médico, el riesgo moral tiene un efecto significativo en la relación médico-paciente, incrementa el costo de la atención de salud e introduce riesgos en la salud. Se trata de una preocupación global. El riesgo moral del comportamiento médico ha evolucionado en respuesta al programa de reforma de atención de salud del gobierno de China, inaugurado en 2009. Un entendimiento científico del comportamiento de los médicos facilitaría la prevención y el control del riesgo moral. El presente estudio usa los principios y metodología de la teoría fundamentada de Glaser y Strauss. Se usaron muestras teóricas y multiplicativas para identificar 60 sujetos y realizar entrevistas semiestructuradas en profundidad. Los temas se identificaron mediante codificación sustancial abierta y teórica. De los datos se extrajeron seis tipos de riesgo moral del comportamiento médico. Se describió y diagramó un modelo de comportamiento para proporcionar una estructura conceptual del riesgo moral del comportamiento médico actual. El modelo conceptual de riesgo moral del comportamiento médico puede usarse de varias maneras para corregir o prevenir acciones no deseadas. Las normas procedimentales de los hospitales pueden fortalecerse y exigirse mediante supervisión y castigo; se puede reducir la asimetría de la información que se da entre el médico y el paciente, incrementar la participación del pacieRisco moral médico tem um efeito significativo na relação médico-paciente, aumenta o custo dos cuidados à saúde e introduz riscos médicos. É uma preocupação global. Comportamento de risco moral médico vem se desenvolvendo em resposta ao programa de reforma de cuidados à saúde da China, que se iniciou em 2009. Uma compreensão científica do comportamento médico facilitaria a prevenção e controle do comportamento de risco moral médico. Este estudo utilizou os princípios da metodologia da Teoria Fundamentada de Glaser e Strauss. Amostragem teóricas e por bola de neve foram utilizadas para identificar 60 participantes. Entrevistas detalhadas semi-estruturadas foram realizadas com cada participante. Temas foram identificados através de codificação (aberta) substancial e codificação teórica. Seis tipos de comportamento de risco moral médico foram obtidos dos dados. Um modelo comportamental foi descrito e diagramado de forma a fornecer um enquadre conceitual do comportamento de risco moral médico. O modelo conceitual de comportamento de risco moral médico pode ser utilizado de diversas formas para corrigir ou prevenir ações indesejáveis. Regras que governam procedimentos em hospitais podem ser fortalecidas e reforçadas por supervisão e punição; a assimetria de informações entre médicos e pacientes pode ser reduzida; a participação dos pacientes nas decisões sobre tratamento pode ser aumentada; e a efetividade da educação ética médica pode ser melhorada

Unlock the Potential of Counterfactually-Augmented Data in Out-Of-Distribution Generalization

Counterfactually-Augmented Data (CAD) -- minimal editing of sentences to flip the corresponding labels -- has the potential to improve the Out-Of-Distribution (OOD) generalization capability of language models, as CAD induces language models to exploit domain-independent causal features and exclude spurious correlations. However, the empirical results of CAD's OOD generalization are not as efficient as anticipated. In this study, we attribute the inefficiency to the myopia phenomenon caused by CAD: language models only focus on causal features that are edited in the augmentation operation and exclude other non-edited causal features. Therefore, the potential of CAD is not fully exploited. To address this issue, we analyze the myopia phenomenon in feature space from the perspective of Fisher's Linear Discriminant, then we introduce two additional constraints based on CAD's structural properties (dataset-level and sentence-level) to help language models extract more complete causal features in CAD, thereby mitigating the myopia phenomenon and improving OOD generalization capability. We evaluate our method on two tasks: Sentiment Analysis and Natural Language Inference, and the experimental results demonstrate that our method could unlock the potential of CAD and improve the OOD generalization performance of language models by 1.0% to 5.9%.Comment: Expert Systems With Applications 2023. arXiv admin note: text overlap with arXiv:2302.0934