Survival of the Confucians: social status and fertility in China, 1400-1900

This paper uses the genealogical records of 35,691 men to test one of the fundamental assumptions of the Malthusian model. Did higher living standards result in increased net reproduction? An empirical investigation of China between 1400 and 1900 finds a positive relationship between social status and fertility. The gentry scholars, the Confucians, produced three times as many sons as the commoners, and this status effect on fertility was stronger in the post-1600 period than in the pre-1600 period. The effect disappears once I control for the number of marriages. Increased marriages among upper-class males drove reproductive success in Imperial China. The results add a demographic perspective to explain the lack of modern economic growth in Imperial China

Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction

Relation prediction is a task designed for knowledge graph completion which aims to predict missing relationships between entities. Recent subgraph-based models for inductive relation prediction have received increasing attention, which can predict relation for unseen entities based on the extracted subgraph surrounding the candidate triplet. However, they are not completely inductive because of their disability of predicting unseen relations. Moreover, they fail to pay sufficient attention to the role of relation as they only depend on the model to learn parameterized relation embedding, which leads to inaccurate prediction on long-tail relations. In this paper, we introduce Relation-dependent Contrastive Learning (ReCoLe) for inductive relation prediction, which adapts contrastive learning with a novel sampling method based on clustering algorithm to enhance the role of relation and improve the generalization ability to unseen relations. Instead of directly learning embedding for relations, ReCoLe allocates a pre-trained GNN-based encoder to each relation to strengthen the influence of relation. The GNN-based encoder is optimized by contrastive learning, which ensures satisfactory performance on long-tail relations. In addition, the cluster sampling method equips ReCoLe with the ability to handle both unseen relations and entities. Experimental results suggest that ReCoLe outperforms state-of-the-art methods on commonly used inductive datasets

A micro-demographic analysis of human fertility from Chinese genealogies, 1368-1911

This thesis is a micro-demographic analysis of human fertility from Chinese genealogies in the Ming (1368-1644) and Qing (1644-1911) dynasties. It exploits a new genealogical dataset comprising 72,861 individuals from six lineages to account for the fertility decisions taken in Chinese families. Following the comprehensive micro-level analyses of a small population, the thesis demonstrates the main features at an individual level of the fertility patterns and the relationships between demographic outcomes and social outcomes in imperial China. This thesis consists of three substantive chapters. The first constructs the marital fertility levels and provides the ongoing debate with quantitative evidence on whether the Chinese consciously practised fertility controls in the pre-modern era. The second substantive chapter shows the social gradients in fertility and examines the mechanisms through which social status affected fertility. The third expands the reproductive success story of a single generation into a multi-generational one, focusing on the process of transmitting fertility choices across generations and the effects of family size on the quality of the children. The three chapters together exhibit the micro-demographic dynamics in Chinese families from the fourteenth to the twentieth centuries. The thesis shows that Ming-Qing China had a moderate fertility level, with no deliberate fertility controls. Throughout the entire period, climbing up the social ladder could significantly increase men’s net reproduction through increasing their marriage chances and the number of marriages they could have. Moreover, elites in traditional China also managed to transmit reproductive success to their offspring, mainly by passing on their high social outcomes. Family size could also affect the quality of the offspring, but the effect was not powerful enough to bring about any change in parents’ fertility choices

Modality to Modality Translation: An Adversarial Representation Learning and Graph Fusion Network for Multimodal Fusion

Learning joint embedding space for various modalities is of vital importance for multimodal fusion. Mainstream modality fusion approaches fail to achieve this goal, leaving a modality gap which heavily affects cross-modal fusion. In this paper, we propose a novel adversarial encoder-decoder-classifier framework to learn a modality-invariant embedding space. Since the distributions of various modalities vary in nature, to reduce the modality gap, we translate the distributions of source modalities into that of target modality via their respective encoders using adversarial training. Furthermore, we exert additional constraints on embedding space by introducing reconstruction loss and classification loss. Then we fuse the encoded representations using hierarchical graph neural network which explicitly explores unimodal, bimodal and trimodal interactions in multi-stage. Our method achieves state-of-the-art performance on multiple datasets. Visualization of the learned embeddings suggests that the joint embedding space learned by our method is discriminative. code is available at: \url{https://github.com/TmacMai/ARGF_multimodal_fusion}Comment: Accepted by AAAI-2020; code is available at: https://github.com/TmacMai/ARGF_multimodal_fusio

Communicative Message Passing for Inductive Relation Reasoning

Relation prediction for knowledge graphs aims at predicting missing relationships between entities. Despite the importance of inductive relation prediction, most previous works are limited to a transductive setting and cannot process previously unseen entities. The recent proposed subgraph-based relation reasoning models provided alternatives to predict links from the subgraph structure surrounding a candidate triplet inductively. However, we observe that these methods often neglect the directed nature of the extracted subgraph and weaken the role of relation information in the subgraph modeling. As a result, they fail to effectively handle the asymmetric/anti-symmetric triplets and produce insufficient embeddings for the target triplets. To this end, we introduce a \textbf{C}\textbf{o}mmunicative \textbf{M}essage \textbf{P}assing neural network for \textbf{I}nductive re\textbf{L}ation r\textbf{E}asoning, \textbf{CoMPILE}, that reasons over local directed subgraph structures and has a vigorous inductive bias to process entity-independent semantic relations. In contrast to existing models, CoMPILE strengthens the message interactions between edges and entitles through a communicative kernel and enables a sufficient flow of relation information. Moreover, we demonstrate that CoMPILE can naturally handle asymmetric/anti-symmetric relations without the need for explosively increasing the number of model parameters by extracting the directed enclosing subgraphs. Extensive experiments show substantial performance gains in comparison to state-of-the-art methods on commonly used benchmark datasets with variant inductive settings.Comment: Accepted by AAAI-202