Using the census data from 2000-2015 and a pseudo-event study design, we estimate the motherhood penalty in China and explore its association with declining fertility. We find that one-third of working women leave their jobs in the year when they give birth, and the penalty persists for over eight years. The motherhood penalty increases significantly across almost all provinces during this period, and provinces with larger increases in the penalty experience greater declines in fertility rates. Using a mover-based design, we find that the rising motherhood penalty has caused a significant decline in the total fertility rate.
Multi-focus image fusion (MFIF) is a critical technique for enhancing depth of field in photography, producing an all-in-focus image from multiple images captured at different focal lengths. While deep learning has shown promise in MFIF, most existing methods ignore the physical model of defocus blurring in their neural architecture design, limiting their interoperability and generalization. This paper presents a novel framework that integrates explicit defocus blur modeling into the MFIF process, leading to enhanced interpretability and performance. Leveraging an atom-based spatially-varying parameterized defocus blurring model, our approach first calculates pixel-wise defocus descriptors and initial focused images from multi-focus source images through a scale-recurrent fashion, based on which soft decision maps are estimated. Afterward, image fusion is performed using masks constructed from the decision maps, with a separate treatment on pixels that are probably defocused in all source images or near boundaries of defocused/focused regions. Model training is done with a fusion loss and a cross-scale defocus estimation loss. Extensive experiments on benchmark datasets have demonstrated the effectiveness of our approach.
WeproposeanODEapproachtosolvingmultiplechoicepolynomialprogram- ming (MCPP) after assuming that the optimum point can be approximated by the ex- pected value of so-called thermal equilibrium as usually did in simulated annealing. The explicit form of the feasible region and the affine property of the objective function are both fully exploited in transforming an MCPP problem into an ODE system. We also show theoretically that a local optimum of the former can be obtained from an equilib- rium point of the latter. Numerical experiments on two typical combinatorial problems, MAX-k-CUT and the calculation of star discrepancy, demonstrate the validity of the ODE approach, and the resulting approximate solutions are of comparable quality to those obtained by the state-of-the-art heuristic algorithms but with much less cost. When compared with the numerical results obtained by using Gurobi to solve MCPP directly, our ODE approach is able to produce approximate solutions of better quality in most instances. This paper also serves as the first attempt to use a continuous algorithm for approximating the star discrepancy.
Recent advances on time series forecasting mainly focus on improving the forecasting models themselves. However, when the time series data suffer from potential structural breaks or concept drifts, the forecasting performance might be significantly reduced. In this paper, we introduce a novel approach called Optimal Starting Point Time Series Forecast (OSP-TSP) for optimal forecasting, which can be combined with existing time series forecasting models. By adjusting the sequence length via leveraging the XGBoost and LightGBM models, the proposed approach can determine the optimal starting point (OSP) of the time series and then enhance the prediction performances of the base forecasting models. To illustrate the effectiveness of the proposed approach, comprehensive empirical analysis have been conducted on the M4 dataset and other real world datasets. Empirical results indicate that predictions based on the OSP-TSP approach consistently outperform those using the complete time series dataset. Moreover, comparison results reveals that combining our approach with existing forecasting models can achieve better prediction accuracy, which also reflect the advantages of the proposed approach.
This study examines how overconfidence shapes individuals' preference for redistribution. We contend that overconfidence inflates individuals' income expectations, which reduces the perceived benefits of redistribution for these individuals and thereby weakens their preference for such policies. Using data from the 2014 China Family Panel Studies, we find that overconfident individuals are more confident in their future life and exhibit less concerns for economic inequality, healthcare, and social security issues—key proxies for preference for redistribution. These results are more pronounced among less wealthy individuals. In addition, our results remain unchanged after controlling for individuals' trust in government and risk preference. These findings highlight the role of biased belief in shaping individuals’ attitude toward redistribution, offering new insights for discussions on redistributive policies.
Mineral crystallization is central to myriad natural processes from the formation of snowflakes to stalagmites, but the molecularscale mechanisms are often far more complex than models reflect. Feedbacks between the hydro-, bio-, and geo-spheres drive complex crystallization processes that challenge our ability to observe and quantify them, motivating an expansion of crystallization theories. In this article, we discuss how the driving forces and timescales of nucleation are influenced by factors ranging from simple geometric confinement to distinct interfacial solution structures involving solvent organization, electrical double layers, and surface charging effects. Taken together, these ubiquitous natural phenomena can preserve metastable intermediates, drive precipitation of undersaturated phases, and modulate crystallization in time and space.
Language and language education are central to studies of Chinese diasporic culture. However, existing scholarship has overwhelmingly focused on how overseas Chinese populations navigate language politics in their host societies. This research adopts a different perspective by examining the crucial roles overseas Chinese played in establishing Indonesian language programs in mainland China between the mid-1940s and mid-1960s. Specifically, overseas Chinese “returnees” were indispensable in founding the National College of Oriental Studies during World War II and launching several Indonesian language programs in the early years of the People’s Republic of China. While these programs served vastly different political purposes over time, they also reveal critical yet often overlooked aspects of—and surprising continuities in—China-Indonesia cultural exchange amid decolonization, domestic conflicts, and the Cold War. Although the primary aim of these programs was to fulfill the operational needs of state agencies and government-affiliated organizations, returnee networks played essential roles in promoting Indonesian culture in China. They actively participated in circulatory cultural diplomacy between the two countries, contributing significantly to China’s long-term knowledge production on Indonesia.
Effective risk assessment and control of environmental antibiotic resistance depend on comprehensive information about antibiotic resistance genes (ARGs) and their microbial hosts. Advances in sequencing technologies and bioinformatics have enabled the identification of ARG hosts using metagenome-assembled contigs and genomes. However, these approaches often suffer from information loss and require extensive computational resources. Here we introduce a bioinformatic strategy that identifies ARG hosts by prescreening ARG-like reads (ALRs) directly from total metagenomic datasets. This ALR-based method offers several advantages: (1) it enables the detection of low-abundance ARG hosts with higher accuracy in complex environments; (2) it establishes a direct relationship between the abundance of ARGs and their hosts; and (3) it reduces computation time by approximately 44–96% compared to strategies relying on assembled contigs and genomes. We applied our ALR-based strategy alongside two traditional methods to investigate a typical human-impacted environment. The results were consistent across all methods, revealing that ARGs are predominantly carried by Gammaproteobacteria and Bacilli, and their distribution patterns may indicate the impact of wastewater discharge on coastal resistome. Our strategy provides rapid and accurate identification of antibiotic-resistant bacteria, offering valuable insights for the high-throughput surveillance of environmental antibiotic resistance. This study further expands our knowledge of ARG-related risk management in future.
Salt crystallization within micro-fractures poses a significant challenge in shale gas production by impeding gas diffusion. This study investigates the real-time behavior of gas flow-induced salt crystallization within a visualized micro-fracture network. Observations reveal that salt crystals initially propagate along the fracture surface before exhibiting perpendicular growth. Crystal nucleation during the saturation stage occurs within a few seconds, while subsequent growth in the supersaturated stage takes approximately 15–20 s. Gas flow drives the evaporation of immobile water, leading to salt precipitation. Furthermore, increasing gas flow rate and decreasing solution salinity are found to accelerate crystal growth. To mitigate plugging damage caused by salt crystallization, controlling pressure differences and solution salinity is crucial.
