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.
Existing methods for moving sound source localization and tracking face significant challenges when dealing withan unknown number of sound sources, which substantially limits their practical applications. This paper proposes amoving sound source tracking method based on source signal envelopes that does not require prior knowledge ofthe number of sources. First, an encoder-decoder attractor (EDA) method is used to estimate the number of sourcesand obtain an attractor for each source, based on which the signal envelope of each source is estimated. This signalenvelope is then used as a clue for tracking the target source. The proposed method has been validated throughsimulation experiments. Experimental results demonstrate that the proposed method can accurately estimate thenumber of sources and precisely track each source.
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.
Generally, facilitating a slick switch to cleaner cooking fuels for households in the countryside has been a challenge. Based on survey data from the China Family Panel Survey for 2022 and provincial statistics, this study examined the effects of relevant factors on household cooking fuel utilization in rural China at the province, household, and neighborhood levels using a multilevel spatial logit model. The findings clearly indicate that new quality productive forces, household cultural consumption, and neighborhood effects significantly support households to adopt cleaner cooking fuels in rural areas. Further studies show that policies on green financial reforms and innovation, straw-burning ban guidelines, and atmospheric priority control areas activate new productive forces to support rural households’ switch to cleaner cooking fuels. These findings contribute to knowledge and action programs for the green transformation of rural energy consumption.
The poor endurance of hafnium oxide (HfO2)-based ferroelectric field-effect transistors (FeFETs) limits their applications. From a novel perspective of ferroelectric domain engineering, we propose and fabricate a high endurance HfO2-based FeFET with monolayer graphene (GR) inserted in the gate oxide for the first time. The introduction of GR between the ferroelectric (FE) layer and the interfacial layer (IL) increases the number of domains in the ferroelectric (FE) layer and reduces the electric field of the IL. Meanwhile, the low density of states (DOS) of monolayer GR suppresses the charge injection to further optimize the endurance. Experimental results show that the endurance of the GR-intercalated FeFET (GR-FeFET) exceeds 108 cycles, which is more than 2 orders of magnitude higher than that of the conventional FeFET. The gate leakage is also effectively suppressed by the GR layer. This work opens a new avenue for improvement of the endurance of FeFETs and demonstrates GR-FeFETs as potential candidates for next-generation embedded memory applications.
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.
