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This paper estimates the returns to membership of the Chinese Communist Party using unique twins data we collected from China. Our OLS estimate shows that being a Party member increases earnings by 10%, but the within-twin-pair estimate becomes zero. One interpretation of these results is that the OLS Party premium is due to omitted ability and family background. This interpretation would suggest that Party members fare well not because of their special political status per se, but because of the superior ability that made them Party members. The estimates are also consistent with an alternative interpretation that Party membership not only has its own effect but also has an external effect on the sibling.

Hyperspectral image (HSI) based detection has attracted considerable attention recently in agriculture, environmental protection and military applications as different wavelengths of light can be advantageously used to discriminate different types of objects. Unfortunately, estimating the background distribution and the detection of interesting local objects is not straightforward, and anomaly detectors may give false alarms. In this paper, a Deep Belief Network (DBN) based anomaly detector is proposed. The high-level features and reconstruction errors are learned through the network in a manner which is not affected by previous background distribution assumption. To reduce contamination by local anomalies, adaptive weights are constructed from reconstruction errors and statistical information. By using the code image which is generated during the inference of DBN and modified by adaptively updated weights, a local Euclidean distance between under test pixels and their neighboring pixels is used to determine the anomaly targets. Experimental results on synthetic and recorded HSI datasets show the performance of proposed method outperforms the classic global Reed-Xiaoli detector (RXD), local RX detector (LRXD) and the-state-of-the-art Collaborative Representation detector (CRD).

This study investigated the impact of indoor illuminance and correlated color temperature (CCT) on healthy adults' cognitive performance, subjective mood, and alertness during daytime office hours and differences in time-of-day effects. A 2(illuminance) × 2(CCT) × 2(morning vs. afternoon) mixed design ( N = 60) was employed. Participants felt less sleepy in the bright light exposure. The low "cool" lighting induced the least positive mood. The effects of illuminance and CCT on subjective feelings were not time-of-day dependent. The results demonstrated the slowest responses in inhibition, working memory, and recognition of facial expression tasks in the low "warm" lighting. The effect on long-term memory was most pronounced under the high "cool" light exposure, but only in the afternoon for recognition of neutral words. The findings suggest that future research on good indoor lighting should consider illuminance levels and CCT as well as other variables to optimize lighting effects during regular daytime hours.