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When Food Became Scarce is about the Great Leap Famine of 1958-61. Yixin Chen adopts a grassroots level analysis to explore an existential question concerning hundreds of millions of Chinese peasants: why did some peasants perish while others from the same villages facing the same collective problems of food scarcity survive? Viewing the famine as a persistent ordeal, Chen identifies environment and lineage as two pivotal factors that influenced the rural populace's destiny. When food quotas under the Maoist communal dining system plummeted below subsistence or came to a halt, most individual villagers in the mountainous regions of southern China turned to their environment for alternative sustenance, ensuring their survival. More remarkably, across the nation, more peasants united in self-preservation strategies, concealing grains to elude excessive state requisitions, orchestrating food and crop riots, and collectively combating desperation. Given that the majority of Chinese villages were historically established on the foundation of consanguine relationships, creating an obligation among villagers to support one another due to shared ancestry, lineage emerged as a microlevel social mechanism that activated diverse forms of collective resistance. In villages where peasants effectively upheld their lineage organizations and adopted self-protective measures, their survival rates exceeded those of villages where the enforcement of Maoist Great Leap initiatives disrupted the lineage structure, leaving the communities more vulnerable. When Food Became Scare reorients the famine narrative, unpacking its intricacies from the perspective of the survival side.
Machine Learning and Statistical Modeling Approaches to Image Retrieval describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results.
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