Energy-efficient Amortized Inference with Cascaded Deep Classifiers

Deep neural networks have been remarkable successful in various AI tasks but often cast high computation and energy cost for energy-constrained applications such as mobile sensing. We address this problem by proposing a novel framework that optimizes the prediction accuracy and energy cost simultaneously, thus enabling effective cost-accuracy trade-off at test time. In our framework, each data instance is pushed into a cascade of deep neural networks with increasing sizes, and a selection module is used to sequentially determine when a sufficiently accurate classifier can be used for this data instance. The cascade of neural networks and the selection module are jointly trained in an end-to-end fashion by the REINFORCE algorithm to optimize a trade-off between the computational cost and the predictive accuracy. Our method is able to simultaneously improve the accuracy and efficiency by learning to assign easy instances to fast yet sufficiently accurate classifiers to save computation and energy cost, while assigning harder instances to deeper and more powerful classifiers to ensure satisfiable accuracy. With extensive experiments on several image classification datasets using cascaded ResNet classifiers, we demonstrate that our method outperforms the standard well-trained ResNets in accuracy but only requires less than 20% and 50% FLOPs cost on the CIFAR-10/100 datasets and 66% on the ImageNet dataset, respectively

Regulation and optimization of cultivated land in different ecological function areas under the guidance of food security goals-a case study of Mengjin County, Henan Province, China

China’s arable land is facing the dual constraints of increasing “non-grain” and tightening ecological control. However, extreme emphasis on food production or excessive attention to ecological protection cannot effectively solve the practical problems of cultivated land utilization. In this paper, evaluation indexes were selected from the aspects of ecological service, landscape integrity, ecological sensitivity, etc., and ecological importance evaluation system for territory space was constructed. The ecological importance of territorial space was divided into three ecological functional areas, namely, the extremely important regions, the relatively important regions and the general regions. The morphological characteristics of cultivated land use in different ecological function areas were described systematically, and the main problems of cultivated land use in different regions were analyzed. On the basis of ensuring the ecological security of territorial space, this paper puts forward the regulation and control plan of cultivated land in different ecological functional areas aiming at food security, and makes an empirical study with Mengjin County as the case area. The results showed that: under the guidance of food security objectives, the implementation of different types of cultivated land remediation programs according to the problems existing in different ecological functional areas could guarantee food security to the greatest extent and amplify the ecological and environmental effects of land remediation. By means of land consolidation and ownership adjustment, the abandoned farmland in general and relatively important ecological regions can be restored for food use, which can not only enhance the food supply capacity, but also without causing damage to the ecological environment. There is a large area of arable land in the ecologically extremely important regions. Large-scale ecological conversion will have a certain impact on food security supply. Promoting ecological farming is an important way to resolve the contradiction between food safety production and ecological environment protection. This study can provide reference for decision making of arable land consolidation in the new period

Bilevel Programming Model of Private Capital Investment in Urban Public Transportation: Case Study of Jinan City

Increasing public transportation subsidies have created fiscal pressures for governments. To ease this financial pressure, Chinese government strongly encourages private capital investment in public transportation. However, previous private capital investments in public transportation operations have largely failed, mainly due to low ticket fares that cannot support sustainable operations. To address this issue, several previous research projects have developed methods to facilitate private capital investment. The majority of the research focuses on qualitative analysis and value for money analysis. Our research proposed a new method of private capital investment in public transportation operations based on the concept of “passenger value.” The feasibility of the proposed method of private investment was analyzed quantitatively by constructing a bilevel programming model. The model was verified based on a sample analysis of Jinan city traffic. Results showed that effective private capital investment increases the total societal benefit from the public transportation system and additionally that the investment method considering “passenger value” is superior to the traditional one. A quantitative tool was provided by the model to evaluate private capital investment effects, design investment policies, and develop further research