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

Helicity hardens the gas

A screw generally works better than a nail, or a complicated rope knot better than a simple one, in fastening solid matter, but a gas is more tameless. However, a flow itself has a physical quantity, helicity, measuring the screwing strength of the velocity field and the degree of the knottedness of the vorticity ropes. It is shown that helicity favors the partition of energy to the vortical modes, compared to others such as the dilatation and pressure modes of turbulence; that is, helicity stiffens the flow, with nontrivial implications for aerodynamics, such as aeroacoustics, and conducting fluids, among others

The Practice of Policy about Corporate Environmental Information Disclosure in China——Data From A-Share Listed Companies of Heavy Polluting Industries

Corporate environmental disclosure policy is a system tool to solve the environmental information asymmetry problem, it has an extremely important significance to improve public participation in environmental activities, and promote the improvement of corporate environmental performance. However, there is less literature about the direct corporate environmental information disclosure policy, and these studies are mostly qualitative or small sample studies, reliability is low. In this paper, all the listed companies of A shares heavily polluting industries are studied as a large sample to research the health of corporate environmental information disclosure system, in order to find out that the system operation runs low performance, the quality of environmental information disclosure is not good enough, revealing the main reason lies in the system is unreasonable design, the poor independence of the regulatory authorities, and then, put forward some specific proposals.Objective: To investigate the practice of corporate environmental information disclosure policy, reveal its cause.Results: around seventy percent of heavily polluting industries listed companies have disclosed environmental information; the disclosure of the information mostly described in text or data; majority of environmental information disclosed in the Report of the Board of Directors or Accounting Statements; the disclosure of the mainly contents of environmental management and environmental governance; large gap between inter-industry enterprises  in the quality and quantity of environmental information disclosure.Research limitations and significance of research: This article examines only the data of corporate environmental disclosure policy operation of 2010, is a cross-sectional study. Follow-up longitudinal studies should be carried out to study the changes in corporate environmental disclosure before and after the implementation of the policy, empirical test whether the policy has a substantial impact on corporate environmental disclosure.Practical significance: this article summarizes the problems of corporate environmental disclosure, reveals its cause, and puts forward a reasonable proposal, which has an important reference value for government decision-making.Innovation and value: select all the listed companies of heavily polluting industries of 2010 as study sample firstly, to reflect the most important aspects of corporate environmental information disclosure policy runs