Previous works utilized "smaller-norm-less-important" criterion to prune filters with smaller norm values in a convolutional neural network. In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two requirements that are not always met: (1) the norm deviation of the filters should be large; (2) the minimum norm of the filters should be small. To solve this problem, we propose a novel filter pruning method, namely Filter Pruning via Geometric Median (FPGM), to compress the model regardless of those two requirements. Unlike previous methods, FPGM compresses CNN models by pruning filters with redundancy, rather than those with "relatively less" importance. When applied to two image classification benchmarks, our method validates its usefulness and strengths. Notably, on CIFAR-10, FPGM reduces more than 52% FLOPs on ResNet-110 with even 2.69% relative accuracy improvement. Moreover, on ILSVRC-2012, FPGM reduces more than 42% FLOPs on ResNet-101 without top-5 accuracy drop, which has advanced the state-of-the-art. Code is publicly available on GitHub: https://github.com/he-y/filter-pruning-geometric-median

Aqueous batteries with zinc metal anodes are promising alternatives to Li-ion batteries for grid storage because of their abundance and benefits in cost, safety, and nontoxicity. However, short cyclability due to zinc dendrite growth remains a major obstacle. Here, we report a cross-linked polyacrylonitrile (PAN)-based cation exchange membrane that is low cost and mechanically robust. Li 2 S 3 reacts with PAN, simultaneously leading to cross-linking and formation of sulfur-containing functional groups. Hydrolysis of the membrane results in the formation of a membrane that achieves preferred cation transport and homogeneous ionic flux distribution. The separator is thin (30 μm-thick), almost 9 times stronger than hydrated Nafion, and made of low-cost materials. The membrane separator enables exceptionally long cyclability (>350 cycles) of Zn/Zn symmetric cells with low polarization and effective dendrite suppression. Our work demonstrates that the design of new separators is a fruitful pathway to enhancing the cyclability of aqueous batteries.

This research paper focuses on a water quality prediction model which requires high-quality data. In the process of construction and operation of smart water quality monitoring systems based on Internet of Things (IoT), more and more big data are produced at a high speed, which has made water quality data complicated. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, a drinking-water quality model was designed and established to predict water quality big data with the help of the advanced deep learning (DL) theory in this paper. The drinking-water quality data measured by the automatic water quality monitoring station of Guazhou Water Source of the Yangtze River in Yangzhou were utilized to analyze the water quality parameters in detail, and the prediction model was trained and tested with monitoring data from January 2016 to June 2018. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and accurately revealed the future developing trend of water quality, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of drinking water.

No abstract