A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification

Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years. However, they are unable to construct the state-of-the-art convolutional neural networks due to their intrinsic architectures. In this regard, we propose a flexible convolutional auto-encoder by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional convolutional auto-encoder. We also design an architecture discovery method by using particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed flexible convolutional auto-encoder with much less computational resource and without any manual intervention. We use the designed architecture optimization algorithm to test the proposed flexible convolutional auto-encoder through utilizing one graphic processing unit card on four extensively used image classification datasets. Experimental results show that our work in this paper significantly outperform the peer competitors including the state-of-the-art algorithm.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems, 201

THE EFFECT OF RUNNING ECONOMY IN MALE RUNNERS WEARING 3 TYPES OF FOOTWEAR

The purpose of this study was to identify the effect of running economy in male runners wearing Nike ZoomX Vaporfly Next%2(N), Qiaodan Feiying pb2(Q), and Xtep2(X) footwear. Twelve male middle-caliber runners (mean±SD, age: 21.0±2.0year, maximum oxygen uptake (VO2max): 51.2±3.7ml/kg/min) attended 4 sessions. The first session consisted of a VO2max test to inform subsequent RE speeds set at 60%, 70%, and 80% of the speed eliciting VO2max (ʋVO2max). In subsequent sessions, treadmill RE was assessed in the 3 footwear conditions in a randomized, counterbalanced crossover design. Oxygen consumption (ml/kg/min) and energy expenditure (W/kg) was lesser in Q vs. X at 80% of ʋVO2max, and there had a significant difference between Q、N and X (p2max (p\u3e0.05). Overall, Qiaodan Feiying pb2 improved RE and energy expenditure in middle-caliber male runners at 80% of ʋVO2max compared to Xtep2, but these improvements had no differences among the 3 types of footwear at 60% and 70% of ʋVO2max