Practical Block-wise Neural Network Architecture Generation

Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset.Comment: Accepted to CVPR 201

A novel overcurrent protection method based on wide area measurement in smart grid

PowerTech is the anchor conference of the IEEE Power & Energy Society in EuropeConventional overcurrent protection settings are fixed to detect faults. Power system operation mode varies while the settings of protection devices remain constant. As a result, overcurrent protection has a small protection range and a long operating time because it is incapable of adjusting its setting online. Wide Area Measurements System (WAMS) provides synchronized and real time data which can be utilized in new protection devices. This paper proposes a novel online setting scheme which utilizes online system data to calculate real-time system operation mode. Based on the real-time operation mode, real-time fault current is calculated before fault occurring. Settings of the protection devices are by this means adjusted in real time to expand the protection area and shorten the operating time. The calculation is expanded from single source model to multi-source with Π model. In addition, interval time of settings adjustment Tchange is proposed and calculated by using hyperbolic function model. Based on this method, power system real-time operation condition can be better monitored and the real-time short circuit current can be obtained to improve protection performance. © 2013 IEEE.published_or_final_versio

Elevation of High-Mobility Group Protein Box-1 in Serum Correlates with Severity of Acute Intracerebral Hemorrhage

High-mobility group protein box-1 (HMGB1) is a proinflammatory involved in many inflammatory diseases. However, its roles in intracerebral hemorrhage (ICH) remain unknown. The purpose of this study was to examine the correlation between changes in serum levels of HMGB1 following acute ICH and the severity of stroke as well as the underlying mechanism. Changes in serum levels of HMGB1 in 60 consecutive patients with primary hemispheric ICH within 12 hours of onset of symptoms were determined. The correlation of HMGB1 with disease severity, IL-6, and TNF-α was analyzed. Changes in HMGB1 levels were detected with ELISA and Western blot. Compared with normal controls, patients with ICH had markedly elevated levels of HMGB1, which was significantly correlated with the levels of IL-6 and TNF-α, NIHSS score at the 10th day, and mRS score at 3 months. In comparison with the control group, the levels of HMGB1 in the perihematomal tissue in mice with ICH increased dramatically, peaked at 72 hours, and decreased at 5 days. Meanwhile, heme could stimulate cultured microglia to release large amounts of HMGB1 whereas Fe2+/3+ ions failed to stimulate HMGB1 production from microglia. Our findings suggest that HMGB1 may play an essential role in the ICH-caused inflammatory injury

Double-edged sword of technological progress to climate change depends on positioning in global value chains

Technological progress (TP) is a double-edged sword to global climate change. This study for the first time reveals rebound and mitigation effects of efficiency-related TP in global value chains (GVCs) on greenhouse gas (GHG) emissions. The integrated effects of TP depend on the positioning of sectors in GVCs. The cost-saving TP in upstream sectors would stimulate downstream demand. This produces stronger rebound effects than mitigation potentials and leads to global GHG emission increments (e.g. TP in the gas sector of China and petroleum and coal products sector of South Korea). In contrast, sectors located in the trailing end of GVCs have greater potentials for GHG emission mitigation through TP, mainly due to the reduction of upstream inputs. (e.g. the construction sector of China and dwelling sector of the United States). Global GHG emissions and production outputs can be either a trade-off or a win-win relationship on account of TP than rebound effects, because TP in different sectors could possibly increase or decrease the emission intensity of GVCs. This study could recognize the most productive spots for GHG emission mitigation through efficiency-related TP. It provides a new perspective for international cooperation to promote global GHG emission mitigation