Gradient-Guided Dynamic Efficient Adversarial Training

Adversarial training is arguably an effective but time-consuming way to train robust deep neural networks that can withstand strong adversarial attacks. As a response to the inefficiency, we propose the Dynamic Efficient Adversarial Training (DEAT), which gradually increases the adversarial iteration during training. Moreover, we theoretically reveal that the connection of the lower bound of Lipschitz constant of a given network and the magnitude of its partial derivative towards adversarial examples. Supported by this theoretical finding, we utilize the gradient's magnitude to quantify the effectiveness of adversarial training and determine the timing to adjust the training procedure. This magnitude based strategy is computational friendly and easy to implement. It is especially suited for DEAT and can also be transplanted into a wide range of adversarial training methods. Our post-investigation suggests that maintaining the quality of the training adversarial examples at a certain level is essential to achieve efficient adversarial training, which may shed some light on future studies.Comment: 14 pages, 8 figure

A Linear LMP Model for Active and Reactive Power with Power Loss

Pricing the reactive power is more necessary than ever before because of the increasing challenge of renewable energy integration on reactive power balance and voltage control. However, reactive power price is hard to be efficiently calculated because of the non-linear nature of optimal AC power flow equation. This paper proposes a linear model to calculate active and reactive power LMP simultaneously considering power loss. Firstly, a linearized AC power flow equation is proposed based on an augmented Generation Shift Distribution Factors (GSDF) matrix. Secondly, a linearized LMP model is derived using GSDF and loss factors. The formulation of LMP is further decomposed into four components: energy, congestion, voltage limitation and power loss. Finally, an iterate algorithm is proposed for calculating LMP with the proposed model. The performance of the proposed model is validated by the IEEE-118 bus system.Comment: 6 pages, 6 figures, accepted by IEEE Sustainable Power & Energy Conference (iSPEC2019

Conditional Perceptual Quality Preserving Image Compression

We propose conditional perceptual quality, an extension of the perceptual quality defined in \citet{blau2018perception}, by conditioning it on user defined information. Specifically, we extend the original perceptual quality $d(p_{X},p_{\hat{X}})$ to the conditional perceptual quality $d(p_{X|Y},p_{\hat{X}|Y})$, where $X$ is the original image, $\hat{X}$ is the reconstructed, $Y$ is side information defined by user and $d(.,.)$ is divergence. We show that conditional perceptual quality has similar theoretical properties as rate-distortion-perception trade-off \citep{blau2019rethinking}. Based on these theoretical results, we propose an optimal framework for conditional perceptual quality preserving compression. Experimental results show that our codec successfully maintains high perceptual quality and semantic quality at all bitrate. Besides, by providing a lowerbound of common randomness required, we settle the previous arguments on whether randomness should be incorporated into generator for (conditional) perceptual quality compression. The source code is provided in supplementary material

Implementation of fast algorithms of discrete fourier transform with FPGA

Fast Fourier transform (FFT) plays an important part as a signal processing function in many applications. This report will represent a single-path pipelined hardware structure and its implementation on field programmable gates-array (FPGA) for discrete Fourier transform (DFT) computation based on the radix-22 FFT algorithm. The proposed structure requires log4N-1 complex multipliers, log2N complex adder/subtractors and 2(N-1) complex data stores. Compared with the previous radix-22 SDF structure, the number of adder/subtractors is reduced by 50%. Compared with the previous radix-22 MDC structure, the number of both complex multipliers and adder/subtractors is reduced by 50%. The report will give the detailed description of the implementation of the structure on FPGA. At the same time, the in depth comparison between the proposed structure and radix-22 SDF structures will be presented.Bachelor of Engineerin

Regulation of nuclear mTORC1

mTORC1 integrates diverse upstream signals to control cell growth and metabolism. We previously showed that mTORC1 activity is spatially compartmentalized to ensure its signaling specificity. In a recently published study, we demonstrated the existence of mTORC1 activity in the nucleus and identified a unique mode of its regulation in the nuclear compartment

Wei zheng zhong gao /

Special collection from London Missionary Society.; On double leaves, oriental style, in case.; Also available in an electronic version via the Internet at http://nla.gov.au/nla.gen-vn1993306.880-03 Mu min zhong ga