Adversarial Defense via Neural Oscillation inspired Gradient Masking

Spiking neural networks (SNNs) attract great attention due to their low power consumption, low latency, and biological plausibility. As they are widely deployed in neuromorphic devices for low-power brain-inspired computing, security issues become increasingly important. However, compared to deep neural networks (DNNs), SNNs currently lack specifically designed defense methods against adversarial attacks. Inspired by neural membrane potential oscillation, we propose a novel neural model that incorporates the bio-inspired oscillation mechanism to enhance the security of SNNs. Our experiments show that SNNs with neural oscillation neurons have better resistance to adversarial attacks than ordinary SNNs with LIF neurons on kinds of architectures and datasets. Furthermore, we propose a defense method that changes model's gradients by replacing the form of oscillation, which hides the original training gradients and confuses the attacker into using gradients of 'fake' neurons to generate invalid adversarial samples. Our experiments suggest that the proposed defense method can effectively resist both single-step and iterative attacks with comparable defense effectiveness and much less computational costs than adversarial training methods on DNNs. To the best of our knowledge, this is the first work that establishes adversarial defense through masking surrogate gradients on SNNs

Spiking sampling network for image sparse representation and dynamic vision sensor data compression

Sparse representation has attracted great attention because it can greatly save storage resources and find representative features of data in a low-dimensional space. As a result, it may be widely applied in engineering domains including feature extraction, compressed sensing, signal denoising, picture clustering, and dictionary learning, just to name a few. In this paper, we propose a spiking sampling network. This network is composed of spiking neurons, and it can dynamically decide which pixel points should be retained and which ones need to be masked according to the input. Our experiments demonstrate that this approach enables better sparse representation of the original image and facilitates image reconstruction compared to random sampling. We thus use this approach for compressing massive data from the dynamic vision sensor, which greatly reduces the storage requirements for event data

A noise based novel strategy for faster SNN training

Spiking neural networks (SNNs) are receiving increasing attention due to their low power consumption and strong bio-plausibility. Optimization of SNNs is a challenging task. Two main methods, artificial neural network (ANN)-to-SNN conversion and spike-based backpropagation (BP), both have their advantages and limitations. For ANN-to-SNN conversion, it requires a long inference time to approximate the accuracy of ANN, thus diminishing the benefits of SNN. With spike-based BP, training high-precision SNNs typically consumes dozens of times more computational resources and time than their ANN counterparts. In this paper, we propose a novel SNN training approach that combines the benefits of the two methods. We first train a single-step SNN(T=1) by approximating the neural potential distribution with random noise, then convert the single-step SNN(T=1) to a multi-step SNN(T=N) losslessly. The introduction of Gaussian distributed noise leads to a significant gain in accuracy after conversion. The results show that our method considerably reduces the training and inference times of SNNs while maintaining their high accuracy. Compared to the previous two methods, ours can reduce training time by 65%-75% and achieves more than 100 times faster inference speed. We also argue that the neuron model augmented with noise makes it more bio-plausible

Improved Performance of d₃₁-Mode Needle-actuating Transducer with PMN-PT Piezocrystal

Prototypes of a PZT-based ultrasound needle-actuating device have shown the ability to reduce needle penetration force and enhance needle visibility with color Doppler imaging during needle insertion for tissue biopsy and regional anesthesia. However, the demand for smaller, lighter devices and the need for high performance transducers have motivated investigation of a different configuration of needle-actuation transducer, utilizing the d 31 -mode of PZT4 piezoceramic, and exploration of further improvement in its performance using relaxor-type piezocrystal. This paper outlines the development of the d 31 -mode needle actuation transducer design from simulation to fabrication and demonstration. Full characterization was performed on transducers for performance comparison. The performance of the proposed smaller, lighter d 31 -mode transducer is comparable with that of previous d 33 -mode transducers. Furthermore, it has been found to be much more efficient when using PMN-PT piezocrystal rather than piezoceramic

A New Model for Capturing the Spread of Computer Viruses on Complex-Networks

Based on complex network, this paper proposes a novel computer virus propagation model which is motivated by the traditional SEIRQ model. A systematic analysis of this new model shows that the virus-free equilibrium is globally asymptotically stable when its basic reproduction is less than one, and the viral equilibrium is globally attractive when the basic reproduction is greater than one. Some numerical simulations are finally given to illustrate the main results, implying that these results are applicable to depict the dynamics of virus propagation

Tanshinone IIA mitigates peritoneal fibrosis by inhibiting EMT via regulation of TGF-β/smad pathway

Purpose: To explore the effects of tanshinone IIA (T-IIA) on Dianeal-N PD-4 (PDF)-induced expression of fibrogenic cytokines in human peritoneal mesothelial cells (HPMCs), and to elucidate the mechanisms of action involved. Methods: Seven groups of HPMCs were used in the study: control group, PDF group, T-IIA group, LY364947 group, and 2 transforming growth factor-β (TGF-β) groups (TGF-β+ 50 μM T-IIA and TGF-β+ 100 μM T- IIA). The expression levels of mRNA and protein of TGF-β, smad2, smad7, α-smooth muscle actin(α-SMA), fibronectin, collagen І, E-cadherin, N-cadherin, matrix metalloprotein-2(MMP-2), and MMP-9 in the various groups were determined by reverse transcription-polymerase chain reaction (RTPCR) and Western blotting as appropriate. Results: The expressions of α-SMA, fibronectin, collagen І, TGF-β and smad2 were significantly upregulated in HPMCs by PDF treatment, but smad7 was down-regulated, relative to the control group (p < 0.01).These PDF-induced effects were reversed by T-IIA (p < 0.05). Inhibition of TGF-β/smad pathway by LY364947 treatment led to significant decrease in the expressions of fibrosis-related proteins, when compared with PDF group (p < 0.05). TGF-β treatment also produced numerous spindleshaped HPMCs characteristic of epithelial-mesenchymal transition (EMT). However, this morphological transition was alleviated, and the expression levels of EMT-related proteins were significantly downregulated by exposure to the two doses of T-IIA (p < 0.05). Conclusion: Tanshinone IIA inhibits EMT in HPMCs by regulating TGF-β/smad pathway, thus mitigating peritoneal fibrosis. Therefore, T-IIA has promising potential as a new drug for the treatment of peritoneal dialysis (PD)-induced fibrosis. Keywords: Peritoneal dialysis, Peritoneal fibrosis, Tanshinone IIA, Epithelial-mesenchymal transitio