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With the prevalence of COVID-19, the modeling of epidemic propagation and its analyses have played a significant role in controlling epidemics. However, individual behaviors, in particular the self-protection and migration, which have a strong influence on epidemic propagation, were always neglected in previous studies. In this paper, we mainly propose two models from the individual and population perspectives. In the first individual model, we introduce the individual protection degree that effectively suppresses the epidemic level as a stochastic variable to the SIRS model. In the alternative population model, an open Markov queueing network is constructed to investigate the individual number of each epidemic state, and we present an evolving population network via the migration of people. Besides, stochastic methods are applied to analyze both models. In various simulations, the infected probability, the number of individuals in each state and its limited distribution are demonstrated. [ABSTRACT FROM AUTHOR]

As the integrated circuit (IC) technology advances into smaller nanometre feature sizes, a fixed-error noise known as device mismatch is introduced owing to the dissimilarity between transistors, and this degrades the accuracy of analog circuits. In this paper, we present an analog co-processor that uses this fixed-pattern noise to its advantage to perform complex computation. This circuit is an extension of our previously published trainable analogue block (TAB) framework and uses multiple inputs that substantially increase functionality. We present measurement results of our two-input analogue co-processor built using a 130-nm process technology and show its learning capabilities for regression and classification tasks. We also show that the co-processor, comprised of 100 neurons, is a low-power system with a power dissipation of only 1.1~\mu \textW . The IC fabrication process contributes to randomness and variability in ICs, and we show that random device mismatch is favorable for the learning capability of our system as it causes variability among the neuronal tuning curves. The low-power capability of our framework makes it suitable for use in various battery-powered applications ranging from biomedical to military as a front-end analog co-processor. [ABSTRACT FROM AUTHOR]

Random device mismatch that arises as a result of scaling of the CMOS (complementary metal-oxide semi-conductor) technology into the deep submicrometer regime degrades the accuracy of analog circuits. Methods to combat this increase the complexity of design. We have developed a novel neuromorphic system called a trainable analog block (TAB), which exploits device mismatch as a means for random projections of the input to a higher dimensional space. The TAB framework is inspired by the principles of neural population coding operating in the biological nervous system. Three neuronal layers, namely input, hidden, and output, constitute the TAB framework, with the number of hidden layer neurons far exceeding the input layer neurons. Here, we present measurement results of the first prototype TAB chip built using a 65 nm process technology and show its learning capability for various regression tasks. Our TAB chip is tolerant to inherent randomness and variability arising due to the fabrication process. Additionally, we characterize each neuron and discuss the statistical variability of its tuning curve that arises due to random device mismatch, a desirable property for the learning capability of the TAB. We also discuss the effect of the number of hidden neurons and the resolution of output weights on the accuracy of the learning capability of the TAB. We show that the TAB is a low power system—the power dissipation in the TAB with 456 neuron blocks is 1.38 \mu\textW. [ABSTRACT FROM AUTHOR]