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Emulation of Brain Metabolic Activities Based on a Dynamically Controllable Optical Phantom

Cyborg and Bionic Systems 2023年第1期4卷 84-92页

作者： Yuxiang Lin Cheng Chen zhouchen ma Nabil Sabor Yanyan Wei Tianhong Zhang Mohamad Sawan Guoxing Wang Jian Zhao Department of Micro-Nano Electronics Shanghai Jiao Tong UniversityShanghaiChina Electrical Engineering Department Faculty of EngineeringAssiut UniversityAssiutEgypt Shanghai Mental Health Center Shanghai Jiao Tong University School of MedicineShanghaiChina CenBRAIN Neurotech School of EngineeringWestlake UniversityHangzhouChina

This paper presents a dynamic optical phantom for the simulation of metabolic activities in the brain,and a linear equivalent model is built for control voltage versus substance concentration.A solid-solid dynamic optical phantom is realized by using liquid crystal film as a voltage-controlled light intensity regulator on the surface of basic phantom,which uses epoxy resin as matrix material and nanometer carbon powder and titanium dioxide powder as absorption and scattering dopants,*** dynamic phantom could mimic near-infrared spectrum(NIRS)signals with sampling rate up to 10 Hz,and the maximum simulation errors for oxy-hemoglobin and deoxy-hemoglobin concentrations varying in the range of 1μmol/l are 7.0%and 17.9%,*** with similar solid biomimetic phantoms,the adjustable mimic substance concentration range is extended by an order of magnitude,which meets the simulation requirements of most brain NIRS signals.

关键词： dynamic powder titanium

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Newton design:designing CNNs with the family of Newton's methods

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Science China(Information Sciences) 2023年第6期66卷 122-137页

作者： Zhengyang SHEN Yibo YANG Qi SHE Changhu WANG Jinwen ma zhouchen LIN School of mathematical Sciences Peking University Bytedance AI Lab JD Explore Academy Key Laboratory of machine Perception School of Intelligence Science and TechnologyPeking University Pazhou Lab

Nowadays, convolutional neural networks(CNNs)have led the developments of machine ***, most CNN architectures are obtained by manual design, which is empirical, time-consuming, and non-transparent. In this paper, we aim at offering better insight into CNN models from the perspective of optimization theory. We propose a unified framework for understanding and designing CNN architectures with the family of Newton’s methods, which is referred to as Newton design. Specifically, we observe that the standard feedforward CNN model(PlainNet)solves an optimization problem via a kind of quasi-Newton method. Interestingly, residual network(ResNet)can also be derived if we use a more general quasi-Newton method to solve this problem. Based on the above observations, we solve this problem via a better method,the Newton-conjugate-gradient(Newton-CG)method, which inspires Newton-CGNet. In the network design,we translate binary-value terms in the optimization schemes to dropout layers, so dropout modules naturally appear in the derived CNN structures with specific locations, rather than being an empirical training *** experiments on image classification and text categorization tasks verify that Newton-CGNets perform very competitively. Particularly, Newton-CGNets surpass their counterparts ResNets by over 4% on CIFAR-10 and over 10% on CIFAR-100, respectively.

关键词： CNN dropout optimization method network design Newton's method

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