Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The PASCAL Visual Object Classes Challenge

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

여성사 쓰기의 가능성과 현주소, 한국여성연구소 여성사연구실 지음, 청년사 1999

Sensors are becoming increasingly significant in our daily life because of the rapid development in electronic and information technologies, including Internet of Things, wearable electronics, home automation, intelligent industry, etc. There is no doubt that their performances are primarily determined by the sensing materials. Among all potential candidates, layered nanomaterials with two-dimensional (2D) planar structure have numerous superior properties to their bulk counterparts which are suitable for building various high-performance sensors. As an emerging 2D material, MXenes possess several advantageous features of adjustable surface properties, tunable bandgap, and excellent mechanical strength, making them attractive in various applications. Herein, we particularly focus on the recent research progress in MXene-based sensors, discuss the merits of MXenes and their derivatives as sensing materials for collecting various signals, and try to elucidate the design principles and working mechanisms of the corresponding MXene-based sensors, including strain/stress sensors, gas sensors, electrochemical sensors, optical sensors, and humidity sensors. In the end, we analyze the main challenges and future outlook of MXene-based materials in sensor applications.

Ti3C2TX MXene for Sensing Applications: Recent Progress, Design Principles, and Future Perspectives.

The computation-intensive circuit simulation makes the analog circuit sizing challenging for large-scale/complicated analog/RF circuits. A Bayesian optimization approach has been proposed recently for the optimization problems involving the evaluations of black-box functions with high computational cost in either objective functions or constraints. In this paper, we propose a weighted expected improvement-based Bayesian optimization approach for automated analog circuit sizing. Gaussian processes (GP) are used as the online surrogate models for circuit performances. Expected improvement is selected as the acquisition function to balance the exploration and exploitation during the optimization procedure. The expected improvement is weighted by the probability of satisfying the constraints. In this paper, we propose a complete Bayesian optimization framework for the optimization of analog circuits with constraints for the first time. The existing GP model-based optimization methods for analog circuits take the GP models as either offline models or as assistance for the evolutionary algorithms. We also extend the Bayesian optimization algorithm to handle multi-objective optimization problems. Compared with the state-of-the-art approaches listed in this paper, the proposed Bayesian optimization method achieves better optimization results with significantly less number of simulations.

An Efficient Bayesian Optimization Approach for Automated Optimization of Analog Circuits

Circuit reliability is affected by various fabrication-time and run-time effects. Fabrication-induced process variation has significant impact on circuit performance and reliability. Various aging effects, such as negative bias temperature instability, cause continuous performance and reliability degradation during circuit run-time usage. In this work, we present a statistical analysis framework that characterizes the lifetime reliability of nanometer-scale integrated circuits by jointly considering the impact of fabrication-induced process variation and run-time aging effects. More specifically, our work focuses on characterizing circuit threshold voltage lifetime variation and its impact on circuit timing due to process variation and the negative bias temperature instability effect, a primary aging effect in nanometer-scale integrated circuits. The proposed work is capable of characterizing the overall circuit lifetime reliability, as well as efficiently quantifying the vulnerabilities of individual circuit elements. This analysis framework has been carefully validated and integrated into an iterative design flow for circuit lifetime reliability analysis and optimization.

/pdf/statistical-reliability-analysis-under-process-variation-and-4b5iv0ryo9.pdf

Statistical reliability analysis under process variation and aging effects

Highly stretchable and self-healing strain sensors for motion detection in wireless human-machine interface

Bayesian optimization methods are promising for the optimization of black-box functions that are expensive to evaluate. In this paper, a novel batch Bayesian optimization approach is proposed. The parallelization is realized via a multi-objective ensemble of multiple acquisition functions. In each iteration, the multi-objective optimization of the multiple acquisition functions is performed to search for the Pareto front of the acquisition functions. The batch of inputs are then selected from the Pareto front. The Pareto front represents the best trade-off between the multiple acquisition functions. Such a policy for batch Bayesian optimization can significantly improve the efficiency of optimization. The proposed method is compared with several state-of-the-art batch Bayesian optimization algorithms using analytical benchmark functions and real-world analog integrated circuits. The experimental results show that the proposed method is competitive compared with the state-of-the-art algorithms.

/pdf/batch-bayesian-optimization-via-multi-objective-acquisition-2vgrmr6aeb.pdf

Fan Yang

Papers

An Efficient Bayesian Optimization Approach for Automated Optimization of Analog Circuits

Statistical reliability analysis under process variation and aging effects

Highly stretchable and self-healing strain sensors for motion detection in wireless human-machine interface

Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design

A skin-like sensor for intelligent Braille recognition