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High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning

Microsystems & Nanoengineering 2022年第1期8卷 259-267页

作者： Jian Zhang Mickael L.Perrin Luis Barba Jan Overbeck Seoho Jung Brock Grassy Aryan Agal Rico Muff Rolf Brönnimann Miroslav Haluska Cosmin Roman Christofer Hierold Martin Jaggi Michel Calame Laboratory for Transport at Nanoscale Interfaces EmpaSwiss Federal Laboratories for Materials Science and TechnologyCH-8600 DübendorfSwitzerland Machine Learning and Optimization Laboratory School of Computer and Communication SciencesEPFLCH-1015 LausanneSwitzerland Department of Physics and Swiss Nanoscience Institute University of BaselCH-4056 BaselSwitzerland Micro-and Nanosystems Department of Mechanical and Process EngineeringETH ZurichCH-8092 ZurichSwitzerland

The identification of nanomaterials with the properties required for energy-efficient electronic systems is usually a tedious human task.A workflow to rapidly localize and characterize nanomaterials at the various stages of their integration into large-scale fabrication processes is essential for quality control and,ultimately,their industrial *** this work,we develop a high-throughput approach to rapidly identify suspended carbon nanotubes(CNTs)by using high-speed Raman imaging and deep learning *** for Raman spectra with extremely low signal-to-noise ratios(SNRs)of 0.9,we achieve a classification accuracy that exceeds 90%,while it reaches 98%for an SNR of *** applying a threshold on the output of the softmax layer of an optimized convolutional neural network(CNN),we further increase the accuracy of the ***,we propose an optimized Raman scanning strategy to minimize the acquisition time while simultaneously identifying the position,amount,and metallicity of CNTs on each *** approach can readily be extended to other types of nanomaterials and has the potential to be integrated into a production line to monitor the quality and properties of nanomaterials during fabrication.

关键词： carbon suspended speed

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Unsupervised robust nonparametric learning of hidden community properties

arXiv

引用

arXiv 2017年

作者： Langovoy, Mikhail Gotmare, Akhilesh Jaggi, Martin Machine Learning and Optimization Laboratory EPFL Station 14 LausanneCH-1015 Switzerland

We consider learning of fundamental properties of communities in large noisy networks, in the prototypical situation where the nodes or users are split into two classes according to a binary property, e.g., according to their opinions or preferences on a topic. For learning these properties, we propose a nonparametric, unsupervised, and scalable graph scan procedure that is, in addition, robust against a class of powerful adversaries. In our setup, one of the communities can fall under the influence of a knowledgeable adversarial leader, who knows the full network structure, has unlimited computational resources and can completely foresee our planned actions on the network. We prove strong consistency of our results in this setup with minimal assumptions. In particular, the learning procedure estimates the baseline activity of normal users asymptotically correctly with probability 1;the only assumption being the existence of a single implicit community of asymptotically negligible logarithmic size. We provide experiments on real and synthetic data to illustrate the performance of our method, including examples with adversaries. Copyright © 2017, The Authors. All rights reserved.

关键词： machine learning

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MEMORY EFFICIENT MIXED PRECISION OPTIMIZERS

arXiv

引用

arXiv 2023年

作者： Lewandowski, Basile Kosson, Atli Machine Learning Optimization laboratory École Polytechnique Fédérale de Lausanne Switzerland

Traditional optimization methods rely on the use of single-precision floating point arithmetic, which can be costly in terms of memory size and computing power. However, mixed precision optimization techniques leverage the use of both single and half-precision floating point arithmetic to reduce memory requirements while maintaining model accuracy. We provide here an algorithm to further reduce memory usage during the training of a model by getting rid of the floating point copy of the parameters, virtually keeping only half-precision numbers. We also explore the benefits of getting rid of the gradient’s value by executing the optimizer step during the back-propagation. In practice, we achieve up to 25% lower peak memory use and 15% faster training while maintaining the same level of accuracy. © 2023, CC BY.

关键词： Digital arithmetic

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Spectral preconditioning for gradient methods on graded non-convex functions 24

Spectral preconditioning for gradient methods on graded non-...

引用

Proceedings of the 41st International Conference on machine learning

作者： Nikita Doikov Sebastian U. Stich Martin Jaggi Machine Learning and Optimization Laboratory (MLO) EPFL Lausanne Switzerland CISPA Helmholtz Center for Information Security Saarbrücken Germany

The performance of optimization methods is often tied to the spectrum of the objective Hessian. Yet, conventional assumptions, such as smoothness, do often not enable us to make finely-grained convergence statements--particularly not for non-convex problems. Striving for a more intricate characterization of complexity, we introduce a unique concept termed graded nonconvexity. This allows to partition the class of nonconvex problems into a nested chain of subclasses. Interestingly, many traditional non-convex objectives, including partially convex problems, matrix factorizations, and neural networks, fall within these subclasses. As a second contribution, we propose gradient methods with spectral preconditioning, which employ inexact top eigenvectors of the Hessian to address the ill-conditioning of the problem, contingent on the grade. Our analysis reveals that these new methods provide provably superior convergence rates compared to basic gradient descent on applicable problem classes, particularly when large gaps exist between the top eigenvalues of the Hessian. Our theory is validated by numerical experiments executed on multiple practical machine learning problems.

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On convergence of incremental gradient for non-convex smooth functions 24

On convergence of incremental gradient for non-convex smooth...

引用

Proceedings of the 41st International Conference on machine learning

作者： Anastasia Koloskova Nikita Doikov Sebastian U. Stich Martin Jaggi Machine Learning and Optimization Laboratory (MLO) EPFL Lausanne Switzerland CISPA Helmholtz Center for Information Security Saarbrücken Germany

In machine learning and neural network optimization, algorithms like incremental gradient, and shuffle SGD are popular due to minimizing the number of cache misses and good practical convergence behavior. However, their optimization properties in theory, especially for nonconvex smooth functions, remain incompletely *** paper delves into the convergence properties of SGD algorithms with arbitrary data ordering, within a broad framework for non-convex smooth functions. Our findings show enhanced convergence guarantees for incremental gradient and single shuffle SGD. Particularly if n is the training set size, we improve n times the optimization term of convergence guarantee to reach accuracy ε from O(n/ε) to O(1/ε).

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Robust Barron-Loss Tucker Tensor Decomposition

TechRxiv

引用

TechRxiv 2021年

作者： Mozaffari, Mahsa Markopoulos, Panos P. Department of Electrical and Microelectronic Engineering Machine Learning Optimization and Signal Processing Laboratory Rochester Institute of Technology RochesterNY14623 United States

Tucker decomposition is a standard method for the analysis of high-order tensor data. Standard Tucker decomposition generalizes singular-value decomposition and is formulated as minimization of the L2-norm of the low-rank approximation error. Due to the quadratic emphasis on peripheral data, the L2-norm based formulation of Tucker has been shown to be sensitive against corruptions. L1-norm-based variants (L1-Tucker) are proposed as robust Tucker decomposition methods, and are formulated as subspace estimators by absolute-projection maximization, and have proven effective in outlier resistant subspace computation. However, Tucker decomposition for robust low-rank tensor approximation is not defined/optimized in the literature. In this work, we propose a new formulation for low-rank tensor approximation, with tunable outlier-robustness, and present a unified algorithmic solution framework. This formulation relies on a new generalized robust loss function (Barron loss), which encompasses several well-known loss-functions with variable outlier-resistance. The robustness of the proposed framework is corroborated by the presented numerical studies on synthetic and real data. © 2021, CC BY.

关键词： Singular value decomposition

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Analysis of the Research Status of Information Technology Education Literature in China and Abroad 24

Analysis of the Research Status of Information Technology Ed...

引用

24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Yulin Liu, Kai Li, Junke School of Information Engineering Suqian University Jiangsu Suqian223800 China School of Computer and Information Qiannan Normal University for Nationalities Duyun558000 China Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment Jiangsu 223800 China Key Laboratory of Machine Learning and Unstructured Data Processing of Qiannan Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun558000 China

ISBN: (纸本)9798350319934

Information technology education contributes to the development of national education. Understanding the research status of information technology education at home and abroad is helpful to the implementation of education informatization. This paper takes the information technology education research literature collected by CNKI and the Web of Science database as the object and uses the bibliometric method to analyze the literature characteristics of information technology education at home and abroad. Specifically, the paper mainly carries out statistical analysis from the time distribution of literature, journal distribution, and hot keywords, and visually describes the literature features of the information technology education discipline, providing references for the research of information technology education discipline at home and abroad. © 2022 IEEE.

关键词： Informatization

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Fast and accurate uncertainty estimation in chemical machine learning

arXiv

引用

arXiv 2018年

作者： Musil, Felix Willatt, Michael J. Langovoy, Mikhail A. Ceriotti, Michele Laboratory of Computational Science and Modeling IMX École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Machine Learning & Optimization Laboratory IC École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland

We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties. The scheme is based on resampling, with multiple models being generated based on sub-sampling of the same training data. The accuracy of the uncertainty prediction can be benchmarked by maximum likelihood estimation, which can also be used to correct for correlations between resampled models, and to improve the performance of the uncertainty estimation by a cross-validation procedure. In the case of sparse Gaussian Process Regression models, this resampled estimator can be evaluated at negligible cost. We demonstrate the reliability of these estimates for the prediction of molecular energetics, and for the estimation of nuclear chemical shieldings in molecular crystals. Extension to estimate the uncertainty in energy differences, forces, or other correlated predictions is straightforward. This method can be easily applied to other machine learning schemes, and will be beneficial to make data-driven predictions more reliable, and to facilitate training-set optimization and active-learning strategies. Copyright © 2018, The Authors. All rights reserved.

关键词： Molecular crystals

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PCT-Cap: Point Cloud Transformer for Accurate 3D Capacitance Extraction

PCT-Cap: Point Cloud Transformer for Accurate 3D Capacitance...

引用

Electronics Design Automation (ISEDA), International Symposium of

作者： Ye Cai Yuyao Liang Zhipeng Luo Biwei Xie Xingquan Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China Peng Cheng Laboratory Shenzhen China Center of Machine Learning and Optimization Minnan Normal University Zhangzhou China State Key Lab of Processors Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (数字)9798350352030

ISBN: (纸本)9798350352047

Accurate parasitic capacitance extraction becomes increasingly essential in advanced technology nodes. 2.5D ex-traction requires more effort to maintain accuracy comparable to the 3D solver. In this work, two experiments reveal that error deviation in the 2.5D method enlarges as wire width shrinks and length increases. A novel Gauss law-based point cloud is proposed to model the 3D capacitance problem. With the proposed data representation, a transformer-based neural network architecture, called PCT-Cap, is designed for 3D pattern matching. PCT-Cap exhibits much better performance than the ResNet-based capacitance models. Extensive experiments on 28nm technology demonstrate that PCT-Cap can accurately predict over 95.41 % of total capacitance with an error margin of less than 5 %. Additionally, PCT-Cap achieves 95.90 % accuracy in predicting coupling capacitance within a margin of error of less than 10%. PCT-Cap achieves at least a 55 × speeding up compared with the commercial extraction tool while consuming negligible memory.

关键词： Point cloud compression Solid modeling Three-dimensional displays Accuracy Capacitance Feature extraction Transformers

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The Spatial-Temporal Evolution and Influencing Factors of Public Attention to 5G: Empirical Analysis Based on Baidu Index from 2011 to 2021

引用

Mathematical Problems in Engineering 2023年第1期2023卷

作者： Liu, Pan Wang, Guanyu Liu, Kai Li, Junke Chen, Yanlin School of Information Engineering Suqian University Jiangsu Suqian223800 China School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China Key Laboratory of Machine Learning and Unstructured Data Processing of Qiannan Guizhou Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun558000 China

The development of 5G (fifth-generation wireless systems) determines the future direction of technology and economy and has received extensive public attention. Studying the changing rules of public attention to 5G can provide an important guiding significance for the sustainable development of 5G. This paper takes Baidu Index as the measurement index of 5G public attention and analyzes the spatial and temporal evolution characteristics and influencing factors of public attention to 5G from 2011 to 2021 by using the elasticity coefficient, Gini coefficient, geographical concentration index, and panel data model. The results of the study show the following. (1) The public concern to 5G is generally on the rise, but the heat has declined in the past two years. (2) The public’s 5G attention shows a seasonal effect, with the highest attention in March and June. (3) The spatial difference of 5G public attention is obvious. The eastern region has a high degree of attention, the internal differences between the eastern and western regions are obvious, and the central region is relatively balanced. (4) The factors such as local economic level, education level, Internet development, and media attention have significantly affected the public focus on 5G. Also, some suggestions are made for the sustainable development of 5G and the planning of 6G (sixth-generation wireless systems). Copyright © 2023 Pan Liu et al.

关键词： Sustainable development

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