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iSplit LBI: Individualized partial ranking with ties via split LBI

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

Ablation characteristics of hybrid target materials for laser triggered vacuum switches

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Ablation characteristics of hybrid target materials for lase...

International Conference on Mechanical, Electric and Industrial Engineering, MEIE 2018

作者： Hui, Chen State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan Hubei430074 China Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy Hubei University of Technology Wuhan430068 China Hubei Key Lab. for High-efficiency Utiliz. of Solar Ener. and Oper. Control of Energy Storage System Hubei University of Technology Wuhan430068 China

The key difference of laser triggered vacuum switch (LTVS) and electrical triggered vacuum switch (ETVS) is the different triggered way, because the initial plasmas were generated by laser irradiation target material of LTVS, which generates different impact on the triggered characteristics of LTVS with different target materials. There are three laser wavelength (1064nm, 532nm and 266nm) which are tested the triggered characteristics of target materials, with decreased conduction time delay under the condition of increasing laser energy. © Published under licence by IOP Publishing Ltd.

关键词： Hybrid materials

Magnetic Modulation of Terahertz Waves via Spin-Polarized Electron Tunneling Based on Magnetic Tunnel Junctions

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Physical Review Applied 2020年第1期14卷 014032-014032页

作者： Zuanming Jin Jugeng Li Wenjie Zhang Chenyang Guo Caihua Wan Xiufeng Han Zhenxiang Cheng Chao Zhang Alexey V. Balakin Alexander P. Shkurinov Yan Peng Guohong Ma Yiming Zhu Jianquan Yao Songlin Zhuang Terahertz Technology Innovation Research Institute Terahertz Spectrum and Imaging Technology Cooperative Innovation Center Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology 516 JunGong Road Shanghai 200093 China Department of Physics Shanghai University 99 Shangda Road Shanghai 200444 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 200092 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing 100190 China Institute for Superconducting and Electronic Materials University of Wollongong Wollongong New South Wales 2500 Australia School of Physics University of Wollongong Wollongong New South Wales 2522 Australia Department of Physics and International Laser Center Lomonosov Moscow State University Leninskie Gory 1 Moscow 19991 Russia STU & SIOM Joint Laboratory for Superintense Lasers and Applications Shanghai 201210 China College of Precision Instrument and Optoelectronics Engineering Tianjin University Tianjin 300110 China

Magnetic tunnel junctions (MTJs) are a key technology in modern spintronics because they are the basis of read-heads of modern hard disk drives, nonvolatile magnetic random access memories, and sensor applications. In this paper, we demonstrate that tunneling magnetoresistance can influence terahertz (THz) wave propagation through a MTJ. In particular, various magnetic configurations between parallel state and antiparallel state of the magnetizations of the ferromagnetic layers in the MTJ have the effect of changing the conductivity, making a functional modulation of the propagating THz electromagnetic fields. Operating in the THz frequency range, a maximal modulation depth of 60% is reached for the parallel state of the MTJ with a thickness of 77.45 nm, using a magnetic field as low as 30 mT. The THz conductivity spectrum of the MTJ is governed by spin-dependent electron tunneling. It is anticipated that the MTJ device and its tunability scheme will have many potential applications in THz magnetic modulators, filtering, and sensing.

关键词： Magnetoresistance Photoconductivity Ultrafast magnetic effects Magnetic tunnel junctions Terahertz time-domain spectroscopy

Mobile big data analysis with machine learning

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arXiv 2018年

作者： Xie, Jiyang Song, Zeyu Li, Yupeng Ma, Zhanyu Pattern Recognition and Intelligent Systems Lab. Beijing University of Posts and Telecommunications Beijing China State Key Lab. of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

This paper investigates to identify the requirement and the development of machine learning-based mobile big data analysis through discussing the insights of challenges in the mobile big data (MBD). Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently adopted methods of data analysis are reviewed. Three typical applications of MBD analysis, namely wireless channel modeling, human online and offline behavior analysis, and speech recognition in the internet of vehicles, are introduced respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis. Copyright © 2018, The Authors. All rights reserved.

关键词： Big data

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

Reinforcement learning for relation classification from noisy data

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arXiv 2018年

作者： Feng, Jun Huang, Minlie Zhao, Li Yang, Yang Zhu, Xiaoyan State Key Lab. of Intelligent Technology and Systems National Lab. for Information Science and Technology Dept. of Computer Science and Technology Tsinghua University Beijing100084 China Microsoft Research Asia College of Computer Science and Technology Zhejiang University

Existing relation classification methods that rely on distant supervision assume that a bag of sentences mentioning an entity pair are all describing a relation for the entity pair. Such methods, performing classification at the bag level, cannot identify the mapping between a relation and a sentence, and largely suffers from the noisy lab.ling problem. In this paper, we propose a novel model for relation classification at the sentence level from noisy data. The model has two modules: an instance selector and a relation classifier. The instance selector chooses high-quality sentences with reinforcement learning and feeds the selected sentences into the relation classifier, and the relation classifier makes sentencelevel prediction and provides rewards to the instance selector. The two modules are trained jointly to optimize the instance selection and relation classification processes. Experiment results show that our model can deal with the noise of data effectively and obtains better performance for relation classification at the sentence level. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

Atomic force microscope tip localization and tracking through deep learning based vision inside an electron microscope

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Atomic force microscope tip localization and tracking throug...

2019 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Shuai Liang Mokrane Boudaoud Catherine Achard Weibin Rong Stéphane Régnier Stéphane Régnier are with the Sorbonne Université campus Pierre et Marie Curie/ ISIR CNRS UMR 72224 Place Jussieu CC 173 Pyramide - T55/65 Paris State Key Lab. of Robotics and System Harbin Institute of Technology 92 West Dazhi Street Nan Gang District Harbin China

Scanning Electron Microscopy (SEM) is an ideal observation tool for small scales robotics. It has the potential to achieve automated nano-robotic tasks such as nano-handling and nano-assembly. Path following control of nano-robot end effectors using SEM vision feedback is a key for an intuitive programming of elementary robotic tasks sequences. It requires the ability to track end effectors under various SEM scan speeds. SEM suffers however from tricky issues that limits robotic tracking capabilities. This paper focuses on one specific issue related to the compromise between the scan speed and the image quality. This restriction seriously limits the performance of conventional vision tracking algorithms when used with electron images. At high scan speed, the image quality is very noisy making very difficult to differentiate the robot end effector from the background, hence limiting the tracking capabilities. The work related in this paper explores for the first time the potential value of Convolutional Neural Networks (ConvNet) in the context of nano-robotic vision tracking inside SEM. The aim is to localize an end-effector, AFM cantilever in the case of the study, from SEM images for any scan speed configuration and despite of low images quality. For that purpose, a data set of AFM tip images is build up from SEM images for the learning algorithm. Network performances are estimated under different SEM scan speeds. Thanks to the learning algorithm, experimental results show robust AFM tip tracking capabilities inside the SEM under various scan speed conditions.

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Convolution Neural Network with Sample Stretch for SAR Target Recognition

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Convolution Neural Network with Sample Stretch for SAR Targe...

2018 China International SAR Symposium, CISS 2018

作者： Zhang, Linbin Xiong, Boli Kuang, Gangyao State Key Lab. of Complex Electromagnetic Environment Effects On Electronics and Information System School of Electronic Science National University of Defense Technology Changsha China

ISBN: (纸本)9781538671931

Convolution neural networks doing well in the field of image classification obeys the precondition that test images are similar to the train images. In SAR vehicle image classification field, the recognition rate drops rapidly as the depression angle increasing. The difference of SAR imaging in depression angle obviously affects the classification accuracy of SAR images. In order to overcome the low accuracy in the classification of three categories of SAR vehicles, experiments with a train set composed of depression angle 17° images and a test set composed of depression angle 45° from MSTAR database are explored. To make the test and train set become more similar, the test images are stretched in both horizontal and vertical directions. The stretched method is verified through the cross-correlation coefficient experiments. Meanwhile, an improved structure of convolutional neural network is adopted in the paper to act as the classifier. The recognition accuracy is compared with other traditional machine learning methods and the A-ConvNets method. The results show that the stretching method and the proposed CNN exceed most of existing classifiers in SAR vehicle recognition. © 2018 IEEE.

关键词： Convolution

Sparse Least Square Support Vector Machines based on Random Entropy 36

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Sparse Least Square Support Vector Machines based on Random ...

第36届中国控制会议

作者： Wenlu Ma Han Liu Faculty of Automation and Information Engineering Xi'an University of Technology Shaanxi Key Lab. of Complex System Control and Intelligent Information Processing Faculty of Automation and Information EngineeringXi’an University of Technology

ISBN: (纸本)9781538629185

Least squares support vector machines(LSSVM) has a good performance in small data samples, but can't solve the large-scale sample problems. In this paper, large data set sparse least squares support vector machines model based on stochastic entropy is proposed, and it can be applied to large-scale data samples. Firstly, the large-scale data set is divided into several subsets. Then the entropy method is used to the sparse samples in each subset. Finally, we use sparse samples sets as training samples, and use least squares support vector machine algorithm to train. The results show that the sparse least squares support vector machine model based on entropy can effectively solve the problem of large-scale data.

关键词： Sparsity LSSVM Large-scale data Random Entropy