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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

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Revisting the impact of regression models for predicting the number of defects 30

Revisting the impact of regression models for predicting the...

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30th International Conference on Software Engineering and Knowledge Engineering, SEKE 2018

作者： Wu, Man Ye, Sizhe Li, Chunhua Ma, Ziyi Fu, Zhongwang Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China

ISBN: (纸本)1891706446

Predicting the number of faults in software modules can be more helpful instead of predicting the modules being faulty or non-faulty. Chen et al. (SEKE 397-402, 2015) and Rathore et al. (Soft Computing 21: 7417-7434, 2017) empirically investigate the feasibility of some regression algorithms for predicting the number of defects. The experimental results showed that the decision tree regression algorithm performed best in terms of average absolute error (AAE), average relative error (ARE) and root mean square error (RMSE). However, they did not consider the imbalanced data distribution problem in defect datasets and employed improper performance measures for evaluating the regression models to evaluate the performance of models for predicting the number of defects. Hence, we revisit the impact of different regression algorithms for predicting the number of defects using Fault-Percentile-Average (FPA) as the performance measure. The experiments on 31 datasets from PROMISE repository show that the prediction performance of models for predicting the number of defects built by different regression algorithms are various, and the gradient boosting regression algorithm and the Bayesian ridge regression algorithm can achieve better performance. © 2018 Universitat zu Koln. All rights reserved.

关键词： Defects

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Small sample learning with high order contractive auto-encoders and application in SAR images

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中国科学（信息科学） 2018年第9期61卷 271-273页

作者： Qianwen YANG Fuchun SUN Department of Computer Science and Technology Tsinghua University Beijing 100084 China State Key Lab of Intelligent Technology and Systems Tsinghua University Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing 100084 China

Dear editor,Recently auto-encoders (AEs) are used as intermediate layers or unsupervised learning stages in deep learning networks [1].However,unlike other deep learning algorithms,which can extract higher-order abstract features using deep structures [2,3],AEs are typically more easily decoded than convolutional networks for their physical *** are generally limited to encoding training data to hidden space.

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Learning to predict explainable plots for neural story generation

arXiv

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arXiv 2019年

作者： Chen, Gang Liu, Yang Luan, Huanbo Zhang, Meng Liu, Qun Sun, Maosong Institute for Artificial Intelligence State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Noah's Ark Lab Paris Huawei Technologies Ltd

Story generation is an important natural language processing task that aims to generate coherent stories automatically. While the use of neural networks has proven effective in improving story generation, how to learn to generate an explainable high-level plot still remains a major challenge. In this work, we propose a latent variable model for neural story generation. The model treats an outline, which is a natural language sentence explainable to humans, as a latent variable to represent a high-level plot that bridges the input and output. We adopt an external summarization model to guide the latent variable model to learn how to generate outlines from training data. Experiments show that our approach achieves significant improvements over state-of-the-art methods in both automatic and human evaluations. Copyright © 2019, The Authors. All rights reserved.

关键词： Natural language processing systems

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Uncertainty-guided domain alignment for layer segmentation in oct images

arXiv

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arXiv 2019年

作者： Wang, Jiexiang Bian, Cheng Li, Meng Yang, Xin Ma, Kai Ma, Wenao Yuan, Jin Ding, Xinghao Zheng, Yefeng School of Information Science and Engineering Xiamen University China Tencent YouTu Lab State Key Laboratory of Ophthalmology ZhongshanOphthalmic Center Sun Yat-sen University Guangzhou510060 China Dept. of Computer Science and Engineering Chinese University of Hong Kong

Automatic and accurate segmentation for retinal and choroidal layers of Optical Coherence Tomography (OCT) is crucial for detection of various ocular diseases. However, because of the variations in different equipments, OCT data obtained from different manufacturers might encounter appearance discrepancy, which could lead to performance uctuation to a deep neural network. In this paper, we propose an uncertainty-guided domain alignment method to aim at alleviating this problem to transfer discriminative knowledge across distinct domains. We disign a novel uncertainty-guided cross-entropy loss for boosting the performance over areas with high uncertainty. An uncertainty-guided curriculum transfer strategy is developed for the self-training (ST), which regards uncertainty as eficient and effective guidance to optimize the learning process in target domain. Adversarial learning with feature recalibration module (FRM) is applied to transfer informative knowledge from the domain feature spaces adaptively. The experiments on two OCT datasets show that the proposed methods can obtain significant segmentation improvements compared with the baseline models. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical tomography

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Contour Detection in Cassini ISS images based on Hierarchical Extreme Learning Machine and Dense Conditional Random Field

arXiv

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arXiv 2019年

作者： Yang, Xiqi Zhang, Qingfeng Li, Zhan Department of computer science Jinan University Guangzhou510632 China State Key Lab of CAD&CG Zhejiang University Hangzhou310000 China Sino-French Joint Laboratory for Astrometry Dynamics and Space Science Jinan University Guangzhou510632 China

In Cassini ISS (Imaging science Subsystem) images, contour detection is often performed on disk-resolved object to accurately locate their center. Thus, the contour detection is a key problem. Traditional edge detection methods, such as Canny and Roberts, often extract the contour with too much interior details and noise. Although the deep convolutional neural network has been applied successfully in many image tasks, such as classification and object detection, it needs more time and computer resources. In the paper, a contour detection algorithm based on H-ELM (Hierarchical Extreme Learning Machine) and DenseCRF (Dense Conditional Random Field) is proposed for Cassini ISS images. The experimental results show that this algorithms performance is better than both traditional machine learning methods such as SVM, ELM and even deep convolutional neural network. And the extracted contour is closer to the actual contour. Moreover, it can be trained and tested quickly on the general configuration of PC, so can be applied to contour detection for Cassini ISS images. Copyright © 2019, The Authors. All rights reserved.

关键词： Knowledge acquisition

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Reciprocity of thermal diffusion in time-modulated systems

arXiv

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arXiv 2021年

作者： Li, Jiaxin Li, Ying Cao, Pei-Chao Zheng, Xu Peng, Yu-Gui Li, Baowen Zhu, Xue-Feng Alù, Andrea Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou310027 China International Joint Innovation Center ZJU-UIUC Institute Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China School of Physics and Innovation Institute Huazhong University of Science and Technology Wuhan430074 China Department of Physics University of Colorado BoulderCO80309 United States Department of Electrical Engineering City University of New York New YorkNY10031 United States Department of Mechanical Engineering University of Colorado BoulderCO80309 United States

The reciprocity principle governs the symmetry in transmission of electromagnetic and acoustic waves, as well as the diffusion of heat between two points in space, with important consequences for thermal management and energy harvesting. There has been significant recent interest in materials with time-modulated properties, which have been shown to efficiently break reciprocity for light, sound, and even charge diffusion. Quite surprisingly, here we show that, from a practical point of view, time modulation cannot generally be used to break reciprocity for thermal diffusion. We establish a theoretical framework to accurately describe the behavior of diffusive processes under time modulation, and prove that thermal reciprocity in dynamic materials is generally preserved by the continuity equation, unless some external bias or special material is considered. We then experimentally demonstrate reciprocal heat transfer in a time-modulated device. Our findings correct previous misconceptions regarding reciprocity breaking for thermal diffusion, revealing the generality of symmetry constraints in heat transfer, and clarifying its differences from other transport processes in what concerns the principles of reciprocity and microscopic reversibility. © 2021, CC BY.

关键词： Energy harvesting

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Deep likelihood network for image restoration with multiple degradation levels

arXiv

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arXiv 2019年

作者： Guo, Yiwen Lu, Ming Zuo, Wangmeng Zhang, Changshui Chen, Yurong Bytedance AI Lab School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Intel Labs China Beijing100190 China Department of Automation State Key Lab of Intelligence Technologies and Systems Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing100084 China

Convolutional neural networks have been proven very effective in a variety of image restoration tasks. Most state-of-the-art solutions, however, are trained using images with a single particular degradation level, and can deteriorate drastically when being applied to some other degradation settings. In this paper, we propose a novel method dubbed deep likelihood network (DL-Net), aiming at generalizing off-the-shelf image restoration networks to succeed over a spectrum of degradation settings while keeping their original learning objectives and core architectures. In particular, we slightly modify the original restoration networks by appending a simple yet effective recursive module, which is derived from a fidelity term for disentangling the effect of degradations. Extensive experimental results on image inpainting, interpolation and super-resolution demonstrate the effectiveness of our DL-Net. Copyright © 2019, The Authors. All rights reserved.

关键词： Image reconstruction

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Transforming heat transfer with thermal metamaterials and devices

arXiv

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arXiv 2020年

作者： Li, Ying Li, Wei Han, Tiancheng Zheng, Xu Li, Jiaxin Li, Baowen Fan, Shanhui Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou310027 China Department of Electrical Engineering Ginzton Laboratory Stanford University StanfordCA94305 United States National Engineering Research Center of Electromagnetic Radiation Control Materials State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China Chengdu610054 China Department of Physics University of Colorado BoulderCO80309 United States School of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China Paul M. Rady Department of Mechanical Engineering University of Colorado BoulderCO80309 United States

| The demand for sophisticated tools and approaches in heat management and control has triggered fast development of emerging fields including conductive thermal metamaterials, nanophononics, far-field and near-field radiative thermal management, etc. In this review, we cast a unified perspective on the control of heat transfer, based on which the related studies can be considered as complementary paradigms toward manipulating physical parameters and realizing unprecedented phenomena in heat transfer using artificial structures, such as thermal conductivity in heat conduction, thermal emissivity in radiation, and properties related to multi-physical effects. The review is divided into three parts that focus on the three main categories of heat flow control, respectively. Thermal conduction and radiation are emphasized in the first and second parts at both macro- and micro-scale. The third part discusses the efforts to actively introduce heat sources or tune the material parameters with multi-physical effects in both conduction and radiation, including works using thermal convection. We conclude the review with challenges in this research topic and new possibilities about topological thermal effects, heat waves, and quantum thermal effects. Copyright © 2020, The Authors. All rights reserved.

关键词： Metamaterials

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iSplit LBI: Individualized partial ranking with ties via split LBI

arXiv

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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

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