检索结果-内蒙古大学图书馆

Computationally Expensive High-Dimensional Multiobjective Optimization via Surrogate-Assisted Reformulation and Decomposition

学校读者我要写书评

暂无评论

IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Pan, Linqiang Lin, Jianqing Wang, Handing He, Cheng Tan, Kay Chen Jin, Yaochu School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence Xidian University Xi’an China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong School of Engineering Trustworth and General Artificial Intelligence Laboratory Westlake University Hangzhou China

In recent decades, various surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to solve computationally expensive multiobjective optimization problems (EMOPs). Nevertheless, designing an SAEA to handle high-dimensional EMOPs and balance convergence, diversity, and computational complexity remains challenging. Here, we propose a two-phase SAEA (TP-SAEA), which follows the idea of convergence first and diversity second, for solving high-dimensional EMOPs. In Phase I, a surrogate-assisted problem reformulation method is proposed to fast-track the Pareto optimal set in association with some reference solutions. Specifically, the high-dimensional EMOP is reformulated into an expensive single-objective one with low-dimensional decision space. Then, the surrogate-assisted optimization is utilized to obtain well-converged solutions. In Phase II, the high-dimensional EMOP is decomposed into two subproblems to explore subregions of the decision space that can effectively promote the diversity of the solutions. The two subproblems are optimized independently via surrogate-assisted optimization, aiming to push the population towards different regions of the Pareto optimal front. Experiments are conducted on EMOPs with 100 to 500 decision variables compared with four state-of-the-art SAEAs. The proposed TP-SAEA obtains well-converged and diverse solutions with only 509 real function evaluations. Moreover, its superiority is examined in six real-world instances with up to 12,000 decision variables. IEEE

关键词： Iron

Incremental Online Learning of Randomized Neural Network with Forward Regularization

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wang, Junda Hu, Minghui Li, Ning Al-Ali, Abdulaziz Suganthan, Ponnuthurai Nagaratnam The Key Laboratory of System Control and Information Processing Ministry of Education of China China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The School of Electrical and Electronic Nanyang Technological University Singapore The KINDI Computing Research Center College of Engineering Qatar University Qatar

Online learning of deep neural networks suffers from challenges such as hysteretic non-incremental updating, increasing memory usage, past retrospective retraining, and catastrophic forgetting. To alleviate these drawbacks and achieve progressive immediate decision-making, we propose a novel Incremental Online Learning (IOL) process of Randomized Neural Networks (Randomized NN), a framework facilitating continuous improvements to Randomized NN performance in restrictive online scenarios. Within the framework, we further introduce IOL with ridge regularization (-R) and IOL with forward regularization (-F). -R generates stepwise incremental updates without retrospective retraining and avoids catastrophic forgetting. Moreover, we substituted -R with -F as it enhanced precognition learning ability using semi-supervision and realized better online regrets to offline global experts compared to -R during IOL. The algorithms of IOL for Randomized NN with -R/-F on non-stationary batch stream were derived respectively, featuring recursive weight updates and variable learning rates. Additionally, we conducted a detailed analysis and theoretically derived relative cumulative regret bounds of the Randomized NN learners with -R/-F in IOL under adversarial assumptions using a novel methodology and presented several corollaries, from which we observed the superiority on online learning acceleration and regret bounds of employing -F in IOL. Finally, our proposed methods were rigorously examined across regression and classification tasks on diverse datasets, which distinctly validated the efficacy of IOL frameworks of Randomized NN and the advantages of forward regularization. Copyright © 2024, The Authors. All rights reserved.

关键词：

Denoiser-Guided Image Deconvolution with Arbitrary Boundaries and Incomplete Observations

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： He, Liangtian Gao, Shaobing Deng, Liang-Jian Wang, Yilun Wang, Chao Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China College of Computer Science Sichuan University Chengdu610065 China Chengdu611731 China American Family Insurance ChicagoIL60654 United States College of Mathematics Physics and Information Zhejiang Ocean University Zhoushan316022 China

Non-blind image deconvolution (NBID) has been a significant and long-standing research topic in the field of imaging sciences. While enduring efforts have been made over the past decades, they are extensively focusing on the scenario where the blur process is artificially assumed, e.g., typically under periodic boundary condition, as the involved matrix inversion can be efficiently tackled using the fast Fourier transforms (FFTs). However, despite its popularity, these boundary conditions are not always realistic assumptions and can lead to severe artifacts in the recovered image. Therefore, image deconvolution with arbitrary boundaries remains as a fundamental problem to be investigated. In this paper, we propose a principled framework with the aid of Plug-and-Play (PnP) scheme to address NBID in a more realistic scenario of unknown boundary. Specifically, the local and nonlocal denoiser priors with implicit optimization objectives are incorporated into a unified model, and the degradation operator is formulated as a composition of a convolution and a spatial mask. Two kinds of experiments: deconvolution, deconvolution with inpainting, all with unknown boundaries are tested. Both quantitative and qualitative evaluations demonstrate that the proposed denoiser-guided PnP framework is highly flexible and effective to cope with the general image deconvolution tasks. © 2023, The Authors. All rights reserved.

关键词： Fast Fourier transforms

学校读者我要写书评

暂无评论

VarBen

The Journal of Molecular Diagnostics 2021年第3期23卷 285-299页

作者： Ziyang Li Shuangsang Fang Rui Zhang Lijia Yu Jiawei Zhang Dechao Bu Liang Sun Yi Zhao Jinming Li National Center for Clinical Laboratories Beijing Hospital National Center of Gerontology Beijing People's Republic of China Graduate School Peking Union Medical College Chinese Academy of Medical Sciences Beijing People's Republic of China Key Laboratory of Intelligent Information Processing Advanced Computer Research Center Institute of Computing Technology Chinese Academy of Medical Sciences Beijing People's Republic of China Beijing Engineering Research Center of Laboratory Medicine Beijing Hospital Beijing People's Republic of China

Next-generation sequencing is increasingly being adopted as a valuable method for the detection of somatic variants in clinical oncology. However, it is still challenging to reach a satisfactory level of robustness and standardization in clinical practice when using the currently available bioinformatics pipelines to detect variants from raw sequencing data. Moreover, appropriate reference data sets are lacking for clinical bioinformatics pipeline development, validation, and proficiency testing. Herein, we developed the Variant Benchmark tool (VarBen), an open-source software for variant simulation to generate customized reference data sets by directly editing the original sequencing reads. VarBen can introduce a variety of variants, including single-nucleotide variants, small insertions and deletions, and large structural variants, into targeted, exome, or whole-genome sequencing data, and can handle sequencing data from both the Illumina and Ion Torrent sequencing platforms. To demonstrate the feasibility and robustness of VarBen, we performed variant simulation on different sequencing data sets and compared the simulated variants with real-world data. The validation study showed that the simulated data are highly comparable to real-world data and that VarBen is a reliable tool for variant simulation. In addition, our collaborative study of somatic variant calling in 20 laboratories emphasizes the need for laboratories to evaluate their bioinformatics pipelines with customized reference data sets. VarBen may help users develop and validate their bioinformatics pipelines using locally generated sequencing data.

关键词：

intelligent ultrasonic distance measurer system with temperature compensation algorithm

学校读者我要写书评

暂无评论

Intelligent ultrasonic distance measurer system with tempera...

International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Yuwen Wu Qiang Yang Kai Tang Jianhang Zou Jianming Zhuo Naixing Feng Yijun Cai Smart Sensing Integrated Circuit Engineering Research Center of Universities in Fujian Province Xiamen University of Technology Xiamen China Xiamen Wish Information Technology Co. Ltd Xiamen China Xiamen Blue Ocean Technology Co. Ltd Xiamen China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

With regard to distance measurement in daily life, the traditional way is to measure the distance between objects through rulers. Nowadays, ultrasonic ranging, laser ranging, radar ranging, sonar ranging and other technologies have enriched the way of distance measurement. At the same time, it is possible to choose a suitable solution according to different actual environments. This paper design an intelligent ultrasonic ranging system through the research of ultrasonic characteristics and the application of existing ultrasonic ranging. The paper introduces the design ideas and process of the intelligent ultrasonic ranging system. Through the design of independent buttons, the normal mode ranging and the high-precision mode ranging are generated to achieve the division of the measurement range and accuracy. When the distance between the measured object and the intelligent ultrasonic ranging system is less than the preset value or exceeds the range of this mode, the buzzer will sound an alarm, so that the system can be used in more fields. The system detects the current ambient temperature in real time, so that the distance measurement can be compensated and modified by the temperature compensation algorithm in the system to achieve self-adaptation and reduce measurement errors.

关键词： Temperature measurement Performance evaluation Temperature distribution Laser radar Ultrasonic variables measurement Current measurement Measurement uncertainty

Learning with multiclass AUC: Theory and algorithms

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Yang, Zhiyong Xu, Qianqian Bao, Shilong Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China School of Cyber Science and Technology Sun Yat-sen University Shenzhen518100 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

The Area under the ROC curve (AUC) is a well-known ranking metric for problems such as imbalanced learning and recommender systems. The vast majority of existing AUC-optimization-based machine learning methods only focus on binary-class cases, while leaving the multiclass cases unconsidered. In this paper, we start an early trial to consider the problem of learning multiclass scoring functions via optimizing multiclass AUC metrics. Our foundation is based on the M metric, which is a well-known multiclass extension of AUC. We first pay a revisit to this metric, showing that it could eliminate the imbalance issue from the minority class pairs. Motivated by this, we propose an empirical surrogate risk minimization framework to approximately optimize the M metric. Theoretically, we show that: (i) optimizing most of the popular differentiable surrogate losses suffices to reach the Bayes optimal scoring function asymptotically;(ii) the training framework enjoys an imbalance-aware generalization error bound, which pays more attention to the bottleneck samples of minority classes compared with the traditional O(p1/N) result. Practically, to deal with the low scalability of the computational operations, we propose acceleration methods for three popular surrogate loss functions, including the exponential loss, squared loss, and hinge loss, to speed up loss and gradient evaluations. Finally, experimental results on 11 real-world datasets demonstrate the effectiveness of our proposed framework.. © 2021, CC BY.

关键词： Machine learning

SEBF: A single-chain based extension model of blockchain for fintech 29

学校读者我要写书评

暂无评论

SEBF: A single-chain based extension model of blockchain for...

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Ji, Yimu Gu, Weiheng Chen, Fei Xiao, Xiaoying Sun, Jing Liu, Shangdong He, Jing Li, Yunyao Zhang, Kaixiang Mei, Fen Wu, Fei School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China Institue of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China Nanjing Center of HPC China Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China Institute of Information Technology Nanjing University of Finance and Economics Nanjing China School of Software and Electrical Engineering Swinburne University of Technology Hawthorn Australia WeBank Co. Ltd. Shenzhen China College of Automation Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9780999241165

The traditional blockchain has the shortcoming that a single-chain can only deal with one or a few specific data types. The research question of how to make blockchain be able to deal with various data types has not been well studied. In this paper, we propose a single-chain based extension model of blockchain for fintech (SEBF). In the financial environment, we design a four-layer architecture for this model. By employing the external trusted oracle group and a financial regulator agency, a variety types of data can be effectively stored in the blockchain, such that the data type extension based on a single-chain is realized. The experimental results indicate that the proposed model can improve the efficiency of simplified payment verification. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Blockchain

Harmonized multimodal learning with Gaussian process latent variable models

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Song, Guoli Wang, Shuhui Huang, Qingming Tian, Qi School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Noah's Ark Lab Shenzhen518129 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

Multimodal learning aims to discover the relationship between multiple modalities. It has become an important research topic due to extensive multimodal applications such as cross-modal retrieval. This paper attempts to address the modality heterogeneity problem based on Gaussian process latent variable models (GPLVMs) to represent multimodal data in a common space. Previous multimodal GPLVM extensions generally adopt individual learning schemes on latent representations and kernel hyperparameters, which ignore their intrinsic relationship. To exploit strong complementarity among different modalities and GPLVM components, we develop a novel learning scheme called Harmonization, where latent model parameters are jointly learned from each other. Beyond the correlation fitting or intra-modal structure preservation paradigms widely used in existing studies, the harmonization is derived in a model-driven manner to encourage the agreement between modality-specific GP kernels and the similarity of latent representations. We present a range of multimodal learning models by incorporating the harmonization mechanism into several representative GPLVM-based approaches. Experimental results on four benchmark datasets show that the proposed models outperform the strong baselines for cross-modal retrieval tasks, and that the harmonized multimodal learning method is superior in discovering semantically consistent latent representation. Copyright © 2019, The Authors. All rights reserved.

关键词： Gaussian distribution

Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Bao, Shilong Xu, Qianqian Yang, Zhiyong Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

The recently proposed Collaborative Metric Learning (CML) paradigm has aroused wide interest in the area of recommendation systems (RS) owing to its simplicity and effectiveness. Typically, the existing literature of CML depends largely on the negative sampling strategy to alleviate the time-consuming burden of pairwise computation. However, in this work, by taking a theoretical analysis, we find that negative sampling would lead to a biased estimation of the generalization error. Specifically, we show that the sampling-based CML would introduce a bias term in the generalization bound, which is quantified by the per-user Total Variance (TV) between the distribution induced by negative sampling and the ground truth distribution. This suggests that optimizing the sampling-based CML loss function does not ensure a small generalization error even with sufficiently large training data. Moreover, we show that the bias term will vanish without the negative sampling strategy. Motivated by this, we propose an efficient alternative without negative sampling for CML named Sampling-Free Collaborative Metric Learning (SFCML), to get rid of the sampling bias in a practical sense. Finally, comprehensive experiments over seven benchmark datasets speak to the superiority of the proposed algorithm. © 2022, CC BY.

关键词： Recommender systems