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Regularized randomized iterative algorithms for factorized linear systems

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arXiv 2022年

作者： Du, Kui School of Mathematical Sciences Fujian Provincial Key Laboratory of Mathematical Modeling and High Performance Scientific Computing Xiamen University Xiamen361005 China

Randomized iterative algorithms for solving the factorized linear system, ABx = b with A ∈ Rm×`, B ∈ R`×n, and b ∈ Rm, have recently been proposed. They take advantage of the factorized form and avoid forming the matrix C = AB explicitly. However, they can only find the minimum norm (least squares) solution. In contrast, the regularized randomized Kaczmarz (RRK) algorithm can find solutions with certain structures from consistent linear systems. In this work, by combining the randomized Kaczmarz algorithm or the randomized Gauss–Seidel algorithm with the RRK algorithm, we propose two new regularized randomized iterative algorithms to find (least squares) solutions with certain structures of ABx = b. We prove linear convergence of the new algorithms. Computed examples are given to illustrate that the new algorithms can find sparse (least squares) solutions of ABx = b and can be better than the existing randomized iterative algorithms for the corresponding full linear system Cx = b with C = *** Codes 65F10, 68W20, 90C25 Copyright © 2022, The Authors. All rights reserved.

关键词： Linear systems

Genetic Algorithm based hyper-parameters optimization for transfer Convolutional Neural Network

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Genetic Algorithm based hyper-parameters optimization for tr...

2022 International Conference on Advanced Algorithms and Neural Networks, AANN 2022

作者： Li, Chen Jiang, JinZhe Zhao, YaQian Li, RenGang Wang, EnDong Zhang, Xin Zhao, Kun State Key Laboratory of High-end Server & Storage Technology Beijing100085 China Inspur Group Co. Ltd Beijing100085 China Shandong Hailiang Information Technology Institutes Shandong Jinan250014 China State Key Laboratory of High-End Server & Storage Technology Shandong Jinan250014 China Inspur Electronic Information Industry Co. Ltd Shandong Jinan250014 China Guangdong Inspur Intelligent Computing Technology Co. Ltd Guangdong Guangzhou510000 China

ISBN: (纸本)9781510655751

Hyperparameter optimization is a challenging problem in developing deep neural networks. Decision of transfer layers and trainable layers is a major task for design of the transfer convolutional neural networks (CNN). Conventional transfer CNN models are usually manually designed based on intuition. In this paper, a genetic algorithm is applied to select trainable layers of the transfer model. The filter criterion is constructed by accuracy and the counts of the trainable layers. The results show that the method is competent in this task. The system will converge with a precision of 97% in the classification of Cats and Dogs datasets, in no more than 15 generations. Moreover, backward inference according the results of the genetic algorithm shows that our method can capture the gradient features in network layers, which plays a part on understanding of the transfer AI models. © 2022 SPIE.

关键词： Genetic algorithms

A novel spectral method for the subdiffusion equation

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arXiv 2022年

作者： Xu, Chuanju Zeng, Wei School of Mathematical Sciences Fujian Provincial Key Laboratory of Mathematical Modeling and High Performance Scientific Computing Xiamen University Xiamen361005 China

In this paper, we design and analyze a novel spectral method for the subdiffusion equation. As it has been known, the solutions of this equation are usually singular near the initial time. Consequently, direct application of the traditional high-order numerical methods is inefficient. We try to overcome this difficulty in a novel approach by combining variable transformation techniques with spectral methods. The idea is to first use suitable variable transformation to re-scale the underlying equation, then construct spectral methods for the re-scaled equation. We establish a new variational framework based on the ψ-fractional Sobolev spaces. This allows us to prove the well-posedness of the associated variational problem. The proposed spectral method is based on the variational problem and generalized Jacobi polynomials to approximate the re-scaled fractional differential equation. Our theoretical and numerical investigation show that the proposed method is exponentially convergent for general right hand side functions, even though the exact solution has very limited regularity. Implementation details are also provided, along with a series of numerical examples to show the efficiency of the proposed method. © 2022, CC BY-NC-SA.

关键词： Spectroscopy

Evaluation of model checkers by verifying message passing programs

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Science China(Information Sciences) 2019年第10期62卷 6-30页

作者： Weijiang HONG Zhenbang CHEN Hengbiao YU Ji WANG College of Computer National University of Defense Technology State Key Laboratory of High Performance Computing National University of Defense Technology

Benchmarks and evaluation are important for the development of techniques and tools. Studies regarding evaluation of model checkers by large-scale benchmarks are few. The lack of such studies is mainly because of the language difference of existing model checkers and the requirement of intensive labor in building models. In this study, we present a large-scale benchmark for evaluating model checkers whose inputs are concurrent models. The benchmark consists of 2318 models that are generated automatically from real-world message passing interface(MPI) programs. The complexities of the models have been inspected to be well distributed and suitable for evaluating model checkers. Based on the benchmark, we have evaluated five state-of-the-art model checkers, i.e., PAT, FDR, Spin, PRISM, and NuSMV, by verifying the deadlock freedom property. The evaluation results demonstrate the ability and performance difference of these model checkers in verifying message passing programs.

关键词： model checker evaluation benchmark MPI symbolic execution

DMRIntTk: Integrating different DMR sets based on density peak clustering

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arXiv 2024年

作者： Zhang, Wenjin Jie, Wenlong Cui, Wanxin Duan, Guihua Zou, You Peng, Xiaoqing Center for Medical Genetics Hunan Key Laboratory of Medical Genetics School of Life Sciences Central South University Changsha410083 China Hunan Key Laboratory of Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China High Performance Computing Center Central South University Changsha410083 China

Background: Identifying differentially methylated regions (DMRs) is a basic task in DNA methylation analysis. However, due to the different strategies adopted, different DMR sets will be predicted on the same dataset, which poses a challenge in selecting a reliable and comprehensive DMR set for downstream analysis. Results: Here, we develop DMRIntTk, a toolkit for integrating DMR sets predicted by different methods on a same dataset. In DMRIntTk, the genome is segmented into bins and the reliability of each DMR set at different methylation thresholds is evaluated. Then, the bins are weighted based on the covered DMR sets and integrated into DMRs by using a density peak clustering algorithm. To demonstrate the practicality of DMRIntTk, DMRIntTk was applied to different scenarios, including different tissues with relatively large methylation differences, cancer tissues versus normal tissues with medium methylation differences, and disease tissues versus normal tissues with subtle methylation differences. The results show that DMRIntTk can effectively trim the regions with small methylation differences in the original DMR sets and therefore it can enhance the proportion of DMRs with higher methylation differences. In addition, the overlap analysis suggests that the integrated DMR sets are quite comprehensive, and the functional analysis indicates the integrated disease-related DMR sets are significantly enriched in biological pathways, which are associated with the pathological mechanisms of the diseases. Conclusions: Conclusively, DMRIntTk can help researchers obtaining a reliable and comprehensive DMR set from many prediction methods. © 2024, CC BY.

关键词： Diseases

Adaptive microwave photonic angle-of-arrival estimation based on BiGRU-CNN [Invited]

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Chinese Optics Letters 2023年第9期21卷 1-6页

作者：李寅蔡乔松杨杰周侗彭元喜江天 Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and Technology.National University of Defense TechnologyChangsha 410073China National Innovation Institute of Defense Technology.Academy of Military Sciences PLA China Beijing 100071China Beijing Institute for Advanced Study National University of Defense TechnologyBeijing 100000China Institute for Quantum Science and Technology College of ScienceNational University of Defense TechnologyChangsha 40073China

An adaptive microwave photonic angle-of-arrival(AOA) estimation approach based on a convolutional neural network with a bidirectional gated recurrent unit(BiGRU-CNN) is proposed and *** with the previously reported AOA estimation methods based on phase-to-power mapping,the proposed method is unnecessary to know the frequency of the signal under test(SUT) in *** envelope voltage correlation matrix is obtained from dual-drive Mach–Zehnder modulator(N-DDMZM,N > 2) optical interferometer arrays first,and then AOA estimations are performed on different frequency signals with the aid of BiGRU-CNN.A three-DDMZM-based experiment is carried out to assess the estimation performance of microwave signals at three different frequencies,and the mean absolute error is only 0.1545°.

关键词： microwave photonics angle-of-arrival deep learning adaptive algorithm.

DEEM: DIFFUSION MODELS SERVE AS THE EYES OF LARGE LANGUAGE MODELS FOR IMAGE PERCEPTION

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arXiv 2024年

作者： Luo, Run Li, Yunshui Chen, Longze He, Wanwei Lin, Ting-En Liu, Ziqiang Zhang, Lei Song, Zikai Xia, Xiaobo Liu, Tongliang Yang, Min Hui, Binyuan Alinejad-Rokny, Hamid Shenzhen Key Laboratory for High Performance Data Mining SIAT CAS China University of Chinese Academy of Sciences China Tsinghua University China Huazhong University of Science and Technology China University of New South Wales Australia School of Computing National University of Singapore Singapore MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China The University of Sydney Australia Alibaba Group China

The development of large language models (LLMs) has significantly advanced the emergence of large multimodal models (LMMs). While LMMs have achieved tremendous success by promoting the synergy between multimodal comprehension and creation, they often face challenges when confronted with out-of-distribution data, such as which can hardly distinguish orientation, quantity, color, structure, etc. This is primarily due to their reliance on image encoders trained to encode images into task-relevant features, which may lead them to disregard irrelevant details. Delving into the modeling capabilities of diffusion models for images naturally prompts the question: Can diffusion models serve as the eyes of large language models for image perception? In this paper, we propose DEEM, a simple but effective approach that utilizes the generative feedback of diffusion models to align the semantic distributions of the image encoder. This addresses the drawbacks of previous methods that solely relied on image encoders like CLIP-ViT, thereby enhancing the model’s resilience against out-of-distribution samples and reducing visual hallucinations. Importantly, this is achieved without requiring additional training modules and with fewer training parameters. We extensively evaluated DEEM on both our newly constructed RobustVQA benchmark and other well-known benchmarks, POPE and MMVP, for visual hallucination and perception. In particular, DEEM improves LMM’s visual perception performance to a large extent (e.g., 4% ↑ on RobustVQA, 6.5% ↑ on MMVP, and 12.8 % ↑ on POPE). Compared to the state-of-the-art interleaved content generation models, DEEM exhibits enhanced robustness and a superior capacity to alleviate model hallucinations while utilizing fewer trainable parameters, less pre-training data (10%), and a smaller base model size. Extensive experiments demonstrate that DEEM enhances the performance of LMMs on various downstream tasks without inferior performance in the long term, includ

关键词： Semantics

TOPOLOGICAL SEQUENCE ENTROPY OF NONAUTONOMOUS DYNAMICAL SYSTEMS

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arXiv 2023年

作者： Shao, Hua Department of Mathematics Nanjing University of Aeronautics and Astronautics Nanjing211106 China Key Laboratory of Mathematical Modelling and High Performance Computing of Air Vehicles Nanjing University of Aeronautics and Astronautics MIIT Nanjing211106 China

Let f0,∞ = {fn}∞n=0 be a sequence of continuous self-maps on a compact metric space X. Firstly, we obtain the relations between topological sequence entropy of a nonautonomous dynamical system (X, f0,∞) and that of its finite-to-one extension. We then prove that the topological sequence entropy of (X, f0,∞) is no less than its corresponding measure sequence entropy if X has finite covering dimension. Secondly, we study the supremum topological sequence entropy of (X, f0,∞), and confirm that it equals to that of its n-th compositions system if f0,∞ is equi-continuous;and we prove the supremum topological sequence entropy of (X, fi,∞) is no larger than that of (X, fj,∞) if i ≤ j, and they are equal if f0,∞ is equi-continuous and surjective. Thirdly, we investigate the topological sequence entropy relations between (X, f0,∞) and (M(X), fˆ0,∞) induced on the space M(X) of all Borel probability measures, and obtain that given any sequence, the topological sequence entropy of (X, f0,∞) is zero if and only if that of (M(X), fˆ0,∞) is zero;the topological sequence entropy of (X, f0,∞) is positive if and only if that of (M(X), fˆ0,∞) is infinite. By applying this result, we obtain some big differences between entropies of nonautonomous dynamical systems and that of autonomous dynamical systems. Finally, we study whether multi-sensitivity of (X, f0,∞) imply positive or infinite topological sequence *** Codes 37B40, 37B55, 54H20 Copyright © 2023, The Authors. All rights reserved.

关键词： Entropy

FlyDB: Query Optimization of Blockchain System Based on Hybrid Storage Architecture

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FlyDB: Query Optimization of Blockchain System Based on Hybr...

Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Yang Liu Zehui Xie Nong Xiao Zhiguang Chen Yang Ou Institute for Quantum Information & State Key Laboratory of High-Performance Computing College of Computer National University of Defense Technology Changsha China School of computer science and Engineering Sun Yat-sen University Guangzhou China College of Computer National University of Defense Technology Changsha China

The hybrid storage architecture incorporates a blockchain dual-index structure optimized for byte addressing in non-volatile memory, low-latency features, and the large capacity of SSD. This paper presents a novel LSM-Tree architecture that leverages the byte-addressing characteristics of non-volatile memory. The layers of the level 0 and level 1 are stored in non-volatile memory while employing a local key-value separation strategy to minimize read and write operations during compaction. A logic compression strategy is proposed to handle data compression tasks efficiently. A dual-index structure design is introduced for users to address the scope and multi-keyword queries in the blockchain system. During the compression process, keyword switching occurs during data reordering, ensuring that data within the same SSTable is rearranged according to user preferences. By effectively harnessing the advantages of each level in the hybrid storage architecture, this design significantly reduces both databases write time and the response time for complex queries.

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