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DPP-HSS: Towards Fast and Scalable Hypervolume Subset Selection for Many-objective Optimization

IEEE Transactions on Evolutionary Computation 2024年

作者： Gong, Cheng Nan, Yang Shang, Ke Guo, Ping Ishibuchi, Hisao Zhang, Qingfu City University of Hong Kong Department of Computer Science Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

Hypervolume subset selection (HSS) has received significant attention since it has a strong connection with evolutionary multi-objective optimization (EMO), such as environment selection and post-processing to identify representative solutions for decision-makers. The goal of HSS is to find the optimal subset that maximizes the hypervolume indicator subject to a given cardinality constraint. However, existing HSS algorithms or related methods are not efficient in achieving good performance in high-dimensional objective spaces. This is primarily because HSS problems become NP-hard when the number of objectives exceeds two, and the calculation of hypervolume contribution is very time-consuming. To efficiently solve HSS problems while maintaining a good solution quality, we propose a fast and scalable hypervolume subset selection method for many-objective optimization based on the determinantal point process (DPP), named DPP-HSS, which is fully free of hypervolume contribution calculation. Specifically, DPP-HSS constructs a hypervolume kernel matrix by extracting the convergence and diversity representations of each solution for a given HSS problem. This matrix is then used to build a DPP model. Subsequently, the original HSS problem is reformulated as a new maximization optimization problem based on the constructed model. A greedy DPP-based hypervolume subset selection algorithm is implemented to solve this transformed problem. Extensive experiments show that the proposed DPP-HSS achieves significant speedup and good hypervolume performance in comparison with state-of-the-art HSS algorithms on benchmark problems. Furthermore, DPP-HSS demonstrates very good scalability with respect to the number of objectives. © 1997-2012 IEEE.

关键词： Optimization algorithms

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U-Star: AN Asymmetric U-Shaped Network Based on Element-Wise Multiplication to Segment Nuclei in H&E Stained Histological Images

U-Star: AN Asymmetric U-Shaped Network Based on Element-Wise...

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IEEE International Symposium on Biomedical Imaging

作者： Guangzhengao Yang Li Zhang Jie Zhao Zifan Chen Haoshen Li Bin Dong Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Nuclei segmentation in Hematoxylin and Eosin (H&E) stained images plays a crucial role in cancer diagnosis and pathological evaluation, enabling pathologists to identify abnormal cells and assess their morphology and distribution. While current automated nuclei segmentation methods predominantly employ convolutional neural networks and attention mechanisms, the potential of element-wise multiplication has been largely unexplored. This paper introduces U-Star, a novel asymmetric segmentation network based on the star block that leverages element-wise multiplication. U-Star adopts the classic encoder-decoder architecture and innovatively implements star-connection as an alternative to traditional skip-connections. In experiments on an H&E stained image dataset, U-Star achieved superior performance with a Dice coefficient of 0.8783, accuracy of 0.9089, and IoU of 0.7929, significantly outperforming baseline models. Extensive ablation studies validate the effectiveness of the star-connection and demonstrate the advantages of our proposed framework. Beyond advancing the application of element-wise multiplication techniques, U-Star shows promising potential for broader applications in medical image segmentation.

关键词： Image segmentation Pathology Technological innovation Image analysis Stars Morphology Network architecture Medical diagnostic imaging Tuning Cancer

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CAT: A Collaborative Annotation Tool for Chinese Genealogy Textual Documents

CAT: A Collaborative Annotation Tool for Chinese Genealogy T...

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International Conference on Computer Supported Cooperative Work in Design

作者： Huan Jiang Zihao Wang Rongrong Li Yuwei Peng Zhiyong Peng Bin Xu School of Computer Science Wuhan University Wuhan China Cultural Heritage Intelligent Computing Laboratory Wuhan University Wuhan China The Big Data Institute Wuhan University Wuhan China School of History Wuhan University Wuhan China

The annotation for Chinese genealogy textual documents is helpful for constructing genealogy knowledge graph, training effective machine learning models for knowledge extraction, etc. However, this kind of documents is difficult to annotate. The primary reason is that the texts are written in both classical and vernacular Chinese. These texts also contain numerous ancient characters and are usually without punctuation. Understanding genealogy texts requires sufficient expertise. When multiple users labeling the same text, conflicts may occur. Existing annotation tools are inappropriate for this work. In this paper, we propose a novel interactive labeling tool, which provides text segmenting, entity and relationship tagging etc. With the annotated information, it is convenient to construct knowledge graph from textual documents, which can be used to analyze Chinese genealogy texts. Furthermore, we introduce a weak supervised mechanism with Hidden Markov Model for collaborative annotating with crowdsourcing. The practice shows that our approach is effective for collaborative annotation. It also facilitates the construction of knowledge graph and obtains more high-quality data sets. At present, this annotation tool has been applied into service.

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Quantum-Inspired Portfolio Optimization In The QUBO Framework

arXiv

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arXiv 2024年

作者： Lu, Ying-Chang Fu, Chao-Ming Yu, Lien-Po Chang, Yen-Jui Chang, Ching-Ray Department of Physics National Taiwan University Taipei Taiwan Institute for Information Industry Taipei Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan

A quantum-inspired optimization approach is proposed to study the portfolio optimization aimed at selecting an optimal mix of assets based on the risk-return trade-off to achieve the desired goal in investment. By integrating conventional approaches with quantum-inspired methods for penalty coefficient estimation, this approach enables faster and accurate solutions to portfolio optimization which is validated through experiments using a real-world dataset of quarterly financial data spanning over ten-year period. In addition, the proposed preprocessing method of two-stage search further enhances the effectiveness of our approach, showing the ability to improve computational efficiency while maintaining solution accuracy through appropriate setting of parameters. This research contributes to the growing body of literature on quantum-inspired techniques in finance, demonstrating its potential as a useful tool for asset allocation and portfolio management. Copyright © 2024, The Authors. All rights reserved.

关键词： Investments

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Martingale Stopping Rule Application in Bayesian A/B Testing With Batch data

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IEEE Access 2025年 13卷 100462-100478页

作者： Yali Wang Haiyan Yu Ching-Chi Yang Ronggui Zhang School of Management Huazhong University of Science and Technology Wuhan Hubei China School of Economics and Management Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China Chongqing Big Data Research Institute Peking University Chongqing China Department of Mathematical Sciences The University of Memphis Memphis TN USA Chongqing Key Laboratory of Emergency Medicine Chongqing Emergency Medical Center Chongqing University Central Hospital School of Medicine Chongqing University Chongqing China NHC Key Laboratory of Birth Defects and Reproductive Health Chongqing Population and Family Planning Science and Technology Research Institute Chongqing China

Continuous monitoring in A/B testing is widely used for selecting options, variants, or treatments in healthcare analytics. However, the appropriate stopping strategy for A/B testing in continuous monitoring remains unclear. This study proposes a Bayesian A/B testing method for continuous monitoring, incorporating a martingale stopping strategy. A uniform Beta distribution is used as the prior in the Bayesian A/B testing method. The posterior expected loss is employed as the criterion for choosing an option. When the posterior expected loss falls below a preset threshold, the result of the continuous monitoring is accepted for the A/B testing. With the proposed method, continuous monitoring of the null hypothesis (that the conversion rate of A is less than that of B) is managed using the posterior expected loss. The results obtained using the martingale stopping strategy enhance the interpretability of the A/B testing process. Finally, the effectiveness of the Bayesian A/B testing method is validated through numerical experiments on both simulated data and real-world applications.

关键词： Testing Bayes methods Monitoring Convergence Standards Decision making Posterior probability Stochastic processes Statistical analysis Maximum likelihood estimation

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UniFace: Unified Cross-Entropy Loss for Deep Face Recognition

UniFace: Unified Cross-Entropy Loss for Deep Face Recognitio...

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International Conference on Computer Vision (ICCV)

作者： Jiancan Zhou Xi Jia Qiufu Li Linlin Shen Jinming Duan National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Computer Vision Institute Shenzhen University Aqara Lumi United Technology Co. Ltd. School of Computer Science University of Birmingham UK Alan Turing Institute UK

As a widely used loss function in deep face recognition, the softmax loss cannot guarantee that the minimum positive sample-to-class similarity is larger than the maximum negative sample-to-class similarity. As a result, no unified threshold is available to separate positive sample-to-class pairs from negative sample-to-class pairs. To bridge this gap, we design a UCE (Unified Cross-Entropy) loss for face recognition model training, which is built on the vital constraint that all the positive sample-to-class similarities shall be larger than the negative ones. Our UCE loss can be integrated with margins for a further performance boost. The face recognition model trained with the proposed UCE loss, UniFace, was intensively evaluated using a number of popular public datasets like MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace. Experimental results show that our approach outperforms SOTA methods like SphereFace, CosFace, ArcFace, Partial FC, etc. Especially, till the submission of this work (Mar. 8, 2023), the proposed UniFace achieves the highest TAR@MR-All on the academic track of the MFR-ongoing challenge. $\color{Blue}{\mathbf{Code}}$ is publicly available.

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NIERT: Accurate Numerical Interpolation through Unifying Scattered data Representations using Transformer Encoder

arXiv

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arXiv 2022年

作者： Ding, Shizhe Xia, Boyang Ren, Milong Bu, Dongbo Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Zhongke Big Data Academy Zhengzhou450046 China

Interpolation for scattered data is a classical problem in numerical analysis, with a long history of theoretical and practical contributions. Recent advances have utilized deep neural networks to construct interpolators, exhibiting excellent and generalizable performance. However, they still fall short in two aspects: 1) inadequate representation learning, resulting from separate embeddings of observed and target points in popular encoder-decoder frameworks and 2) limited generalization power, caused by overlooking prior interpolation knowledge shared across different domains. To overcome these limitations, we present a Numerical Interpolation approach using Encoder Representation of Transformers (called NIERT). On one hand, NIERT utilizes an encoder-only framework rather than the encoder-decoder structure. This way, NIERT can embed observed and target points into a unified encoder representation space, thus effectively exploiting the correlations among them and obtaining more precise representations. On the other hand, we propose to pre-train NIERT on large-scale synthetic mathematical functions to acquire prior interpolation knowledge, and transfer it to multiple interpolation domains with consistent performance gain. On both synthetic and real-world datasets, NIERT outperforms the existing approaches by a large margin, i.e., 4.3∼14.3× lower MAE on TFRD subsets, and 1.7/1.8/8.7× lower MSE on Mathit/PhysioNet/PTV datasets. The source code of NIERT is available at https://***/DingShizhe/*** Codes 68T07, 65D05 (Primary) 68T05 (Secondary) Copyright © 2022, The Authors. All rights reserved.

关键词： Interpolation

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Intelligent Automatic Test Pattern Generation for Digital Circuits Based on Reinforcement Learning

Intelligent Automatic Test Pattern Generation for Digital Ci...

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Asian Test Symposium (ATS)

作者： Wenxing Li Hongqin Lyu Shengwen Liang Tiancheng Wang Pengyu Tian Huawei Li State Key Lab of Processors Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Public Big Data Guizhou University Guiyang China CASTEST Beijing China

Automatic Test Pattern Generation (ATPG) is a crucial technology in the testing of digital circuits. The excessive backtracks during the ATPG process can consume considerable computational resources and deleteriously affect performance. In this study, we introduce an intelligent ATPG method based on reinforcement learning to reduce the number of backtracks and enhance performance. Specifically, the Q-learning algorithm is employed to learn an optimal backtracing strategy pattern from the ATPG data produced through path-oriented decision-making (PODEM). The learned model is then utilized to guide the backtracing decisions within the PODEM, thereby improving the performance of the ATPG process. Experimental results demonstrate that, compared with traditional heuristic strategies and the backtrace path selection strategy based on artificial neural network (ANN), the proposed method can reduce backtrack occurrences and enhance performance more effectively.

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Paddy Diseases Multi-Class Classification using CNN Variants

Paddy Diseases Multi-Class Classification using CNN Variants

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Information Technology (ACIT)

作者： Danial Izzat Anwar Bin Abdul Aziz Marina Yusoff Nurain Ibrahim Raed Alazaidah College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

Agriculture is critical to ensuring and securing food supply for human consumption. Paddy crops are an agricultural practice that produces one of the highest food yields in agriculture. Paddy diseases must now be detected and diagnosed as soon as possible rather than later. Farmers currently use the naked eye to observe suspected infected paddies. The suspected infected paddy will then be sampled and sent to the laboratory for further examination. These methods are highly inefficient, time-consuming, and error prone. Most paddy farmers lack the knowledge to make an informed and accurate diagnosis of which paddies are infected by which diseases. This paper explores using deep learning algorithms to detect and classify various paddy diseases automatically. This method will take an image of paddy leaves and classify it based on the disease category indicated by the idea. The PaddyDoctor dataset from the Kaggle website was used to train and test the deep-learning models. Convolutional Neural Network (CNN) and Transfer Learning models, including MobileNet, MobileNetv2, and InceptionV3, have been constructed. The results of an experiment involving different dataset split ratios, learning rates, and batch sizes. CNN with MobileNetV2 outperformed CNN with MobileNetV1 and CNN with InceptionV3 by 98.05 percent, 80.08 percent, and 83.98 percent, respectively.

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Enhancing Medical Image Report Generation Through Regional and Global Feature Interaction 2nd

Enhancing Medical Image Report Generation Through Regional a...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Teng, Pengrui Yang, Hongyu Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology Tongxin Road No. 568 Zhuangshi Street Zhejiang Ningbo315201 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China University of Science and Technology of China No. 96 Jinzhai Road Baohe District Anhui Hefei230022 China

ISBN: (纸本)9789819619061

Medical image report generation has gained significant attention as an essential task in automated diagnosis, aiming to reduce the workload of physicians and expedite the diagnostic process. However, existing methods predominantly rely on coarse-grained global features, neglecting the fine-grained regional analysis. This limitation potentially leads to the loss of crucial diagnostic information. To address this challenge, we propose the Regional-Global Interaction Network (RGI), an innovative architecture designed to provide more granular information to the model. The RGI introduces two key components: (1) a region selection agent that precisely locates and selects relevant image patches based on input regional word embeddings, and (2) a bidirectional cross-attention mechanism that fuses the selected regional features with global features. This approach enables the model to capture local details while retaining global context. Extensive experiments on the IU X-Ray dataset demonstrate that our RGI architecture significantly improves the accuracy of medical image analysis and report generation. The proposed method outperforms existing state-of-the-art models across multiple evaluation metrics, including BLEU and ROUGE scores. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distribution transformers

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