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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Pang, Lie Meng Ishibuchi, Hisao Nan, Yang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789819635054

To handle constrained multi-objective optimization problems (CMOPs), many constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed. However, a recent study has shown that many of these CMOEAs do not perform well on real-world CMOPs. In contrast, NSGA-II, proposed over 20 years ago with a simple constrained dominance principle, has demonstrated better performance than recent CMOEAs on many real-world CMOPs. Motivated by NSGA-II’s promising results, this paper aims to further enhance its performance. We explore the idea of enhancing NSGA-II with a knee point for solving CMOPs. Specifically, a knee point is identified using the minimum distance from the estimated ideal point, and always included in the next population of NSGA-II. Experimental results show that this simple idea improves the performance of NSGA-II on real-world CMOPs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Channel-Feedback-Free Transmission for Downlink FD-RAN:A Radio Map Based Complex-Valued Precoding Network Approach

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China Communications 2024年第4期21卷 10-22页

作者： Zhao Jiwei Chen Jiacheng Sun Zeyu Shi Yuhang Zhou Haibo Xuemin(Sherman)Shen School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310058China Department of Mathematics and Theories Peng Cheng LaboratoryShenzhen 518000China Department of Electrical and Computer Engineering University of WaterlooWaterlooOntarioN2L 3G1Canada

As the demand for high-quality services proliferates,an innovative network architecture,the fully-decoupled RAN(FD-RAN),has emerged for more flexible spectrum resource utilization and lower network ***,with the decoupling of uplink base stations and downlink base stations in FDRAN,the traditional transmission mechanism,which relies on real-time channel feedback,is not suitable as the receiver is not able to feedback accurate and timely channel state information to the *** paper proposes a novel transmission scheme without relying on physical layer channel ***,we design a radio map based complex-valued precoding network(RMCPNet)model,which outputs the base station precoding based on user *** comprises multiple subnets,with each subnet responsible for extracting unique modal features from diverse input ***,the multimodal embeddings derived from these distinct subnets are integrated within the information fusion layer,culminating in a unified *** also develop a specific RMCPNet training algorithm that employs the negative spectral efficiency as the loss *** evaluate the performance of the proposed scheme on the public DeepMIMO dataset and show that RMCPNet can achieve 16%and 76%performance improvements over the conventional real-valued neural network and statistical codebook approach,respectively.

关键词： beamforming complex neural networks deep learning FD-RAN

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Integrated electro-optic isolator on thin-film lithium niobate (Jun, 10.1038/s41566-023-01227-8, 2023)

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NATURE PHOTONICS 2023年第8期17卷 732-732页

作者： Yu, Mengjie Cheng, Rebecca Reimer, Christian He, Lingyan Luke, Kevin Puma, Eric Shao, Linbo Shams-Ansari, Amirhassan Ren, Xinyi Grant, Hannah R. R. Johansson, Leif Zhang, Mian Loncar, Marko John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA HyperLight Cambridge MA USA Draper Scholar The Charles Stark Draper Laboratory Cambridge MA USA Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USA Freedom Photonics Goleta CA USA

Optical isolators are indispensable components of almost any optical system and are used to protect a laser from unwanted reflections for phase-stable coherent operation. The emergence of chip-scale optical systems, powered by semiconductor lasers that are integrated on the same chip, has generated a demand for a fully integrated optical isolator. Conventional approaches, which rely on the use of magneto-optic materials to break Lorentz reciprocity, present substantial challenges in terms of material integration. Although alternative magnetic-free approaches have been explored, an integrated isolator with a low insertion loss, high isolation ratio, broad bandwidth and low power consumption on a monolithic material platform is yet to be achieved. Here we realize a non-reciprocal travelling-wave-based electro-optic isolator on thin-film lithium niobate. The isolator enables a maximum optical isolation of 48.0 dB with an on-chip insertion loss of 0.5 dB and uses a single-frequency microwave drive power of 21 dBm. The isolation ratio remains larger than 37 dB across a tunable optical wavelength range from 1,510 to 1,630 nm. We realize a hybrid distributed feedback laser–lithium niobate isolator module that successfully protects the single-mode operation and linewidth of the laser from reflection. Our result represents an important step towards a practical high-performance optical isolator on chip.

关键词： Integrated optics Nanophotonics and plasmonics Nonlinear optics

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Evolutionary Algorithm-Based Energy-Aware Path Planning with a Quadrotor for Warehouse Inventory Management

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第4期4卷 829-837页

作者： De Guzman, C.J.P. Chua, A.Y. Chu, T.S. Secco, E.L. Department of Mechanical Engineering De La Salle University 2401 Taft Ave. Malate Manila Philippines Robotics Laboratory School of Mathematics Computer Science & Engineering Liverpool Hope University United Kingdom

Quadrotors have been vital for automating warehouse processes. However, a significant gap in recent studies is that they use a single quadrotor with limited battery life, considering that their objective involves navigation in a large-scale environment such as a warehouse. Using an energy consumption model to enable more efficient navigation can be explored. Conventional data-driven energy models and path planning algorithms are insufficient for describing the various motions that a quadrotor can perform in warehouse operations, such as changes in yaw. This study aims to design a novel exhaustive data-driven energy consumption model and evolutionary algorithm-based path planning algorithm to consider various quadrotor movements involved in warehouse operations. The quadrotor is tasked with performing a set of movements to each be represented as a power equation in terms of their velocity. The obtained equations were subsequently used as the primary optimization objective for the path planning algorithm, which included yaw angle objectives and constraints. A set of experiments was performed with Crazyflie quadrotors to verify the model and the algorithm. The results showcased the accuracy of the energy consumption model, which was kept at a maximum difference of 0.6%. The designed path planning algorithm obtained greater energy efficiency in the generated paths compared to other state-of-the-art evolutionary algorithms with similar objectives and constraints. © Authors retain all.

关键词： Motion planning

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Defending Poisoning Attacks in Federated Learning via Loss Value Normal Distribution 26

Defending Poisoning Attacks in Federated Learning via Loss V...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Han, Fei Zhang, Yao Zhao, Meng Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Key Laboratory of Computer Vision and System of Ministry of Education Tianjin300384 China

ISBN: (纸本)9798350331684

As an emerging distributed machine learning paradigm, federated learning (FL) only shares model updates between clients and servers, and training data are secured on each client's device. Due to local data privacy, FL is vulnerable to poisoning attacks, which causes serious degradation in the model performance. In this paper, we analyze the targeted poisoning attacks of FL and reveal that when the data held by each client of FL is independent and identically distributed (iid), the loss of all non-poisoned models on some clean dataset agrees with a normal distribution (ND), while the loss value of the poisoned model deviates from this distribution. Based on this observation, we propose a method named ND-defense to defend FL targeted poisoning attacks. We set a small clean test dataset on the server, compute the loss of each client model on it, detect the abnormal ones with the proposed loss-value-based inspection algorithm, and then only aggregate the benign clients' models to get the global model. We conducted experiments on three benchmark datasets and compared several state-of-the-art defense methods. The results show that our NDdefense is effective in defending against targeted poisoning attacks. © 2023 IEEE.

关键词： Normal distribution

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Segmentation of CAD models using hybrid representation

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 188-202页

作者： Claude UWIMANA Shengdi ZHOU Limei YANG Zhuqing LI Norbelt MUTAGISHA Edouard NIYONGABO Bin ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peace-Bay Academy of Virtual Reality Shenyang 110004China China Aerospace Systems Engineering Corporation Beijing 100070China Department of Signal and Information Processing Beihang UniversityBeijing 100191China Department of Computer Science University of SherbrookeSherbrooke J1C0A1Canada

In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD *** previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point ***,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological ***,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation *** combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh *** complex CAD models,model segmentation is crucial for model retrieval and *** partial retrieval,it aims to segment a complex CAD model into several simple *** first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh *** second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD *** combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object *** study uses the Fusion 360 Gallery *** results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.

关键词： B-RepNet hybrid segmentation CAD models classification MeshCNN MeshCAD-Net

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DPT‐tracker:Dual pooling transformer for efficient visual tracking

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 948-959页

作者： Yang Fang Bailian Xie Uswah Khairuddin Zijian Min Bingbing Jiang Weisheng Li Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Mechanical Precision Engineering Malaysia‐Japan International Institute of TechnologyUniversity of Technology MalaysiaKuala LumpurMalaysia Department of Electrical and Computer Engineering Inha UniversityIncheonRepublic of Korea School of Information Science and Technology Hangzhou Normal UniversityHangzhouChina

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input *** alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time *** aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking *** tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking *** experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

关键词： human‐computer interfacing image motion analysis pattern recognition signal processing tracking

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Visual Explanations of Some Problematic Search Behaviors of Frequently-Used EMO Algorithms 1

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Ishibuchi, Hisao Pang, Lie Meng Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819635382

ISBN: (纸本)9789819635375

Recently, evolutionary multi-objective optimization (EMO) algorithms have been used in various application fields. Whereas many new EMO algorithms are proposed every year, well-known EMO algorithms such as NSGA-II, MOEA/D, SMS-EMOA and NSGA-III have been still frequently used in application papers as shown by their number of citations. Since most EMO algorithms have been designed for and evaluated by artificial test problems such as DTLZ and WFG, they do not always work well on real-world problems. Main difficulties of real-world problems are as follows: (i) their Pareto front shapes are irregular, (ii) some objectives have much larger values than others, (iii) some decision variables have much larger values than others, (iv) some objectives are highly correlated, and (v) solution evaluation is expensive. In this paper, we focus on the first four difficulties (except for expensive solution evaluation). Our goal is to clearly explain problematic search behaviors of NSGA-II, MOEA/D, SMS-EMOA and NSGA-III caused by these difficulties using simple distance minimization problems. We formulate various distance minimization problems with the above-mentioned four difficulties by changing the location of each target point on the map as well as the size and shape of the map. We also suggest simple remedies for performance improvement of each algorithm for real-world applications without changing the algorithm itself. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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AN ADAPTIVE POLICY TO EMPLOY SHARPNESS-AWARE MINIMIZATION 11

AN ADAPTIVE POLICY TO EMPLOY SHARPNESS-AWARE MINIMIZATION

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11th International Conference on Learning Representations, ICLR 2023

作者： Jiang, Weisen Yang, Hansi Zhang, Yu Kwok, James Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Peng Cheng Laboratory China

Sharpness-aware minimization (SAM), which searches for flat minima by min-max optimization, has been shown to be useful in improving model generalization. However, since each SAM update requires computing two gradients, its computational cost and training time are both doubled compared to standard empirical risk minimization (ERM). Recent state-of-the-arts reduce the fraction of SAM updates and thus accelerate SAM by switching between SAM and ERM updates randomly or periodically. In this paper, we design an adaptive policy to employ SAM based on the loss landscape geometry. Two efficient algorithms, AE-SAM and AE-LookSAM, are proposed. We theoretically show that AE-SAM has the same convergence rate as SAM. Experimental results on various datasets and architectures demonstrate the efficiency and effectiveness of the adaptive policy. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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Leveraging Data from Vast Unexplored Seas: Positive Unlabeled Learning for Refining Prediction Area in Good Fishing Ground Prediction 27th

Leveraging Data from Vast Unexplored Seas: Positive Unlabel...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Konii, Haruki Nakano, Teppei Miyazawa, Yasumasa Ogawa, Tetsuji Department of Communications and Computer Engineering Waseda University Tokyo Japan Intelligent Framework Laboratory Tokyo Japan Application Laboratory Japan Agency for Marine-Earth Science and Technology Yokohama Japan

ISBN: (纸本)9783031781919

This paper proposes a Positive Unlabeled (PU) learning approach to narrow down the prediction area in the context of good fishing ground prediction. PU learning, a type of semi-supervised learning, is particularly suitable for scenarios characterized by limited positive examples and abundant unlabeled data, as often encountered in fishing ground prediction tasks, where the explored sea areas identified as productive fishing spots are treated as positive instances and the vast unexplored sea areas as unlabeled data. Conventional methods often struggle to accurately model the characteristics of good fishing grounds, resulting in overly broad prediction areas or overly restrictive constraints. To tackle this challenge, we present a PU learning-based method designed to identify negative examples from the unlabeled data and consequently refine the area predicted as positive. Specifically, we train a prediction model for fishing duration, which can be considered a surrogate indicator of good fishing grounds. Subsequently, we apply this model to predict the fishing duration for unexplored areas;those areas exhibiting short fishing durations are deemed reliable negative examples. By incorporating both past positive examples and selected negative examples into a binary classification framework for predicting good fishing grounds, we aim to fine-tune the prediction area. To the best of our knowledge, this study represents the first application of PU learning in the domain of good fishing ground prediction. Experimental comparisons conducted using data from bullet tuna trolling validate the efficacy of the proposed methodology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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