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International research initiative on genomicsguided sugarcane breeding

Molecular Plant 2025年第2期18卷 171-174页

作者： Xingtan Zhang Jungang Wang Noor-ul-Ain Youxiong Que Jisen Zhang Javeria Abid Peifang Zhao Yuebin Zhang Jishan Lin Li-yu Chen Yixue Bao Robert Herry Sithichoke Tangphatsornruang Qinnan Wang Dongliang Huang Jian Ye Muqing Zhang Ming Luo Mike Butterfield Ren Wang Ray Ming National Key Laboratory of Tropical Crop Breeding Institute of Tropical Bioscience and BiotechnologyChinese Academy of Tropical Agricultural SciencesSanyaHainanChina National Key Laboratory for Tropical Crop Breeding Agricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenGuangdong 518120China State Key Laboratory for Conservation and Utilization of Subtropical Agri-Biological Resources&Guangxi Key Laboratory for Sugarcane Biology Guangxi UniversityNanningGuangxiChina National Key Laboratory of Tropical Crop Breeding Yunan Academy of Agricultural SciencesKaiyuanYunnanChina National Engineering Research Center for Sugarcane Fujian Provincial Key Laboratory of Haixia Applied Plant Systems BiologyKey Laboratory of GeneticsBreeding and Multiple Utilization of CropsMinistry of EducationFujian Agriculture and Forestry UniversityFuzhouFujianChina Queensland Alliance for Agriculture and Food Innovation University of QueenslandBrisbaneQLDAustralia National Omics Center National Science and Technology Development AgencyPathum ThaniThailand Institute of Nanfan&Seed Industry Guangdong Academy of Sciencesand Ministry of Agriculture of Guangdong Sugarcane Germplasm Resources and Utilization of Scientific Observation Test StationNo.10 Shiliugang RdGuangzhouGuangdong China Guangxi Key Laboratory of Sugarcane Genetic Improvement Guangxi Academy of Agricultural SciencesNanningGuangxiChina State Key Laboratory of Plant Genomics Institute of MicrobiologyChinese Academy of SciencesBeijingChina State Key Laboratory of Plant Diversity and Specialty Crops Guangdong Provincial Key Laboratory of Applied BotanySouth China Botanical Garden Chinese Academy of SciencesGuangzhou 510650China 12Centro De Tecnologia CanavieiraPiracicabaSão PauloBrazil

Addressing global challenges such as energy sustainability,food security,and environmental conservation requires innovative agricultural *** sugarcane(Saccharum hybrid)is a crucial crop that contributes not only to the sugar industry but also to bioenergy *** its traditional role,sugarcane supports global food security,environmental sustainability,and economic empowerment of *** 80%of the worldwide sugarcane production is dedicated to sugar,a dietary staple(OECD-FAO,2024).The remaining 19%aid biofuel production,with sugarcane accounting for 24%of the global ethanol feedstock by *** a C4 plant,sugarcane efficiently captures atmospheric carbon and acts as an important carbon ***,sugarcane cultivation can lower local temperatures by nearly 1°C compared to pastures/natural vegetation(Loarie et al.,2011).Economically,sugarcane is a high-value cash crop,generating$43 billion annually in Brazil alone(Voora et al.,2023).

关键词： annually sugar breeding

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Analysis of Schema Formation in Genetic Algorithms: A Review 16th

Analysis of Schema Formation in Genetic Algorithms: A Review

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16th International Conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Zhang, Tiancong Chu, Shu-Chuan Weng, Shaowei Dao, Thi-Kien Nguyen, Trong-The School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qing-Dao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789819615346

Genetic algorithms (GAs) are a powerful class of optimization techniques inspired by the principles of natural selection and genetics. One of the theoretical cornerstones of GAs is schema theory, which provides a framework for understanding how building blocks of solutions, known as schemas, are propagated and combined through generations. This review paper aims to provide a comprehensive theoretical analysis of schema formation in genetic algorithms. It delves into the foundational principles of schema theory, including the schema theorem and its implications for the efficiency and behavior of GAs. The paper also explores advanced topics such as schema disruption mechanisms, the impact of multimodal and epistatic fitness landscapes, and the integration of schema theory with hybrid approaches. Additionally, practical implications and real-world applications of schema theory are discussed through case studies and best practices. By elucidating the mechanisms of schema formation and their influence on genetic algorithm performance, this review seeks to offer valuable insights and directions for future research. Ultimately, this theoretical exploration aims to bridge the gap between abstract concepts and practical application, contributing to the advancement of genetic algorithms as robust optimization tools. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

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Evolving Effective Drug Therapies with Multi-objective Genetic Algorithm 16th

Evolving Effective Drug Therapies with Multi-objective Genet...

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16th International Conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Zhang, Tiancong Weng, Shaowei Chu, Shu-Chuan Nguyen, Trong-The Dao, Thi-Kien School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qing-dao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789819615346

The development of effective drug therapies is a complex and multifaceted problem involving various biological, chemical, and computational challenges. Traditional drug development methods are often time-consuming and costly, necessitating innovative approaches. Multi-objective genetic algorithms (MOGAs) have emerged as a powerful tool for optimizing drug design by simultaneously considering multiple conflicting objectives, such as efficacy, safety, and cost. This paper reviews the application of MOGAs in drug discovery, highlighting their principles, advantages, and case studies, and discussing future directions in the field. Enhancing MOGAs in drug discovery are also outlined, emphasizing the integration with machine learning, advancements in computational infrastructure, and the potential for personalized medicine. The paper concludes by affirming the significant promise of MOGAs in accelerating the development of safe and effective drug therapies, thereby addressing critical needs in modern pharmacology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drug discovery

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Ultrafast Online Clutter Filtering for Ultrasound Microvascular Imaging

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Chen, Yinran Fang, Baohui Li, Huaying Huang, Lijie Luo, Jianwen Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen361005 China Tsinghua University School of Biomedical Engineering Beijing100084 China

Spatiotemporal clutter filtering via robust principal component analysis (rPCA) has been widely used in ultrasound microvascular imaging. However, the performance of the rPCA clutter filtering highly relies on low-rank modeling for tissue signals and sparse modeling for blood flow signals. Moreover, current rPCA clutter filters are typically based on static processing and have to access a batch of beamformed frames for optimization. This prevents these filters from ultrafast realization. This paper adopts the iteratively reweighted least squares (IRLS) rPCA framework to model tissue and blood flow signals for improved clutter filtering. More importantly, the static IRLS-rPCA filter is upgraded to a spatiotemporal-constrained online method to instantaneously extract blood flow signals from the ongoing beamformed frame. Simulations and in-vivo experiments on a contrast-enhanced rat kidney and a contrast-free human liver demonstrated that the IRLS-rPCA clutter filter achieves higher sensitivity, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) than other rPCA methods. Particularly, the static IRLS-rPCA clutter filter obtains more than 2 dB improvements in CNR over the compared methods in the human liver dataset. The proposed online clutter filter achieves comparable image quality to the static version and processing time of 0.028 ± 0.004 seconds per frame. The corresponding acceleration factor of the online clutter filter over all the tested methods is more than 20. © 2025 IEEE.

关键词： Radar clutter

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Admissibly Randomized Coordinate Descent Methods for computing Extreme Eigenpairs of Symmetric Matrices

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Numerical Linear Algebra with Applications 2025年第2期32卷

作者： Bai, Zhong-Zhi Chen, Yan-Qi State Key Laboratory of Mathematical Sciences State Key Laboratory of Scientific/Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China

For solving symmetric eigenvalue problems of extremely large matrices that cannot be stored in a whole in the computer memory, we propose a class of admissibly randomized coordinate descent methods by minimizing the corresponding Rayleigh quotients. These iteration methods only call of and operate on a column of the matrix at each of their iteration steps, so they require a small computer memory and have a small computational complexity. We analyze the convergence property of these admissibly randomized coordinate descent methods and confirm their implemental effectiveness by numerical experiments. © 2025 John Wiley & Sons Ltd.

关键词： convergence property coordinate descent method randomized iteration symmetric eigenvalue problem

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Inverse Lax-Wendroff boundary treatment for solving conservation laws with finite difference HWENO methods

arXiv

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arXiv 2025年

作者： Zhu, Guanghao Jiang, Yan Zhao, Zhuang Zhang, Mengping School of Mathematical Sciences University of Science and Technology of China Anhui Hefei230026 China Laoshan Laboratory Qingdao266237 China School of Mathematical Sciences Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computing Xiamen University Fujian Xiamen361005 China

This paper presents a novel inverse Lax-Wendroff (ILW) boundary treatment for finite difference Hermite weighted essentially non-oscillatory (HWENO) schemes to solve hyperbolic conservation laws on arbitrary geometries. The complex geometric domain is divided by a uniform Cartesian grid, resulting in challenge in boundary treatment. The proposed ILW boundary treatment could provide high order approximations of both solution values and spatial derivatives at ghost points outside the computational domain. Distinct from existing ILW approaches, our boundary treatment constructs the extrapolation via optimized through a least squares formulation, coupled with the spatial derivatives at the boundary obtained via the ILW procedure. Theoretical analysis indicates that compared with other ILW methods, our proposed one would require fewer terms by using the relatively complicated ILW procedure and thus improve computational efficiency while preserving accuracy and stability. The effectiveness and robustness of the method are validated through numerical experiments. Copyright © 2025, The Authors. All rights reserved.

关键词： Numerical methods

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Statistical Multiplexing Gain Analysis of Centralized Open Radio Access Networks with Limited Cells and Heterogeneous Traffics

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Lu Zhou, Yiqing Liu, Ya-Feng Liu, Ling Shi, Ningzhe Tian, Lin Shi, Jinglin National Key Laboratory of Transient Impact Beijing102202 China 208 Research Institute of China Ordnance Industries Beijing102202 China Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing100190 China Chinese Academy of Sciences State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China Nanjing Institute of InforSuperBahn Nanjing211100 China

Centralized open radio access networks (O-RANs) aggregate computing resources of multiple cells to improve resource utilization and achieve statistical multiplexing gain (SMG), i.e., the ratio of the number of deployed resources (DepR) for distributed radio access network (D-RAN) to that for O-RAN. SMG of O-RAN has been investigated with infinite number of cells with homogeneous bursty-temporal traffic features. However, in practice, the number of cells is limited, and in the era of Internet of Things (IoT), cells with heterogeneous bursty-temporal traffics (het-traffics), should be considered. This paper analyzes the SMG of O-RAN with limited het-traffic cells. First of all, given traffic models of het-traffic cells following different bursty lognormal distribution and periodic time-varying features, required resources (ReqR) for D-RAN can be derived. Then, to obtain the distribution of ReqR for O-RAN with limited het-traffic cells, a Gaussian mixture model (GMM) based resource distribution approximation (GMM-RDA) is proposed. Thus SMG can be obtained for any given service rate. In simulations, the effectiveness of GMM-RDA is verified by Kolmogorov-Smirnov (K-S) test, and the accuracy of achieved SMG can be up to 99.7%, which is about 33% higher than that obtained by the law of large numbers (LLN) for infinite cells. Moreover, het-traffic cells resulting from service diversification can enhance SMG in O-RAN by 34.7%. © 1967-2012 IEEE.

关键词： Radio access networks

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QoS-Based Beamforming and Compression Design for Cooperative Cellular Networks via Lagrangian Duality

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IEEE Transactions on Signal Processing 2025年

作者： Fan, Xilai Liu, Ya-Feng Liu, Liang Chang, Tsung-Hui Chinese Academy of Sciences State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong The Chinese University of HongKong School of Science and Engineering Shenzhen Shenzhen China Shenzhen Research Institute of Big Data China

This paper considers the quality-of-service (QoS)-based joint beamforming and compression design problem in the downlink cooperative cellular network, where multiple relay-like base stations (BSs), connected to the central processor via ratelimited fronthaul links, cooperatively transmit messages to the users. The problem of interest is formulated as the minimization of the total transmit power of the BSs, subject to all users' signal-to-interference-plus-noise ratio (SINR) constraints and all BSs' fronthaul rate constraints. In this paper, we first show that there is no duality gap between the considered joint optimization problem and its Lagrangian dual by showing the tightness of its semidefinite relaxation (SDR). Then, we propose an efficient algorithm based on the above duality result for solving the considered problem. The proposed algorithm judiciously exploits the special structure of an enhanced Karush-Kuhn-Tucker (KKT) conditions of the considered problem and approaches the solution that satisfies the enhanced KKT conditions via two fixed point iterations. Two key features of the proposed algorithm are: (1) it is able to detect whether the considered problem is feasible or not and find its globally optimal solution when it is feasible;(2) it is highly efficient because both of the fixed point iterations in the proposed algorithm are linearly convergent and function evaluations in the fixed point iterations are computationally cheap. Numerical results show the global optimality and efficiency of the proposed algorithm. © 1991-2012 IEEE.

关键词： Lagrange multipliers

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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High-order diversity feature learning for pedestrian attribute recognition

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Neural Networks 2025年 188卷 107463页

作者： Wu, Junyi Huang, Yan Gao, Min Niu, Yuzhen Chen, Yuzhong Wu, Qiang Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fujian Fuzhou China Australian Artificial Intelligence Institute University of Technology Sydney NSW Australia Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fujian Fuzhou China School of Electrical and Data Engineering University of Technology Sydney NSW Australia

Pedestrian attribute recognition (PAR) involves accurately identifying multiple attributes present in pedestrian images. There are two main approaches for PAR: part-based method and attention-based method. The former relies on existing segmentation or region detection methods to localize body parts and learn corresponding attribute-specific feature from the corresponding regions, where the performance heavily depends on the accuracy of body region localization. The latter adopts the embedded attention modules or transformer attention to exploit detailed feature. However, it can focus on certain body regions but often provide coarse attention, failing to capture fine-grained details, the learned feature may also be interfered with by irrelevant information. Meanwhile, these methods overlook the global contextual information. This work argues for replacing coarse attention with detailed attention and integrating it with global contextual feature from ViT to jointly represent attribute-specific regions. To tackle this issue, we propose a High-order Diversity Feature Learning (HDFL) method for PAR based on ViT. We utilize a polynomial predictor to design an Attribute-specific Detailed Feature Exploration (ADFE) module, which can construct the high-order statistics and gain more fine-grained feature. Our ADFE module is a parameter-friendly method that provides flexibility in deciding its utilization during the inference phase. A Soft-redundancy Perception Loss (SPLoss) is proposed to adaptively measure the redundancy between feature of different orders, which can promote diverse characterization of features. Experiments on several PAR datasets show that our method achieves a new state-of-the-art (SOTA) performance. On the most challenging PA100K dataset, our method outperforms previous SOTA by 1.69% and achieves the highest mA of 84.92%. © 2025 The Authors

关键词： Contrastive Learning

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