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Emergent classical spin liquid phases in an Ising lattice via size effects

Physical Review B 2024年第2期109卷 L020303-L020303页

作者： Pratik Brahma Sayeef Salahuddin Department of Electrical Engineering and Computer Sciences University of California Berkeley California 94720 USA Materials Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA

We show that a classical spin liquid phase can emerge from an ordered magnetic state in the two-dimensional frustrated Shastry-Sutherland Ising lattice due to lateral confinement. Two distinct classical spin liquid states are stabilized: (i) long-range spin-correlated dimers, and (ii) exponentially decaying spin-correlated disordered states, depending on widths of W=3n, 3n+1 or W=3n+2,n being a positive integer. Stabilization of spin liquids in a square-triangular lattice moves beyond the conventional geometric paradigm of kagome, triangular, or tetrahedral arrangements of antiferromagnetic ions, where spin liquids have been discussed conventionally.

关键词： Phase diagrams Spin liquid Classical spin models Lattice types Ising model Transfer matrix calculations

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Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

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BMEMat(BioMedical engineering Materials) 2025年第1期3卷 158-181页

作者： Suparna Das Hirak Mazumdar Kamil Reza Khondakar Ajeet Kaushik Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for WomenHyderabadIndia School of Engineering and Technology Department of Computer Science and EngineeringAdamas UniversityKolkataIndia School of Technology Woxsen UniversityHyderabadIndia NanoBiotech Laboratory Department of Environmental EngineeringFlorida Polytechnic UniversityLakelandUSA

Machine learning(ML)and nanotechnology interfacing are exploring opportunities for cancer treatment *** improve cancer therapy,this article investigates the synergistic combination of Graphene Oxide(GO)‐based devices with ML *** production techniques and functionalization tactics used to modify the physicochemical characteristics of GO for specific drug delivery are explained at the outset of the *** is a great option for treating cancer because of its natural biocompatibility and capacity to absorb medicinal ***,complicated biological data are analyzed using ML algorithms,which make it possible to identify the best medicine formulations and individualized treatment plans depending on each patient's particular *** study also looks at optimizing and predicting the interactions between GO carriers and cancer cells using *** modeling helps ensure effective payload release and therapeutic efficacy in the design of customized drug delivery ***,tracking treatment outcomes in real time is made possible by ML algorithms,which permit adaptive modifications to therapy *** optimizing medication doses and delivery settings,the combination of ML and GO in cancer therapy not only decreases adverse effects but also enhances treatment accuracy.

关键词： artificial intelligence cancer diagnosis graphene oxide machine learning real‐time monitoring sensing

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Improving selection strategies in zeroth-level classifier systems based on average reward reinforcement learning

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1201-1211页

作者： Zang, Zhaoxiang Li, Zhao Dan, Zhiping Wang, Junying Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Hubei Yichang443002 China College of Computer and Information Technology China Three Gorges University Hubei Yichang443002 China

As a genetics-based machine learning technique, zeroth-level classifier system (ZCS) based on average reward reinforcement learning (ZCSAR) evolves solutions to optimize average reward per time step. However, initial experimental results have shown that, in some cases, the performance of ZCSAR oscillates heavily during the learning period, or cannot reach the optimum during the testing period. In this paper, we modify the selection strategies in ZCSAR to improve its performance, under conditions of minimal changes of ZCSAR. The proposed selection strategies take tournament selection method to choose parents in Genetic Algorithm (GA), and take roulette wheel selection method to choose actions in match set and to choose classifiers for deletion in both GA and covering. Experimental results show that ZCSAR with the new selection strategies can evolve more promising solutions with enough parameter independence, and also with slighter oscillation during the learning period. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Reinforcement learning

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An efficient Quasi-Affine Transformation Evolutionary algorithm with fixed dimension updating and its application in UAV 3D path planning

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9755-9781页

作者： Sung, Tien-Wen Zhao, Baohua Zhang, Xin Lee, Chao-Yang Fang, Qingjun Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou China Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm's practicability. © 2024 - IOS Press. All rights reserved.

关键词： Optimization

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Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 319-332页

作者： Xiaoqing Zhang Xiao Wu Zunjie Xiao Lingxi Hu Zhongxi Qiu Qingyang Sun Risa Higashita Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhenChina Tomey Corporation NagoyaJapan Guangdong Provincial Key Laboratory of Brain‐inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhenChina Singapore Eye Research Institute SingaporeSingapore

Eye health has become a global health concern and attracted broad *** the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and ***,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model *** alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular *** MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer *** conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 *** results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and ***,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

关键词： artificial intelligence deep learning deep neural networks image analysis image classification medical applications medical image processing

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Automated Test Case Generation for Prime Path Coverage with Jaya Algorithm 14

Automated Test Case Generation for Prime Path Coverage with ...

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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Yadegari, Saba Keyvanpour, Mohammad-Reza Alzahra University Data Mining Laboratory Department of Computer Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350349412

Automatic test case generation is critical in software testing because it can significantly reduce testing time and cost while improving the software's overall quality. One of the critical objectives of test case generation is to improve the coverage of the program, with a focus on achieving a high prime path coverage. In this paper, we suggest a self-Adaptive Jaya algorithm for generating test cases that achieve a high coverage of prime paths in software. We conducted a comparative study against a well-established, state-of-The-Art approach utilizing eleven Java benchmark programs to evaluate its performance. Our results show that the suggested method achieves a higher average coverage of prime paths than the existing approach. © 2023 IEEE.

关键词： Software testing

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Leveraging Transfer Learning for Efficient Classification of Coffee Leaf Diseases 15

Leveraging Transfer Learning for Efficient Classification of...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ali, Md Hasan Mahmud, Tanjim Aziz, Mohammad Tarek Aziz, Md. Faisal Bin Abdul Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati Bangladesh Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Chittagong University of Engineering & Technology Department of Computer Science and Engineering Chittagong Bangladesh Comilla University Department of Computer Science and Engineering Comilla Bangladesh University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

Coffee leaf diseases pose a significant threat to the quality and yield of coffee crops, necessitating early and precise identification for effective disease management. This study introduces a robust approach leveraging transfer learning to classify seven prevalent coffee leaf diseases. By employing a ResNet50v2 model, the research aims to enhance classification accuracy while mitigating bias. The proposed methodology integrates data preparation, preprocessing, data augmentation, partial layer freezing, feature fusion, and fully connected layers to develop a reliable disease classifier. The ResNet50v2 model initially distinguishes healthy from unhealthy leaves, achieving an impressive test accuracy of 96.99%. In subsequent stages, the model classifies unhealthy leaves into sooty molds, brown spots, and rust leaf diseases with 94.40% accuracy, and further identifies red spider mite, leaf miner, phoma, and cercospora diseases with 92.66% accuracy. Overall, the model demonstrates a classification accuracy of 94.20% across the entire dataset, underscoring its efficacy in detecting and classifying multiple coffee leaf diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Evaluation of OCT Image Synthesis for Choroidal and Retinal Layer Segmentation Using Denoising Diffusion Probabilistic Models

Evaluation of OCT Image Synthesis for Choroidal and Retinal ...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Yamauchi, Yudai Wu, Yuli Okada, Eiji Keio University Department of Electronics and Electrical Engineering Japan Institute of Imaging and Computer Vision RWTH Aachen University Germany

Machine learning can automatically conduct the layer segmentation task of retinal optical coherence tomog raphy (OCT) image, but annotated data is required to train these models. Synthetic retinal OCT images are generated using denoising diffusion probabilistic models (DDPMs), which can be used to train segmentation models effectively and automatically create annotated data. However, the extent to which these synthetic im ages contribute to segmentation accuracy compared to real data has not been investigated. In this study, we synthesized retinal OCT images from sketch images using DDPMs, trained a segmentation model using syn thetic and real images, and evaluated how the use of synthetic images influenced the accuracy of choroidal and retinal layer segmentation compared to results using only real images. Through a comparison of the Dice score, we confirmed that training with both synthetic and real OCT images led to higher Dice scores than train ing with only real OCT images. These findings suggest that using synthetic images can enhance segmentation accuracy, offering a promising approach to improving model performance in situations with limited annotated real data. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Image segmentation

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Topic Controlled Steganography via Graph-to-Text Generation

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computer Modeling in engineering & Sciences 2023年第7期136卷 157-176页

作者： Bowen Sun Yamin Li Jun Zhang Honghong Xu Xiaoqiang Ma Ping Xia School of Computer Science and Information Engineering Hubei UniversityWuhan430062China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China Yichang Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China Department of CSIS Douglas CollegeNewWestminsterBCV3L 5B2Canada College of Computer and Information Technology China Three Gorges UniversityYichang443002China

Generation-based linguistic steganography is a popular research area of information *** text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention ***,in the course of our experiment,we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text,which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases,and that the topic of generated texts is uncontrollable,so there is still room for improvement in *** this paper,we propose a topic-controlled steganography method which is guided by graph-to-text *** proposed model can automatically generate steganographic texts carrying secret messages from knowledge graphs,and the topic of the generated texts is *** also provide a graph path coding method with corresponding detailed algorithms for graph-to-text *** from traditional linguistic steganography methods,we encode the secret information during graph path coding rather than using conditional *** test our method in different aspects and compare it with other text generative steganographic *** experimental results show that the model proposed in this paper can effectively improve the quality of the generated text and significantly improve the concealment of steganographic text.

关键词： Information hiding linguistic steganography knowledge graph topic controlled text generation

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The Distributed Intelligent Collaboration to UAV-Assisted VEC: Joint Position Optimization and Task Scheduling

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IEEE Internet of Things Journal 2025年第12期12卷 21473-21487页

作者： Yi, Meng Lee, Vincent C.S. Yang, Peng Li, Peisong Zhang, Yifan Wei, Wei Gao, Honghao Southeast University School of Computer Science and Engineering Nanjing211189 China Monash University Faculty of IT Department of DS and AI ClaytonVIC3800 Australia Southeast University School of Computer Science and Engineering the Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Southeast University-Monash University Joint Graduate School Suzhou215123 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Deploying Unmanned Aerial Vehicles (UAVs) as aerial base stations enhances the coverage and performance of communication networks in Vehicular Edge Computing (VEC) scenarios. However, due to the limited communication range and energy capacity of UAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of UAVs and efficient task offloading remains a significant challenge. To address this problem, the UAV-assisted Two-stage Intelligent Collaboration (UTIC) method is proposed in this paper to tackle the joint position optimization and task scheduling issue. Firstly, a UAV-assisted Two-stage Task Scheduling (UTTS) system model is designed to optimize the allocation process. Secondly, an Enhanced Particle Swarm Optimization (E-PSO) algorithm is designed to determine the optimal positions of UAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of UAVs. Thirdly, Deep Deterministic Policy Gradient (DDPG) method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in UAV deployment and outperform three other classical reinforcement learning (RL) algorithms in terms of reducing system cost. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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