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Bond sensitive graph neural networks for predicting high temperature superconductors

Materials Genome engineering Advances 2024年第2期2卷 126-133页

作者： Liang Gu Yang Liu Pin Chen Haiyou Huang Ning Chen Yang Li Turab Lookman Yutong Lu Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijingChina Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijingChina National Supercomputer Center in Guangzhou School of Computer Science and EngineeringSun Yat-sen UniversityGuangzhouChina School of Materials Science and Engineering University of Science and Technology BeijingBeijingChina Department of Engineering Science and Materials University of Puerto RicoMayaguezPuerto RicoUSA AiMaterials Research LLC Santa FeNew MexicoUSA

Finding high temperature superconductors(HTS)has been a continuing challenge due to the difficulty in predicting the transition temperature(Tc)of ***,the efficiency of predicting Tc has been greatly improved via ma-chine learning(ML).Unfortunately,prevailing ML models have not shown adequate generalization ability to find new HTS,*** this work,a graph neural network model is trained to predict the maximal Tc(Tc max)of various *** model reveals a close connection between Tc max and chemical *** sug-gests that shorter bond lengths are favored by high Tc,which is in coherence with previous domain *** importantly,it also indicates that chemical bonds consisting of some specific chemical elements are responsible for high Tc,which is new even to the human *** can provide a convenient guidance to the materials scientists in search of HTS.

关键词： graph neural network machine learning superconductivity superconductors transition temperature

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Enhancing IoT Security: Federated Learning with GANs for Effective Attack Detection

Enhancing IoT Security: Federated Learning with GANs for Eff...

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International Bhurban Conference on Applied Sciences & technology, IBCAST

作者： Bakhtawar Saeed Sobia Arshad Sanay Muhammad Umar Saeed Muhammad Awais Azam Computer Engineering Department Deep Packet Inspection Lab (DPI) National Center of Cyber Security (NCCS) UET Taxila Technology Innovation Research Group School of Information Technology Whitecliffe NZ

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

The rapid proliferation of the Internet of Things (IoT) has given rise to security challenges, necessitating the real-time detection and mitigation of cyberattacks. Federated Learning (FL) is promising because it enables collaborative global model training in IoT for attack detection while maintaining data privacy. However, FL-enabled systems for IoT face challenges related to class imbalance, inconsistent data distributions, and increased training time. This paper presents a novel FL-enabled approach for IoT attack detection that effectively resolves class imbalance by utilizing Generative Adversarial Networks (GANs). The proposed approach also handles non-iid data distributions through the implementation of Vertical FL. Additionally, ordinal encoding techniques are employed to enhance detection accuracy. The effectiveness of the proposed approach is extensively evaluated on the ToN-IoT and N-BaIoT datasets, the DAEs and MLPs both deep learning (DL) algorithms consistently models achieve high precision, recall, and F1 score on ToN-IoT (MLPs: 99.39%, DAEs: 99.62%) and N-BaIoT datasets (DAEs: 93.66%, MLPs: 94.63 %), demonstrating its capability in improving attack detection accuracy and mitigating the limitations of FL-enabled systems in IoT scenarios.

关键词： Training Accuracy Federated learning Prevention and mitigation Generative adversarial networks Real-time systems Encoding Internet of Things Security Faces

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Communication-Constrained Distributed Learning: TSI-Aided Asynchronous Optimization with Stale Gradient

Communication-Constrained Distributed Learning: TSI-Aided As...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Siyuan Yu Wei Chen H. Vincent Poor Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist) Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

Distributed machine learning including federated learning has attracted considerable attention due to its potential of scaling the computational resources, reducing the training time, and helping protect the user privacy. As one of key enablers of distributed learning, asynchronous optimization allows multiple workers to process data simultaneously without paying a cost of synchronization delay. However, given limited communication bandwidth, asynchronous optimization can be hampered by gradient staleness, which severely hinders the learning process. In this paper, we present a communication-constrained distributed learning scheme, in which asynchronous stochastic gradients generated by parallel workers are transmitted over a shared medium or link. Our aim is to minimize the average training time by striking the optimal tradeoff between the number of parallel workers and their gradient staleness. To this end, a queueing theoretic model is formulated, which allows us to find the optimal number of workers participating in the asynchronous optimization. Furthermore, we also leverage the packet arrival time at the parameter server, also referred to as Timing Side Information (TSI), to compress the staleness information for the stalenessaware Asynchronous Stochastic Gradients Descent (Asyn-SGD). Numerical results demonstrate the substantial reduction of training time owing to both the worker selection and TSI-aided compression of staleness information.

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The Realization Factors of Taiwan's Low Altitude Airspace Economy and UAM: Analysis and Evaluation 20

The Realization Factors of Taiwan's Low Altitude Airspace Ec...

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20th IEEE International Conference on e-Business engineering, ICEBE 2024

作者： Wu, Tung-Ling Kung, Chih-Ming Lin, Guan-Ting Tsai, Kuen-Yu Meng, Guang-Yun Huang, Yu-Che Cheng, Wan-Shu Institute of Transportation at Ministry of Transportation and Communications Taiwan Shin Chin Univeristy Kaohsiung Campus Dep. of Information Technology&Communication Taiwan Providence University Dep. of Computer Science and Information Management Taichung Taiwan National Taiwan Univeristy Department of Electrical Engineering Taipei Taiwan Executive Officer Center for Research on High-Performance Remote Sensing and Urban Informatics Ntu Taiwan

ISBN: (纸本)9798350365856

Low altitude airspace economy applications will be of highly effective in a wide range of civil activities and will be a critical element in the smart cities of the future. In view of the main features of unmanned aerial vehicle (UAV), such as automatic piloting system, access to uninhabited areas, low cost compared to road and manned aircraft, shorter delivery time, and the ability to carry heavy payloads, it is important to recognize that UAVs can be used for a wide range of civil activities. With the development of these applications in full swing around the world, in addition to advanced communications technology and other related infrastructure, business demand and a favorable investment climate, clarity, definiteness and feasible regulations in the Taiwanese environment are also essential. Based on the premise of local economic strength, the technical level of the telecommunication industry, and commercial demands, Taiwan should establish a rational standard for the UAV regulatory system as soon as possible, so as to facilitate the development of UAM and to ensure the international competitiveness of the related technologies for market development © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Conditional Diffusion Model for Electrical Impedance Tomography

arXiv

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arXiv 2025年

作者： Shi, Duanpeng Zheng, Wendong Guo, Di Liu, Huaping School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China Department of Computer Science and Technology Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology China

Electrical impedance tomography (EIT) is a noninvasive imaging technique, which has been widely used in the fields of industrial inspection, medical monitoring and tactile sensing. However, due to the inherent non-linearity and ill-conditioned nature of the EIT inverse problem, the reconstructed image is highly sensitive to the measured data, and random noise artifacts often appear in the reconstructed image, which greatly limits the application of EIT. To address this issue, a conditional diffusion model with voltage consistency (CDMVC) is proposed in this study. The method consists of a pre-imaging module, a conditional diffusion model for reconstruction, a forward voltage constraint network and a scheme of voltage consistency constraint during sampling process. The pre-imaging module is employed to generate the initial reconstruction. This serves as a condition for training the conditional diffusion model. Finally, based on the forward voltage constraint network, a voltage consistency constraint is implemented in the sampling phase to incorporate forward information of EIT, thereby enhancing imaging quality. A more complete dataset, including both common and complex concave shapes, is generated. The proposed method is validated using both simulation and physical experiments. Experimental results demonstrate that our method can significantly improves the quality of reconstructed images. In addition, experimental results also demonstrate that our method has good robustness and generalization performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Inverse problems

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Improving Botnet Detection with a Generative Adversarial Network-Based Technique

Improving Botnet Detection with a Generative Adversarial Net...

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International Bhurban Conference on Applied Sciences & technology, IBCAST

作者： Shehla Gul Sobia Arshad Sanay Muhammad Umar Saeed Adeel Akram Bakhtawar Saeed Muhammad Awais Azam Computer Engineering Department Deep Packet Inspection Lab (DPI) National Center of Cyber Security (NCCS) UET Taxila Technology Innovation Research Group School of Information Technology Whitecliffe NZ

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

The number of internet users has increased rapidly, leading to significant concern for individuals about cyber attacks. These attacks have evolved and have become more complex, posing a challenge to their detection and prevention. Botnets are high-risk cyber-attacks that are constantly emerging and changing with time. Many studies have suggested using Machine Learning (ML) techniques to detect and classify botnet a ttack s . However, traditional algorithms often miss many attacks or generate false alarms in large-scale network environments with high bandwidth. The scarcity of data about attacks results in imbalanced training sets that can impact the analysis performance of these methods. We propose a Generative Adversarial Networks (GANs) based oversampling technique to address this problem. We use Wasserstein GANs (WGANs) to simulate botnet attacks and create synthetic attack instances that mimic the high-dimensional distribution of botnet attacks. The experiments have been performed on the CICIDS-2017 dataset. After addressing the issue of imbalanced class, we employed several ML classifiers - Random Forest (RF), K-Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Gradient Boosting Decision Trees (GBDT) to detect botnet attacks. We attained the highest accuracy of 99.9% for the GBDT classifier. The outcomes have shown that the suggested method can successfully address the issue of class imbalance, enhance ML models' detection performance, and outperform state-of-the-art techniques.

关键词： Support vector machines Training Machine learning algorithms Botnet Prevention and mitigation Nearest neighbor methods Generative adversarial networks Cyberattack Random forests Classification tree analysis

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High Swing VCO with Current-Reused Frequency Doubler and Darlington Amplifier

High Swing VCO with Current-Reused Frequency Doubler and Dar...

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technology, Systems and Applications (VLSI-TSA/VLSI-DAT), International VLSI Symposium on

作者： Sheng-Jen Cheng Sheng-Lyang Jang Hui Chen Chung-Hung Hong Chung-Ping Chen Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Chang Gung Memorial Hospital Kidney Research Center Taoyuan Taiwan

This letter proposes a current-reused frequency doubler-Darington amplifier (FD-DA) aiming at enlarging the doubler signal. Conventional active balanced frequency doubler can output a signal with the frequency beyond $f_{max}$ and $f_{\mathrm{t}}$ of transistor, but the output strength is small. Normal transistor amplifier can't amplify this signal. Darlington amplifier (DA) is also called $f_{\mathrm{t}}$ -doubler can amplify the signal at a frequency beyond $f_{\mathrm{t}}$ . In the proposed circuit, the DA stacks on a frequency doubler (FD). The current-reused circuit accepts an input signal from a voltage-controlled-oscillator (VCO). To reduce the power consumption and to improve the phase-noise performance, a capacitance-splitting and a gate–drain impedance-balancing technique are realized with an inductive divider. The 16 GHz frequency source has been designed and fabricated in the TSMC $0.18\ \mu\mathrm{m}$ 1P6M CMOS technology. The measured output power of the fundamental signal and second-harmonic signal are −4.61 and −4.95 dBm, respectively. The measured tuning ranges of the fundamental signal and second-harmonic signal are from 7.52 to 7.885 and 15.04 to 15.77 GHz, respectively. The measured phase noise of the fundamental signal and second-harmonic is −104.96 and −105.12 dBc/Hz at 1MHz offsets, respectively. The frequency source total power consumption is 8.74 mW. The whole chip occupies a small area of 1.07×1.19 mm 2 .

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Intelligent Polarization Reconfigurable Surface for S-Band Applications

Intelligent Polarization Reconfigurable Surface for S-Band A...

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Antennas and Propagation Society International Symposium

作者： Muhammad Sumaid Wajeeh Hassan Ahmad Javed Adnan Nadeem Nosherwan Shoaib David Chatzichristodoulou Symeon Nikolaou School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical Engineering Frederick Research Center (FRC) Frederic University Nicosia Cyprus

To realize the concept of reconfigurable (cross/circular) polarization of the reflected wave, a reflective intelligent polarization reconfigurable surface (IPRS), operating in the S-band (3.8 GHz) is presented. IPRS consists of periodic unit cells consisting of square shaped structures with a diagonal element and two PIN diodes as switching elements. The simultaneous biasing of the PIN diodes allows the switching of the polarization state of the reflected wave to either cross or circular polarization. The incident wave is linearly polarized, and with the diodes in OFF state, cross polarization is achieved, while with the diodes in ON state, circular polarization is caused on the reflected wave.

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research of mask wearing detection method using Improved YOLO network

Research of mask wearing detection method using Improved YOL...

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Chinese Control and Decision Conference, CCDC

作者： Fei Xiao Zixi Liu Shengbin Zhao Shuai Yuan School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China Daqing Oilfield Limited Company DaQing China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Mask wearing detection technology is a practical and research-oriented area within object detection. Due to the complexity of mask wearing detection scenarios, issues such as small target detection and low image clarity often result in missed detections and false alarms. The YOLO algorithm, known for its fast detection speed, is commonly used in mask wearing detection. However, the original YOLO algorithm struggles to achieve satisfactory results in dense crowd scenarios. Therefore, this paper proposes an improved YOLOv5 algorithm based on the BiFPN structure and incorporating attention mechanisms to enhance the accuracy of mask wearing detection while maintaining real-time performance. The proposed approach firstly introduces the Bidirectional Feature Pyramid (BiFPN) structure into the backbone network to enhance the feature extraction capability. Additionally, attention mechanisms are incorporated to filter out irrelevant information, conserve computational resources, and improve the detection performance for small targets. Experimental results on a custom dataset demonstrate that the YOLOv5_BiFPN algorithm achieves a 4% improvement in accuracy and an 11.7% increase in mAP50 compared to the original YOLOv5. Moreover, the inclusion of attention mechanisms further boosts the accuracy by an additional 1.6%. These findings confirm that the improved YOLOv5s model exhibits superior performance in the task of mask wearing detection.

关键词： YOLO Attention mechanisms Accuracy Filtering algorithms Feature extraction Information filters Real-time systems

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