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Light People:*** May Lau,newly elected US NAE member in Hong Kong,talks about future of photonics and women in science

Light(science & Applications) 2024年第4期13卷 602-608页

作者： Yating Wan Chenzi Guo Electrical and Computer Engineering the ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology23955-6900 ThuwalSaudi Arabia Changchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of SciencesChangchunChina

Editorial Photonics technology remains a driving force in today’s scientific landscape,marked by continuous innovation and crossdisciplinary *** an enlighting conversation with Light:science&Applications,*** May Lau,a pioneer in photonics research,shares her deep insights on the evolution of technologies of LEDs,lasers,challenges of hetero-epitaxy,and the future of micro-LEDs and quantum dot *** honored as a member of the US National Academy of Engineering(NAE)for her significant contributions to photonics and electronics using III-V semiconductors on silicon,*** stands out as the sole Hong Kong scholar inducted into the NAE this year,joining 114 new and 21 international *** this exclusive Light People interview,*** shares her journey as a pioneering woman in engineering,her commitment to mentorship and academia,and her perspective on advancing female representation in *** summary provided is distilled from ***’s thoughtful responses during the *** a deeper exploration of ***’s experiences and advice,the full interview is available in the Supplementary material.

关键词： interview lighting driving

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Light People:*** Dai,*** Lo,and *** Su-innovators in silicon photonics

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Light(science & Applications) 2024年第12期13卷 2880-2885页

In this edition of Light People,we are excited to feature *** Dai(Zhejiang University),*** Su(Shanghai Jiao Tong University),and *** Lo(Advanced Micro Foundry Pte Ltd,Singapore),three prominent researchers shaping the future of silicon *** collaborative work addresses critical issues in silicon photonics,including reducing propagation losses,enlarging the functionalities and enhancing building blocks,integrating efficient laser sources,expanding applications,and pushing the boundaries of optical and electronic *** this interview,we delve into their academic journeys,challenges,and future visions,offering insights into the ongoing evolution of silicon photonics and its potential to transform *** a deeper exploration of their experiences and advice,the full interview is available in the Supplementary material.

关键词： interview offering photonics

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EPM: Evolutionary Perception Method for Anomaly Detection in Noisy Dynamic Graphs

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IEEE Transactions on Knowledge and Data Engineering 2025年第7期37卷 4035-4048页

作者： Wang, Huan Chen, Junyang Wu, Yirui Leung, Victor C. M. Wang, Di King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Saudi Arabia Huazhong Agricultural University College of Informatics China Shenzhen University College of Computer Science and Software Engineering China Hohai University College of Computer and Information China

With the rapid expansion of interactions across various domains such as knowledge graphs and social networks, anomaly detection in dynamic graphs has become increasingly critical for mitigating potential risks. However, existing anomaly detection methods often assume noise-free dynamic graphs, overlooking the prevalence of noisy dynamic graphs in real-world applications. Specifically, noisy dynamic graphs affected by structural noises-such as spurious and missing nodes and edges-struggle to consistently provide reliable structural evidence for anomaly detection. To tackle this challenge, we propose an Evolutionary Perception Method (EPM) for identifying anomalous nodes in noisy dynamic graphs by resisting the interference of structural noises. EPM primarily consists of two components: a dynamic fitter and a filtering reviser. The dynamic fitter characterizes the interaction dynamics of nodes that removes and generates links at each period as a multiple superposition state, utilizing various link prediction algorithms to fit evolutionary mechanisms. Additionally, the filtering reviser designs evolutional entropies to quantify the evolutional uncertainty in multiple superposition states, further designing the Kalman filter to optimize these entropies. Extensive experiments show that the proposed EPM method surpasses state-of-the-art approaches in detecting anomalous nodes in noisy dynamic graphs. © 1989-2012 IEEE.

关键词： Kalman filters

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Editorial—A Time for Reflection

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Proceedings of the IEEE 2024年第12期112卷 1758-1760页

作者： Setti, Gianluca King Abdullah University of Science and Technology (KAUST) Computer Electrical and Mathematical Sciences and Engineering Division Thuwal 23955 Saudi Arabia

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A Python Class for Neutrosophic Morphology

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Neutrosophic Sets and Systems 2025年 76卷 500-519页

作者： Affè, Lorenzo Nordo, Giorgio Smarandache, Florentin MIFT Department – Mathematical and Computer Science Physical Sciences and Earth Sciences University of Messina Sant’Agata Messina98166 Italy Mathematics Physics and Natural Science Division University of New Mexico 705 Gurley Ave GallupNM87301 United States

In this work, we introduce a Python class, named NSmorph, developed to facilitate image manipulation through neutrosophic morphological operations. This innovative approach extends traditional image processing methods by leveraging the flexibility of neutrosophic logic to handle uncertainty, indeterminacy, and noise in digital images. The class offers implementations of essential morphological operators, such as neutrosophic dilation, erosion, opening, and closing, providing a robust tool for applications where image clarity is often compromised, like medical imaging and surveillance. We detail the class structure and functions and provide multiple examples to demonstrate its practical applications and comparative advantages over classical morphological methods. © 2025, University of New Mexico. All rights reserved.

关键词： Python

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An End-To-End Neuromorphic Radio Classification System With an Efficient Sigma-Delta-Based Spike Encoding Scheme

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1869-1881页

作者： Guo, Wenzhe Yang, Kuilian Stratigopoulos, Haralampos-G. Aboushady, Hassan Salama, Khaled Nabil King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955 Saudi Arabia Sorbonne Université Cnrs LIP6 Paris75005 France

Rapid advancements in 5G communication and the Internet-of-things have prompted the development of cognitive radio sensing for spectrum monitoring and malicious attack detection. An end-to-end radio classification system is essential to realize efficient real-time monitoring at the edge. This work presents an end-to-end neuromorphic system enabled by an efficient spiking neural network (SNN) for radio classification. A novel hardware-efficient spiking encoding method is proposed leveraging the Sigma-Delta modulation mechanism in analog-to-digital converters. It requires no additional hardware components, simplifies the system design, and helps reduce conversion latency. Following a designed hardware-emulating conversion process, the classification performance is verified on two benchmark radio modulation datasets. A comparable accuracy to an artificial neural network baseline with a difference of 0.30% is achieved on the dataset RADIOML 2018 with more realistic conditions. Further analysis reveals that the proposed method requires less power-intensive computational operations, leading to 22× lower computational energy consumption. In addition, this method exhibits more than 99% accuracy on the dataset when the signal-to-noise ratio is above 0 dB. The SNN-based classification module is realized on field programmable gate array (FPGA) with a heterogeneous streaming architecture, achieving high throughput and low resource utilization. Therefore, this work demonstrates a promising solution for constructing an efficient high-performance end-to-end radio classification system. © 2020 IEEE.

关键词： Wireless sensor networks

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Discrete Anomalous Hall Resistance-based Quantized Convolutional Neural Network

Discrete Anomalous Hall Resistance-based Quantized Convoluti...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Lone, Aijaz H. Zou, Xuecui Li, Hanrui El-Atab, Nazek Fariborzi, Hossein King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences and Engineering Division Saudi Arabia

ISBN: (纸本)9798350338362

Spintronic devices have shown promise for energy-efficient storage and neuromorphic computing. In this abstract, we present the realization of a spintronic device exhibiting discrete anomalous Hall resistance states. We attribute this discrete resistance behavior to the magnetic domain wall pinning and depinning and gradual switching of different magnetic layers. The number of resistance states is a function of the temperature. Furthermore, this discrete resistance behavior of the device allows us to employ these resistance states as weights in a quantized convolutional neural network. The network is trained and tested on the CIFAR-10 data set and the system achieves an accuracy of around 86.95%. © 2023 IEEE.

关键词： Energy efficiency

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The p-Subspace Sketch Problem in Small Dimensions with Applications to Support Vector Machines 34

The p-Subspace Sketch Problem in Small Dimensions with Appli...

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34th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2023

作者： Li, Yi Lin, Honghao Woodruff, David P. Division of Mathematical Sciences School of Physical and Mathematical Sciences Nanyang Technological University Singapore Computer Science Department Carnegie Mellon University United States

ISBN: (纸本)9781611977554

In the p-subspace sketch problem, we are given an n × d matrix A with n > d, and asked to build a small memory data structure Q(A, Ε) so that, for any query vector x ∈ Rd, we can output a number in (1 ± Ε)Axpp given only Q(A, Ε). This problem is known to require Ω(equation presented)(dΕ-2) bits of memory for d = Ω(log(1/Ε)). However, for d = o(log(1/Ε)), no data structure lower bounds were known. Small constant values of d are particularly important for estimating point queries for support vector machines (SVMs) in a stream (Andoni et al. 2020), where only tight bounds for d = 1 were known. We resolve the memory required to solve the p-subspace sketch problem for any constant d and integer p, showing that it is Ω(equation presented) bits and Õ(equation presented) words, where the Õ(·) notation hides poly(log(1/Ε)) factors. This shows that one can beat the Ω(Ε-2) lower bound, which holds for d = Ω(log(1/Ε)), for any constant d. Further, we show how to implement the upper bound in a single pass stream, with an additional multiplicative poly(log log n) factor and an additive poly(log n) cost in the memory. Our bounds extend to loss functions other than the p-norm, and notably they apply to point queries for SVMs with additive error, where we show an optimal bound of Θ(equation presented) for every constant d. This is a near-quadratic improvement d+1 over the Ω(equation presented) lower bound of Andoni et al. Further, previous upper bounds for SVM point query were noticeably lacking: for d = 1 the bound was Õ(Ε-1/2) and for d = 2 the bound was Õ(Ε-4/5), but all existing techniques failed to give any upper bound better than Õ(Ε-2) for any other value of d. Our techniques, which rely on a novel connection to low dimensional techniques from geometric functional analysis, completely close this gap. Copyright © 2023 by SIAM.

关键词： Support vector machines

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Supervised Local Training With Backward Links for Deep Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 854-867页

作者： Guo, Wenzhe Fouda, Mohammed E. Eltawil, Ahmed M. Salama, Khaled Nabil King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955 Saudi Arabia University of California Center for Embedded and Cyber-Physical Systems IrvineCA92612 United States

The restricted training pattern in the standard backpropagation (BP) requires end-to-end error propagation, causing large memory costs and prohibiting model parallelization. Existing local training methods aim to resolve the training obstacles by completely cutting off the backward path between modules and isolating their gradients. These methods prevent information exchange between modules and result in inferior performance. This work proposes a novel local training algorithm, BackLink, which introduces intermodule backward dependence and facilitates information to flow backward along with the network. To preserve the computational advantage of local training, BackLink restricts the error propagation length within the module. Extensive experiments performed in various deep convolutional neural networks demonstrate that our method consistently improves the classification performance of local training algorithms over other methods. For example, our method can surpass the conventional greedy local training method by 6.45% in accuracy in ResNet32 classifying CIFAR100 and recent work by 2.58% in ResNet110 classifying STL-10 with much lower complexity. Analysis of computational costs reveals that small overheads are incurred in GPU memory costs and runtime on multiple GPUs. Our method can lead up to a 79% reduction in memory cost and 52% in simulation runtime in ResNet110 compared to the standard BP. Therefore, our method could create new opportunities for improving training algorithms toward better efficiency for real-time learning applications. © 2023 IEEE.

关键词： Deep neural networks

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Can blockchain link the future?

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Digital Communications and Networks 2022年第5期8卷 687-694页

作者： Liang Qiao Shuping Dang Basem Shihada Mohamed-Slim Alouini Robert Nowak Zhihan Lv College of Computer Science&Technology Qingdao UniversityQingdao266071China Computer Electrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal23955-6900Saudi Arabia Institute of Computer Science Warsaw University of TechnologyNowowiejska 15/1900-665WarsawPoland

Recently,decentralization has been extensively explored by ***,as a representation of decentralized technology,has attracted attention with its unique characteristics,such as irrevocability and ***,herein,we introduce cutting-edge blockchain technologies from four directions:blockchain system,consensus algorithms,smart contract,and ***,we analyze the current lack of consensus mechanism,fault tolerance,and block capacity of the blockchain,and the integration of blockchain into 5G/6G mobile ***,we discuss the possible applications of blockchain in intellectual property protection,the Internet of Things,digital twins,standardization,and epidemic prevention and ***,explore the impacts and solutions of blockchain on human society beyond technology.

关键词： Blockchain Consensus algorithm Scalability Smart contract Internet of things Digital twins

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