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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Pitilakis, A. Tsilipakos, O. Tasolamprou, A.C. Tsioliaridou, A. Kantartzis, N.V. Ioannidis, S. Liaskos, C. Kafesaki, M. Institute of Electronic Structure and Laser Foundation for Research and Technology Hellas Greece Aristotle University of Thessaloniki School of Electrical and Computer Engineering Greece Theoretical and Physical Chemistry Institute National Hellenic Research Foundation Greece National and Kapodistrian University of Athens Department of Physics Greece Institute of Computer Science Foundation for Research and Technology Hellas Greece Technical University of Crete School of Electrical & Computer Engineering Greece University of Ioannina Department of Computer Science and Engineering Greece University of Crete Department of Materials Science and Technology Greece

ISBN: (纸本)9798350375909

In this work we present a comprehensive framework for the analysis and design of vector holographic metasurfaces (VHMS). The term vector refers to the metasurface ability to manipulate the polarization of the scattered wave, while holographic refers to the configuration of the metasurface aperture, which can have any arbitrary complex profile in its equivalent surface impedance (phase and/or magnitude). We start from the presentation of the analysis of such VHMS, i.e., the calculation of its scattering under given equivalent surface impedance configuration and illumination;the analysis relies on physical optics and can, in theory, model the scattering and diffraction by a finite-aperture metasurface of arbitrary convex shape under arbitrary spatially coherent wavefront illumination. We then present the VHMS design and synthesis procedure, i.e., we compute its equivalent and physical configuration, when given the illuminating wavefront and the desired scattering response or functionality, e.g., beam steering or focusing. For the synthesis, we use a simple scalable unit cell topology incorporating square conductor patches interconnected with lumped complex loads;these can locally and continuously tune the VHMS phase and amplitude response, with independent control over both polarizations, thus allowing arbitrary incident-to-scattered wavefront transformations. These versatile devices, especially when fused with wavefront sensing capabilities, can be used in applications spanning wireless communications (e.g., beamforming, integrated/joint sensing and communication, or programmable wireless environments), radar scattering (e.g., stealth), and holography/imaging. © 2024 IEEE.

关键词： Wavefronts

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A Quality Diversity Study in EvoDevo Processes for engineering Design 13

A Quality Diversity Study in EvoDevo Processes for Engineeri...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Buchanan, Edgar Hickinbotham, Simon Dubey, Rahul Friel, Imelda Colligan, Andrew Price, Mark Tyrrell, Andy M. School of Physics Engineering and Technology University of York Intelligent Systems & Robotics Research Group United Kingdom Missouri State University Computer Science United States School of Mechanical & Aerospace Engineering Queen's University Belfast United Kingdom

ISBN: (纸本)9798350308365

For a long time engineering design has relied on human engineers manually crafting and refining designs using their expertise and experience. In Bio-inspired Evolutionary Development (EvoDevo), generative algorithms are employed to investigate a broader design space that may go beyond what human engineers have considered. Previous literature has demonstrated the use of quality and diversity (QD) algorithms in evolutionary approaches to drive the process to better quality solutions. This paper provides a study to understand the effects of using QD algorithms in EvoDevo processes for engineering design. This paper also analyses the impact of using different behavioural characterisations (BC) in the performance of the quality of the solutions found. The results demonstrate that quality and diversity algorithms can find better solutions than other EAs for engineering design problems. It was also found that the characterisation of the BC is important to get the best results. © 2024 IEEE.

关键词： evodevo generative design neural networks quality diversity structural engineering

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DNMDA: Deep Non-negative Matrix Factorization with Multi-level Integration for MiRNA-Drug Interaction Prediction

DNMDA: Deep Non-negative Matrix Factorization with Multi-lev...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qi, Yujie Yang, Jiaxin Huang, Yu-An You, Zhu-Hong Zhang, Zimai Hu, Lun Zhou, Xi Hu, Pengwei Xinjiang University The School of Software Xinjiang Technical Institute of Physics Chemistry Chinese Academy of Sciences Urumqi China Columbia University School of Engineering and Applied Science NY United States Northwestern Polytechnical University School of Computer Science Xian China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi China

ISBN: (纸本)9798350386226

Numerous studies have demonstrated that the interaction between miRNAs and drugs plays a pivotal role in regulating gene expression and cellular function. Therefore, predicting these interactions is crucial for the development of novel drugs and personalized therapies. Existing methods for predicting miRNA-drug interactions often fail to leverage the full spectrum of molecular and biological features and overlook complex high-dimensional patterns. Deep non-negative matrix factorization (DNMF) addresses these limitations by extracting higher-level representations, thereby enhancing prediction accuracy and robustness. Building on this, this paper proposes a model called DNMDA. In this model, we integrate multiple similarity networks for both miRNAs and drugs and then extract their features through three key modules. Moreover, autoencoders are used to combine various feature sets, allowing for the capture of complementary information and enhancing the model's capacity for making precise and reliable predictions. The resulting features are consolidated into a unified feature vector for each miRNA-drug pair. Ultimately, these feature vectors and their associated labels are provided to the classifier for training. To verify the predictions, a five-fold cross-validation was conducted. The five-fold cross-validation demonstrated a clear advantage in DNMDA's metrics, underscoring its reliability in predicting potential miRNA-drug interactions. This claim is further supported by the predictive results section in the paper, offering concrete evidence of DNMDA's efficacy in this field. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 352-364页

作者： Babu, Jampani Satish G, Dinesh Suganya, R. Radhakrishnan, P. Sutar, Anand S, Alex David Rao, L. Malleswara Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Karnataka Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai India Department of Physics Sri Y N College (A) West Godavari District Andhra Pradesh Narsapur India

Water scarcity in arid regions necessitates innovative solutions to uncover and manage subsurface water resources for environmental health. This study explores the potential of GeoHydroNet, a specialized deep learning framework, in identifying hidden water resources within arid regions. Utilizing remote sensing data such as multispectral and hyperspectral imagery, geological maps, and soil moisture datasets, GeoHydroNet combines graph neural Networks (GNNs), AutoEncoders, and generative adversarial networks (GANs) to address challenges in subsurface hydrology. The model achieved an accuracy of 93.5%, surpassing traditional models like CNNs (85.4%), LSTMs (88.1%), and RNNs (82.6%). GeoHydroNet demonstrates significant precision (0.88), recall (0.87), and sensitivity to data imbalance (0.91). Its physics-informed learning approach incorporates hydrological principles, ensuring realistic predictions. By analyzing subsurface water patterns, recharge zones, and geological influences, the model not only enhances prediction accuracy but also aids in sustainable groundwater management. GeoHydroNet’s performance in identifying groundwater levels, with minimal errors ranging from 0.62 to 3.07%, indicates its robustness in tackling water scarcity challenges in data-constrained environments. These findings highlight its transformative role in hydrological modeling and sustainable water resource management in arid regions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquifers Arid regions Deep learning Generative adversarial networks GeoHydroNet physics-informed learning Remote sensing Subsurface hydrology Sustainable water management

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Sparse Recovery Space Time Adaptive Processing Based on Log-Determinant 15

Sparse Recovery Space Time Adaptive Processing Based on Log-...

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15th International Conference on Signal Processing Systems, ICSPS 2023

作者： Chang, Yuqing Du, Xiaolin Jin, Biao Tian, Tuanwei Li, Jianbo School of Computer and Control Engineering Yantai University Yantai China Ocean College Jiangsu University of Science and Technology Zhenjiang China School of Physics and Electronics Henan University Kaifeng China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781510675056

Airborne radars usually face non-uniform clutter environments, and it is difficult to obtain enough independent and identical distributed (i.i.d.) training samples, which degrades the clutter suppression performance of space time adaptive processing (STAP). To address the problem, a log-determinant sparse rocovery based STAP (LSR-STAP) method is proposed in this paper. Through the sparse recovery theory of low-rank matrices and the prior knowledge of clutter covariance matrices, the corresponding problem is modeled using the log-determinant (LogDet) approximation of the rank function, and the solution of the resulting nonconvex optimization problem is derived in the framework of the symmetric alternating direction method of multipliers (S-ADMM). The simulation results show the superiority of the proposed method over similar algorithms. © 2024 SPIE.

关键词： Space time adaptive processing

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The Immense Impact of Reverse Edges on Large Hierarchical Networks

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engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

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Stroboscopic x-ray diffraction microscopy of dynamic strain in diamond thin-film bulk acoustic resonators for quantum control of nitrogen-vacancy centers

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Physical Review Applied 2024年第2期22卷 024016-024016页

作者： Anthony D’Addario Johnathan Kuan Noah F. Opondo Ozan Erturk Tao Zhou Sunil A. Bhave Martin V. Holt Gregory D. Fuchs Department of Physics Elmore Family School of Electrical and Computer Engineering Center for Nanoscale Materials School of Applied and Engineering Physics Kavli Institute at Cornell for Nanoscale Science Ithaca New York USA

Bulk-mode acoustic waves in a crystalline material exert lattice strain through the thickness of the sample, which couples to the spin Hamiltonian of defect-based qubits such as the nitrogen-vacancy (N-V) center defect in diamond. This mechanism has previously been harnessed for unconventional quantum spin control, spin decoherence protection, and quantum sensing. Bulk-mode acoustic wave devices are also important in the microelectronics industry as microwave filters. A key challenge in both applications is a lack of appropriate operando microscopy tools for quantifying and visualizing gigahertz-frequency dynamic strain. In this work, we directly image acoustic strain within N-V center-coupled diamond thin-film bulk acoustic wave resonators using stroboscopic scanning hard x-ray diffraction microscopy at the Advanced Photon Source. The far-field scattering patterns of the nanofocused x-ray diffraction encode strain information entirely through the illuminated thickness of the resonator. These patterns have a real-space spatial variation that is consistent with the bulk strain’s expected modal distribution and a momentum-space angular variation from which the strain amplitude can be quantitatively deduced. We also perform optical measurements of strain-driven Rabi precession of of the N-V center spin ensemble, providing an additional quantitative measurement of the strain amplitude. As a result, we directly measure one of the six N-V spin-stress coupling parameters, b=2.73(2) MHz/GPa, by correlating these measurements at the same spatial position and applied microwave power. Our results demonstrate a unique technique for directly imaging ac lattice strain in micromechanical structures and provide a direct measurement of a fundamental constant for the N-V center defect spin Hamiltonian.

关键词： Quantum control Spin dynamics Strain Stress Micromechanical devices Nitrogen vacancy centers in diamond Acoustic techniques X-ray diffraction

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Stable Task Allocation with Simulated Annealing in Taxi Ride-hailing Services

Stable Task Allocation with Simulated Annealing in Taxi Ri...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Lv, Jingwei Wei, Ziyu Yao, Shuzhen Tan, Huobin School of Computer Science and Engineering Beihang University Beijing100191 China School of Physics Beihang University Beijing100191 China School of Software Beihang University Beijing100191 China

ISBN: (纸本)9789811966125

Taxis have been becoming an important part of the urban public transportation and the ride-hailing systems offer services that would make life more convenient. One of the most important tasks in a taxi ride-hailing platform is to match the passengers and drivers into pairs. However, the current technologies focus more on overall interests but less on personal preferences of passengers and drivers. We proposed a novel algorithm based on Simulated Annealing to achieve the tradeoff between stable matching and the total pickup distance. Compared to a baseline approach for stable matching, the total distance decreases significantly, while the proportion of unstable pairs can still be controlled within a certain range. Finally, evaluations on experimental data verified the effectiveness of the algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Simulated annealing

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CrisisHateMM: Multimodal Analysis of Directed and Undirected Hate Speech in Text-Embedded Images from Russia-Ukraine Conflict

CrisisHateMM: Multimodal Analysis of Directed and Undirected...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Bhandari, Aashish Shah, Siddhant B. Thapa, Surendrabikram Naseem, Usman Nasim, Mehwish Delhi Technological University Department of CSE India Virginia Tech Department of Computer Science United States School of Computer Science The University of Sydney Australia School of Physics Mathematics and Computing The University of Western Australia Australia College of Science and Engineering Flinders University Australia

ISBN: (纸本)9798350302493

Text-embedded images are frequently used on social media to convey opinions and emotions, but they can also be a medium for disseminating hate speech, propaganda, and extremist ideologies. During the Russia-Ukraine war, both sides used text-embedded images extensively to spread propaganda and hate speech. To aid in moderating such content, this paper introduces CrisisHateMM, a novel multimodal dataset of over 4,700 text-embedded images from the Russia-Ukraine conflict, annotated for hate and non-hate speech. The hate speech is annotated for directed and undirected hate speech, with directed hate speech further annotated for individual, community, and organizational targets. We benchmark the dataset using unimodal and multimodal algorithms, providing insights into the effectiveness of different approaches for detecting hate speech in text-embedded images. Our results show that multimodal approaches outperform unimodal approaches in detecting hate speech, highlighting the importance of combining visual and textual features. This work provides a valuable resource for researchers and practitioners in automated content moderation and social media analysis. The CrisisHateMM dataset and codes are made publicly available at https://***/aabhandari/CrisisHateMM. © 2023 IEEE.

关键词： Modal analysis

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physics-informed neural networks for classical and quantum communication with multimode fibers

Physics-informed neural networks for classical and quantum c...

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2024 Frontiers in Optics, FiO 2024

作者： Zhang, Qian Sun, Jiali Rothe, Stefan Czarske, Jürgen Faculty of Electrical and Computer Engineering TU Dresden Dresden01069 Germany Department of Applied Physics Yale University New HavenCT06520 United States Institute of Applied Physics School of Science TU Dresden Germany

physics-informed neural networks provide mode decomposition based on intensity-only images in high-dimensional quantum communication using multimode fibers. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Multimode fibers

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