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2024 Frontiers in Optics, FiO 2024

作者： Endo, Mamoru Takahashi, Kazuma Nomura, Takefumi Sonoyama, Tatsuki Miki, Shigehito Yabuno, Masahiro Terai, Hirotaka Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States

Superconducting-nanostrip photon detectors with optical sampling method now function as true photon-number resolving detectors in real-time without multiplexing. We applied this technique for quantum state generation ... 详细信息

关键词： Quantum computers

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A Novel Optimization Technique for Designing Frequency Reconfigurable Pixelated Planar Antenna

A Novel Optimization Technique for Designing Frequency Recon...

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Antennas and Propagation Society International Symposium

作者： Tianrui Qiao Shiwen Tang Junhui Rao Yujie Zhang Chi-Yuk Chiu Qingsha S. Cheng Ross Murch Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Institute for Advanced Study The Hong Kong University of Science and Technology Hong Kong

A novel method incorporating joint perturbation sensitivity analysis is proposed in this paper to design frequency reconfigurable planar antennas with a pixelated surface. Using our method, an antenna design having two switched working frequency bands and omnidirectional radiation patterns, is optimized. The simulation results verify its efficacy and potential computation efficiency.

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Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach

arXiv

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arXiv 2024年

作者： Kakooei, Mohammad Solska, Klaudia Daoud, Adel Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Institute for Analytical Sociology Linköping University Sweden Center for Advanced Study in the Behavioral Sciences Stanford University United States

Reducing global poverty is a key objective of the Sustainable Development Goals (SDGs). Achieving this requires high-frequency, granular data to capture neighborhood-level changes, particularly in data scarce regions such as low- and middle-income countries. To fill in the data gaps, recent computer vision methods combining machine learning (ML) with earth observation (EO) data to improve poverty estimation. However, while much progress have been made, they often omit intra-annual variations, which are crucial for estimating poverty in agriculturally dependent countries. We explored the impact of integrating intra-annual NDVI information with annual multi-spectral data on model accuracy. To evaluate our method, we created a simulated dataset using Landsat imagery and nighttime light data to evaluate EO-ML methods that use intra-annual EO data. Additionally, we evaluated our method against the Demographic and Health Survey (DHS) dataset across Africa. Our results indicate that integrating specific NDVI-derived features with multi-spectral data provides valuable insights for poverty analysis, emphasizing the importance of retaining intra-annual information. © 2024, CC BY.

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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FedSNN: Training Slimmable Neural Network With Federated Learning in Edge Computing

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IEEE/ACM Transactions on Networking 2024年

作者： Xu, Yang Liao, Yunming Xu, Hongli Wang, Zhiyuan Wang, Lun Liu, Jianchun Qian, Chen University Of Science And Technology Of China School Of Computer Science And Technology Anhui Hefei230027 China University Of Science And Technology Of China Suzhou Institute For Advanced Study Jiangsu Suzhou215123 China University Of California At Santa Cruz Jack Baskin School Of Engineering Department Of Computer Science And Engineering Santa CruzCA95064 United States

To provide a flexible tradeoff between inference accuracy and resource requirement at runtime, the slimmable neural network (SNN), a single network executable at different widths with the same deploying and management cost as that of a single model, has been proposed. However, how to effectively train SNN among massive devices in edge computing without revealing their local data remains an open problem. To this end, we leverage a novel distributed machine learning paradigm, i.e., federated learning, to realize effective on-device SNN training. As current FL schemes often train only one model with fixed architecture, and the existing SNN training algorithm is resource-intensive, integrating FL and SNN is non-trivial. Furthermore, two intrinsic features in edge computing, i.e., data and system heterogeneity, exacerbate the difficulty. Motivated by this, we redesign the model distribution, local training, and model aggregation phases in traditional FL, and propose FedSNN, a framework that ensures all widths in SNN can obtain high accuracy with less resource consumption. Specifically, for devices with heterogeneous training capacities and data distributions, the parameter server will distribute each of them with one proper width for adaptive local training guided by their uploaded model features, and their trained models will be weighted-averaged using the proposed multi-width SNN aggregation to improve their statistical utility. Extensive experiments on a distributed testbed show that FedSNN improves the model accuracy by about 2.18%-8.1%, and accelerates training by about 1.31 × -6.84 ×, compared with existing solutions. © 1993-2012 IEEE.

关键词： Federated learning

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Stabilizing Non-Abelian Topological Order against Heralded Noise via Local Lindbladian Dynamics

arXiv

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arXiv 2024年

作者： Chirame, Sanket Prem, Abhinav Gopalakrishnan, Sarang Burnell, Fiona J. School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States School of Natural Sciences Institute for Advanced Study PrincetonNJ08540 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

An important open question for the current generation of highly controllable quantum devices is understanding which phases can be realized as stable steady-states under local quantum dynamics. In this work, we show how robust steady-state phases with both Abelian and non-Abelian mixed-state topological order can be stabilized against generic "heralded" noise using active dynamics that incorporate measurement and feedback, modeled as a fully local Lindblad master equation. These topologically ordered steady states are two-way connected to pure topologically ordered ground states using local quantum channels, and preserve quantum information for a time that is exponentially large in the system size. Specifically, we present explicit constructions of families of local Lindbladians for both Abelian (Z2) and non-Abelian (D4) topological order whose steady-states host mixed-state topological order when the noise is below a threshold strength. As the noise strength is increased, these models exhibit first-order transitions to intermediate mixed state phases where they encode robust classical memories, followed by (first-order) transitions to a trivial steady state at high noise rates. When the noise is imperfectly heralded, steady-state order disappears but our active dynamics significantly enhances the lifetime of the encoded logical information. To carry out the numerical simulations for the non-Abelian D4 case, we introduce a generalized stabilizer tableau formalism that permits efficient simulation of the non-Abelian Lindbladian dynamics. © 2024, CC BY.

关键词： Topology

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Adaptive Vehicle Control System for Improved Safety With Image Processing 5

Adaptive Vehicle Control System for Improved Safety With Ima...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Vijayalakshmi, N. Priya, L. Ramaraj, S. Nanjundan, Menakadevi Palanisamy, Satheesh Kumar Olalekan Salau, Ayodeji Braide, Sepiribo Lucky Alliance University Alliance School of Advanced Compuitng Department of Computer Science and Engineering Bangalore India Department of Ece Kinathukadavu Tamilnadu Coimbatore641202 India Karpagam College of Engineering Department of Computer Science and Engineering India Karpagam Institute of Technology Department of Ece Coimbatore India Bms Institute of Technology and Management Department of Ece Karnataka Bengaluru India Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Rivers State University Department of Electrical and Electronics Engineering Port Harcourt Nigeria

ISBN: (纸本)9798331542559

We propose the development of an Adaptive Vehicle Control (AVC) system using readily available components such as a Raspberry Pi microcontroller, a motor, a motor driver, and a Raspberry Pi camera. This system aims to enhance safety and convenience by automatically adjusting vehicle speed based on surrounding traffic conditions and recognizing relevant road signs like school zones, speed limits, and hospital zones. The process begins with real-time image capture using the Raspberry Pi camera module. These images are then analyzed using Python-based computer vision algorithms, primarily leveraging OpenCV. The image processing pipeline employs techniques such as object detection and template matching to identify specific road signs within the captured frames. Upon sign detection, the system interprets their meanings and determines the appropriate speed limits associated with them. Using the information from the detected signs and their corresponding speed limits, the system regulates the motor connected to the vehicle's throttle through GPIO pins on the Raspberry Pi. By adjusting the motor's speed, the AVC system ensures compliance with the specified speed limits across various road conditions. Safety measures are paramount, with fail-safe mechanisms implemented to prevent accidents in case of system errors or malfunctions. This AVC system offers a cost-effective and adaptable solution for integrating intelligent cruise control capabilities into vehicles. Leveraging the computational power of the Raspberry Pi and the versatility of image processing techniques, the system can adapt to diverse driving environments, thereby enhancing overall driving safety. This project contributes to the progression of smart transportation systems and lays the foundation for further advancements in autonomous driving technologies. © 2024 IEEE.

关键词： Adaptive cruise control

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A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective

arXiv

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arXiv 2023年

作者： Cui, Chenhang Ren, Yazhou Pu, Jingyu Li, Jiawei Pu, Xiaorong Wu, Tianyi Shi, Yutao He, Lifang School of Computer Science and Engineering Shenzhen Institute for Advanced Study China School of Mathematical Sciences School of Information and Communication Engineering} University of Electronic Science and Technology of China China Department of Computer Science and Engineering Lehigh University Bethlehem United States

Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consistent information while often neglecting the issue of redundancy across multiple views. This study presents a new approach called Sufficient Multi-View Clustering (SUMVC) that examines the multi-view clustering framework from an information-theoretic standpoint. Our proposed method consists of two parts. Firstly, we develop a simple and reliable multi-view clustering method SCMVC (simple consistent multi-view clustering) that employs variational analysis to generate consistent information. Secondly, we propose a sufficient representation lower bound to enhance consistent information and minimise unnecessary information among views. The proposed SUMVC method offers a promising solution to the problem of multi-view clustering and provides a new perspective for analyzing multi-view data. To verify the effectiveness of our model, we conducted a theoretical analysis based on the Bayes Error Rate, and experiments on multiple multi-view datasets demonstrate the superior performance of SUMVC. © 2023, CC BY-NC-ND.

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MR Spatiospectral Reconstruction Integrating Subspace Modeling and Self-Supervised Spatiotemporal Denoising

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zhao, Ruiyang Wang, Zepeng Anderson, Aaron Huesmann, Graham Lam, Fan University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and Technology United States University of Illinois Urbana-Champaign Department of Bioengineering Beckman Institute for Advanced Science and Technology United States University of Illinois Urbana-Champaign Beckman Institute for Advanced Science and Technology United States Carle Foundation Hospital United States University of Illinois Urbana-Champaign Department of Bioengineering Beckman Institute for Advanced Science and Technology IL61801 United States

We present a new method that integrates subspace modeling and a pre-learned spatiotemporal denoiser for reconstruction from highly noisy magnetic resonance spectroscopic imaging (MRSI) data. The subspace model imposes an explicit low-dimensional representation of the high-dimensional spatiospectral functions of interest for noise reduction, while the denoiser serves as a complementary spatiotemporal prior to constrain the subspace reconstruction. A self-supervised learning strategy was proposed to train a denoiser that can distinguish the spatio-temporally correlated signals from uncorrelated noise. An iterative reconstruction formalism was developed based on the Plug-and-Play (PnP)-ADMM framework to synergize the subspace constraint, plug-in denoiser and spatiospectral encoding model. We evaluated the proposed method using numerical simulations and in vivo data, demonstrating improved performance over state-of-the-art subspacebased methods. We also provided theoretical analysis on the utility of combining subspace projection and iterative denoising in terms of both algorithm convergence and performance. Our work demonstrated the potential of integrating self-supervised denoising priors and low-dimensional representations for high-dimensional imaging problems. © 2025 IEEE.

关键词： Magnetic resonance imaging

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Energy-optimal transport trajectory planning and online trajectory modification for holonomic robots

Energy-optimal transport trajectory planning and online traj...

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作者： Kim, Hongjun Kim, Byung Kook Department of Computer Engineering Daejeon University Daejeon Korea Republic of School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

To maximize energy efficiency, we suggest minimum-energy trajectory planning and online trajectory modification algorithms for three-wheeled omni-directional mobile robots (TOMRs). First, minimum-energy multisection trajectory planning for various constraints is performed using Pontryagin's minimum principle. Two strategies are established to formulate an efficient minimum-energy, multisection trajectory generation algorithm: an online trajectory modification and a control algorithm that maintains energy efficiency are investigated using novel heuristic functions. Compared with the conventional trajectory using the trapezoidal velocity profile, the generated transport trajectory can save energy. © 2020 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

关键词： Mobile robots

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