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Butler Matrix Enabled Multi-Beam Optical Phased Array for Two-Dimensional Beam-Steering 27

Butler Matrix Enabled Multi-Beam Optical Phased Array for Tw...

27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022

作者： Zhou, Zuoyu Xu, Weihan Li, Xinhang Lu, Liangjun Chen, Jianping Zhou, Linjie Shanghai Institute for Advanced Communication and Data Science State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai Key Lab of Navigation and Location Services Department of Electronic Engineering Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China

We demonstrate a multi-beam optical phased array based on a 4×4 Butler matrix. The device shows the capability of two-dimensional multi-beam steering with a pm 30^{circ}×8^{circ} field-of-view through a shar... 详细信息

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

关键词： Beamforming

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Feature-wise and Sample-wise Adaptive Transfer Learning for High-dimensional Linear Regression

arXiv

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arXiv 2024年

作者： He, Zelin Sun, Ying Liu, Jingyuan Li, Runze Department of Statistics Pennsylvania State University United States School of Electrical Engineering and Computer Science Pennsylvania State University United States MOE Key Laboratory of Econometrics Department of Statistics and Data Science School of Economics Wang Yanan Institute for Studies in Economics Fujian Key Lab of Statistics Xiamen University China

We consider the transfer learning problem in the high dimensional linear regression setting, where the feature dimension is larger than the sample size. To learn transferable information, which may vary across features or the source samples, we propose an adaptive transfer learning method that can detect and aggregate the feature-wise (F-AdaTrans) or sample-wise (S-AdaTrans) transferable structures. We achieve this by employing a fused-penalty, coupled with weights that can adapt according to the transferable structure. To choose the weight, we propose a theoretically informed, data-driven procedure, enabling F-AdaTrans to selectively fuse the transferable signals with the target while filtering out non-transferable signals, and S-AdaTrans to obtain the optimal combination of information transferred from each source sample. We show that, with appropriately chosen weights, F-AdaTrans achieves a convergence rate close to that of an oracle estimator with known transferable structure, and S-AdaTrans recovers existing near-minimax optimal rates as a special case. The effectiveness of the proposed method is validated using both synthetic and real data, demonstrating favorable performance compared to the existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Transfer learning

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ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism

arXiv

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arXiv 2024年

作者： Yu, Xinlei Jin, Hui Wu, Qing Elazab, Ahmed Jiang, Xinchen Shi, Qinglei Ge, Ruiquan Jia, Gangyong Wang, Changmiao School of Computer Science Hangzhou Dianzi University Hangzhou China School of Biomedical Engineering Shenzhen University Shenzhen China School of Management and Economics The Chinese University of Hong Kong Shenzhen Shenzhen China School of Medicine The Chinese University of Hong Kong Shenzhen Shenzhen China Medical Big Data Lab Shenzhen Research Institute of Big Data Shenzhen China

Intracerebral hemorrhage (ICH) is the most fatal subtype of stroke and is characterized by a high incidence of disability. Accurate segmentation of the ICH region and prognosis prediction are critically important for developing and refining treatment plans for post-ICH patients. However, existing approaches address these two tasks independently and predominantly focus on imaging data alone, thereby neglecting the intrinsic correlation between the tasks and modalities. This paper introduces a multi-task network, ICH-SCNet, designed for both ICH segmentation and prognosis classification. Specifically, we integrate a SAM-CLIP cross-modal interaction mechanism that combines medical text and segmentation auxiliary information with neuroimaging data to enhance cross-modal feature recognition. Additionally, we develop an effective feature fusion module and a multi-task loss function to improve performance further. Extensive experiments on an ICH dataset reveal that our approach surpasses other state-of-the-art methods. It excels in the overall performance of classification tasks and outperforms competing models in all segmentation task metrics. © 2024, CC BY.

关键词： Neuroimaging

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Q&A: Query-Based Representation Learning for Multi-Track Symbolic Music re-Arrangement

arXiv

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arXiv 2023年

作者： Zhao, Jingwei Xia, Gus Wang, Ye School of Computing NUS Singapore Institute of Data Science NUS Singapore Integrative Sciences and Engineering Programme NUS Graduate School Singapore Music X Lab NYU Shanghai China MBZUAI United Arab Emirates

Music rearrangement is a common music practice of reconstructing and reconceptualizing a piece using new composition or instrumentation styles, which is also an important task of automatic music generation. Existing studies typically model the mapping from a source piece to a target piece via supervised learning. In this paper, we tackle rearrangement problems via self-supervised learning, in which the mapping styles can be regarded as conditions and controlled in a flexible way. Specifically, we are inspired by the representation disentanglement idea and propose Q&A, a query-based algorithm for multi-track music rearrangement under an encoder-decoder framework. Q&A learns both a content representation from the mixture and function (style) representations from each individual track, while the latter queries the former in order to rearrange a new piece. Our current model focuses on popular music and provides a controllable pathway to four scenarios: 1) re-instrumentation, 2) piano cover generation, 3) orchestration, and 4) voice separation. Experiments show that our query system achieves high-quality rearrangement results with delicate multi-track structures, significantly outperforming the baselines. © 2023, CC BY.

关键词： Music

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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Canonical Shape Reconstruction with SE(3) Equivariance Learning for Weakly-Supervised Object Pose Estimation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhou, Jun Chen, Kai Wei, Mingqiang Zhang, Xiao-Ping Dou, Qi Qin, Jing Hong Kong Polytechnic University Center of Smart Health School of Nursing Hong Kong Hong Kong Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong Nanjing University of Aeronautics and Astronautics School of Computer Science and Technology Nanjing China Tsinghua University Shenzhen Ubiquitous Data Enabling Key Lab Shenzhen International Graduate School Shenzhen China

6D object pose estimation from a single RGB-D image is a fundamental problem in computer vision and robot manipulation. Despite recent advancements, existing methods still suffer several limitations. First of all, the object shape representation extracted from the depth map is often less expressive because the object point cloud parsed from the depth map is highly incomplete due to the object self-occlusion and noisy due to the sensor artifacts. This shape representation issue further intensifies when lacking sufficient labeled data for model training, which unfortunately is another typical problem for object pose estimation considering the heavy annotation cost for real-world pose labeling. In this study, we propose to tackle the above issues in a unified way. First, we enhance the object shape representation from the partial point cloud with a novel canonical shape reconstruction module, in which an implicit canonical frame is established by incorporating the SE(3) equivariance, achieving implicit feature alignment of the partial point cloud inputs, leading to robust shape recovery. Second, based on the enhanced object representation, we further utilize the decanonicalized and pose-dependent completed object shape as the training signal, and develop a novel weakly-supervised learning framework to leverage both labeled synthetic data and unlabeled real data to train the pose estimation model in a label-efficient way. Extensive experiments on three widely used benchmarks demonstrate the effectiveness, and superiority of our framework over state-of-the-art methods. © 1991-2012 IEEE.

关键词： Self-supervised learning

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Optimal Design of Color Laparoscopic Super-Resolution Image Quality Based on Generative Adversarial Networks

Optimal Design of Color Laparoscopic Super-Resolution Image ...

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Computer Graphics and Image Processing (CGIP), International Conference on

作者： Norifumi Kawabata Toshiya Nakaguchi Education and Research Center for Mathematical and Data Science Hokkaido University Sapporo Japan Research Division of Computational Imaging Computational Imaging Lab Sapporo Japan Center for Frontier Medical Engineering Chiba University Chiba Japan

The Generative Adversarial Networks (GAN) is unsupervised learning enabled to transform according to data characteristics, though this generate unreal data by learning characteristics from data. As past our study, we discussed from the viewpoint of image quality for super-resolution of color laparoscopic image including SRCNN (Super-Resolution Convolutional Neural Network). However, it was not enough to compare to other neural network methods in our discussion. We consider that it is possible to support the medical image diagnosis by measuring whether the difference of both neural network method and image contents is affected or not for image quality. In this paper, first we carried out the objective image quality assessment by designing optimally of color laparoscopic super-resolution image using Generative Adversarial Networks (GAN). And then, we discussed for performance between methods comparing to result of SRCNN. On the other hand, from a view of information science, we consider that we need to verify experimentally for affect between network learning effect and generated image, and then to improve method. Therefore, we also discussed relationship between image quality and learning effect in color laparoscopic image generation using SRGAN.

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ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism

ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognos...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinlei Yu Ahmed Elazab Ruiquan Ge Hui Jin Xinchen Jiang Gangyong Jia Qing Wu Qinglei Shi Changmiao Wang School of Computer Science Hangzhou Dianzi University Hangzhou China School of Biomedical Engineering Shenzhen University Shenzhen China School of Management and Economics The Chinese University of Hong Kong Shenzhen Shenzhen China School of Medicine The Chinese University of Hong Kong Shenzhen Shenzhen China Medical Big Data Lab Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

关键词： Neuroimaging Measurement Image segmentation Correlation Text recognition Refining Multitasking Prognostics and health management Hemorrhaging Biomedical imaging

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Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding Using Lstm and Seq2seq Lstm Surrogate Model for Real-Time Application: A Case Study for Norfolk, Virginia

SSRN

引用

SSRN 2023年

作者： Roy, Binata Goodall, Jonathan L. McSpadden, Diana Goldenberg, Steven Schram, Malachi Dept. of Civil and Environmental Engineering University of Virginia CharlottesvilleVA22904 United States Link Lab School of Engineering and Applied Science University of Virginia CharlottesvilleVA22904 United States Data Science Department Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States

In urban-coastal cities facing an elevated risk of nuisance flooding (caused by rain and tide) due to an increase in heavy rainfall, sea level rise, urbanization, and aging drainage systems that result in disruptions in transportation systems, real-time flood forecasting at the street scale can provide useful information to decision-makers. However, Physics-Based Models (PBMs) that offer high accuracy come with high computational runtimes and costs that limit their application for real-time flood forecasting. To address this challenge, we proposed a Machine Learning (ML) surrogate model trained from PBMs to provide street-scale flood forecasts. Previous related studies have focused on nowcasting, but our study explores forecasting water levels on streets with sufficient lead hours for effective preparedness against impending flood situations. In this study, we applied Long Short-Term Memory (LSTM) and sequence-to-sequence (seq2seq) LSTM – two state-of-art ML forecasting model architectures – to predict hourly flood depth for 22 flood-vulnerable streets in Norfolk, Virginia, with a 4-hour (short-term) and 8-hour (long-term) lead time. This study represents one of the first applications of LSTM and seq2seq LSTM as surrogate models for street-scale flooding forecasting, particularly for localized nuisance flooding, which involves more complex input-output relationships and unpredictable, faster response dynamics compared to other kinds of flooding. The surrogate models were trained with environmental (rainfall and tide) and topographic (Elevation, TWI, and DTW) features along with PBM (TUFLOW) derived water depths for different storm events. The results demonstrated satisfactory performance of both ML forecasting models throughout the forecast period when compared to the PBM output (short-term average RMSE ≤ 0.0293 m;long-term average RMSE ≤ 0.0373 m). However, the seq2seq LSTM model showed lower MAE/RMSE and higher correlation compared to the LSTM model across most le

关键词： Floods

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Admission Control with Latency Considerations for 5G Mobile Edge Computing

Admission Control with Latency Considerations for 5G Mobile ...

引用

IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Ye Zhang Wuyungerile Li Winston K.G. Seah Inner Mongolia Key Lab. of Wireless Networking & Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education School of Computer Science Inner Mongolia University Hohhot China Wireless Networks Research Group School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

The fifth generation (5G) mobile network is a new generation of broadband mobile communication technology with the potential to address the increasing demands of new user services and applications that have stringent low latency and high bandwidth requirements. Besides the enhanced mobile broadband (eMBB) which is an evolution of broadband services from previous generations, the ultra reliable low latency communication (URLLC) service comes with stringent delay requirements that are needed to support new applications like autonomous vehicles, augmented/virtual reality, etc. Mobile or multiple access edge computing (MEC) emerged to provide services and computing resources for users at the network edge to provide faster access speeds and lower end-to-end delays. To better meet user needs and maximize resource utilization, network resources need to be allocated and managed efficiently. Admission control for user requests is one of the methods used that can effectively prevent network congestion, thereby improving the overall performance of the system. In this paper, we propose a RED-based Admission Control with Latency Considerations (REDAL) algorithm for user admission control that aims to increase throughput, meet users’ delay requirements and reduce packet discard rate. By explicitly accounting for user traffic delay constraints and bandwidth requirements, we are able to meet the strict delay constraints of URLLC traffic while meeting the bandwidth requirements of eMBB traffic. We validate our approach in an MEC scenario to demonstrate high resource utilization and also keeping the request discard rate below 20%.

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