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Comparative assessment of established and deep learning-based segmentation methods for hippocampal volume estimation in brain magnetic resonance imaging analysis

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NMR in Biomedicine 2024年第9期37卷 e5169-e5169页

作者： Wang, Hsi-Chun Chen, Chia-Sho Kuo, Chung-Chin Huang, Teng-Yi Kuo, Kuei-Hong Chuang, Tzu-Chao Lin, Yi-Ru Chung, Hsiao-Wen Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Division of Medical Image Far Eastern Memorial Hospital New Taipei Taiwan School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

In this study, our objective was to assess the performance of two deep learning-based hippocampal segmentation methods, SynthSeg and TigerBx, which are readily available to the public. We contrasted their performance with that of two established techniques, FreeSurfer-Aseg and FSL-FIRST, using three-dimensional T1-weighted MRI scans (n = 1447) procured from public databases. Our evaluation focused on the accuracy and reproducibility of these tools in estimating hippocampal volume. The findings suggest that both SynthSeg and TigerBx are on a par with Aseg and FIRST in terms of segmentation accuracy and reproducibility, but offer a significant advantage in processing speed, generating results in less than 1 min compared with several minutes to hours for the latter tools. In terms of Alzheimer's disease classification based on the hippocampal atrophy rate, SynthSeg and TigerBx exhibited superior performance. In conclusion, we evaluated the capabilities of two deep learning-based segmentation techniques. The results underscore their potential value in clinical and research environments, particularly when investigating neurological conditions associated with hippocampal structures. © 2024 John Wiley & Sons Ltd.

关键词： Magnetic resonance imaging

Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments

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arXiv 2024年

作者： Liao, Haicheng Liu, Shangqian Li, Yongkang Li, Zhenning Wang, Chengyue Li, Yunjian Li, Shengbo Eben Xu, Chengzhong State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China Department of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China State Key Laboratory of Internet of Things for Smart City Department of Civil and Environmental Engineering and Computer and Information Science University of Macau China State Key Laboratory of Internet of Things for Smart City Department of Civil and Environmental Engineering University of Macau China Faculty of Innovation Engineering Macau University of Science and Technology China School of Vehicle and Mobility Tsinghua University Beijing China

In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. Traditional approaches often rely on computational methods such as time-series analysis. Our research diverges significantly by adopting an interdisciplinary approach that integrates principles of human cognition and observational behavior into trajectory prediction models for AVs. We introduce a novel "adaptive visual sector" mechanism that mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. Additionally, we develop a "dynamic traffic graph" using Convolutional Neural Networks (CNN) and Graph Attention Networks (GAT) to capture spatio-temporal dependencies among agents. Benchmark tests on the NGSIM, HighD, and MoCAD datasets reveal that our model (GAVA) outperforms state-of-the-art baselines by at least 15.2%, 19.4%, and 12.0%, respectively. Our findings underscore the potential of leveraging human cognition principles to enhance the proficiency and adaptability of trajectory prediction algorithms in AVs. The code for the proposed model is available at our Github. © 2024, CC BY-NC-SA.

关键词： Time series analysis

A Facile Approach for Fabricating Ordered Submicrometer-Wide Surface Patterns by Imprinting Polydimethylsiloxane Cracks

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A Facile Approach for Fabricating Ordered Submicrometer-Wide...

International Conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： Yang Bu Sheng Ni Levent Yobas Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR Division of Bioengineering The Hong Kong University of Science and Technology Hong Kong SAR

A novel and cost-effective method for creating ordered submicrometer-wide surface patterns using PDMS cracks is presented in this study. Cracks are first formed on a flat PDMS substrate by stretching an oxidized PDMS slab, and then the cracks are imprinted onto a target substrate to create uniform and ordered surface patterns. The resulting submicrometer-wide patterns are difficult to achieve using conventional fabrication methods limited by photolithography's resolution. Applications of the imprinted patterns, such as selective particle capture and DNA combing, are demonstrated. This approach offers a new way to utilize PDMS cracks and provides a simple and low-cost method for creating submicrometer-wide surface patterns with potential applications in various fields.

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Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy

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arXiv 2024年

作者： Huo, Wei Liu, Changxin Ding, Kemi Johansson, Karl Henrik Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures StockholmSE-10044 Sweden School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

This paper explores the cubic-regularized Newton method within a federated learning framework while addressing two major concerns: privacy leakage and communication bottlenecks. We propose the Differentially Private Federated Cubic Regularized Newton (DP-FCRN) algorithm, which leverages second-order techniques to achieve lower iteration complexity than first-order methods. We incorporate noise perturbation during local computations to ensure privacy. Furthermore, we employ sparsification in uplink transmission, which not only reduces the communication costs but also amplifies the privacy guarantee. Specifically, this approach reduces the necessary noise intensity without compromising privacy protection. We analyze the convergence properties of our algorithm and establish the privacy guarantee. Finally, we validate the effectiveness of the proposed algorithm through experiments on a benchmark dataset. © 2024, CC BY.

关键词： Federated learning

A Differential Dynamic Programming Framework for Inverse Reinforcement Learning

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arXiv 2024年

作者： Cao, Kun Xu, Xinhang Jin, Wanxin Johansson, Karl H. Xie, Lihua School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore School for Engineering of Matter Transport and Energy Arizona State University United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden

A differential dynamic programming (DDP)-based framework for inverse reinforcement learning (IRL) is introduced to recover the parameters in the cost function, system dynamics, and constraints from demonstrations. Different from existing work, where DDP was used for the inner forward problem with inequality constraints, our proposed framework uses it for efficient computation of the gradient required in the outer inverse problem with equality and inequality constraints. The equivalence between the proposed method and existing methods based on Pontryagin’s Maximum Principle (PMP) is established. More importantly, using this DDP-based IRL with an open-loop loss function, a closed-loop IRL framework is presented. In this framework, a loss function is proposed to capture the closed-loop nature of demonstrations. It is shown to be better than the commonly used open-loop loss function. We show that the closed-loop IRL framework reduces to a constrained inverse optimal control problem under certain assumptions. Under these assumptions and a rank condition, it is proven that the learning parameters can be recovered from the demonstration data. The proposed framework is extensively evaluated through four numerical robot examples and one real-world quadrotor system. The experiments validate the theoretical results and illustrate the practical relevance of the approach. © 2024, CC BY.

关键词： Cost functions

Blockchain Based Employee Rewarding System 16

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Blockchain Based Employee Rewarding System

16th IEEE International Conference on Industrial and Information Systems, ICIIS 2021

作者： Bandara, Assajie Illankoon, Anjana Jayaweera, Rasanga Dassanayake, Hashim Sri Lanka Telecom PLC R D Division Colombo Sri Lanka University of Peradeniya Department of Computer Engineering Peradeniya Sri Lanka University of Peradeniya Department of Electrical and Electronic Engineering Peradeniya Sri Lanka

ISBN: (纸本)9781665426374

In this technical era which changes every second, Blockchain plays an advanced role by extending its application in several areas such as telecommunication, networking, and mobile applications, so, now blockchain technology as a decentralized application is made an efficient, immutable, and secured platform to economical society in many aspects specially for database analysis and networking purposes. Recently, many organizations all around the world are using this technology to meet with the competitive business society because they understand the potential of this decentralized management platform. Although there are many systems existing and developing for bracing the bond between organization and customers, there is a lack of systems for employee evaluation. Decentralized Ethereum private blockchain network which this paper has described will enable company generated token transactions among the employees in the organization. The system allows an employee to reward a number of tokens to his subordinates on their performance base. © 2021 IEEE.

关键词： Blockchain

Joint UAV Deployment and Resource Allocation in THz-Assisted MEC-Enabled Integrated Space-Air-Ground Networks

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arXiv 2024年

作者： Tun, Yan Kyaw Dán, György Park, Yu Min Hong, Choong Seon The Department of Electronic Systems Aalborg University A. C. Meyers Vænge 15 København2450 Denmark The Division of Network and Systems Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Brinellvägen 8 Stockholm114 28 Sweden The Department of Computer Science and Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of

Multi-access edge computing (MEC)-enabled integrated space-air-ground (SAG) networks have drawn much attention recently, as they can provide communication and computing services to wireless devices in areas that lack terrestrial base stations (TBSs). Leveraging the ample bandwidth in the terahertz (THz) spectrum, in this paper, we propose MEC-enabled integrated SAG networks with collaboration among unmanned aerial vehicles (UAVs). We then formulate the problem of minimizing the energy consumption of devices and UAVs in the proposed MEC-enabled integrated SAG networks by optimizing tasks offloading decisions, THz sub-bands assignment, transmit power control, and UAVs deployment. The formulated problem is a mixed-integer nonlinear programming (MILP) problem with a non-convex structure, which is challenging to solve. We thus propose a block coordinate descent (BCD) approach to decompose the problem into four sub-problems: 1) device task offloading decision problem, 2) THz sub-band assignment and power control problem, 3) UAV deployment problem, and 4) UAV task offloading decision problem. We then propose to use a matching game, concave-convex procedure (CCP) method, successive convex approximation (SCA), and block successive upper-bound minimization (BSUM) approaches for solving the individual subproblems. Finally, extensive simulations are performed to demonstrate the effectiveness of our proposed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

Recent Advances on Static Passive EMSs for Fixed Wireless Access Applications

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Recent Advances on Static Passive EMSs for Fixed Wireless Ac...

Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Francesco Zardi Mohammad A. Hannan Gino Sorbello Renzo Azaro Giacomo Oliveri Andrea Massa DICAM - Department of Civil Environmental and Mechanical Engineering ELEDIA Research Center (ELEDIA@UniTN - University of Trento) Trento Italy Department of Electrical Electronic and Computer Engineering ELEDIA Research Center (ELEDIA@UniCT - University of Catania) Catania Italy ELEDIA Research Center (ELEDIA@UESTC - UESTC) School of Electronic Science and Engineering Chengdu China ELEDIA Research Center (ELEDIA@TSINGHUA - Tsinghua University) Beijing Haidian China School of Electrical Engineering Tel Aviv University Tel Aviv Israel

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

This paper explores the potential of passive, flat-patterned electromagnetic skins (EMSs) in enhancing point-to-point fixed wireless access links beyond the asymptotic free-space total path attenuation (TPA) limit. The work delves into the design methodologies, technological challenges, and potential applications of these proposed solutions. Additionally, selected proof-of-concept results to highlight the practical feasibility of the suggested solution strategy in real-world scenarios are discussed. The features and potentialities of the resulting architectural solution in non line-of-sight fixed wireless access scenarios are addressed.

关键词： Wireless communication Electric potential Design methodology Conferences Line-of-sight propagation Attenuation Antennas and propagation

Calibration of Ordinal Regression Networks

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arXiv 2024年

作者： Kim, Daehwan Chung, Haejun Jang, Ikbeom Department of Artificial Intelligence Semiconductor Engineering Hanyang University Seoul Korea Republic of Department of Artificial Intelligence Hanyang University Seoul Korea Republic of Department of Electronic Engineering Hanyang University Seoul Korea Republic of Division of Computer Engineering Hankuk University of Foreign Studies Yongin Korea Republic of Division of AI Data Convergence Hankuk University of Foreign Studies Yongin Korea Republic of Division of Language & AI Hankuk University of Foreign Studies Yongin Korea Republic of

Recent studies have shown that deep neural networks are not well-calibrated and often produce over-confident predictions. The miscalibration issue primarily stems from using cross-entropy in classifications, which aims to align predicted softmax probabilities with one-hot labels. In ordinal regression tasks, this problem is compounded by an additional challenge: the expectation that softmax probabilities should exhibit unimodal distribution is not met with cross-entropy. The ordinal regression literature has focused on learning orders and overlooked calibration. To address both issues, we propose a novel loss function that introduces ordinal-aware calibration, ensuring that prediction confidence adheres to ordinal relationships between classes. It incorporates soft ordinal encoding and ordinal-aware regularization to enforce both calibration and unimodality. Extensive experiments across four popular ordinal regression benchmarks demonstrate that our approach achieves state-of-the-art calibration without compromising classification accuracy. © 2024, CC BY-NC-SA.

关键词： Deep neural networks