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Sequential-Scanning Dual-Energy CT Imaging Using High Temporal Resolution Image Reconstruction and Error-Compensated Material Basis Image Generation

arXiv

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arXiv 2024年

作者： Li, Qiaoxin Chen, Ruifeng Wang, Peng Quan, Guotao Du, Yanfeng Liang, Dong Li, Yinsheng Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Laboratory of Image Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China CT RPA Department United Imaging Healthcare Company Ltd. Shanghai China Research Center for Medical Artificial Intelligence Key Laboratory of Biomedical Imaging Science and System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Dual-energy computed tomography (DECT) has been widely used to obtain quantitative elemental composition of imaged subjects for personalized and precise medical diagnosis. Compared with DECT leveraging advanced X-ray source and/or detector technologies, the use of the sequential-scanning data acquisition scheme to implement DECT may make a broader impact on clinical practice because this scheme requires no specialized hardware designs and can be directly implemented into conventional CT systems. However, since the concentration of iodinated contrast agent in the imaged subject varies over time, sequentially scanned data sets acquired at two tube potentials are temporally inconsistent. As existing material basis image reconstruction approaches assume that the data sets acquired at two tube potentials are temporally consistent, the violation of this assumption results in inaccurate quantification of material concentration. In this work, we developed sequential-scanning DECT imaging using high temporal resolution image reconstruction and error-compensated material basis image generation, ACCELERATION in short, to address the technical challenge induced by temporal inconsistency of sequentially scanned data sets and improve quantification accuracy of material concentration in sequential-scanning DECT. ACCELERATION has been validated and evaluated using numerical simulation data sets generated from clinical human subject exams and experimental human subject studies. Results demonstrated the improvement of quantification accuracy and image quality using ACCELERATION. Copyright © 2024, The Authors. All rights reserved.

关键词： computerized tomography

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Interconnectedness of Circular Economy, Artificial Intelligence and Vertical Farming as a Technosignature Model for Sustainable Space Food Production

Interconnectedness of Circular Economy, Artificial Intellige...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Ronnie Concepcion II Carlo Cabaccan Jullian Dominic Ducut R-Jay Relano Jonnel Alejandrino Ronnel Agulto Argel Bandala Roslina Mohamad Elmer Dadios Alvin Culaba Department of Manufacturing Engineering and Management De La Salle University Manila Philippines Intelligent Systems Research Unit Center for Engineering and Sustainability Development Research De La Salle University Manila Philippines Bio-energy Environment and Renewables Laboratory Center for Engineering and Sustainability Development Research De La Salle University Manila Philippines Plant and Soil Health Research Unit Center for Natural Sciences and Environmental Research De La Salle University Manila Philippines School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines Wireless High Speed Network Research Group (WHiSNet) School of Electrical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Malaysia Department of Electronics and Computer Engineering De La Salle University Manila Philippines Department of Mechanical Engineering De La Salle University Manila Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

The degradation of Earth's natural resources and environmental health due to improper anthropological activities, mainly through unoptimized food production, is manifested through climate change that negatively alters the productivity in the agricultural sector. Because of this challenge in open terrestrial agriculture, the concept of space farming is believed to be significant in delivering fresh and quality food for the people on top of providing for astronauts and making them healthy in space. However, another problem emerges in the view of circular economy (CE) which is a production and consumption idealism that avoids degradation of natural resources and environment in producing one food unit and despite the collaborative advances in space technology, particularly in astrobotany, the materialization of CE and the absolutism of artificial intelligence (AI) are still partly imaginary. This study employed a bibliometric, scientometric and systematic analysis to highlight the advantages, limitations, and connections of CE, AI, and vertical farming (VF) which led to establishing a technosignature model for sustainable space food production. Here, CE, AI and VF were singularized as a model for sustainable space food production and identified their potential interplay. Based on scientific correlations, it is undoubtedly true that CE, AI, and VF have existing mutualism in Earth-based systems, but the degree of its importance as a whole is not yet completely appreciated for space farming. The developed CE-AI-VF technosignature model is scalable and adaptable if the government and scientific community put it into action accompanied by science policies.

关键词： Natural resources Degradation Productivity Adaptation models systematics Space technology Bibliometrics

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FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence

TechRxiv

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TechRxiv 2024年

作者： Wu, Zhiyuan Sun, Sheng Wang, Yuwei Liu, Min Xu, Ke Wang, Wen Jiang, Xuefeng Gao, Bo Lu, Jinda Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Computer and Information Technology The Engineering Research Center of Network Management Technology for High-Speed Railway Ministry of Education Beijing Jiaotong University Beijing China School of Information Science and Technology University of Science and Technology of China Hefei China

Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data sources. To protect data privacy and unify data silos distributed among end devices in EI, Federated Learning (FL) is proposed for collaborative training of shared AI models across multiple devices without compromising data privacy. However, the prevailing FL approaches cannot guarantee model generalization and adaptation on heterogeneous clients. Recently, Personalized Federated Learning (PFL) has drawn growing awareness in EI, as it enables a productive balance between local-specific training requirements inherent in devices and global-generalized optimization objectives for satisfactory performance. However, most existing PFL methods are based on the Parameters Interaction-based Architecture (PIA) represented by FedAvg, which suffers from unaffordable communication burdens due to large-scale parameters transmission between devices and the edge server. In contrast, Logits Interaction-based Architecture (LIA) allows to update model parameters with logits transfer and gains the advantages of communication lightweight and heterogeneous on-device model allowance compared to PIA. Nevertheless, previous LIA methods attempt to achieve satisfactory performance either relying on unrealistic public datasets or increasing communication overhead for additional information transmission other than logits. To tackle this dilemma, we propose a knowledge cache-driven PFL architecture, named FedCache, which reserves a knowledge cache on the server for fetching personalized knowledge from the samples with similar hashes to each given on-device sample. During the training phase, ensemble distillation is applied to on-device models for constructive optimization with personalized knowledge transferred from the server-side knowledge cache. Empirical experiments on four datasets demonstrate that FedCache achiev

关键词： Federated learning

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When Sign Language Meets Semantic Communications

When Sign Language Meets Semantic Communications

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Vasileios Kouvakis Stylianos E. Trevlakis Alexandros-Apostolos A. Boulogeorgos Theodoros Tsiftsis Keshav Singh Nan Qi Department of Research and Development InnoCube P.C. 17th Noemvriou 79 Thessaloniki Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics & Telecommunications University of Thessaly Lamia Greece Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

This paper presents an American sign language (ASL) semantic communications scheme. The scheme consists of a semantic encoder that leverages a convolutional neural network to effectively utilize the ASL alphabet. The encoded information is transmitted with the 24-QAM quadrature amplitude modulation (QAM). Additionally, this paper introduces a dataset that involves the overlaying of red-green-blue landmarks and key-points onto the acquired images, thereby augmenting the depiction of hand posture. The quantification of the proposed system’s training, testing, and communication performance is accomplished through numerical results, which serve to emphasize the attainable benefits and stimulate meaningful discussions.

关键词： Training Hands Sign language Quadrature amplitude modulation Assistive technologies Semantic communication Numerical models Convolutional neural networks Testing Land mobile radio

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial network

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computer systems Science & engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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Reliability-Latency-Rate Tradeoff in Low-Latency Communications with Finite-Blocklength Coding

arXiv

引用

arXiv 2023年

作者： Li, Lintao Chen, Wei Popovski, Petar Letaief, Khaled B. Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of Space Network and Communications The Beijing National Research Center for Information Science and Technology Beijing100084 China Department of Electronic Systems Aalborg University Aalborg9220 Denmark Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong

Low-latency communication plays an increasingly important role in delay-sensitive applications by ensuring the real-time information exchange. However, due to the constraint on the maximum instantaneous power, guaranteeing bounded latency is challenging. In this paper, we investigate the reliability-latency-rate tradeoff in low-latency communication systems with finite-blocklength coding (FBC). Specifically, we are interested in the fundamental tradeoff between error probability, delay-violation probability (DVP), and service rate. Based on the effective capacity (EC), we present the gain-conservation equations to characterize the reliability-latency-rate tradeoffs in low-latency communication systems. In particular, we investigate the low-latency transmissions over an additive white Gaussian noise (AWGN) channel and a Nakagami-m fading channel. By defining the service rate gain, reliability gain, and real-time gain, we conduct an asymptotic analysis to reveal the fundamental reliability-latency-rate tradeoff of ultra-reliable and low-latency communications in the high signal-to-noise-ratio (SNR) regime. To analytically evaluate and optimize the quality-of-service-constrained throughput of low-latency communication systems adopting FBC, an EC-approximation method is conceived to derive the closed-form expression of that throughput. Our results may offer some insights into the efficient scheduling of low-latency wireless communications, in which statistical latency and reliability metrics are crucial. © 2023, CC BY.

关键词： Asymptotic analysis

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SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention

arXiv

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arXiv 2023年

作者： Xu, Rui Liu, Zhi Luo, Yong Hu, Han Shen, Li Du, Bo Kuang, Kaiming Yang, Jiancheng The School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Hubei Luojia Laboratory Wuhan China The Department of Computer and Network Engineering The University of Electro-Communications 1-5-1 Chofugaoka Chofushi Tokyo182-8585 Japan The School of Information and Electronics Beijing Insititute of Technology Beijing100081 China The JD Explore Academy Beijing China The Dianei Technology Shanghai200120 China The Shanghai Jiao Tong University Shanghai China Dianei Technology Shanghai China EPFL Lausanne Switzerland

Lung cancer is the leading cause of cancer death worldwide. The best solution for lung cancer is to diagnose the pulmonary nodules in the early stage, which is usually accomplished with the aid of thoracic computed tomography (CT). As deep learning thrives, convolutional neural networks (CNNs) have been introduced into pulmonary nodule detection to help doctors in this labor-intensive task and demonstrated to be very effective. However, the current pulmonary nodule detection methods are usually domain-specific, and cannot satisfy the requirement of working in diverse real-world scenarios. To address this issue, we propose a slice grouped domain attention (SGDA) module to enhance the generalization capability of the pulmonary nodule detection networks. This attention module works in the axial, coronal, and sagittal directions. In each direction, we divide the input feature into groups, and for each group, we utilize a universal adapter bank to capture the feature subspaces of the domains spanned by all pulmonary nodule datasets. Then the bank outputs are combined from the perspective of domain to modulate the input group. Extensive experiments demonstrate that SGDA enables substantially better multi-domain pulmonary nodule detection performance compared with the state-of-the-art multi-domain learning methods. © 2023, CC BY.

关键词： Convolutional neural networks

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Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

SSRN

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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform(MEMIP)

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Ecological Processes 2022年第1期11卷 222-237页

作者： Cuijuan Liao Yizhao Chen Jingmeng Wang Yishuang Liang Yansong Huang Zhongyi Lin Xingjie Lu Yuanyuan Huang Feng Tao Danica Lombardozzi Almut Arneth Daniel SGoll Atul Jain Stephen Sitch Yanluan Lin Wei Xue Xiaomeng Huang Yiqi Luo Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijingChina Joint Innovation Center for Modern Forestry Studies College of Biology and the EnvironmentNanjing Forestry UniversityNanjingChina Department of Geography University of CambridgeCambridgeUK School of Life Science Nanjing UniversityNanjingChina Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Guangdong Province Key Laboratory for Climate Change and Natural Disaster StudiesSchool of Atmospheric SciencesSun Yat-Sen UniversityGuangzhouChina CSIRO Oceans and Atmosphere Aspendale 3195Australia Terrestrial Sciences Section Climate and Global DynamicsNational Center for Atmospheric ResearchBoulderCO 80305USA Karlsruhe Institute of Technology Institute of Meteorology and Climate Research/Atmospheric Environmental ResearchGarmisch-PartenkirchenGermany UniversitéParis Saclay CEA-CNRS-UVSQLSCE/IPSLGif sur YvetteFrance Department of Atmospheric Sciences University of IllinoisUrbanaILUSA College of Life and Environmental Sciences University of ExeterExeterUK Department of Computer Science and Technology Tsinghua UniversityBeijingChina Center for Ecosystem Science and Society Department of Biological SciencesNorthern Arizona UniversityFlagstaffAZUSA

Background:Large uncertainty in modeling land carbon(C)uptake heavily impedes the accurate prediction of the global C *** the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth system Models(ESMs).Here we present a Matrix-based Ensemble Model Inter-comparison Platform(MEMIP)under a unified model traceability framework to evaluate multiple soil organic carbon(SOC)*** the MEMIP,we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter(SOM)*** comparing the model outputs from the C-only and CN modes,the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly ***:Results showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation(1900–2000).The SOC difference between the multi-layer models was remarkably higher than between the single-layer *** analysis indicated that over 80%of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes,while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth ***:The output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction,especially between models with similar process *** analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model *** stressed the importance of analyzing ensemble outputs from the fundamental model structures,and holding a holistic view in understanding the en

关键词： Soil organic carbon Inter-model comparison Uncertainty analysis Carbon-nitrogen coupling Vertical resolved soil biogeochemistry structure

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Stochastic-Robust Planning of networked Hydrogen-Electrical Microgrids: A Study on Induced Refueling Demand

arXiv

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arXiv 2024年

作者： Sun, Xunhang Cao, Xiaoyu Zeng, Bo Zhai, Qiaozhu Başar, Tamer Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States Smart Integrated Energy Department Sichuan Digital Economy Industry Development Research Institute Sichuan Chengdu610037 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

Hydrogen-electrical microgrids are increasingly assuming an important role on the pathway toward decarbonization of energy and transportation systems. This paper studies networked hydrogen-electrical microgrids planning (NHEMP), considering a critical but often-overlooked issue, i.e., the demand-inducing effect (DIE) associated with infrastructure development decisions. Specifically, higher refueling capacities will attract more refueling demand of hydrogen-powered vehicles (HVs). To capture such interactions between investment decisions and induced refueling demand, we introduce a decision-dependent uncertainty (DDU) set and build a trilevel stochastic-robust formulation. The upper-level determines optimal investment strategies for hydrogen-electrical microgrids, the lower-level optimizes the risk-aware operation schedules across a series of stochastic scenarios, and, for each scenario, the middle-level identifies the "worst" situation of refueling demand within an individual DDU set to ensure economic feasibility. Then, an adaptive and exact decomposition algorithm, based on Parametric Column-and-Constraint Generation (PC&CG), is customized and developed to address the computational challenge and to quantitatively analyze the impact of DIE. Case studies on an IEEE exemplary system validate the effectiveness of the proposed NHEMP model and the PC&CG algorithm. It is worth highlighting that DIE can make an important contribution to the economic benefits of NHEMP, yet its significance will gradually decrease when the main bottleneck transits to other system restrictions. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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