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Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions

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arXiv 2022年

作者： Fan, Xin Li, Zi Li, Ziyang Wang, Xiaolin Liu, Risheng Luo, Zhongxuan Huang, Hao The DUT-RU International School of Information Science & Engineering The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China The Institute of Artificial Intelligence Guilin University of Electronic Technology Guilin541004 China The Department of Radiology Children’s Hospital of Philadelphia PhiladelphiaPA United States The Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States

Deformable image registration plays a critical role in various tasks of medical image analysis. A successful registration algorithm, either derived from conventional energy optimization or deep networks, requires tremendous efforts from computer experts to well design registration energy or to carefully tune network architectures with respect to medical data available for a given registration task/scenario. This paper proposes an automated learning registration algorithm (AutoReg) that cooperatively optimizes both architectures and their corresponding training objectives, enabling non-computer experts to conveniently find off-the-shelf registration algorithms for various registration scenarios. Specifically, we establish a triple-level framework to embrace the searching for both network architectures and objectives with a cooperating optimization. Extensive experiments on multiple volumetric datasets and various registration scenarios demonstrate that AutoReg can automatically learn an optimal deep registration network for given volumes and achieve state-of-the-art performance. The automatically learned network also improves computational efficiency over the mainstream UNet architecture from 0.558 to 0.270 seconds for a volume pair on the same configuration. Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture

Non-Gaussian Lagrangian Stochastic Model for Wind Field Simulation in the Surface Layer

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Advances in Atmospheric Sciences 2020年第1期37卷 90-104页

作者： Chao LIU Li FU Dan YANG David R.MILLER Junming WANG Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing UniversityChongqing 401331China School of Big Data and Software Engineering Chongqing UniversityChongqing 401331China Department of Natural Resources and Environment University of ConnecticutStorrsCT 06268USA Climate and Atmospheric Science Section Illinois State Water SurveyPrairie Research InstituteUniversity of Illinois at Urbana-ChampaignChampaignIL 61820USA

Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality,gene flow(through pollen drift),and plant disease transmission(spore dispersion).Although Lagrangian stochastic(LS)models describe stochastic wind behaviors,such models assume that wind velocities follow Gaussian ***,measured surface-layer wind velocities show a strong skewness and *** paper presents an improved model,a non-Gaussian LS model,which incorporates controllable non-Gaussian random variables to simulate the targeted non-Gaussian velocity distribution with more accurate skewness and *** velocity statistics generated by the non-Gaussian model are evaluated by using the field data from the Cooperative Atmospheric Surface Exchange Study,October 1999 experimental dataset and comparing the data with statistics from the original Gaussian *** show that the non-Gaussian model improves the wind trajectory simulation by stably producing precise skewness and kurtosis in simulated wind velocities without sacrificing other features of the traditional Gaussian LS model,such as the accuracy in the mean and variance of simulated *** improvement also leads to better accuracy in friction velocity(i.e.,a coupling of three-dimensional velocities).The model can also accommodate various non-Gaussian wind fields and a wide range of skewness–kurtosis ***,improved skewness and kurtosis in the simulated velocity will result in a significantly different dispersion for wind/particle ***,the non-Gaussian model is worth applying to wind field simulation in the surface layer.

关键词： Lagrangian stochastic model wind field simulation non-Gaussian wind velocity surface layer

Wireless Large AI Model: Shaping the AI-Native Future of 6G and Beyond

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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Li, Xinyi Zhang, Maojun Chen, Yixuan Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Jin, Richeng Huang, Yongming Feng, Wei Yang, Tingting Bai, Baoming Gao, Feifei Yang, Kun Liu, Yuanwen Muhaidat, Sami Yuen, Chau Huang, Kaibin Wong, Kai-Kit Niyato, Dusit Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Automation Southeast University Nanjing210096 China Tsinghua University Beijing100084 China Pengcheng Laboratory Shenzhen518066 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China The University of Hong Kong Hong Kong KU 6G Research Center Computer and Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Sorting

DeCoCo: Blockchain-based Decentralized Compensation of Digital Content Purchases

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DeCoCo: Blockchain-based Decentralized Compensation of Digit...

Blockchain, Robotics and AI for Networking Security Conference (BRAINS)

作者： Marcel Müller Jacek Aleksander Janczura Peter Ruppel Service-centric Networking Technische Universität Berlin Berlin Germany Department of Software Engineering CODE University of Applied Sciences Berlin Germany

ISBN: (数字)9781728170916

ISBN: (纸本)9781728170923

Current centralized e-commerce platforms have lock-in effects. These platform-binding effects are constructed towards the content creator and the consumer in the process of purchasing digital content. The lock-in effects originate from the situation in which a centralized platform offers a variety of value propositions towards content creators and consumers. In this article, we propose an approach to decentralize the value propositions and distribute them among different actors. Therefore, we develop a decentralized application based on smart contracts and the blockchain technology. With that application as a base, we offer an alternative to the current centralized marketplaces without lock-in effects.

关键词： Blockchain Smart Contract Decentralized Application Electronic Commerce electronic commerce Decentralized Digital content Locking Consumers

service Integration Methodology for Convergence service in Science and Technology 10th

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Service Integration Methodology for Convergence Service in S...

10th International Conference on Computer Science and its Applications, CSA 2018 and the 13th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2018

作者： Kang, YunHee Young Chul Kim, R. Choi, HeeSeok Division of Information and Communication Baekseok University 115 Anseo-dong CheonanChungnam330-704 Korea Republic of Department of Software and Communications Engineering Hongik University Seoul30016 Korea Republic of Convergence Service Center KISTI 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of

ISBN: (纸本)9789811393402

To facilitate the open science in research lifecycle, reconstructing individual services into modular and independent function blocks that can be reusable and interoperable. Over the years, more REST based web services have been deployed in the enterprise application that is integrated with services from different sources with existing functionality over HTTP. This results in an increasing interoperability and supporting seamless integration compared to traditional web services. Despite this trend, there are still no standards or guidelines about how to develop a RESTful web service for S&T knowledge infrastructure. In this paper we introduce the methodology for service integration for modelling conceptual architecture and understand the practices on the portal composed of services of convergence of S&T. In the research lifecycle, S&T services are used to support resource activities from developing ideas to sharing to proliferating research results. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Web services

QoSBERT: A Uncertainty-Aware Approach based on Pre-trained Language Models for service Quality Prediction

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arXiv 2025年

作者： Wang, Ziliang Zhang, Xiaohong Li, Ze Shi Yan, Meng Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Computer Science University of Victoria Canada

Accurate prediction of Quality of service (QoS) metrics is fundamental for selecting and managing cloud-based services. Traditional QoS models rely on manual feature engineering and yield only point estimates, offering no insight into the confidence of their predictions. In this paper, we present QoSBERT, a novel end-to-end framework that leverages pre-trained language models to automatically encode rich, natural-language descriptions of services and integrates a Monte Carlo Dropout–based uncertainty estimation mechanism to quantify predictive confidence. QoSBERT applies attentive pooling over contextualized embeddings and a lightweight multi-layer perceptron regressor, fine-tuned jointly to minimize absolute error. We further exploit the resulting uncertainty estimates to select high-quality training samples, improving robustness in low-resource settings. On standard QoS benchmark datasets, QoSBERT achieves an average reduction of 11.7% in MAE and 6.7% in RMSE for response time prediction, and 6.9% in MAE for throughput prediction compared to the strongest baselines, while providing well-calibrated confidence intervals for robust and trustworthy service quality estimation. Our approach not only advances the accuracy of service quality prediction but also delivers reliable uncertainty quantification, paving the way for more trustworthy, data-driven service selection and optimization. © 2025, CC0.

关键词： Network performance

The SA Management Scheme Based on Blockchain for Convergence service in S&T 10th

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The SA Management Scheme Based on Blockchain for Convergence...

10th International Conference on Computer Science and its Applications, CSA 2018 and the 13th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2018

ISBN: (纸本)9789811393402

A convergence service can be built by referring the specification of well-designed services and putting together them in bottom-up manner. In the service system for delivering an S&T convergence service, a service system needs to define a service agreement (SA) and to establish to support continuously and dependably the SA. In the paper we propose a SA management scheme that is used for storing the SA values co-created into the blockchain. The proposed scheme can provide a guideline for designing the S&T service system on SOA framework with a blockchain for providing information about SA as part of service metadata. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Blockchain

Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training

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arXiv 2022年

作者： Liu, Risheng Liu, Xuan Zeng, Shangzhi Zhang, Jin Zhang, Yixuan DUT-RU International School of Information Science and Engineering Dalian University of Technology Liaoning Dalian China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Liaoning Dalian China Peng Cheng Laboratory Guangdong Shenzhen China Department of Mathematics and Statistics University of Victoria VictoriaBC Canada Department of Mathematics SUSTech International Center for Mathematics Southern University of Science and Technology Guangdong Shenzhen China National Center for Applied Mathematics Shenzhen Guangdong Shenzhen China

Recently, Optimization-Derived Learning (ODL) has attracted attention from learning and vision areas, which designs learning models from the perspective of optimization. However, previous ODL approaches regard the training and hyper-training procedures as two separated stages, meaning that the hyper-training variables have to be fixed during the training process, and thus it is also impossible to simultaneously obtain the convergence of training and hyper-training variables. In this work, we design a Generalized Krasnoselskii-Mann (GKM) scheme based on fixed-point iterations as our fundamental ODL module, which unifies existing ODL methods as special cases. Under the GKM scheme, a Bilevel Meta Optimization (BMO) algorithmic framework is constructed to solve the optimal training and hyper-training variables together. We rigorously prove the essential joint convergence of the fixed-point iteration for training and the process of optimizing hyper-parameters for hyper-training, both on the approximation quality, and on the stationary analysis. Experiments demonstrate the efficiency of BMO with competitive performance on sparse coding and real-world applications such as image deconvolution and rain streak removal. © 2022, CC BY-SA.

关键词： Iterative methods

Dizygotic Conditional Variational AutoEncoder for multi-modal and partial modality absent few-shot learning

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arXiv 2021年

作者： Zhang, Yi Huang, Sheng Peng, Xi Yang, Dan Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing400044 China The School of Big Data and Software Engineering Chongqing University Chongqing400044 China Department of Computer and Information Sciences University of Delaware NewarkDE19716 United States

Data augmentation is a powerful technique for improving the performance of the few-shot classification task. It generates more samples as supplements, and then this task can be transformed into a common supervised learning issue for solution. However, most mainstream data augmentation based approaches only consider the single modality information, which leads to the low diversity and quality of generated features. In this paper, we present a novel multi-modal data augmentation approach named Dizygotic Conditional Variational AutoEncoder (DCVAE) for addressing the aforementioned issue. DCVAE conducts feature synthesis via pairing two Conditional Variational AutoEncoders (CVAEs) with the same seed but different modality conditions in a dizygotic symbiosis manner. Subsequently, the generated features of two CVAEs are adaptively combined to yield the final feature, which can be converted back into its paired conditions while ensuring these conditions are consistent with the original conditions not only in representation but also in function. DCVAE essentially provides a new idea of data augmentation in various multi-modal scenarios by exploiting the complement of different modality prior information. Extensive experimental results demonstrate our work achieves state-of-the-art performances on miniImageNet, CIFAR-FS and CUB datasets, and is able to work well in the partial modality absence case. © 2021, CC BY-NC-SA.

关键词： Learning systems