检索结果-内蒙古大学图书馆

A privacy-preserving platform for recording COVID-19 vaccine passports

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Barati, Masoud Buchanan, William J. Lo, Owen Rana, Omer Blockpass ID Lab Edinburgh Napier University Edinburgh United Kingdom School of Computer Science & Informatics Cardiff University Cardiff United Kingdom

Digital vaccine passports are one of the main solutions which would allow the restart of travel in a post COVID-19 world. Trust, scalability and security are all key challenges one must overcome in implementing a vaccine passport. Initial approaches attempt to solve this problem by using centralised systems with trusted authorities. However, sharing vaccine passport data between different organisations, regions and countries has become a major challenge. This paper designs a new platform architecture for creating, storing and verifying digital COVID-19 vaccine certifications. The platform makes use of InterPlanetary File System (IPFS) to guarantee there is no single point of failure and allow data to be securely distributed globally. Blockchain and smart contracts are also integrated into the platform to define policies and log access rights to vaccine passport data while ensuring all actions are audited and verifiably immutable. Our proposed platform realises General Data Protection Regulation (GDPR) requirements in terms of user consent, data encryption, data erasure and accountability obligations. We assess the scalability and performance of the platform using IPFS and Blockchain test networks. © 2021, CC BY.

关键词： Smart contract

Novel Power-Imbalanced Dense Codebooks for Reliable Multiplexing in Nakagami Channels

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Gui, Yiming Liu, Zilong Yu, Lisu Li, Chunlei Fan, Pingzhi Xihua University Chengdu610039 China The School of Computer Science and Electronics Engineering University of Essex United Kingdom The School of Information Engineering Nanchang University Nanchang330031 China The Department of Informatics Selmer Center University of Bergen Norway The Key Lab of Information Coding and Transmission Southwest Jiaotong University Chengdu China

This paper studies enhanced dense code multiple access (DCMA) system design for downlink transmission over the Nakagami-m fading channels. By studying the DCMA pairwise error probability (PEP) in a Nakagami-m channel, a novel design metric called minimum logarithmic sum distance (MLSD) is first derived. With respect to the proposed MLSD, we introduce a new family of power-imbalanced dense codebooks by deleting certain rows of a special non-unimodular circulant matrix. Simulation results demonstrate that our proposed dense codebooks lead to both larger minimum Euclidean distance and MLSD, thus yielding significant improvements of error performance over the existing sparse code multiple access and conventional unimodular DCMA schemes in Nakagami-m fading channels under different overloading factors. Copyright © 2023, The Authors. All rights reserved.

关键词： Fading channels

B-SPLINE CNNS ON LIE GROUPS 8

学校读者我要写书评

暂无评论

B-SPLINE CNNS ON LIE GROUPS

8th International Conference on Learning Representations, ICLR 2020

作者： Bekkers, Erik J. Amsterdam Machine Learning Lab Informatics Institute University of Amsterdam Netherlands Centre for Analysis and Scientific Computing Applied Mathematics and Computer Science Eindhoven University of Technology Netherlands

Group convolutional neural networks (G-CNNs) can be used to improve classical CNNs by equipping them with the geometric structure of groups. Central in the success of G-CNNs is the lifting of feature maps to higher dimensional disentangled representations in which data characteristics are effectively learned, geometric data-augmentations are made obsolete, and predictable behavior under geometric transformations (equivariance) is guaranteed via group theory. Currently, however, the practical implementations of G-CNNs are limited to either discrete groups (that leave the grid intact) or continuous compact groups such as rotations (that enable the use of Fourier theory). In this paper we lift these limitations and propose a modular framework for the design and implementation of G-CNNs for arbitrary Lie groups. In our approach the differential structure of Lie groups is used to expand convolution kernels in a generic basis of B-splines that is defined on the Lie algebra. This leads to a flexible framework that enables localized, atrous, and deformable convolutions in G-CNNs by means of respectively localized, sparse and non-uniform B-spline expansions. The impact and potential of our approach is studied on two benchmark datasets: cancer detection in histopathology slides in which rotation equivariance plays a key role and facial landmark localization in which scale equivariance is important. In both cases, G-CNN architectures outperform their classical 2D counterparts and the added value of atrous and localized group convolutions is studied in detail. © 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： Lie groups

StackTHP: A stacking ensemble model for accurate prediction of tumor-homing peptides in cancer therapy

学校读者我要写书评

暂无评论

computers in Biology and Medicine 2025年 189卷 109958-109958页

作者： Raihan, Fazla Rabby Abdulrazak, Lway Faisal Rahman, Md. Ashikur Ali, Md Mamun Ibrahim, Sobhy M. Ahmed, Kawsar Bui, Francis M. Mahmud, Imran Department of Software Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh Electrical Engineering Technical College Middle Technical University Baghdad Iraq Department of Computer Science Cihan University Sulaimaniya Kurdistan Region Sulaimaniya46001 Iraq Division of Biomedical Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Department of Biochemistry College of Science King Saud University P.O. Box: 2455 Riyadh11451 Saudi Arabia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh

The tumor-homing peptides (THPs) have emerged as one of the attractive resources for targeted cancer therapy, being able to bind and penetrate tumor cells selectively while ignoring adjacent healthy tissues. Therefore, the computational models to predict THPs became popular very rapidly, since laboratory methods are slow and resourceful. Herein, we are proposing StackTHP, a newly developed stacking-ensemble model aimed at further improving THP prediction accuracy. StackTHP implements multiple feature extraction methods, including amino acid composition (AAC), and pseudo amino acid composition (PAAC) together with classical machine learning classifiers like Extra Trees, Random Forest, and AdaBoost, while the logistic regression-based meta-classifier is used for the stacking framework. StackTHP outperformed all other models, producing an accuracy of 91.92 %, Matthew's correlation coefficient (MCC) of 0.8415, AUC of 0.977 on benchmark datasets, indicates that it is better than approaches attempted earlier and provides a robust solution for proceeding towards the discovery and development of peptide-based cancer therapies. Future research will focus on the application of StackTHP over more diverse sets of data along with some hybrid methods to enhance the prediction capability. The dataset and the code are available at the following link: https://***/Ashikur562/StackTHP. © 2025 The Authors

关键词： Prediction models

CCGRID 2023: A Holistic Approach to Inclusion and Belonging

学校读者我要写书评

暂无评论

CCGRID 2023: A Holistic Approach to Inclusion and Belonging

IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Beth Plale Preeti Malakar Meenakshi D'Souza Hemangee K. Kapoor Yogesh Simmhan Ilkay Altintas Manohar Swaminathan School of Informatics Computing and Engineering Indiana University Bloomington IN USA Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kalyanpur Kanpur India International Institute of Information Technology Bangalore Bengaluru Karnataka India Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati Assam India Department of Computational and Data Sciences Indian Institute of Science Bangalore India San Diego Supercomputer Center University of California San Diego MC USA Microsoft Research Lab - India Bengaluru Karnataka India

“CCGRID will act with responsibility as its primary consideration; with equity, diversity, and inclusion as its central goals.” from the CCGRID 2023 web site [1]

关键词：

The classical iterative HHL-based hemodynamic simulation quantum linear equation algorithm for abdominal aortic aneurysm

学校读者我要写书评

暂无评论

The European Physical Journal Special Topics 2024年 1-11页

作者： You, Yujie Zhou, Fei Yue, Yimin Qiu, Yue Wang, Xinhang Yu, Yangchao Li, Bing Li, Ran Zhang, Le College of Computer Science Sichuan University Chengdu China Jinan Institute of Quantum Technology Jinan China Jinan Branch Hefei National Laboratory Jinan China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu People’s Republic of China Med-X Center for Informatics Sichuan University Chengdu People’s Republic of China West China Hospital- SenseTime Joint Lab Chengdu People’s Republic of China Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China

The abdominal aortic aneurysm (AAA) rupture is an extremely dangerous surgical acute abdominal disease. An important preventive method for AAA rupture is to simulate the hemodynamic parameters of AAA for medical evaluation through computational fluid dynamics. To fill the gap between the application of quantum computing and the field of biomedical engineering, we report a classical iterative HHL-based quantum algorithm for AAA hemodynamic simulation, and provide a Hamiltonian simulation approach for the coefficient matrix of linearized one-dimensional hemodynamic equations. Simulation experiments on IBM’s Qiskit (virtual machine) show that our proposed algorithm cannot only significantly reduce quantum resource consumption, but also improve simulation accuracy compared with the HHL algorithm. In addition, we have established a search-based circuit mapping which considers the hardware structure of the ‘Xiaohong’ quantum computer and selects mapping circuits with lower noise to improve the accuracy of measured results.

关键词：

Predicting cognitive scores with graph neural networks through sample selection learning

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Hanik, Martin Demirtaş, Mehmet Arif Gharsallaoui, Mohammed Amine Rekik, Islem Zuse Institute Berlin Berlin Germany BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Tunis Tunisia BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Computing School of Science and Engineering University of Dundee Dundee United Kingdom

Analyzing the relation between intelligence and neural activity is of the utmost importance in understanding the working principles of the human brain in health and disease. In existing literature, functional brain connectomes have been used successfully to predict cognitive measures such as intelligence quotient (IQ) scores in both healthy and disordered cohorts using machine learning models. However, existing methods resort to flattening the brain connectome (i.e., graph) through vectorization which overlooks its topological properties. To address this limitation and inspired from the emerging graph neural networks (GNNs), we design a novel regression GNN model (namely RegGNN) for predicting IQ scores from brain connectivity. On top of that, we introduce a novel, fully modular sample selection method to select the best samples to learn from for our target prediction task. However, since such deep learning architectures are computationally expensive to train, we further propose a learning-based sample selection method that learns how to choose the training samples with the highest expected predictive power on unseen samples. For this, we capitalize on the fact that connectomes (i.e., their adjacency matrices) lie in the symmetric positive definite (SPD) matrix cone. Our results on full-scale and verbal IQ prediction outperforms comparison methods in autism spectrum disorder cohorts and achieves a competitive performance for neurotypical subjects using 3-fold cross-validation. Furthermore, we show that our sample selection approach generalizes to other learning-based methods, which shows its usefulness beyond our GNN architecture. © 2021, CC BY.

关键词： Forecasting

Benchmarking Neural Radiance Fields for Autonomous Robots: An Overview

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Ming, Yuhang Yang, Xingrui Wang, Weihan Chen, Zheng Feng, Jinglun Xing, Yifan Zhang, Guofeng School of Computer Science Hangzhou Dianzi University Hangzhou310018 China High-speed Aerodynamics Institute CARDC Mianyang621000 China Stevens Institute of Technology HobokenNJ07030 United States Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47405 United States City College of New York New YorkNY10031 United States School of Computer Science University of Bristol BristolBS8 1UB United Kingdom State Key Lab of CAD&CG Zhejiang University Hangzhou310058 China

Neural Radiance Fields (NeRF) have emerged as a powerful paradigm for 3D scene representation, offering high-fidelity renderings and reconstructions from a set of sparse and unstructured sensor data. In the context of autonomous robotics, where perception and understanding of the environment are pivotal, NeRF holds immense promise for improving performance. In this paper, we present a comprehensive survey and analysis of the state-of-the-art techniques for utilizing NeRF to enhance the capabilities of autonomous robots. We especially focus on the perception, localization and navigation, and decision-making modules of autonomous robots and delve into tasks crucial for autonomous operation, including 3D reconstruction, segmentation, pose estimation, simultaneous localization and mapping (SLAM), navigation and planning, and interaction. Our survey meticulously benchmarks existing NeRF-based methods, providing insights into their strengths and limitations. Moreover, we explore promising avenues for future research and development in this domain. Notably, we discuss the integration of advanced techniques such as 3D Gaussian splatting (3DGS), large language models (LLM), and generative AIs, envisioning enhanced reconstruction efficiency, scene understanding, decision-making capabilities. This survey serves as a roadmap for researchers seeking to leverage NeRFs to empower autonomous robots, paving the way for innovative solutions that can navigate and interact seamlessly in complex environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

Benchmarking Neural Radiance Fields for Autonomous Robots: An Overview

学校读者我要写书评

暂无评论

SSRN

SSRN 2024年

Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene representation, offering high-fidelity renderings and reconstructions from a set of sparse and unstructured sensor data. In the context of autonomous robotics, where perception and understanding of the environment are pivotal, NeRF holds immense promise for improving performance. In this paper, we present a comprehensive survey and analysis of the state-of-the-art techniques for utilizing NeRF to enhance the capabilities of autonomous robots. We especially focus on the perception, localization and navigation, and decision-making modules of autonomous robots and delve into tasks crucial for autonomous operation, including 3-dimensional reconstruction, segmentation, pose estimation, simultaneous localization and mapping, navigation and planning, and interaction. Our survey meticulously benchmarks existing NeRF-based methods, providing insights into their strengths and limitations. Moreover, we explore promising avenues for future research and development in this domain. Notably, we discuss the integration of advanced techniques such as 3-dimensional Gaussian splatting, large language models, and generative artificial intelligence, envisioning enhanced reconstruction efficiency, scene understanding, decision-making capabilities. This survey serves as a roadmap for researchers seeking to leverage NeRF to empower autonomous robots, paving the way for innovative solutions that can navigate and interact seamlessly in complex environments. © 2024, The Authors. All rights reserved.

关键词： Decision making