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arXiv 2022年

作者： Zhang, Tinghao Lam, Kwok-Yan Zhao, Jun Li, Feng Han, Huimei Jamil, Norziana The Strategic Centre for Research in Privacy-Preserving Technologies and Systems The School of Computer Science and Engineering at Nanyang Technological University Singapore The Strategic Centre for Research in Privacy-Preserving Technologies and Systems at Nanyang Technological University Singapore College of Information Engineering Zhejiang University of Technology Zhejiang Hangzhou310032 China College of Computing & Informatics University Tenaga Nasional Putrajaya Campus Selangor Malaysia

Machine learning (ML) is a widely accepted means for supporting customized services for mobile devices and applications. Federated Learning (FL), which is a promising approach to implement machine learning while addressing data privacy concerns, typically involves a large number of wireless mobile devices to collect model training data. Under such circumstances, FL is expected to meet stringent training latency requirements in the face of limited resources such as demand for wireless bandwidth, power consumption, and computation constraints of participating devices. Due to practical considerations, FL selects a portion of devices to participate in the model training process at each iteration. Therefore, the tasks of efficient resource management and device selection will have a significant impact on the practical uses of FL. In this paper, we propose a spectrum allocation optimization mechanism for enhancing FL over a wireless mobile network. Specifically, the proposed spectrum allocation optimization mechanism minimizes the time delay of FL while considering the energy consumption of individual participating devices;thus ensuring that all the participating devices have sufficient resources to train their local models. In this connection, to ensure fast convergence of FL, a robust device selection is also proposed to help FL reach convergence swiftly, especially when the local datasets of the devices are not independent and identically distributed (non-iid). Experimental results show that (1) the proposed spectrum allocation optimization method optimizes time delay while satisfying the individual energy constraints;(2) the proposed device selection method enables FL to achieve the fastest convergence on non-iid datasets. © 2022, CC BY.

关键词： Time delay

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Exponential Qubit Reduction in Optimization for Financial Transaction Settlement

arXiv

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arXiv 2023年

作者： Huber, Elias X. Tan, Benjamin Y.L. Griffin, Paul R. Angelakis, Dimitris G. ETH Zürich D-CHAB Interdisciplinary Sciences Vladimir-Prelog-Weg 1-5/10 Zurich8093 Switzerland Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore117543 Singapore AngelQ Quantum Computing 531A Upper Cross Street 04-95 Hong Lim Complex 051531 Singapore School of Computing and Information Systems Singapore Management University 80 Stamford Road Singapore178902 Singapore School of Electrical and Computer Engineering Technical University of Crete Chania73100 Greece

We extend the qubit-efficient encoding presented in [1] and apply it to instances of the financial transaction settlement problem constructed from data provided by a regulated financial exchange. Our methods are directly applicable to any QUBO problem with linear inequality constraints. Our extension of previously proposed methods consists of a simplification in varying the number of qubits used to encode correlations as well as a new class of variational circuits which incorporate symmetries thereby reducing sampling overhead, improving numerical stability and recovering the expression of the cost objective as a Hermitian observable. We also propose optimality-preserving methods to reduce variance in real-world data and substitute continuous slack variables. We benchmark our methods against standard QAOA for problems consisting of 16 transactions and obtain competitive results. Our newly proposed variational ansatz performs best overall. We demonstrate tackling problems with 128 transactions on real quantum hardware, exceeding previous results bounded by NISQ hardware by almost two orders of magnitude. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

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Entanglement as the cross-symmetric part of quantum discord

arXiv

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arXiv 2023年

作者： Xiong, Chunhe Kim, Sunho Kumar, Asutosh Chen, Zeyu Wu, Minghui Wu, Junde Interdisciplinary Center for Quantum Information School of Physics Zhejiang University Hangzhou310027 China School of Computer and Computing Science Hangzhou City University Hangzhou310015 China School of Mathematical Sciences Harbin Engineering University Harbin150001 China P.G. Department of Physics Gaya College Magadh University Gaya Rampur823001 India Vaidic and Modern Physics Research Centre Bhagal Bhim Jalore Bhinmal343029 India School of Mathematical Sciences Zhejiang University Hangzhou310027 China

In this paper we show that the minimal quantum discord over "cross-symmetric" state extensions is an entanglement monotone. In particular, we show that the minimal Bures distance of discord over cross-symmetric extensions is equivalent to the Bures distance of entanglement. At last, we refute a long-held but unstated convention that only contractive distances can be used to construct entanglement monotones by showing that the entanglement quantifier induced by the Hilbert-Schmidt distance, which is not contractive under quantum operations, is also an entanglement monotone. © 2023, CC BY-NC-ND.

关键词： Quantum entanglement

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Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting

arXiv

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arXiv 2022年

作者： He, Hui Zhang, Qi Yi, Kun Shi, Kaize Niu, Zhendong Cao, Longbing The School of Medical Technology Beijing Institute of Technology Beijing100081 China The School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China The Department of Computer Science Tongji University Shanghai201804 China The Data Science and Machine Intelligence Lab University of Technology SydneyNSW2007 Australia The Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education China The DataX Research Centre School of Computing Macquarie University SydneyNSW2109 Australia

Due to the non-stationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift. Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time. Existing methods address distribution drift via adapting to the latest arrived data or self-correcting per the meta knowledge derived from future data. Despite their great success in MTS forecasting, these methods hardly capture the intrinsic distribution changes, especially from a distributional perspective. Accordingly, we propose a novel framework temporal conditional variational autoencoder (TCVAE) to model the dynamic distributional dependencies over time between historical observations and future data in MTSs and infer the dependencies as a temporal conditional distribution to leverage latent variables. Specifically, a novel temporal Hawkes attention mechanism represents temporal factors subsequently fed into feed-forward networks to estimate the prior Gaussian distribution of latent variables. The representation of temporal factors further dynamically adjusts the structures of Transformer-based encoder and decoder to distribution changes by leveraging a gated attention mechanism. Moreover, we introduce conditional continuous normalization flow to transform the prior Gaussian to a complex and form-free distribution to facilitate flexible inference of the temporal conditional distribution. Extensive experiments conducted on six real-world MTS datasets demonstrate the TCVAE’s superior robustness and effectiveness over the state-of-the-art MTS forecasting baselines. We further illustrate the TCVAE applicability through multifaceted case studies and visualization in real-world *** Codes 68Txx Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning

arXiv

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arXiv 2022年

作者： Fang, Jianwu Zhu, Chen Zhang, Pu Yu, Hongkai Xue, Jianru Chang'an University Xi’an China NExT++ Centre School of Computing National University of Singapore Singapore The KargoBot Beijing China The Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an China The Department of Electrical Engineering and Computer Science Cleveland State University Cleveland United States

Heterogeneous trajectory forecasting is critical for intelligent transportation systems, but it is challenging because of the difficulty of modeling the complex interaction relations among the heterogeneous road agents as well as their agent-environment constraints. In this work, we propose a risk and scene graph learning method for trajectory forecasting of heterogeneous road agents, which consists of a Heterogeneous Risk Graph (HRG) and a Hierarchical Scene Graph (HSG) from the aspects of agent category and their movable semantic regions. HRG groups each kind of road agent and calculates their interaction adjacency matrix based on an effective collision risk metric. HSG of the driving scene is modeled by inferring the relationship between road agents and road semantic layout aligned by the road scene grammar. Based on this formulation, we can obtain effective trajectory forecasting in driving situations, and comparable performance to other state-of-the-art approaches is presented by extensive experiments on the nuScenes, ApolloScape, and Argoverse datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Charlie and the CryptoFactory: Towards Secure and Trusted Manufacturing Environments

Charlie and the CryptoFactory: Towards Secure and Trusted Ma...

引用

Mediterranean Electrotechnical Conference (MELECON)

作者： Antonis Michalas Tamas Kiss Network and Information Security Group Tampere University Tampere Finland Research Centre for Parallel Computing School of Computer Science and Engineering University of Westminster London U.K.

ISBN: (数字)9781728152004

ISBN: (纸本)9781728152011

The modernization that stems from Industry 4.0 started populating the manufacturing sector with networked devices, complex sensors, and a significant proportion of physical actuation components. However, new capabilities in networked cyber-physical systems demand more complex infrastructure and algorithms and often lead to new security flaws and operational risks that increase the attack surface area exponentially. The interconnected nature of Industry 4.0-driven operations and the pace of digital transformation mean that cyberattacks can have far more extensive effects than ever before. Based on that, the core ideas of this paper are driven by the observation that cybersecurity is one of the key enablers of Industry 4.0. Having this in mind, we propose CryptoFactory - a forward-looking design of a layered-based architecture that can be used as a starting point for building secure and privacy-preserving smart factories. CryptoFactory aims to change the security outlook in smart manufacturing by discussing a set of fundamental requirements and functionality that modern factories should support in order to be resistant to both internal and external attacks. To this end, CryptoFactory first focuses on how to build trust relationships between the hardware devices in the factory. Then, we look on how to use several cryptographic approaches to allow IoT devices to securely collect, store and share their data while we also touch upon the emerging topic of secure and privacy-preserving communication and collaboration between manufacturing environments and value chains. Finally, we look into the problem of how to perform privacy-preserving analytics by leveraging Trusted Execution Environments and the promising concept of Functional Encryption.

关键词： Cryptography Manufacturing Industries Cloud computing Hardware computer security

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Pareto Set Learning through Genetic Programming for Multi-Objective Dynamic Scheduling

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IEEE Transactions on Evolutionary Computation 2025年

作者： Xu, Meng Mei, Yi Zhang, Fangfang Ong, Yew Soon Zhang, Mengjie Agency for Science Technology and Research Singapore Institute of Manufacturing Technology Singapore Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Agency for Science Technology and Research College of Computing and Data Science Nanyang Technological University Centre for Frontier AI Research Institute of High Performance Computing Singapore Singapore

The multi-objective dynamic flexible job shop scheduling (MO-DFJSS) problem is crucial in modern manufacturing, impacting productivity and operational costs. Genetic Programming (GP) has emerged as a prominent method for MO-DFJSS due to its ability to evolve real-time responsible and effective scheduling heuristics. However, existing GP approaches often learn multiple heuristics for different regions of the Pareto front, making their management and selection complicated in real-world applications. This paper proposes a novel Pareto set learning GP (PSLGP) framework that addresses this limitation by learning a single, preference-conditioned heuristic that encompasses the entire Pareto front based on user preferences. This simplifies scheduling and allows for real-time adaptation to user-defined priorities. The framework employs a novel preference-conditioned heuristic representation that incorporates user preferences as additional inputs, enabling dynamic heuristic adjustments. To efficiently evaluate fitness without increasing training time, a surrogate model is used to estimate individual performance across different preferences, and three new fitness aggregation strategies are designed to ensure effective heuristic alignment across the Pareto front. Experimental results demonstrate that PSLGP significantly outperforms the state-of-the-art multi-objective GP approach, particularly in less busy MO-DFJSS environments, providing a more adaptable and efficient solution for dynamic scheduling challenges. Further analyses of preference influence, solution distribution, and heuristic structure provide evidence that the proposed PSLGP effectively learns preference-conditioned scheduling heuristics that align user preferences with various regions of the Pareto front. © 1997-2012 IEEE.

关键词： Decision making

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Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data (vol 12, 2078, 2021)

引用

NATURE COMMUNICATIONS 2021年第1期12卷 2078页

作者： Eshaghi, Arman Young, Alexandra L. Wijeratne, Peter A. Prados, Ferran Arnold, Douglas L. Narayanan, Sridar Guttmann, Charles R. G. Barkhof, Frederik Alexander, Daniel C. Thompson, Alan J. Chard, Declan Ciccarelli, Olga Queen Square Multiple Sclerosis Centre Department of Neuroinflammation UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. a.eshaghi@ucl.ac.uk. Centre for Medical Image Computing (CMIC) Department of Computer Science Faculty of Engineering Sciences University College London London UK. Queen Square Multiple Sclerosis Centre Department of Neuroinflammation UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. McConnell Brain Imaging Centre Montreal Neurological Institute McGill University Montreal QC Canada. Center for Neurological Imaging Brigham and Women's Hospital Harvard Medical School Boston MA USA.

Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials.

关键词： Functional magnetic resonance imaging Learning algorithms Multiple sclerosis

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Fair Evaluation of Federated Learning Algorithms for Automated Breast Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated Learning Challenge

arXiv

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arXiv 2024年

作者： Schmidt, Kendall Bearce, Benjamin Chang, Ken Coombs, Laura Farahani, Keyvan Elbatel, Marawan Mouheb, Kaouther Marti, Robert Zhang, Ruipeng Zhang, Yao Wang, Yanfeng Hu, Yaojun Ying, Haochao Xu, Yuyang Testagrose, Conrad Demirer, Mutlu Gupta, Vikash Akünal, Ünal Bujotzek, Markus Maier-Hein, Klaus H. Qin, Yi Li, Xiaomeng Kalpathy-Cramer, Jayashree Roth, Holger R. American College of Radiology United States The Massachusetts General Hospital United States University of Colorado United States National Institutes of Health National Cancer Institute United States Computer Vision and Robotics Institute University of Girona Spain Cooperative Medianet Innovation Center Shanghai Jiao Tong University China Shanghai AI Laboratory China Real Doctor AI Research Centre Zhejiang University China School of Public Health Zhejiang University China College of Computer Science and Technology Zhejiang University China University of North Florida College of Computing Jacksonville United States Mayo Clinic Florida Radiology United States Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Electronic and Computer Engineering Hong Kong University of Science and Technology China NVIDIA United States

The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography systems, models built using data from one system do not generalize well to other systems. Though federated learning (FL) has emerged as a way to improve the generalizability of AI without the need to share data, the best way to preserve features from all training data during FL is an active area of research. To explore FL methodology, the breast density classification FL challenge was hosted in partnership with the American College of Radiology, Harvard Medical schools' Mass General Brigham, University of Colorado, NVIDIA, and the National Institutes of Health National Cancer Institute. Challenge participants were able to submit docker containers capable of implementing FL on three simulated medical facilities, each containing a unique large mammography dataset. The breast density FL challenge ran from June 15 to September 5, 2022, attracting seven finalists from around the world. The winning FL submission reached a linear kappa score of 0.653 on the challenge test data and 0.413 on an external testing dataset, scoring comparably to a model trained on the same data in a central location. © 2024, CC BY.

关键词： Learning algorithms

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Deep Predictive Coding with Bi-directional Propagation for Classification and Reconstruction

arXiv

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arXiv 2023年

作者： Qiu, Senhui Bhattacharyya, Saugat Coyle, Damien Dora, Shirin The Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University LondonderryBT48 7JL United Kingdom The Bath Institute for the Augmented Human University of Bath BathBA2 7AY United Kingdom The Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom

This paper presents a new learning algorithm, termed Deep Bi-directional Predictive Coding (DBPC) that allows developing networks to simultaneously perform classification and reconstruction tasks using the same weights. Predictive Coding (PC) has emerged as a prominent theory underlying information processing in the brain. The general concept for learning in PC is that each layer learns to predict the activities of neurons in the previous layer which enables local computation of error and in-parallel learning across layers. In this paper, we extend existing PC approaches by developing a network which supports both feedforward and feedback propagation of information. Each layer in the networks trained using DBPC learn to predict the activities of neurons in the previous and next layer which allows the network to simultaneously perform classification and reconstruction tasks using feedforward and feedback propagation, respectively. DBPC also relies on locally available information for learning, thus enabling in-parallel learning across all layers in the network. The proposed approach has been developed for training both, fully connected networks and convolutional neural networks. The performance of DBPC has been evaluated on both, classification and reconstruction tasks using the MNIST and FashionMNIST datasets. The classification and the reconstruction performance of networks trained using DBPC is similar to other approaches used for comparison but DBPC uses a significantly smaller network. Further, the significant benefit of DBPC is its ability to achieve this performance using locally available information and in-parallel learning mechanisms which results in an efficient training protocol. This results clearly indicate that DBPC is a much more efficient approach for developing networks that can simultaneously perform both classification and reconstruction. © 2023, CC BY.

关键词： Classification (of information)

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