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arXiv 2024年

作者： Hsieh, Chihcheng Moreira, Catarina Nobre, Isabel Blanco Sousa, Sandra Costa Ouyang, Chun Brereton, Margot Jorge, Joaquim Nascimento, Jacinto C. School of Information Systems Queensland University of Technology 2 George Street BrisbaneQLD4000 Australia School of Computer Science Queensland University of Technology 2 George Street BrisbaneQLD4000 Australia Data Science Institute University of Technology Sydney 61 Broadway Ultimo SydneyNSW2007 Australia Imagiology Department Grupo Lusiadas Rua Abílio Mendes 12 Lisboa1500-458 Portugal Instituto Superior Tecnico Universidade de Lisboa Av. Rovisco Pais 1 Lisboa1049-001 Portugal INESC-ID Av. Rovisco Pais 1 Lisboa1049-001 Portugal

X-ray images are vital in medical diagnostics, but their effectiveness is limited without clinical context. Radiologists often find chest X-rays insufficient for diagnosing underlying diseases, necessitating the integration of structured clinical features with radiology reports. To address this, we introduce DALL-M, a novel framework that enhances clinical datasets by generating contextual synthetic data. DALL-M augments structured patient data, including vital signs (e.g., heart rate, oxygen saturation), radiology findings (e.g., lesion presence), and demographic factors. It integrates this tabular data with contextual knowledge extracted from radiology reports and domain-specific resources (e.g., Radiopaedia, Wikipedia), ensuring clinical consistency and reliability. DALL-M follows a three-phase process: (i) clinical context storage, (ii) expert query generation, and (iii) context-aware feature augmentation. Using large language models (LLMs), it generates both contextual synthetic values for existing clinical features and entirely new, clinically relevant features. Applied to 799 cases from the MIMIC-IV dataset, DALL-M expanded the original 9 clinical features to 91. Empirical validation with machine learning models—including Decision Trees, Random Forests, XGBoost, and TabNET—demonstrated a 16.5% improvement in F1 score and a 25% increase in Precision and Recall. DALL-M bridges an important gap in clinical data augmentation by preserving data integrity while enhancing predictive modeling in healthcare. Our results show that integrating LLM-generated synthetic features significantly improves model performance, making DALL-M a scalable and practical approach for AI-driven medical diagnostics. © 2024, CC BY-NC-ND.

关键词： Radiology

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Inverse learning of black-box aggregator for robust Nash equilibrium

arXiv

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Extensive Composable Entropy for the Analysis of Cosmological Data

arXiv

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arXiv 2024年

作者： Tsallis, Constantino Jensen, Henrik Jeldtoft Centro Brasileiro de Pesquisas Físicas National Institute of Science and Technology of Complex Systems Rua Xavier Sigaud 150 RJ Rio de Janeiro22290-180 Brazil Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States Complexity Science Hub Vienna Josefstädter Strasse 39 Vienna1080 Austria Centre for Complexity Science Department of Mathematics Imperial College London South Kensington Campus LondonSW7 2AZ United Kingdom School of Computer Science Tokyo Institute of Technology 4259 Nagatsuta-cho Yokohama226-8502 Japan

Along recent decades, an intensive worldwide research activity is focusing both black holes and cosmos (e.g. the dark-energy phenomenon) on the basis of entropic approaches. The Boltzmann-Gibbs-based Bekenstein-Hawking entropy SBH ∝ A/lP2 (A ≡ area;lP ≡ Planck length) systematically plays a crucial theoretical role although it has a serious drawback, namely that it violates the thermodynamic extensivity of spatially-three-dimensional systems. Still, its intriguing area dependence points out the relevance of considering the form W(N) ∼ µNγ (µ > 1;γ > 0), W and N respectively being the total number of microscopic possibilities and the number of components;γ = 1 corresponds to standard Boltzmann-Gibbs (BG) statistical mechanics. For this W(N) asymptotic behavior, we introduce here, on a group-theory basis, the entropic functional (formula presented). This functional simultaneously is extensive (as required by thermodynamics) and composable (as required for logic consistency), ∀(α, γ). We further show that (α, γ) = (1, 2/3) satisfactorily agrees with cosmological data measuring neutrinos, Big Bang nucleosynthesis and the relic abundance of cold dark matter particles, as well as dynamical and geometrical cosmological data sets. © 2024, CC BY.

关键词： Entropy

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Ranking influential nodes in networks from aggregate local information

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Physical Review Research 2023年第3期5卷 033123-033123页

作者： Silvia Bartolucci Fabio Caccioli Francesco Caravelli Pierpaolo Vivo Department of Computer Science University College London 66–72 Gower Street London WC1E 6EA United Kingdom Centre for Financial Technology Imperial College Business School South Kensington London SW7 2AZ United Kingdom Systemic Risk Centre London School of Economics and Political Sciences London WC2A 2AE United Kingdom London Mathematical Laboratory London WC 8RH United Kingdom Theoretical Division (T4) Condensed Matter & Complex Systems Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Mathematics King's College London Strand London WC2R 2LS United Kingdom

Many complex systems exhibit a natural hierarchy in which elements can be ranked according to a notion of “influence”. While the complete and accurate knowledge of the interactions between constituents is ordinarily required for the computation of nodes' influence, using a low-rank approximation we show that—in a variety of contexts—local and aggregate information about the neighborhoods of nodes is enough to reliably estimate how influential they are without the need to infer or reconstruct the whole map of interactions. Our framework is successful in approximating with high accuracy different incarnations of influence in systems as diverse as the WWW PageRank, trophic levels of ecosystems, upstreamness of industrial sectors in complex economies, and centrality measures of social networks, as long as the underlying network is not exceedingly sparse. We also discuss the implications of this “emerging locality” on the approximate calculation of nonlinear network observables.

关键词： Complex systems Degree correlations Network structure Centrality

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LPV-Based Control Design with Guarantees: a Case Study for Automated Steering of Road Vehicles

LPV-Based Control Design with Guarantees: a Case Study for A...

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European Control Conference (ECC)

作者： Balázs Németh Máté Fazekas Zoltán Bagoly Péter Gáspár Olivier Sename Eötvös Loránd Research Network (ELKH) Kende u. 13-17 Institute for Computer Science and Control (SZTAKI) Budapest Hungary Department of Control for Transportation and Vehicle Systems (KJIT) Budapest University of Technology and Economics (BME) Stoczek u. 2. Budapest Hungary Grenoble INP GIPSAlab Univ. Grenoble Alpes CNRS Grenoble France

This paper proposes a Linear Parameter Varying (LPV) based steering control design method, which contains data aided control elements, e.g., learning-based agents. The framework is based on a supervisory control structure, which contains a supervisor, a LPV controller and the data aided control element. The goal of this paper is to provide a safe steering control, with which the human steering intervention can be effectively imitated. Moreover, in the proposed framework the data aided control can be adapted to the actual requirements on driving style, without re-designing the LPV control. Thus, a general control structure with performance guarantees on path following constraints is provided, in which the data aided steering control element can be varied. The effectiveness of the proposed method through driver-in-the-loop scenarios is illustrated, in which different settings on the control system are analyzed.

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Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble

arXiv

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arXiv 2024年

作者： Chen, Chuangtao Zhao, Qinglin Zhou, MengChu He, Zhimin Sun, Zhili Situ, Haozhen Faculty of Innovation Engineering Macau University of Science and Technology Macao999078 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States School of Electronic and Information Engineering Foshan University Foshan528000 China Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

Classical diffusion models have shown superior generative results. Exploring them in the quantum domain can advance the field of quantum generative learning. This work introduces Quantum Generative Diffusion Model (QGDM) as their simple and elegant quantum counterpart. Through a non-unitary forward process, any target quantum state can be transformed into a completely mixed state that has the highest entropy and maximum uncertainty about the system. A trainable backward process is used to recover the former from the latter. The design requirements for its backward process includes non-unitarity and small parameter count. We introduce partial trace operations to enforce non-unitary and reduce the number of trainable parameters by using a parameter-sharing strategy and incorporating temporal information as an input in the backward process. We present QGDM’s resource-efficient version to reduce auxiliary qubits while preserving generative capabilities. QGDM exhibits faster convergence than Quantum Generative Adversarial Network (QGAN) because its adopted convex-based optimization can result in better convergence. The results of comparing it with QGAN demonstrate its effectiveness in generating both pure and mixed quantum states. It can achieve 53.02% higher fidelity in mixed-state generation than QGAN. The results highlight its great potential to tackle challenging quantum generation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Knowledge and Data Dual-Driven Channel Estimation and Feedback for Ultra-Massive MIMO systems under Hybrid Field Beam Squint Effect

arXiv

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arXiv 2024年

作者： Wang, Kuiyu Gao, Zhen Chen, Sheng Ning, Boyu Chen, Gaojie Su, Yu Wang, Zhaocheng Poor, H. Vincent Beijing100081 China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom The National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China The 5GIC & 6GIC Institute for Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom The China Mobile Chengdu Institute of Research and Development Chengdu610000 China The Department of Electronic Engineering Tsinghua University Beijing100084 China The Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Acquiring accurate channel state information (CSI) at an access point (AP) is challenging for wideband millimeter wave (mmWave) ultra-massive multiple-input and multiple-output (UM-MIMO) systems, due to the high-dimensional channel matrices, hybrid near- and far- field channel feature, beam squint effects, and imperfect hardware constraints, such as low-resolution analog-to-digital converters, and in-phase and quadrature imbalance. To overcome these challenges, this paper proposes an efficient downlink channel estimation (CE) and CSI feedback approach based on knowledge and data dual-driven deep learning (DL) networks. Specifically, we first propose a data-driven residual neural network de-quantizer (ResNet-DQ) to pre-process the received pilot signals at user equipment (UEs), where the noise and distortion brought by imperfect hardware can be mitigated. A knowledge-driven generalized multiple measurement vector learned approximate message passing (GMMV-LAMP) network is then developed to jointly estimate the channels by exploiting the approximately same physical angle shared by different subcarriers. In particular, two wideband redundant dictionaries (WRDs) are proposed such that the measurement matrices of the GMMV-LAMP network can accommodate the far-field and near-field beam squint effect, respectively. Finally, we propose an encoder at the UEs and a decoder at the AP by a data-driven CSI residual network (CSI-ResNet) to compress the CSI matrix into a low-dimensional quantized bit vector for feedback, thereby reducing the feedback overhead substantially. Simulation results show that the proposed knowledge and data dual-driven approach outperforms conventional downlink CE and CSI feedback methods, especially in the case of low signal-to-noise ratios. Copyright © 2024, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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LPV-Fuzzy control approach for road adaptive semi-active suspension system

LPV-Fuzzy control approach for road adaptive semi-active sus...

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IEEE Symposium on Intelligent Vehicle

作者： Hakan Basargan Andrá s Mihá ly Pé ter Gá spá r Oliver Sename Department of Control for Transportation and Vehicle Systems BME Stoczek u. 2 Budapest Hungary Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) E&#x00F6 tv&#x00F6 s Lor&#x00E1 nd Research Network (ELKH) Kende utca 13-17 Budapest Hungary GIPSA-lab INPG Universit&#x00E9 Grenoble Alpes 11 Rue des Math&#x00E9 matiques Grenoble France

This paper proposed a road adaptive semi-active suspension control method, where a trade-off between driving comfort and vehicle stability/road-holding is accomplishable in order to achieve desirable performance results at different road irregularities and velocities by modifying the scheduling variable that is designed by Fuzzy Logic Control. The proposed semi-active controller is founded on the Linear Parameter-Varying framework. Hungarian highway route data has been implemented into the TruckSim simulation environment based on real geographical data having road irregularities in order to compare the proposed adaptive method with a non-adaptive scenario. Simulation results show that all performances have been improved with the proposed method in different road irregularities and velocities.

关键词： Fuzzy logic Suspensions (mechanical systems) Adaptation models Adaptive systems Intelligent vehicles Roads Simulation

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Double-free-layer stochastic magnetic tunnel junctions with synthetic antiferromagnets

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Physical Review Applied 2024年第5期21卷 054002-054002页

作者： Kemal Selcuk Shun Kanai Rikuto Ota Hideo Ohno Shunsuke Fukami Kerem Y. Camsari Department of Electrical and Computer Engineering University of California Santa Barbara California 93106 USA Research Institute of Electrical Communication Tohoku University 2-1-1 Katahira Aoba-ku Sendai 980-8577 Japan Graduate School of Engineering Tohoku University 6-6 Aramaki Aza Aoba Aoba-ku Sendai 980-8579 Japan WPI Advanced Institute for Materials Research (WPI-AIMR) Tohoku University 2-1-1 Katahira Aoba-ku Sendai 980-8577 Japan Center for Science and Innovation in Spintronics (CSIS) Tohoku University 2-1-1 Katahira Aoba-ku Sendai 980-8577 Japan PRESTO Japan Science and Technology Agency (JST) Kawaguchi 332-0012 Japan Division for the Establishment of Frontier Sciences of Organization for Advanced Studies at Tohoku University Tohoku University Sendai 980-8577 Japan National Institutes for Quantum Science and Technology Takasaki 370-1207 Japan Center for Innovative Integrated Electronic Systems (CIES) Tohoku University 468-1 Aramaki Aza Aoba Aoba-ku Sendai 980-0845 Japan Inamori Research Institute of Science (InaRIS) Kyoto 600-8411 Japan

Stochastic magnetic tunnel junctions (SMTJs) using low-barrier nanomagnets have shown promise as fast, energy-efficient, and scalable building blocks for probabilistic computing. Despite recent experimental and theoretical progress, SMTJs exhibiting the ideal characteristics necessary for probabilistic bits (p-bits) are still lacking. Ideally, the SMTJs should have (a) voltage bias independence, preventing read disturbance; (b) uniform randomness in the magnetization angle between the two magnetic layers; and (c) fast fluctuations without requiring external magnetic fields, while being robust to magnetic field perturbations. Here, we propose a design that satisfies all of these requirements, using double-free-layer SMTJs with synthetic antiferromagnets (SAFs). We evaluate the proposed SMTJ design with experimentally benchmarked spin-circuit models, accounting for transport physics, coupled with the stochastic Landau-Lifshitz-Gilbert equation for magnetization dynamics. We find that the use of low-barrier SAF layers reduces dipolar coupling, achieving uncorrelated fluctuations at zero-magnetic field, surviving up to diameters exceeding D≈100nm if the nanomagnets can be made thin enough (≈1–2nm). The double-free-layer structure retains bias independence and the circular nature of the nanomagnets provides near-uniform randomness with fast fluctuations. Combining our full SMTJ model with advanced transistor models, we estimate the energy to generate a random bit to be about 3.6fJ, with fluctuation rates of about 3.3GHz per p-bit. Our results will guide the experimental development of superior stochastic magnetic tunnel junctions for large-scale and energy-efficient probabilistic computation for problems relevant to machine learning and artificial intelligence.

关键词： Machine learning Neuromorphic computing Probabilistic computing Spintronics Stochastic processes Devices for digital logic, storage & processing Magnetic tunnel junctions Synthetic antiferromagnetic multilayers

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The Age of Generative AI and AI-Generated Everything

arXiv

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arXiv 2023年

作者： Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Ping Cui, Shuguang Shen, Xuemin Mao, Shiwen Han, Zhu Jamalipour, Abbas Vincent Poor, H. Kim, Dong In School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative AI (GAI) has emerged as a significant advancement in artificial intelligence, renowned for its language and image generation capabilities. This paper presents "AIGenerated Everything" (AIGX), a concept that extends GAI beyond mere content creation to real-time adaptation and control across diverse technological domains. In networking, AIGX collaborates closely with physical, data link, network, and application layers to enhance real-time network management that responds to various system and service settings as well as application and user requirements. Networks, in return, serve as crucial components in further AIGX capability optimization through the AIGX lifecycle, i.e., data collection, distributed pre-training, and rapid decision-making, thereby establishing a mutually enhancing interplay. Moreover, we offer an in-depth case study focused on power allocation to illustrate the interdependence between AIGX and networking systems. Through this exploration, the article analyzes the significant role of GAI for networking, clarifies the ways networks augment AIGX functionalities, and underscores the virtuous interactive cycle they form. This article paves the way for subsequent future research aimed at fully unlocking the potential of GAI and networks. © 2023, CC BY.

关键词： Decision making

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