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arXiv 2024年

作者： Hu, Xinqi Dong, Gaogao Christensen, Kim Sun, Hanlin Fan, Jingfang Tian, Zihao Gao, Jianxi Havlin, Shlomo Lambiotte, Renaud Meng, Xiangyi School of Mathematical Sciences Jiangsu University Jiangsu Zhenjiang212013 China Blackett Laboratory Centre for Complexity Science Imperial College London LondonSW7 2AZ United Kingdom Nordita KTH Royal Institute of Technology Stockholm University Hannes Alfvéns väg 12 StockholmSE-106 91 Sweden School of Systems Science Institute of Nonequilibrium Systems Beijing Normal University Beijing100875 China School of Management and Engineering Nanjing University Nanjing210093 China Network Science and Technology Center Rensselaer Polytechnic Institute TroyNY12180 United States Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Department of Physics Bar-Ilan University Ramat Gan52900 Israel Mathematical Institute University of Oxford OxfordOX2 6GG United Kingdom Turing Institute LondonNW1 2DB United Kingdom Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States

Quantum networks (QNs) exhibit stronger connectivity than predicted by classical percolation, yet the origin of this phenomenon remains unexplored. We apply a statistical physics model—concurrence percolation—to uncover the origin of stronger connectivity on hierarchical scale-free networks, the (U, V) flowers. These networks allow full analytical control over path connectivity through two adjustable path-length parameters, U ≤ V. This precise control enables us to determine critical exponents well beyond current simulation limits, revealing that classical and concurrence percolations, while both satisfying the hyperscaling relation, fall into distinct universality classes. This distinction arises from how they "superpose" parallel, nonshortest path contributions into overall connectivity. Concurrence percolation, unlike its classical counterpart, is sensitive to nonshortest paths and shows higher resilience to detours as these paths lengthen. This enhanced resilience is also observed in real-world hierarchical, scale-free Internet networks. Our findings highlight a crucial principle for QN design: when nonshortest paths are abundant, they notably enhance QN connectivity beyond what is achievable with classical percolation. © 2024, CC BY.

关键词： Percolation (computer storage)

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A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges

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Informatics in Medicine Unlocked 2024年 51卷

作者： Mienye, Ibomoiye Domor Obaido, George Jere, Nobert Mienye, Ebikella Aruleba, Kehinde Emmanuel, Ikiomoye Douglas Ogbuokiri, Blessing Institute for Intelligent Systems University of Johannesburg Auckland Park Johannesburg 2006 South Africa Center for Human-Compatible Artificial Intelligence (CHAI) Berkeley Institute for Data Science (BIDS) University of California Berkeley 94720 CA United States Department of Computer Science University of Fort Hare Alice Campus Eastern Cape Alice South Africa School of Computing and Mathematical Sciences University of Leicester Leicester LE1 7RH United Kingdom School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Computer Science Brock University Niagara Region St. Catharines L2S 3A1 ON Canada

Explainable AI (XAI) has the potential to transform healthcare by making AI-driven medical decisions more transparent, reliable, and ethically compliant. Despite its promise, the healthcare sector faces several challenges, including the need to balance interpretability and accuracy, integrating XAI into clinical workflows, and ensuring adherence to rigorous regulatory standards. This paper provides a comprehensive review of XAI in healthcare, covering techniques, challenges, opportunities, and advancements, thereby enhancing the understanding and practical application of XAI in healthcare. The study also explores responsible AI in healthcare, discussing new perspectives and emerging trends, offering valuable insights for researchers and practitioners. The insights and recommendations presented aim to guide future research and policy-making, fostering the development of transparent, trustworthy, and effective AI-driven solutions. © 2024 The Authors

关键词： AI Bias Ethics Fairness Healthcare Machine learning

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Fast Estimation of Outcome Probabilities for Quantum Circuits

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PRX Quantum 2022年第2期3卷 020361-020361页

作者： Hakop Pashayan Oliver Reardon-Smith Kamil Korzekwa Stephen D. Bartlett Institute for Quantum Computing and Department of Combinatorics and Optimization University of Waterloo Ontario N2L 3G1 Canada Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków 30-348 Poland Centre for Engineered Quantum Systems School of Physics The University of Sydney Sydney New South Wales 2006 Australia

We present two classical algorithms for the simulation of universal quantum circuits on n qubits constructed from c instances of Clifford gates and t arbitrary-angle Z-rotation gates such as T gates. Our algorithms complement each other by performing best in different parameter regimes. The Estimate algorithm produces an additive precision estimate of the Born-rule probability of a chosen measurement outcome with the only source of run-time inefficiency being a linear dependence on the stabilizer extent (with scaling approximately equal to 1.17t for T gates). Our algorithm is state of the art for this task: as an example, in approximately 13 h (on a standard desktop computer), we estimate the Born-rule probability to within an additive error of 0.03, for a 50-qubit, 60 non-Clifford gate quantum circuit with more than 2000 Clifford gates. Our second algorithm, Compute, calculates the probability of a chosen measurement outcome to machine precision with run time O(2t−rt), where r is an efficiently computable, circuit-specific quantity. With high probability, r is very close to min{t,n−w} for random circuits with many Clifford gates, where w is the number of measured qubits. Compute can be effective in surprisingly challenging parameter regimes, e.g., we can randomly sample Clifford+T circuits with n=55, w=5, c=105, and t=80T gates, and then compute the Born-rule probability with a run time consistently less than 10 min using a single core of a standard desktop computer. We provide a C+Python implementation of our algorithms and benchmark them using random circuits, the hidden-shift algorithm, and the quantum approximate optimization algorithm.

关键词： Quantum simulation

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Exploring Contextual Representation and Multi-Modality for End-to-End Autonomous Driving

arXiv

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arXiv 2022年

作者： Azam, Shoaib Munir, Farzeen Kyrki, Ville Jeon, Moongu Pedrycz, Witold Department of Electrical Engineering and Automation Aalto University Espoo Finland Finnish Center for Artificial Intelligence Finland School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

Learning contextual and spatial environmental representations enhances autonomous vehicle’s hazard anticipation and decision-making in complex scenarios. Recent perception systems enhance spatial understanding with sensor fusion but often lack full environmental context. Humans, when driving, naturally employ neural maps that integrate various factors such as historical data, situational subtleties, and behavioral predictions of other road users to form a rich contextual understanding of their surroundings. This neural map-based comprehension is integral to making informed decisions on the road. In contrast, even with their significant advancements, autonomous systems have yet to fully harness this depth of human-like contextual understanding. Motivated by this, our work draws inspiration from human driving patterns and seeks to formalize the sensor fusion approach within an end-to-end autonomous driving framework. We introduce a framework that integrates three cameras (left, right, and center) to emulate the human field of view, coupled with top-down bird-eye-view semantic data to enhance contextual representation. The sensor data is fused and encoded using a self-attention mechanism, leading to an auto-regressive waypoint prediction module. We treat feature representation as a sequential problem, employing a vision transformer to distill the contextual interplay between sensor modalities. The efficacy of the proposed method is experimentally evaluated in both open and closed-loop settings. Our method achieves displacement error by 0.67m in open-loop settings, surpassing current methods by 6.9% on the nuScenes dataset. In closed-loop evaluations on CARLA’s Town05 Long and Longest6 benchmarks, the proposed method enhances driving performance, route completion, and reduces infractions. © 2022, CC BY.

关键词： Autonomous vehicles

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Stimulated Raman scattering spectro-microscopy using multiple-plate continuum

Stimulated Raman scattering spectro-microscopy using multipl...

引用

Label-free Biomedical Imaging and Sensing (LBIS) 2023

作者： Huang, Guan-Jie Lai, Pei-Chen Shen, Ming-Wei Su, Jia-Xuan Guo, Jhan-Yu Chao, Kuo-Chuan Lin, Peng Cheng, Ji-Xin Chu, Li-An Chiang, Ann-Shyn Chen, Bo-Han Lu, Chih-Hsuan Chu, Shi-Wei Yang, And Shang-Da Department of Physics National Taiwan University 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan Institute of Photonics Technologies National Tsing Hua University 101 Sec. 2 Kuang-Fu Rd. Hsinchu300044 Taiwan Brain Research Center National Tsing Hua University 101 Sec. 2 Kuang-Fu Rd. Hsinchu300044 Taiwan Department of Electrical and Computer Engineering Boston University 8 St. Mary’s St. BostonMA02215 United States Department of Biomedical Engineering Boston University 44 Cummington Mall BostonMA02215 United States Department of Biomedical Engineering & Environmental Sciences National Tsing Hua University 101 Sec 2 Kuang-Fu Rd. Hsinchu30044 Taiwan Institute of Systems Neuroscience Department of Life Science National Tsing Hua University 101 Sec 2 Kuang-Fu Rd. Hsinchu30044 Taiwan Molecular Imaging Center National Taiwan University 1 Sec 4 Roosevelt Rd. Taipei10617 Taiwan

ISBN: (纸本)9781510658875

Stimulated Raman scattering (SRS) has attracted significant attention recently for providing high-sensitivity and background-free chemical characterization without exogenous labeling. For the laser system of SRS, the current benchmark is the combination of a pico- or femtosecond mode-locked solid-state oscillator and a synchronously pumped optical parametric oscillator (OPO), offering two central wavelengths λ(Greek Passage) and λ(Greek Passage) to match the desired Raman modes. Despite great success, the phase-sensitive nature of degenerate OPO hampers its access to low-frequency Raman shifts, and the inability to independently adjust the second wavelength prevents the OPO source from electronic preresonance (EPR) detection for desirable molecules. In this work, we demonstrated an SRS spectro-microscopy system driven by a multiple-plate continuum (MPC) supercontinuum laser source, whose spectrum spans from 600 nm to 1300 nm, offering capabilities of dual-wavelength tunability across the entire Raman active region (0-4000 cm-1). We demonstrated SRS microspectroscopy across the fingerprint, silent, and C-H stretch Raman regions in acetonitrile solution. This novel light source allows significant contrast enhancement through EPR-SRS by tuning the pump wavelengths toward the absorption peak of dye molecules, exemplified by the C=C mode of Alexa 635. Moreover, single-wavelength SRS imaging of the Drosophila brain was presented. We envision that utilizing an MPC light source will substantially enhance the sensitivity and specificity of SRS by implementing EPR mode and spectral multiplexing. © 2023 SPIE

关键词： Stimulated Raman scattering

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Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications

arXiv

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arXiv 2023年

作者： Ma, Zhe Wu, Wen Gao, Feifei Shen, Xuemin The Institute for Artificial Intelligence Tsinghua University State Key Lab of Intelligent Technologies and Systems Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University Beijing100084 China The Frontier Research Center Peng Cheng Laboratory Guangdong Shenzhen518055 China The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

In this paper, we investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme, where data bits are embedded in the pilot sequences and the base station simultaneously detects active devices and their embedded data bits without explicit channel estimation. Due to the correlated sparsity pattern introduced by the noncoherent transmission scheme, the traditional approximate message passing (AMP) algorithm cannot achieve satisfactory performance. Therefore, we propose a deep learning (DL) modified AMP network (DL-mAMPnet) that enhances the detection performance by effectively exploiting the pilot activity correlation. The DL-mAMPnet is constructed by unfolding the AMP algorithm into a feedforward neural network, which combines the principled mathematical model of the AMP algorithm with the powerful learning capability, thereby benefiting from the advantages of both techniques. Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient. Moreover, a refinement module is designed to further advance the performance by utilizing the spatial feature caused by the correlated sparsity pattern. Simulation results demonstrate that the proposed DL-mAMPnet can significantly outperform traditional algorithms in terms of the symbol error rate performance. © 2023, CC BY-NC-SA.

关键词： Message passing

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Dynamic AI-Driven Network Slicing with O-RAN for Continuous Connectivity in Connected Vehicles and Onboard Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Shah, Syed Danial Ali Bashir, Ali Kashif Al-Otaibi, Yasser D. Dabel, Maryam M. Al Ali, Farman University of Leeds School of Electronic and Electrical Engineering LeedsLS2 9JT United Kingdom Manchester Metropolitan University Department of Computing and Mathematics M15 6BH United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Jeddah21589 Saudi Arabia University of Hafr Al Department of Computer Science and Engineering Batin Saudi Arabia Sungkyunkwan University School of Convergence Seoul Korea Republic of

The rise of connected and autonomous vehicles signifies an era of intelligent transportation systems, where robust and continued network connectivity is essential for critical applications and enhanced in-vehicle Consumer Electronics (CE) experiences. Slicing at the network's edge offers tailored and dedicated logical networks for diverse and low-latency vehicular demands, including Advanced Driver Assistance systems (ADAS) and in-car infotainment. However, seamless migration of network slices as vehicles traverse coverage areas of different network operators presents formidable challenges, such as ensuring continuous connectivity and uninterrupted service for both safety-critical systems and consumer-oriented services. In this paper, we introduced dynamic network slicing for continuous connectivity in connected vehicles and onboard CE using the Open Radio Access Network (O-RAN) framework in a highly dynamic and mobile environment. We implemented an xAPP within O-RAN that enables Deep Reinforcement Learning (DRL) agent to learn optimal policies through interaction with the network, guiding intelligent decisions on slice migration, resource allocation, and handover optimization. We conducted simulations and evaluations to demonstrate the effectiveness of the proposed xAPP in maintaining optimal Quality of Service (QoS), ensuring efficient RAN resource utilization, minimizing service interruptions, and prioritizing safety-critical slices, all while supporting seamless operation of CE within vehicles during mobility. © 1975-2011 IEEE.

关键词： Advanced driver assistance systems

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Machine Learning and Physics-Driven Modelling and Simulation of Multiphase systems

SSRN

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SSRN 2024年

作者： Basha, Nausheen Arcucci, Rossella Angeli, Panagiota Anastasiou, Charitos Abadie, Thomas Casas, César Quilodrán Chen, Jianhua Cheng, Sibo Chagot, Loïc Galvanin, Federico Heaney, Claire E. Hossein, Fria Hu, Jinwei Kovalchuk, Nina Kalli, Maria Kahouadji, Lyes Kerhouant, Morgan Lavino, Alessio Liang, Fuyue Nathanael, Konstantia Magri, Luca Lettieri, Paola Materazzi, Massimiliano Erigo, Matteo Pico, Paula Pain, Christopher C. Shams, Mosayeb Simmons, Mark Traverso, Tullio Valdes, Juan Pablo Wolffs, Zef Zhu, Kewei Zhuang, Yilin Matar, Omar K. Department of Chemical Engineering Imperial College London United Kingdom Department of Earth Science & Engineering Imperial College London United Kingdom Department of Chemical Engineering University College London United Kingdom School of Chemical Engineering University of Birmingham United Kingdom Data Science Institute Department of Computing Imperial College London United Kingdom State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences Beijing China Centre for AI-Physics Modelling Imperial-X White City Campus Imperial College London United Kingdom The Alan Turing Institute British Library 96 Euston Road LondonNW1 2DB United Kingdom Department of Aeronautics Imperial College London United Kingdom Institute of Physics University of Amsterdam Science Park Amsterdam904 Netherlands Nikhef Science Park Amsterdam105 Netherlands Department of Computer Science University of York United Kingdom

We highlight the work of a multi-university collaborative programme, PREMIERE (PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE systems), which is at the intersection of multi-physics and machine learning, aiming to enhance predictive capabilities in complex multiphase flow systems across diverse length and time scales. Our contributions encompass a variety of approaches, including the Design of Experiments for nanoparticle synthesis optimisation, Generalised Latent Assimilation models for drop coalescence prediction, Bayesian regularised artificial neural networks, eXtreme Gradient Boosting for microdroplet formation prediction, and a sub-sampling based adversarial neural network for predicting slug flow behaviour in two-phase pipe flows. Additionally, we introduce a generalised latent assimilation technique, Long Short-Term Memory networks for sequence forecasting mixing performance in stirred and static mixers, active learning via Bayesian optimisation to recover coalescence model parameters for high current density electrolysers, Gaussian process regression for drop size distribution predictions for sprays, and acoustic emission signal inversion using gradient boosting machines to characterise particle size distribution in fluidised beds. We also offer perspectives on the development of a shape optimisation framework that leverages the use of a multi-fidelity multiphase emulator. The results presented have applications in chemical synthesis, microfluidics, product manufacturing, and green hydrogen generation. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Deterministic particle flows for constraining stochastic nonlinear systems

引用

Physical Review Research 2022年第4期4卷 043035-043035页

作者： Dimitra Maoutsa Manfred Opper Department of Theoretical Computer Science Technical University of Berlin Marchstraße 23 10587 Berlin Germany Institute of Mathematics University of Potsdam Karl-Liebknecht-Str. 24/25 14476 Potsdam Germany Centre for Systems Modelling and Quantitative Biomedicine University of Birmingham B15 2TT United Kingdom

Devising optimal interventions for constraining stochastic systems is a challenging endeavor that has to confront the interplay between randomness and dynamical nonlinearity. Existing intervention methods that employ stochastic path sampling scale poorly with increasing system dimension and are slow to converge. Here we propose a generally applicable and practically feasible methodology that computes the optimal interventions in a noniterative scheme. We formulate the optimal dynamical adjustments in terms of deterministically sampled probability flows approximated by an interacting particle system. Applied to several biologically inspired models, we demonstrate that our method provides the necessary optimal controls in settings with terminal, transient, or generalized collective state constraints and arbitrary system dynamics.

关键词： Coupled oscillators Diffusion Mathematical physics Stochastic processes Stochastic dynamical systems Fokker–Planck equation Stochastic analysis Stochastic differential equations

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Constrained Optimization with Decision-Dependent Distributions

arXiv

引用

arXiv 2023年

作者： Wang, Zifan Liu, Changxin Parisini, Thomas Zavlanos, Michael M. Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy The KIOS Research and Innovation Center of Excellence University of Cyprus NicosiaCY-1678 Cyprus The Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States

In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distributions has assumed either the absence of constraints or fixed constraints. This work considers a more general setting where the constraints can also dynamically adjust in response to changes in the decision variables. Specifically, we consider linear constraints and analyze the effect of decision-dependent distributions in both the objective function and constraints. Firstly, we establish a sufficient condition for the existence of a constrained equilibrium point, at which the distributions remain invariant under retraining. Morevoer, we propose and analyze two algorithms: repeated constrained optimization and repeated dual ascent. For each algorithm, we provide sufficient conditions for convergence to the constrained equilibrium point. Furthermore, we explore the relationship between the equilibrium point and the optimal point for the constrained decision-dependent optimization problem. Notably, our results encompass previous findings as special cases when the constraints remain fixed. To show the effectiveness of our theoretical analysis, we provide numerical experiments on both a market problem and a dynamic pricing problem for parking based on real-world data. Copyright © 2023, The Authors. All rights reserved.

关键词： Constrained optimization

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